Tuesday, 30 June 2015

Data Scraping - Increasing Accessibility by Scraping Information From PDF

You may have heard about data scraping which is a method that is being used by computer programs in extracting data from an output that comes from another program. To put it simply, this is a process which involves the automatic sorting of information that can be found on different resources including the internet which is inside an html file, PDF or any other documents. In addition to that, there is the collection of pertinent information. These pieces of information will be contained into the databases or spreadsheets so that the users can retrieve them later.

Most of the websites today have text that can be accessed and written easily in the source code. However, there are now other businesses nowadays that choose to make use of Adobe PDF files or Portable Document Format. This is a type of file that can be viewed by simply using the free software known as the Adobe Acrobat. Almost any operating system supports the said software. There are many advantages when you choose to utilize PDF files. Among them is that the document that you have looks exactly the same even if you put it in another computer so that you can view it. Therefore, this makes it ideal for business documents or even specification sheets. Of course there are disadvantages as well. One of which is that the text that is contained in the file is converted into an image. In this case, it is often that you may have problems with this when it comes to the copying and pasting.

This is why there are some that start scraping information from PDF. This is often called PDF scraping in which this is the process that is just like data scraping only that you will be getting information that is contained in your PDF files. In order for you to begin scraping information from PDF, you must choose and exploit a tool that is specifically designed for this process. However, you will find that it is not easy to locate the right tool that will enable you to perform PDF scraping effectively. This is because most of the tools today have problems in obtaining exactly the same data that you want without personalizing them.

Nevertheless, if you search well enough, you will be able to encounter the program that you are looking for. There is no need for you to have programming language knowledge in order for you to use them. You can easily specify your own preferences and the software will do the rest of the work for you. There are also companies out there that you can contact and they will perform the task since they have the right tools that they can use. If you choose to do things manually, you will find that this is indeed tedious and complicated whereas if you compare this to having professionals do the job for you, they will be able to finish it in no time at all. Scraping information from PDF is a process where you collect the information that can be found on the internet and this does not infringe copyright laws.

Source: http://ezinearticles.com/?Increasing-Accessibility-by-Scraping-Information-From-PDF&id=4593863

Tuesday, 23 June 2015

Web Scraping: working with APIs

APIs present researchers with a diverse set of data sources through a standardised access mechanism: send a pasted together HTTP request, receive JSON or XML in return. Today we tap into a range of APIs to get comfortable sending queries and processing responses.

These are the slides from the final class in Web Scraping through R: Web scraping for the humanities and social sciences

This week we explore how to use APIs in R, focusing on the Google Maps API. We then attempt to transfer this approach to query the Yandex Maps API. Finally, the practice section includes examples of working with the YouTube V2 API, a few ‘social’ APIs such as LinkedIn and Twitter, as well as APIs less off the beaten track (Cricket scores, anyone?).

I enjoyed teaching this course and hope to repeat and improve on it next year. When designing the course I tried to cram in everything I wish I had been taught early on in my PhD (resulting in information overload, I fear). Still, hopefully it has been useful to students getting started with digital data collection, showing on the one hand what is possible, and on the other giving some idea of key steps in achieving research objectives.

Download the .Rpres file to use in Rstudio here

A regular R script with code-snippets only can be accessed here

Slides from the first session here

Slides from the second session here

Slides from the third session here

Source: http://www.r-bloggers.com/web-scraping-working-with-apis/

Thursday, 18 June 2015

Three Common Methods For Data Extraction

Probably the most common technique used traditionally to extract data from web pages this is to cook up some regular expressions that match the pieces you want (e.g., URL's and link titles). Our screen-scraper software actually started out as an application written in Perl for this very reason. In addition to regular expressions, you might also use some code written in something like Java or Active Server Pages to parse out larger chunks of text. Using raw regular expressions to pull out the data can be a little intimidating to the uninitiated, and can get a bit messy when a script contains a lot of them. At the same time, if you're already familiar with regular expressions, and your scraping project is relatively small, they can be a great solution.

Other techniques for getting the data out can get very sophisticated as algorithms that make use of artificial intelligence and such are applied to the page. Some programs will actually analyze the semantic content of an HTML page, then intelligently pull out the pieces that are of interest. Still other approaches deal with developing "ontologies", or hierarchical vocabularies intended to represent the content domain.

There are a number of companies (including our own) that offer commercial applications specifically intended to do screen-scraping. The applications vary quite a bit, but for medium to large-sized projects they're often a good solution. Each one will have its own learning curve, so you should plan on taking time to learn the ins and outs of a new application. Especially if you plan on doing a fair amount of screen-scraping it's probably a good idea to at least shop around for a screen-scraping application, as it will likely save you time and money in the long run.

So what's the best approach to data extraction? It really depends on what your needs are, and what resources you have at your disposal. Here are some of the pros and cons of the various approaches, as well as suggestions on when you might use each one:

Raw regular expressions and code

Advantages:

- If you're already familiar with regular expressions and at least one programming language, this can be a quick solution.

- Regular expressions allow for a fair amount of "fuzziness" in the matching such that minor changes to the content won't break them.

- You likely don't need to learn any new languages or tools (again, assuming you're already familiar with regular expressions and a programming language).

- Regular expressions are supported in almost all modern programming languages. Heck, even VBScript has a regular expression engine. It's also nice because the various regular expression implementations don't vary too significantly in their syntax.

Disadvantages:

- They can be complex for those that don't have a lot of experience with them. Learning regular expressions isn't like going from Perl to Java. It's more like going from Perl to XSLT, where you have to wrap your mind around a completely different way of viewing the problem.

- They're often confusing to analyze. Take a look through some of the regular expressions people have created to match something as simple as an email address and you'll see what I mean.

- If the content you're trying to match changes (e.g., they change the web page by adding a new "font" tag) you'll likely need to update your regular expressions to account for the change.

- The data discovery portion of the process (traversing various web pages to get to the page containing the data you want) will still need to be handled, and can get fairly complex if you need to deal with cookies and such.

When to use this approach: You'll most likely use straight regular expressions in screen-scraping when you have a small job you want to get done quickly. Especially if you already know regular expressions, there's no sense in getting into other tools if all you need to do is pull some news headlines off of a site.

Ontologies and artificial intelligence

Advantages:

- You create it once and it can more or less extract the data from any page within the content domain you're targeting.

- The data model is generally built in. For example, if you're extracting data about cars from web sites the extraction engine already knows what the make, model, and price are, so it can easily map them to existing data structures (e.g., insert the data into the correct locations in your database).

- There is relatively little long-term maintenance required. As web sites change you likely will need to do very little to your extraction engine in order to account for the changes.

Disadvantages:

- It's relatively complex to create and work with such an engine. The level of expertise required to even understand an extraction engine that uses artificial intelligence and ontologies is much higher than what is required to deal with regular expressions.

- These types of engines are expensive to build. There are commercial offerings that will give you the basis for doing this type of data extraction, but you still need to configure them to work with the specific content domain you're targeting.

- You still have to deal with the data discovery portion of the process, which may not fit as well with this approach (meaning you may have to create an entirely separate engine to handle data discovery). Data discovery is the process of crawling web sites such that you arrive at the pages where you want to extract data.

When to use this approach: Typically you'll only get into ontologies and artificial intelligence when you're planning on extracting information from a very large number of sources. It also makes sense to do this when the data you're trying to extract is in a very unstructured format (e.g., newspaper classified ads). In cases where the data is very structured (meaning there are clear labels identifying the various data fields), it may make more sense to go with regular expressions or a screen-scraping application.

Screen-scraping software

Advantages:

- Abstracts most of the complicated stuff away. You can do some pretty sophisticated things in most screen-scraping applications without knowing anything about regular expressions, HTTP, or cookies.

- Dramatically reduces the amount of time required to set up a site to be scraped. Once you learn a particular screen-scraping application the amount of time it requires to scrape sites vs. other methods is significantly lowered.

- Support from a commercial company. If you run into trouble while using a commercial screen-scraping application, chances are there are support forums and help lines where you can get assistance.

Disadvantages:

- The learning curve. Each screen-scraping application has its own way of going about things. This may imply learning a new scripting language in addition to familiarizing yourself with how the core application works.

- A potential cost. Most ready-to-go screen-scraping applications are commercial, so you'll likely be paying in dollars as well as time for this solution.

- A proprietary approach. Any time you use a proprietary application to solve a computing problem (and proprietary is obviously a matter of degree) you're locking yourself into using that approach. This may or may not be a big deal, but you should at least consider how well the application you're using will integrate with other software applications you currently have. For example, once the screen-scraping application has extracted the data how easy is it for you to get to that data from your own code?

When to use this approach: Screen-scraping applications vary widely in their ease-of-use, price, and suitability to tackle a broad range of scenarios. Chances are, though, that if you don't mind paying a bit, you can save yourself a significant amount of time by using one. If you're doing a quick scrape of a single page you can use just about any language with regular expressions. If you want to extract data from hundreds of web sites that are all formatted differently you're probably better off investing in a complex system that uses ontologies and/or artificial intelligence. For just about everything else, though, you may want to consider investing in an application specifically designed for screen-scraping.

As an aside, I thought I should also mention a recent project we've been involved with that has actually required a hybrid approach of two of the aforementioned methods. We're currently working on a project that deals with extracting newspaper classified ads. The data in classifieds is about as unstructured as you can get. For example, in a real estate ad the term "number of bedrooms" can be written about 25 different ways. The data extraction portion of the process is one that lends itself well to an ontologies-based approach, which is what we've done. However, we still had to handle the data discovery portion. We decided to use screen-scraper for that, and it's handling it just great. The basic process is that screen-scraper traverses the various pages of the site, pulling out raw chunks of data that constitute the classified ads. These ads then get passed to code we've written that uses ontologies in order to extract out the individual pieces we're after. Once the data has been extracted we then insert it into a database.

Source: http://ezinearticles.com/?Three-Common-Methods-For-Web-Data-Extraction&id=165416


Saturday, 6 June 2015

On-line directory tree webscraping

As you surf around the internet — particularly in the old days — you may have seen web-pages like this:

The former image is generated by Apache SVN server, and the latter is the plain directory view generated for UserDir on Apache.

In both cases you have a very primitive page that allows you to surf up and down the directory tree of the resource (either the SVN repository or a directory file system) and select links to resources that correspond to particular files.

Now, a file system can be thought of as a simple key-value store for these resources burdened by an awkward set of conventions for listing the keys where you keep being obstructed by the ‘/‘ character.

My objective is to provide a module that makes it easy to iterate through these directory trees and produce a flat table with the following helpful entries:

Although there is clearly redundant data between the fields url, abspath, fname, name, ext, having them in there makes it much easier to build a useful front end.

The function code (which I won’t copy in here) is at https://scraperwiki.com/scrapers/apache_directory_tree_extractor/. This contains the functions ParseSVNRevPage(url) and ParseSVNRevPageTree(url), both of which return dicts of the form:

{'url', 'rev', 'dirname', 'svnrepo',

 'contents':[{'url', 'abspath', 'fname', 'name', 'ext'}]}

I haven’t written the code for parsing the Apache Directory view yet, but for now we have something we can use.

I scraped the UK Cave Data Registry with this scraper which simply applies the ParseSVNRevPageTree() function to each of the links and glues the output into a flat array before saving it:

lrdata = ParseSVNRevPageTree(href)

ldata = [ ]

for cres in lrdata["contents"]:

    cres["svnrepo"], cres["rev"] = lrdata["svnrepo"], lrdata["rev"]

    ldata.append(cres)

scraperwiki.sqlite.save(["svnrepo", "rev", "abspath"], ldata)

Now that we have a large table of links, we can make the cave image file viewer based on the query:

select abspath, url, svnrepo from swdata where ext=’.jpg’ order by abspath limit 500

By clicking on a reference to a jpg resource on the left, you can preview what it looks like on the right.

If you want to know why the page is muddy, a video of the conditions in which the data was gathered is here.

Image files are usually the most immediately interesting out of any unknown file system dump. AOn-line directory tree webscraping

As you surf around the internet — particularly in the old days — you may have seen web-pages like this:

The former image is generated by Apache SVN server, and the latter is the plain directory view generated for UserDir on Apache.

In both cases you have a very primitive page that allows you to surf up and down the directory tree of the resource (either the SVN repository or a directory file system) and select links to resources that correspond to particular files.

Now, a file system can be thought of as a simple key-value store for these resources burdened by an awkward set of conventions for listing the keys where you keep being obstructed by the ‘/‘ character.

My objective is to provide a module that makes it easy to iterate through these directory trees and produce a flat table with the following helpful entries:

Although there is clearly redundant data between the fields url, abspath, fname, name, ext, having them in there makes it much easier to build a useful front end.

The function code (which I won’t copy in here) is at https://scraperwiki.com/scrapers/apache_directory_tree_extractor/. This contains the functions ParseSVNRevPage(url) and ParseSVNRevPageTree(url), both of which return dicts of the form:

{'url', 'rev', 'dirname', 'svnrepo',

 'contents':[{'url', 'abspath', 'fname', 'name', 'ext'}]}

I haven’t written the code for parsing the Apache Directory view yet, but for now we have something we can use.

I scraped the UK Cave Data Registry with this scraper which simply applies the ParseSVNRevPageTree() function to each of the links and glues the output into a flat array before saving it:

lrdata = ParseSVNRevPageTree(href)

ldata = [ ]

for cres in lrdata["contents"]:

    cres["svnrepo"], cres["rev"] = lrdata["svnrepo"], lrdata["rev"]

    ldata.append(cres)

scraperwiki.sqlite.save(["svnrepo", "rev", "abspath"], ldata)

Now that we have a large table of links, we can make the cave image file viewer based on the query:

select abspath, url, svnrepo from swdata where ext=’.jpg’ order by abspath limit 500

By clicking on a reference to a jpg resource on the left, you can preview what it looks like on the right.

If you want to know why the page is muddy, a video of the conditions in which the data was gathered is here.

Image files are usually the most immediately interesting out of any unknown file system dump. And they can be made more interesting by associating meta-data with them (given that no convention for including interesting information in the EXIF sections of their file formats). This meta-data might be floating around in other files dumped into the same repository — eg in the form of links to them from html pages which relate to picture captions.

But that is a future scraping project for another time.

Source: https://scraperwiki.wordpress.com/2012/09/14/on-line-directory-tree-webscraping/

Sunday, 31 May 2015

Data Scraping Services - Scraping Yelp Business Data With Python Scraping Script

Yelp is a great source of business contact information with details like address, postal code, contact information; website addresses etc. that other site like Google Maps just does not. Yelp also provides reviews about the particular business. The yelp business database can be useful for telemarketing, email marketing and lead generation.

Are you looking for yelp business details database? Are you looking for scraping data from yelp website/business directory? Are you looking for yelp screen scraping software? Are you looking for scraping the business contact information from the online Yelp? Then you are at the right place.

Here I am going to discuss how to scrape yelp data for lead generation and email marketing. I have made a simple and straight forward yelp data scraping script in python that can scrape data from yelp website. You can use this yelp scraper script absolutely free.

I have used urllib, BeautifulSoup packages. Urllib package to make http request and parsed the HTML using BeautifulSoup, used Threads to make the scraping faster.

Yelp Scraping Python Script

import urllib from bs4 import BeautifulSoup import re from threading import Thread #List of yelp urls to scrape url=['http://www.yelp.com/biz/liman-fisch-restaurant-hamburg','http://www.yelp.com/biz/casa-franco-caramba-hamburg','http://www.yelp.com/biz/o-ren-ishii-hamburg','http://www.yelp.com/biz/gastwerk-hotel-hamburg-hamburg-2','http://www.yelp.com/biz/superbude-hamburg-2','http://www.yelp.com/biz/hotel-hafen-hamburg-hamburg','http://www.yelp.com/biz/hamburg-marriott-hotel-hamburg','http://www.yelp.com/biz/yoho-hamburg'] i=0 #function that will do actual scraping job def scrape(ur): html = urllib.urlopen(ur).read() soup = BeautifulSoup(html) title = soup.find('h1',itemprop="name") saddress = soup.find('span',itemprop="streetAddress") postalcode = soup.find('span',itemprop="postalCode") print title.text print saddress.text print postalcode.text print "-------------------" threadlist = [] #making threads while i<len(url): t = Thread(target=scrape,args=(url[i],)) t.start() threadlist.append(t) i=i+1 for b in
threadlist: b.join()

import urllib

from bs4 import BeautifulSoup

import re

from threading import Thread

 #List of yelp urls to scrape

url=['http://www.yelp.com/biz/liman-fisch-restaurant-hamburg','http://www.yelp.com/biz/casa-franco-caramba-hamburg','http://www.yelp.com/biz/o-ren-ishii-hamburg','http://www.yelp.com/biz/gastwerk-hotel-hamburg-hamburg-2','http://www.yelp.com/biz/superbude-hamburg-2','http://www.yelp.com/biz/hotel-hafen-hamburg-hamburg','http://www.yelp.com/biz/hamburg-marriott-hotel-hamburg','http://www.yelp.com/biz/yoho-hamburg']

 i=0

#function that will do actual scraping job

def scrape(ur):

           html = urllib.urlopen(ur).read()

          soup = BeautifulSoup(html)

       title = soup.find('h1',itemprop="name")

          saddress = soup.find('span',itemprop="streetAddress")

          postalcode = soup.find('span',itemprop="postalCode")

          print title.text

          print saddress.text

          print postalcode.text

          print "-------------------"

 threadlist = []

#making threads

while i<len(url):

          t = Thread(target=scrape,args=(url[i],))

          t.start()

          threadlist.append(t)

          i=i+1

for b in threadlist:

          b.join()

Recently I had worked for one German company and did yelp scraping project for them and delivered data as per their requirement. If you looking for scraping data from business directories like yelp then send me your requirement and I will get back to you with sample.

Source: http://webdata-scraping.com/scraping-yelp-business-data-python-scraping-script/

Wednesday, 27 May 2015

Data Mining Services

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Types of data mining services delivered by us

With an extensive variety of data mining services provided by us, you will definitely be able to find the most perfect service package to cater to your requirements. Below listed are just some of the data mining services offered by us:

•    Web data mining
•    Data extraction
•    Data capture
•    Data gathering
•    Collection of required data
•    Validation of data

Outsource data mining requirements to us, and we are sure that the data mining India unit of Hi-Tech BPO Services will be able to formulate the most appropriate and cost effective solutions to include your entire requirements.

Highlights of our data mining services:

•    Most affordable rates
•    Dedicated data mining India unit
•    Latest data mining technologies used to mine all required data
•    Data will be mined, gathered, processed and validated as per your requirements
•    Mined data can be directly included into your database

Competitive advantage of using our data mining services

To mine accurate and relevant data, some level of internet knowledge is essential. And it would also consume a lot of your valuable time. With our data mining services, we will take care of all your data mining tasks, while you look after your business and its core functions.

The affordably priced data mining services delivered by the data mining India unit will also help you to save considerable amount of your money, which you can put into more productive purposes.

Source: http://www.hitechbposervices.com/data-mining.php

Monday, 25 May 2015

Improving performance for web scraping code

2 down vote favorite

I have a website in which the code scrapes other websites for getting the accurate data. While the code works good but there a decent lag in performance because the code firsts downloads the html stream from various sites(some times 9 websites), extracts the relative part and then renders the html page.

What should I do to get an optimal performance. Should I change from shared hosting (godaddy) to my own server or it has nothing to do with my hosting and I need to make changes to my code?

1 Answer

API/CSV

Ask those websites if they provide an API, or, if you don't need an up-to-date information or the information you need doesn't change frequently, if they can sell/give you for free the data itself (for example in an CSV file). Some small websites may have fancier ways to access data, like a CSV file for the older information, and an RSS feed for the changed one.

Those websites would probably be happy to help you, since providing you with an API would reduce their own CPU and bandwidth usage by you.

Profile

Screen scrapping is really ugly when it comes to performance and scaling. You may be limited by:

  •     your machine performance, since parsing, sometimes an invalid HTML file, takes time,
  •     your network speed,
  •     their network speed usage, i.e. how fast can you access the pages of their website depending on the restrictions they set, like the DOS protection and the number of requests per second for screen scrappers and search engine crawlers,
  •     their machine performance: if they spend 500 ms. to generate every page, you can't do anything to reduce this delay.

If, despite your requests to them, those websites cannot provide any convenient way to access their data, but they give you a written consent to screen scrape their website, then profile your code to determine the bottleneck. It may be the internet speed. It may be your database queries. It may be anything.

For example, you may discover that you spend too much time finding with regular expressions the relevant information in the received HTML. In that case, you would want to stop doing it wrong and use a parser instead of regular expressions, then see how this improve the performance.

You may also find that the bottleneck is the time the remote server spends generating every page. In this case, there is nothing to do: you may have the fastest server, the fastest connection and the most optimized code, the performance will be the same.

Do things in parallel:

Remember to use parallel computing wisely and to always profile what you're doing, instead of doing premature optimization, in hope that you're smarter than the profiler.

Especially when it comes to using network, you may be very surprised. For example, you may believe that making more requests in parallel will be faster, but as Steve Gibson explains in episode 345 of Security Now, this is not always the case.

Legal aspects

Also note that screen scrapping is explicitly forbidden by the conditions of use (like on IMDB) on many websites. And if nothing is said on this subject in conditions of use, it doesn't mean that you can screen scrape those websites.

The fact that the information is available publicly on the internet doesn't give you the right to copy and reuse it this way neither.

Why? you may ask. For two reasons:
  •     Most websites are relying on advertisement and marketing. When people use one of those websites directly, they waste some CPU/network bandwidth of the website, but in response, they may click on an ad or buy something sold on the website. When you screen scrape, your bot waste their CPU/network bandwidth, but will never click on an ad or buy something.
  •     Displaying the information you screen scrapped on your website can have even worse effects. Example: in France, there are two major websites selling hardware. The first one is easy and fast to use, has a nice visual design, better SEO, and in general is very well done. The second one is a crap, but the prices are lower. If you screen scrape them and give the raw results (prices with links) to your users, they will obviously click on the lower price every time, which means that the website with pretty design will have less chances to sell the products.
  •     People made an effort in collecting, processing and displaying some data. Sometimes they paid to get it. Why would they enjoy seeing you pulling this data conveniently and for free?

Source: http://programmers.stackexchange.com/questions/141403/improving-performance-for-web-scraping-code/141406#141406

Saturday, 23 May 2015

Scraping Data: Site-specific Extractors vs. Generic Extractors

Scraping is becoming a rather mundane job with every other organization getting its feet wet with it for their own data gathering needs. There have been enough number of crawlers built – some open-sourced and others internal to organizations for in-house utilities. Although crawling might seem like a simple technique at the onset, doing this at a large-scale is the real deal. You need to have a distributed stack set up to take care of handling huge volumes of data, to provide data in a low-latency model and also to deal with fail-overs. This still is achievable after crossing the initial tech barrier and via continuous optimizations. (P.S. Not under-estimating this part because it still needs a team of Engineers monitoring the stats and scratching their heads at times).

Social Media Scraping

Focused crawls on a predefined list of sites

However, you bump into a completely new land if your goal is to generate clean and usable data sets from these crawls i.e. “extract” data in a format that your DB can process and aid in generating insights. There are 2 ways of tackling this:

a. site-specific extractors which give desired results

b. generic extractors that result in few surprises

Assuming you still do focused crawls on a predefined list of sites, let’s go over specific scenarios when you have to pick between the two-

1. Mass-scale crawls; high-level meta data – Use generic extractors when you have a large-scale crawling requirement on a continuous basis. Large-scale would mean having to crawl sites in the range of hundreds of thousands. Since the web is a jungle and no two sites share the same template, it would be impossible to write an extractor for each. However, you have to settle in with just the document-level information from such crawls like the URL, meta keywords, blog or news titles, author, date and article content which is still enough information to be happy with if your requirement is analyzing sentiment of the data.

cb1c0_one-size

A generic extractor case

Generic extractors don’t yield accurate results and often mess up the datasets deeming it unusable. Reason being

programatically distinguishing relevant data from irrelevant datasets is a challenge. For example, how would the extractor know to skip pages that have a list of blogs and only extract the ones with the complete article. Or delineating article content from the title on a blog page is not easy either.

To summarize, below is what to expect of a generic extractor.

Pros-

•    minimal manual intervention
•    low on effort and time
•    can work on any scale

Cons-

•    Data quality compromised
•    inaccurate and incomplete datasets
•    lesser details suited only for high-level analyses
•    Suited for gathering- blogs, forums, news
•    Uses- Sentiment Analysis, Brand Monitoring, Competitor Analysis, Social Media Monitoring.

2. Low/Mid scale crawls; detailed datasets – If precise extraction is the mandate, there’s no going away from site-specific extractors. But realistically this is do-able only if your scope of work is limited i.e. few hundred sites or less. Using site-specific extractors, you could extract as many number of fields from any nook or corner of the web pages. Most of the times, most pages on a website share similar templates. If not, they can still be accommodated for using site-specific extractors.

cutlery

Designing extractor for each website

Pros-

•    High data quality
•    Better data coverage on the site

Cons-

High on effort and time

Site structures keep changing from time to time and maintaining these requires a lot of monitoring and manual intervention

Only for limited scale

Suited for gathering – any data from any domain on any site be it product specifications and price details, reviews, blogs, forums, directories, ticket inventories, etc.

Uses- Data Analytics for E-commerce, Business Intelligence, Market Research, Sentiment Analysis

Conclusion

Quite obviously you need both such extractors handy to take care of various use cases. The only way generic extractors can work for detailed datasets is if everyone employs standard data formats on the web (Read our post on standard data formats here). However, given the internet penetration to the masses and the variety of things folks like to do on the web, this is being overly futuristic.

So while site-specific extractors are going to be around for quite some time, the challenge now is to tweak the generic ones to work better. At PromptCloud, we have added ML components to make them smarter and they have been working well for us so far.

What have your challenges been? Do drop in your comments.

Source: https://www.promptcloud.com/blog/scraping-data-site-specific-extractors-vs-generic-extractors/

Friday, 22 May 2015

The Features of the "Holographic Meridian Scraping Therapy"

1. Systematic nature: Brief introduction to the knowledge of viscera, meridians and points in traditional Chinese medicine, theory of holographic diagnosis and treatment; preliminary discussion of the treatment and health care mechanism of scraping therapy; systemat­ic introduction to the concrete methods of the holographic meridian scraping therapy; enumerating a host of therapeutic methods of scraping for disorders in both Chinese and Western medicine to em­body a combination of disease differentiation and syndrome differen­tiation; and summarizing the health care scraping methods. It is a practical handbook of gua sha.

2. Scientific: Applying the theories of Chinese and Western medicine to explain the health care and treatment mechanism and clinical applications of scraping therapy; introducing in detail the practical manipulations, items for attention, and indications and contraindications of the scraping therapy. Here are introduced repre­sentative diseases in different clinical departments, for which scrap­ing therapy has a better curative effect and the therapeutic methods of scraping for these diseases. Stress is placed on disease differentia­tion in Western medicine and syndrome differentiation in Chinese medicine, which should be combined in practical application.

Although there are more than 140,000 kinds of disease known to modem medicine, all diseases are related to dysfunction of the 14 meridians and internal organs, according to traditional Chinese med­icine. The object of scraping therapy is to correct the disharmony in the meridians and internal organs to recover the normal bodily func­tions. Thus, the scraping of a set of meridian points can be used to treat many diseases. In the section on clinical application only about 100 kinds of common diseases are discussed, although the actual number is much more than that. For easy reference the "Index of Diseases and Symptoms" is appended at the back of the book.

3. Practical: Using simple language and plenty of pictures and diagrams to guarantee that readers can easily leam, memorize and apply the principles of scraping therapy. As long as they master the methods explained in Chapter Three, readers without any medical knowledge can apply scraping therapy to themselves or others, with reference to the pictures in Chapters Four and Five. Besides scraping therapy, herbal treatment for each disease or syndrome is explained and may be used in combination with the scraping techniques.

Referring to the Holographic Meridian Hand Diagnosis and pic­tures at the back of the book will enhance accuracy of diagnosis and increase the effectiveness of scraping therapy.

Since the first publication and distribution of the Chinese edition of the book in July 1995, it has been welcomed by both medical specialists and lay people. In March 1996 this book was republished and adopted as a textbook by the School for Advanced Studies of Traditional Chinese Medicine affiliated to the Institute of the Acu­puncture and Moxibustion of the China Academy of Traditional Chi­nese Medicine.

In order to bring this health care method to more and more peo­ple and to make traditional Chinese medicine better appreciated They have modified and replenished this book in the spirit of constant im­provement. They hope that they may make a contribution to the health care of mankind with this natural therapy which has no side-effects and causes no pollution.

They hope that the Holographic Meridian Scraping Therapy can help the health and happiness of more and more families in the world.

Source: http://ezinearticles.com/?The-Features-of-the-Holographic-Meridian-Scraping-Therapy&id=5005031

Monday, 18 May 2015

Dapper: The Scraper for the Common Man

Sometimes, especially with Web 2.0 companies, jargon can get a little bit out of hand. When someone says that a service allows you to "build an API for any website", it can be a bit difficult to understand what that really means.

However, put simply, Dapper is a scraper. Nothing more. It allows you to scrape content from a Web page and convert it into an XML document that can be easily used at another location. Though you won't find the words "scrape" or "scraper" anywhere on its site, that is exactly what it does.

What separates Dapper from other scrapers, both legitimate and illegitimate, is that it is both free and easy to use. In short, it makes the process of setting up the scraper simple enough for your every day Internet user. While one has never needed to be a geek to scrape RSS feeds, now the technologically impaired can scrape content from any site, even those that don't publish RSS feeds.

Though the TechCrunch profile of the service says that Dapper "aims to offer some legitimate, valuable services and set up a means to respect copyright" others are expressing concern about the potential for copyright violations, especially by spam bloggers.

Either way though, both the cause for concern and the potential dangers are very, very real.

What is Dapper

When a user goes to create a new "Dapp", he or she first needs to provide a series of links. These links must be on the same domain and in similar formats (IE: Google searches for different terms or different blog posts on a single site) for the service to work. Once the links have been defined, the user is then taken to a GUI where they pick out fields.

In a simple example where the user would create their own RSS feed for a blog, the post title might be one field, perhaps called "post title" and the body would be a second, perhaps called "post body". Dapper, much like the service social bookmarking Clipmarks, is able able to intelligently select blocks of text on a Web page, making it easy to ensure that the entire post body is selected and that extraneous information is omitted.

Once the fields have been selected, the user can then either create groups based upon those fields or simply save the dapp for future use. Once the Dapp has been saved, they can then use it to create both raw XML data, an RSS feed, a Google Gadget or any number of other output files that can be easily used in other services.

If you are interested in viewing a demo of Dapper, you can do so at this link.

There is little doubt that Dapper is an impressive service. It has taken the black art of scraping and made it into a simple, easy-to-use application that just about anyone can pick up. Though it might take a few tries to create a working Dapp, and certainly spending some time reading up on the service is required, most will find it easy to use, especially when compared to the alternatives.

However, it's this ease of use that has so many worried. Though scrapers have been around for many years, they have been either difficult to use or expensive. Dapper's power, when combined with its price tag and sheer ease of use, has many wondered that it might be ushering not a new age for the Web, but a new age for scrapers seeking to abuse other's hard work.

Cause for Concern

While being easy to use or free is not necessarily a problem in and of itself, in the rush to enable users to make an API for any site, they forget that many sites don't have one or restrict access to their APIs for very good reasons. RSS scraping is perhaps the biggest copyright issue bloggers face. It enables a plagiarist or spammer to not only steal all of the content on the blog right then, but also all of the content that will be posted in the future. This is a huge concern for many bloggers, especially those concerned about performing well in the search engines.

This has prompted many blogs to either disable their RSS feeds, truncate them or move them to a feed monitoring service such as Feedburner. However, if users can simply create their own RSS feeds with ease, these protections are circumvented and Webmasters lose control over their content.

Even with potential copyright abuse issues aside, Dapper creates potential problems for Webmasters. It bypasses the usual metrics that site owners have. A user who reads a site, or large portions of it, through a Dapp will not be counted in either the feed statistics or, depending on how Dapper is set up, even in the site's logs. All the while, the site is spending precious resources to feed the Dapp, taking money out of the Webmaster's pocket.

This combination of greater expense, less traffic and less accurate metrics can be dangerous to Webmasters who are working to get accurate traffic counts, visitor feedback or revenue.

Worse still, Dapp users also bypass any ads or other monetization tools that might be included in the site or the original RSS feed. This has a direct impact on sites trying to either turn a profit or, like this one, recoup some of the costs of hosting.

Despite this, it's the copyright concerns that reign supreme. Though screen scraping is not necessarily an evil technology, it is the sinister uses that have gotten the most attention and, sadly, seem to be the most common, especially in regards to blogs.

Even if the makers of Dapper is aiming to add copyright protection at a later date, the service is fully functional today and, though the FAQ states that they will "comply with any verified request by the lawful owner of the content to cease using his content," there is no opt-out procedure, no DMCA information on the United States Copyright Office Web site, no information on how to prevent Dapper from accessing your site and nothing but a contact page to get in touch with the makers of the service.

(Note: An email sent to the makers of Dapper on the 22nd has, as of yet, gone unanswered)

In addition to creating a potential copyright nightmare for Webmasters the site seems to be setting itself up for a lawsuit. In addition to not being DMCA Safe Harbor compliant (PDF), thus opening it up to copyright infringement lawsuits directly, the service seems to be vulnerable to a lawsuit under the MGM v. Grokster case, which found that service providers can be sued for infringement conducted by its users if they fail an "inducement" test. Sadly for Dapper, simply saying that it is the user's responsibility is not adequate to pass such a test, as Grokster found out. The failure to offer filtering technology and encouragement to create API's for "any" site are both likely strikes against Dapper in that regard.

To make matters more grim, copyright is not the only issue scrapers have to worry about, as one pair of lawyers put it, there are at least four different different legal theories that make scraping illegal including the computer fraud and abuse act, trespass against chattels and breach of contract. All in all, copyright is practically the least of Dapper's problems.

When it's all said and done, there is a lot of room for concern, not just on the part of Webmasters that might be affected by Dapper or its users, but also its makers. These intellectual property and other legal issues could easily sink the entire project.

Conclusions

It is obvious that a lot of time and effort went into creating Dapper. It's a very powerful, easy to use service that opens up interesting possibilities. I would hate to see the service used for ill and I would hate even worse to see all of the hard work that went into it lost because of intellectual property issues.

However, in its current incarnation, it seems likely that Dapper is going to encounter significant resistance on the IP front. There is little, if any protection or regard for intellectual property under the current system and, once bloggers find out that their content is being syndicated without their permission by the service, many are likely to start raising a fuss.

Even though Dapper has gotten rave reviews in the Web 2.0 community, it seems likely that traditional bloggers and other Web site owners will have serious objections to it. Those people, sadly, most likely have never heard of Dapper at this point.

With that being said, it is a service everyone needs to make note of. The one thing that is for certain is that it will be in the news again. The only question is what light will it be under.

Source: https://www.plagiarismtoday.com/2006/08/24/dapper-the-scraper-for-the-common-man/

Thursday, 14 May 2015

Web Scraping Services Are Important Tools For Knowledge

Data extraction and web scraping techniques are important tools to find relevant data and information for personal or business use. Many companies, self-employed to copy and paste data from web pages. This process is very reliable, but very expensive as it is a waste of time and effort to get results. This is because the data collected and spent less resources and time required to collect these data are compared.

At present, several mining companies and their websites effective web scraping technique specifically for the thousands of pages of information developed culture can be traced. The information from a CSV file, database, XML file, or any other source with the required format is alameda. understanding of correlations and patterns in the data, so that policies can be designed to assist decision making. The information can also be stored for future reference.

The following are some common examples of data extraction process:

In order to rule through a government portal, citizens who are reliable for a given survey name removed.

Competitive pricing and data products include scraping websites

To access the web site or web design Stock download the videos and photos of scratching

Automatic Data Collection

It regularly collects data on a regular basis. Automated data collection techniques are very important because they find the company’s customer trends and market trends to help. By determining market trends, it is possible to understand customer behavior and predict the likelihood of the data will change.

The following are some examples of automated data collection:

Monitoring of special hourly rates for stocks

collects daily mortgage rates from various financial institutions

on a regular basis is necessary to check the weather

By using web scraping services, you can extract all data related to your business. Then analyzed the data to a spreadsheet or database can be downloaded and compared. Storing data in a database or in a required format and interpretation of the correlations to understand and makes it easier to identify hidden patterns.

Data extraction services, it is possible pricing, email, databases, profile data, and consistently to competitors for information about the data. Different techniques and processes designed to collect and analyze data, and has developed over time. Web Scraping for business processes that have beaten the market recently is one. It is a process from various sources such as websites and databases with large amounts of data provides.

Some of the most common methods used to scrape web crawling, text, fun, DOM analysis and include matching expression. After the process is only analyzers, HTML pages or meaning can be achieved through annotations. There are many different ways of scaling data, but more importantly is working toward the same goal. The main purpose of using web scraping service to retrieve and compile data in databases and web sites. In the business world is to remain relevant to the business process.

The central question about the relevance of web scraping contact. The process is relevant to the business world? The answer is yes. The fact that it is used by large companies in the world and many awards speaks derivatives.

Source: http://www.selfgrowth.com/articles/web-scraping-services-are-important-tools-for-knowledge

Monday, 4 May 2015

Customized Web Data Extraction Solutions for Business

As you begin leading your business on the path to success, competitive analysis forms a major part of your homework. You have already mobilized your efforts in finding the appropriate website data scrapping tool that will help you to collect relevant data from competitive websites and shape them up into useable information. There is however a need to look for a customized approach in your search for Data Extraction tools in order to leverage its benefits in the best possible way.

Off-the-shelf Tools Impede Data Extraction

 In the current scenario, Internet Technologies are evolving in abundance. Every organization leverages this development and builds their websites using a different programming language and technology. Off-the-shelf Website Data extraction tools are unable to interpret this difference. They fail to understand the data elements that need to be captured and end up in gathering data without any change in the software source codes.

As a result of this incapability in their technology, off-the-shelf solutions often deliver unclean, incomplete and also inaccurate data. Developers need to contribute a humungous effort in cleaning up and structuring the data to make it useable. However, despite the time-consuming activity, data seldom metamorphoses into the desired information. Also the personnel dealing with the clean-up process needs to have sufficient technical expertise in order to participate in the activities. The endeavor however results in an impediment to the whole process of data extraction leaving you thirsting for the required information to augment business growth.

Understanding how Web Extraction tools work

Web Scrapping tools are designed to extract data from the web automatically. They are usually small pieces of code written using programming languages such as Python, Ruby or PHP depending upon the expertise of the community building it. There are however several single-click models available which tends to make life easier for non-technical personnel.

The biggest challenge faced by a successful web extractor tool is to know how to tackle the right page and the right elements on that page in order to extract the desired information. Consequently, a web extractor needs to be designed to understand the anatomy of a web page in order to accomplish its task successfully. It should be designed to interpret the meaning of HTML elements like , table rows () within those tables, and table data (<td>) cells within those rows in order to extract the exact data. It will also be interfacing with the

element which are blocks of text and know how to extract the desired information from it.

Customized Solutions for your business

 Customized Solutions are provided by most Data Scraping experts. These software's help to minimize the cumbersome effort of writing elaborate codes to successfully accomplish the feat of data extraction. They are designed to seamlessly search competitive websites,identify relevant data elements, and extract appropriate data that will be useful for your business. Owing to their focused approach, these tools provide clean and accurate data thereby eliminating the need to waste valuable time and effort in any clean-up effort.

Most customized data extraction tools are also capable of delivering the extracted data in customized formats like XML or CSV. It also stores data in local databases like Microsoft Access, MySQL, or Microsoft SQL.

Customized Data scraping solutions therefore help you take accurate and informed decisions in order to define effective business strategies.

Source: http://scraping-solutions.blogspot.in/2014_07_01_archive.html 

Wednesday, 29 April 2015

Web Scraping – An Illegal Activity or Simple Data Collection?

Gone are the days when skillful extraction of information pertaining to real estate such as foreclosures, homes for sale, or mortgage records was considered difficult. Now, it is not only easy to extract data from real estate websites but also scrape real estate data on a consistent basis to add more value to your portal, or ensure that updated data is available to your visitors at all times. From downloading actual scanned documents in the form of PDF files to scraping websites for deeds or mortgages, smartly designer data extraction tools can do it all.

However, the one question that still manages to come to the front in the minds of those who scrape real estate listings and others are whether the act is illegal in nature or a simple way of collecting data.

Take a look.

Web Scraping—What is it?

Generally speaking, web scraping refers to programs that are designed to simulate human internet surfing and access websites on behalf of their users. These tools are effective in collecting large quantities of data that are otherwise difficult for end users to access. They process semi-structured or unstructured data pages of targeted websites and transform available data into a more structured format that can be extracted or manipulated by the user easily.

Quite similar to web indexing that is used by search engines, the end motivation of web scraping is much different. While web indexing makes search engines far more efficient, the latter is used for reasons like market research, change detection, data monitoring, or in some events, theft. But then, it is not always a bad thing. You just need to know if a website allows web scraping before proceeding with the act.

Fine Line between Stealing and Collecting Information

Web scraping rides an extremely fine line between the acts of collecting relevant information and stealing the same. The websites that have copyright disclosure statements in place to protect their website information are offended by outsiders raiding their data without due permission. In other words, it amounts to trespassing on their portal, which is unacceptable—both ethically and legally. So, it is very important for you to read all disclosure statements carefully and follow along in the right way. As web scraping cases may turn into felony offenses, it is best to guard against any kind of scrupulous activity and take permission before scraping data.

The Good News

However, all is not grey in data extraction processes. Reputed agencies are helping their clients scrape valuable data for gaining more value through legal means and carefully used tools. If you are looking for such services, then do get in touch with a reliable web scraping company of your choice and take your business to the next levels of success.

Source: https://3idatascraping.wordpress.com/2015/03/11/web-scraping-an-illegal-activity-or-simple-data-collection/

Monday, 27 April 2015

I Don’t Need No Stinking API: Web Scraping For Fun and Profit

If you’ve ever needed to pull data from a third party website, chances are you started by checking to see if they had an official API. But did you know that there’s a source of structured data that virtually every website on the internet supports automatically, by default?

scraper toolThat’s right, we’re talking about pulling our data straight out of HTML — otherwise known as web scraping. Here’s why web scraping is awesome:

Any content that can be viewed on a webpage can be scraped. Period.

If a website provides a way for a visitor’s browser to download content and render that content in a structured way, then almost by definition, that content can be accessed programmatically. In this article, I’ll show you how.

Over the past few years, I’ve scraped dozens of websites — from music blogs and fashion retailers to the USPTO and undocumented JSON endpoints I found by inspecting network traffic in my browser.

There are some tricks that site owners will use to thwart this type of access — which we’ll dive into later — but they almost all have simple work-arounds.

Why You Should Scrape

But first we’ll start with some great reasons why you should consider web scraping first, before you start looking for APIs or RSS feeds or other, more traditional forms of structured data.

Websites are More Important Than APIs

The biggest one is that site owners generally care way more about maintaining their public-facing visitor website than they do about their structured data feeds.

We’ve seen it very publicly with Twitter clamping down on their developer ecosystem, and I’ve seen it multiple times in my projects where APIs change or feeds move without warning.

Sometimes it’s deliberate, but most of the time these sorts of problems happen because no one at the organization really cares or maintains the structured data. If it goes offline or gets horribly mangled, no one really notices.

Whereas if the website goes down or is having issues, that’s a more of an in-your-face, drop-everything-until-this-is-fixed kind of problem, and gets dealt with quickly.

No Rate-Limiting

Another thing to think about is that the concept of rate-limiting is virtually non-existent for public websites.

Aside from the occasional captchas on sign up pages, most businesses generally don’t build a lot of defenses against automated access. I’ve scraped a single site for over 4 hours at a time and not seen any issues.

Unless you’re making concurrent requests, you probably won’t be viewed as a DDOS attack, you’ll just show up as a super-avid visitor in the logs, in case anyone’s looking.

Anonymous Access

There are also fewer ways for the website’s administrators to track your behavior, which can be useful if you want gather data more privately.

With APIs, you often have to register to get a key and then send along that key with every request. But with simple HTTP requests, you’re basically anonymous besides your IP address and cookies, which can be easily spoofed.

The Data’s Already in Your Face

Web scraping is also universally available, as I mentioned earlier. You don’t have to wait for a site to open up an API or even contact anyone at the organization. Just spend some time browsing the site until you find the data you need and figure out some basic access patterns — which we’ll talk about next.

Let’s Get to Scraping

So you’ve decided you want to dive in and start grabbing data like a true hacker. Awesome.

Just like reading API docs, it takes a bit of work up front to figure out how the data is structured and how you can access it. Unlike APIs however, there’s really no documentation so you have to be a little clever about it.

I’ll share some of the tips I’ve learned along the way.

Fetching the Data

So the first thing you’re going to need to do is fetch the data. You’ll need to start by finding your “endpoints” — the URL or URLs that return the data you need.

If you know you need your information organized in a certain way — or only need a specific subset of it — you can browse through the site using their navigation. Pay attention to the URLs and how they change as you click between sections and drill down into sub-sections.

The other option for getting started is to go straight to the site’s search functionality. Try typing in a few different terms and again, pay attention to the URL and how it changes depending on what you search for. You’ll probably see a GET parameter like q= that always changes based on you search term.

Try removing other unnecessary GET parameters from the URL, until you’re left with only the ones you need to load your data. Make sure that there’s always a beginning ? to start the query string and a & between each key/value pair.

Dealing with Pagination

At this point, you should be starting to see the data you want access to, but there’s usually some sort of pagination issue keeping you from seeing all of it at once. Most regular APIs do this as well, to keep single requests from slamming the database.

Usually, clicking to page 2 adds some sort of offset= parameter to the URL, which is usually either the page number or else the number of items displayed on the page. Try changing this to some really high number and see what response you get when you “fall off the end” of the data.

With this information, you can now iterate over every page of results, incrementing the offset parameter as necessary, until you hit that “end of data” condition.

The other thing you can try doing is changing the “Display X Per Page” which most pagination UIs now have. Again, look for a new GET parameter to be appended to the URL which indicates how many items are on the page.

Try setting this to some arbitrarily large number to see if the server will return all the information you need in a single request. Sometimes there’ll be some limits enforced server-side that you can’t get around by tampering with this, but it’s still worth a shot since it can cut down on the number of pages you must paginate through to get all the data you need.

AJAX Isn’t That Bad!

Sometimes people see web pages with URL fragments # and AJAX content loading and think a site can’t be scraped. On the contrary! If a site is using AJAX to load the data, that probably makes it even easier to pull the information you need.

The AJAX response is probably coming back in some nicely-structured way (probably JSON!) in order to be rendered on the page with Javscript.

All you have to do is pull up the network tab in Web Inspector or Firebug and look through the XHR requests for the ones that seem to be pulling in your data.

Once you find it, you can leave the crufty HTML behind and focus instead on this endpoint, which is essentially an undocumented API.

(Un)structured Data?

Now that you’ve figured out how to get the data you need from the server, the somewhat tricky part is getting the data you need out of the page’s markup.

Use CSS Hooks

In my experience, this is usually straightforward since most web designers litter the markup with tons of classes and ids to provide hooks for their CSS.

You can piggyback on these to jump to the parts of the markup that contain the data you need.

Just right click on a section of information you need and pull up the Web Inspector or Firebug to look at it. Zoom up and down through the DOM tree until you find the outermost <div> around the item you want.

This <div> should be the outer wrapper around a single item you want access to. It probably has some class attribute which you can use to easily pull out all of the other wrapper elements on the page. You can then iterate over these just as you would iterate over the items returned by an API response.

A note here though: the DOM tree that is presented by the inspector isn’t always the same as the DOM tree represented by the HTML sent back by the website. It’s possible that the DOM you see in the inspector has been modified by Javascript — or sometime even the browser, if it’s in quirks mode.

Once you find the right node in the DOM tree, you should always view the source of the page (“right click” > “View Source”) to make sure the elements you need are actually showing up in the raw HTML.

This issue has caused me a number of head-scratchers.

Get a Good HTML Parsing Library

It is probably a horrible idea to try parsing the HTML of the page as a long string (although there are times I’ve needed to fall back on that). Spend some time doing research for a good HTML parsing library in your language of choice.

Most of the code I write is in Python, and I love BeautifulSoup for its error handling and super-simple API. I also love its motto:

    You didn’t write that awful page. You’re just trying to get some data out of it. Beautiful Soup is here to help. :)

You’re going to have a bad time if you try to use an XML parser since most websites out there don’t actually validate as properly formed XML (sorry XHTML!) and will give you a ton of errors.

A good library will read in the HTML that you pull in using some HTTP library (hat tip to the Requests library if you’re writing Python) and turn it into an object that you can traverse and iterate over to your heart’s content, similar to a JSON object.

Some Traps To Know About

I should mention that some websites explicitly prohibit the use of automated scraping, so it’s a good idea to read your target site’s Terms of Use to see if you’re going to make anyone upset by scraping.

For two-thirds of the website I’ve scraped, the above steps are all you need. Just fire off a request to your “endpoint” and parse the returned data.

But sometimes, you’ll find that the response you get when scraping isn’t what you saw when you visited the site yourself.

When In Doubt, Spoof Headers

Some websites require that your User Agent string is set to something they allow, or you need to set certain cookies or other headers in order to get a proper response.

Depending on the HTTP library you’re using to make requests, this is usually pretty straightforward. I just browse the site in my web browser and then grab all of the headers that my browser is automatically sending. Then I put those in a dictionary and send them along with my request.

Note that this might mean grabbing some login or other session cookie, which might identify you and make your scraping less anonymous. It’s up to you how serious of a risk that is.

Content Behind A Login

Sometimes you might need to create an account and login to access the information you need. If you have a good HTTP library that handles logins and automatically sending session cookies (did I mention how awesome Requests is?), then you just need your scraper login before it gets to work.

Note that this obviously makes you totally non-anonymous to the third party website so all of your scraping behavior is probably pretty easy to trace back to you if anyone on their side cared to look.

Rate Limiting

I’ve never actually run into this issue myself, although I did have to plan for it one time. I was using a web service that had a strict rate limit that I knew I’d exceed fairly quickly.

Since the third party service conducted rate-limiting based on IP address (stated in their docs), my solution was to put the code that hit their service into some client-side Javascript, and then send the results back to my server from each of the clients.

This way, the requests would appear to come from thousands of different places, since each client would presumably have their own unique IP address, and none of them would individually be going over the rate limit.

Depending on your application, this could work for you.

Poorly Formed Markup

Sadly, this is the one condition that there really is no cure for. If the markup doesn’t come close to validating, then the site is not only keeping you out, but also serving a degraded browsing experience to all of their visitors.

It’s worth digging into your HTML parsing library to see if there’s any setting for error tolerance. Sometimes this can help.

If not, you can always try falling back on treating the entire HTML document as a long string and do all of your parsing as string splitting or — God forbid — a giant regex.

Source: https://blog.hartleybrody.com/web-scraping/

Friday, 24 April 2015

Hard-Scraped Hardwood Flooring: Restoration of History

Throughout History hardwood flooring has undergone dramatic changes from the meticulous hard-scraped hardwood polished floors of majestic plantations of the Deep South, to modern day technology providing maintenance free wood flooring designed for comfort and appearance. The hand-scraped hardwood floors of the South, depicted charm with old rustic nature and character that was often associated with this time era. To date, hand-scraped hardwood flooring is being revitalized and used in up-scale homes and places of businesses to restore the old country charm that once faded into oblivion.

As the name implies, hand-scraped flooring involves the retexturing the top layer of flooring material by various methods in an attempts to mimic the rustic appearance of flooring in yesteryears. Depending on the degree of texture required, hand scraping hardwood material is often accomplished by highly skilled craftsmen with specialized tools and years of experience perfecting this procedure. When properly done, hand-scraped hardwood floors add texture, richness and uniqueness not offered in any similar hardwood flooring product.

Rooted with history, these types of floors are available in finished or unfinished surfaces. The majority of the individuals selecting hand-scraped hardwood flooring elect a prefinished floor to reduce costs per square foot in installation and finishing labor charges, allowing for budget guidelines to bend, not break. As expected, hand-scraped flooring is expensive and depending on the grade and finish selected, can range from $15-40$ per square foot and beyond for material only. Preparation of the material is labor intensive adding to the overall cost per square foot dramatically. Recommended professional installation can and often does increase the cost per square foot as well, placing this method of hardwood flooring well out of reach of the average hardwood floor purchaser.

With numerous selections of hand-scraped finishes available, each finish is designed to bring out a different appearance making it a one-of-a-kind work of art. These numerous finish selections include:

• Time worn aged, dark coloring stain application bringing out grain characteristics

• Wire brushed, providing a highlighted "grainy" effect with obvious rough texture

• Hand sculpted, smoother distressed uniform appearance

• French Bleed, staining of edges and side joints with a much darker stain to give a bleeding effect to the wood

• Hand Hewn or Rough Sawn, with visible and noticeable saw marks

Regardless of the selection made, scraped flooring cannot be compared to any other available flooring material based on durability, strength and visual appearance. Limited by only the imagination and creativity, several wood species can be used to create unusual floor patterns, highlighting main focal points of personal libraries and art collections.

The precise process utilized in the creation of scraped floors projects a custom look with deep color and subtle warm highlights. With radiant natural light reflecting off this type of floor, the effect of beauty and depth is radiated in a fashion that fills the room with solitude and serenity encompassing all that enter. Hand-scraped hardwood floors speak of the past, a time of decent, a time or war and ambiguity towards other races and the blood- shed so that all men could be treated as equals. More than exquisite flooring, hand-scraped hardwood flooring is the restoration of History.

Source: http://ezinearticles.com/?Hard-Scraped-Hardwood-Flooring:-Restoration-of-History&id=6333218

Tuesday, 21 April 2015

Data Mining and Predictive Analysis

Data collection and curing is the core foundation of most businesses. Database building thus is an important function and activity where enterprises invest heavily. With information now available on the Internet and easily obtained, it raises the importance of having professionals who crawl data and offer web scraping services.

Once the data is accessed, though, it is important to filter out the relevant data based on the business need. Although Many DaaS provider convert the unstructured web data into meaningful structured data it is recommended to be internally equipped to use the data to its maximum.

This understanding has given rise to the field of Data Mining. Data Mining is designed to explore large amounts of data in search of consistent patterns and connections between the variables and validate the findings by applying the detected patterns to the new sets of the data. Once these connections are established and understood, the end goal is to be able to predict the possible outcomes using predictive analysis techniques.

Together, both Data Mining and predictive analysis aid in making marketing campaigns more efficient. While predictive analysis helps simulate and understand what may happen, data mining helps identify exciting data patterns and connections.

The process of Data Mining and Predictive analysis consists of 3 steps

Exploration


Once a database is compiled, it needs to be cleaned, analysed and potential connections need to be built. This process involves filtering the relevant data and identifying the possible predictors. Data Exploration also sets a premise for preliminary feature selection to manage number of variables. This data is then prepared for statistical analysis using a wide variety of graphical and statistical parameters. This helps identify the most relevant variables and setups the predictive models to be built.

Data mining process

Validation


Next comes building various models and choosing the most relevant ones. This decision is based on their possible predictive performance and of being able to produce stable results across all the samples. Simple as it sounds, to truly get the results, all possible models must be treated with data to simulate scenarios. The model with most stable statistical feature is validated.

Application


Once the relevant models are finalised, the same is applied to new data to understand and predict the estimated outcomes. Application of data models is an ongoing and complex process since every new dataset needs to be configured in the model.

Data Mining and predictive analysis essentially involves blending statistical methodology where the traditional statistics machine learning and complex algorithms. This greatly increases the need for efficient and skilled data handlers. This could include data analysts and scientists.

See how you can become data scientist here:

Data crunchers use data mining and predictive analysis actively to get an edge in the big data management. Database platforms like Hadoop assist in database management and large-scale distribution. But the costs involved in setting up data centres and big data management capacity are high. Budgets allocated within the enterprise are more project-focussed and analytics budgets are usually limited. Quite often, big data and analytics project fail to launch because of this problem! The other problem is that to run effective predictive models, data requires to be handled by scientists with experience. Finding and setting together a technologically-advanced team is a daunting task most enterprises face outside the tech domain.

Predictive Analysis model

A predictive analysis model is essentially predicting the all possible outcomes from a given set of data. Here are a few steps that can be taken to help build and identify the “ideal” predictive analysis model. These steps more or less mirror the usual statistical methodology of building a test model.

Defining an objective

This is the first and a critical step. Unless the objective is identified and defined there can be no concrete results since there wouldn’t be clarity to compare the final outcome to the expected result. It also helps understand the scope of the project.

Preparing the data

This is more to do with data mining. Historic data used for training the model is scattered across multiple platforms and sources. To compound the problem, data can be unstructured with possible duplicate accounts and missing values! Data quality determines the quality of the model, and thus it becomes imperative that data is healthy and relevant.

Data Sampling


Once mined, Data is essentially split into 2 parts. One set is for training that is used to build the model and the second is the ‘test’ set that is used to verify the accuracy of the final output. This also helps identify and filter the noise component.

Model Building

Sampling cam equally result in a single algorithm or parallel & connected algorithms. In such a case the data goes through multiple testing and a decision is based on the final output.

Execution

Once a model gets finalised, the other teams in the organization need to be involved to build a deployable model and understand its impact on the overall business.

The possibilities with Data mining & Predictive analysis are huge. It also gives a huge room for learning and experimenting. There are several tools available in the industry to aid through all the steps of data mining and predictive analysis. The combination of human expertise and intellect along with the help of the available tools and the overall cooperation within the multiple channels within the organization essentially ensures a stronger grip on the ability to build a solid predictive model.

When used together, predictive analytics and data mining help marketing professionals anticipate and get ready for customer needs, rather than just reacting to them.

Source: https://www.promptcloud.com/blog/data-mining-and-predictive-analysis/

Wednesday, 8 April 2015

rvest: easy web scraping with R

rvest is new package that makes it easy to scrape (or harvest) data from html web pages, inspired by libraries like beautiful soup. It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces. Install it with:

install.packages("rvest")

rvest in action

To see rvest in action, imagine we’d like to scrape some information about The Lego Movie from IMDB. We start by downloading and parsing the file with html():

library(rvest)

lego_movie <- html("http://www.imdb.com/title/tt1490017/")

To extract the rating, we start with selectorgadget to figure out which css selector matches the data we want: strong span. (If you haven’t heard of selectorgadget, make sure to read vignette("selectorgadget") – it’s the easiest way to determine which selector extracts the data that you’re interested in.) We use html_node() to find the first node that matches that selector, extract its contents with html_text(), and convert it to numeric with as.numeric():

lego_movie %>%

  html_node("strong span") %>%

  html_text() %>%

  as.numeric()

#> [1] 7.9

We use a similar process to extract the cast, using html_nodes() to find all nodes that match the selector:

lego_movie %>%

  html_nodes("#titleCast .itemprop span") %>%

  html_text()

#>  [1] "Will Arnett"     "Elizabeth Banks" "Craig Berry"   

#>  [4] "Alison Brie"     "David Burrows"   "Anthony Daniels"

#>  [7] "Charlie Day"     "Amanda Farinos"  "Keith Ferguson"

#> [10] "Will Ferrell"    "Will Forte"      "Dave Franco"   

#> [13] "Morgan Freeman"  "Todd Hansen"     "Jonah Hill"

The titles and authors of recent message board postings are stored in a the third table on the page. We can use html_node() and [[ to find it, then coerce it to a data frame with html_table():

lego_movie %>%

  html_nodes("table") %>%

  .[[3]] %>%

  html_table()

#>                                              X 1            NA

#> 1 this movie is very very deep and philosophical   mrdoctor524

#> 2 This got an 8.0 and Wizard of Oz got an 8.1...  marr-justinm

#> 3                         Discouraging Building?       Laestig

#> 4                              LEGO - the plural      neil-476

#> 5                                 Academy Awards   browncoatjw

#> 6                    what was the funniest part? actionjacksin

Other important functions

•    If you prefer, you can use xpath selectors instead of css: html_nodes(doc, xpath = "//table//td")).

•    Extract the tag names with html_tag(), text with html_text(), a single attribute with html_attr() or all attributes with html_attrs().

•    Detect and repair text encoding problems with guess_encoding() and repair_encoding().

•    Navigate around a website as if you’re in a browser with html_session(), jump_to(), follow_link(), back(), and forward(). Extract, modify and submit forms with html_form(), set_values() and submit_form(). (This is still a work in progress, so I’d love your feedback.)

To see these functions in action, check out package demos with demo(package = "rvest").

Source: http://blog.rstudio.org/2014/11/24/rvest-easy-web-scraping-with-r/

Monday, 6 April 2015

How to Generate Sales Leads Using Web Scraping Services

The first stage of any selling process is what is popularly known as “lead generation”. This phase is what most businesses place at the apex of their sales concerns. It is a driving force that governs decision-making at its highest levels, and influences business strategy and planning. If you are about to embark on an outbound sales campaign and are in the process of looking for leads, you would acknowledge the fact that lead generation process is of extreme importance for any business.

Different lead generation techniques have been used over and over again by companies around the world to satiate this growing business need. Newer, more innovative methods have also emerged to help marketers in this process. One such method of lead generation that is fast catching on, and is poised to play a big role for businesses in the coming years, is web scraping. With web scraping, you can easily get access to multiple relevant and highly customized leads – a perfect starting point for any marketing, promotional or sales campaign.

The prominence of Web Scraping in overall marketing strategy

At present, levels of competition have risen sky high for most businesses. For success, lead generation and gaining insight about customer behavior and preferences is an essential business requirement. Web scraping is the process of scraping or mining the internet for information. Different tools and techniques can be used to harvest information from multiple internet sources based on relevance, and the structured and organized in a way that makes sense to your business. Companies that provide web scraping services essentially use web scrapers to generate a targeted lead database that your company can then integrate into its marketing and sales strategies and plans.

The actual process of web scraping involves creating scraping scripts or algorithms which crawl the web for information based on certain preset parameters and options. The scraping process can be customized and tuned towards finding the kind of data that your business needs. The script can extract data from websites automatically, collate and put together a meaningful collection of leads for business development.

Lead Generation Basics

At a very high level, any person who has the resources and the intent to purchase your product or service qualifies as a lead. In the present scenario, you need to go far deeper than that. Marketers need to observe behavior patterns and purchasing trends to ensure that a particular person qualifies as a lead. If you have a group of people you are targeting, you need to decide who the viable leads will be, acquire their contact information and store it in a database for further action.

List buying used to be a popular way to get leads, but their efficacy has dwindled over time. Web scraping is the fast coming up as a feasible lead generation technique, allowing you to find highly focused and targeted leads in short amounts of time. All you need is a service provider that would carry out the data mining necessary for lead generation, and you end up with a list of actionable leads that you can try selling to.

How Web Scraping makes a substantial difference

With web scraping, you can extract valuable predictive information from websites. Web scraping facilitates high quality data collection and allows you to structure marketing and sales campaigns better. To drive sales and maximize revenue, you need strong, viable leads. To facilitate this, you need critical data which encompasses customer behavior, contact details, buying patterns and trends, willingness and ability to spend resources, and a myriad of other aspects critical to ascertain the potential of an entity as a rewarding lead. Data mining through web scraping can be a great way to get to these factors and identifying the leads that would make a difference for your business.

web-scraping-service

Crawling through many different web locales using different techniques, web scraping services pick up a wealth of information. This highly relevant and specialized information instantly provides your business with actionable leads. Furthermore, this exercise allows you to fine-tune your data management processes, make more accurate and reliable predictions and projections, arrive at more effective, strategic and marketing decisions and customize your workflow and business development to better suit the current market.

The Process and the Tools

Lead generation, being one of the most important processes for any business, can prove to be an expensive proposition if not handled strategically. Companies spend large amounts of their resources acquiring viable leads they can sell to. With web scraping, you can dramatically cut down the costs involved in lead generation and take your business forward with speed and efficiency. Here are some of the time-tested web scraping tools which can come in handy for lead generation –

•    Website download software – Used to copy entire websites to local storage. All website pages are downloaded and the hierarchy of navigation and internal links preserve. The stored pages can then be viewed and scoured for information at any later time.     Web scraper – Tools that crawl through bulk information on the internet, extracting specific, relevant data using a set of pre-defined parameters.

•    Data grabber – Sifts through websites and databases fast and extracts all the information, which can be sorted and classified later.

•    Text extractor – Can be used to scrape multiple websites or locations for acquiring text content from websites and web documents. It can mine data from a variety of text file formats and platforms.

With these tools, web scraping services scrape websites for lead generation and provide your business with a set of strong, actionable leads that can make a difference.

Covering all Bases

The strength of web scraping and web crawling lies in the fact that it covers all the necessary bases when it comes to lead generation. Data is harvested, structured, categorized and organized in such a way that businesses can easily use the data provided for their sales leads. As discussed earlier, cold and detached lists no longer provide you with enough actionable leads. You need to look at various factors and consider them during your lead generation efforts –

•    Contact details of the prospect

•    Purchasing power and purchasing history of the prospect

•    Past purchasing trends, willingness to purchase and history of buying preferences of the prospect

•    Social markers that are indicative of behavioral patterns

•    Commercial and business markers that are indicative of behavioral patterns

•    Transactional details

•    Other factors including age, gender, demography, social circles, language and interests

All these factors need to be taken into account and considered in detail if you have to ensure whether a lead is viable and actionable, or not. With web scraping you can get enough data about every single prospect, connect all the data collected with the help of onboarding, and ascertain with conviction whether a particular prospect will be viable for your business.

Let us take a look at how web scraping addresses these different factors –

1. Scraping website’s


During the scraping process, all websites where a particular prospect has some participation are crawled for data. Seemingly disjointed data can be made into a sensible unit by the use of onboarding- linking user activities with their online entities with the help of user IDs. Documents can be scanned for participation. E-commerce portals can be scanned to find comments and ratings a prospect might have delivered to certain products. Service providers’ websites can be scraped to find if the prospect has given a testimonial to any particular service. All these details can then be accumulated into a meaningful data collection that is indicative of the purchasing power and intent of the prospect, along with important data about buying preferences and tastes.

2. Social scraping


According to a study, most internet users spend upwards of two hours every day on social networks. Therefore, scraping social networks is a great way to explore prospects in detail. Initially, you can get important identification markers like names, addresses, contact numbers and email addresses. Further, social networks can also supply information about age, gender, demography and language choices. From this basic starting point, further details can be added by scraping social activity over long periods of time and looking for activities which indicate purchasing preferences, trends and interests. This exercise provides highly relevant and targeted information about prospects can be constructively used while designing sales campaigns.

Check out How to use Twitter data for your business

3. Transaction scraping


Through the scraping of transactions, you get a clear idea about the purchasing power of prospects. If you are looking for certain income groups or leads that invest in certain market sectors or during certain specific periods of time, transaction scraping is the best way to harvest meaningful information. This also helps you with competition analysis and provides you with pointers to fine-tune your marketing and sales strategies.

get-results-from-your-lead-generation-campaign

Using these varied lead generation techniques and finding the right balance and combination is key to securing the right leads for your business. Overall, signing up for web scraping services can be a make or break factor for your business going forward. With a steady supply of valuable leads, you can supercharge your sales, maximize returns and craft the perfect marketing maneuvers to take your business to an altogether new dimension.

Source: https://www.promptcloud.com/blog/how-to-generate-sales-leads-using-web-scraping-services/