Using External Input Data in Off-the-shelf Web Scrapers

There is a question I’ve wanted to shed some light upon for a long time already: “What if I need to scrape several URL’s based on data in some external database?“.

For example, recently one of our visitors asked a very good question (thanks, Ed):

    “I have a large list of amazon.com asin. I would like to scrape 10 or so fields for each asin. Is there any web scraping software available that can read each asin from a database and form the destination url to be scraped like http://www.amazon.com/gp/product/{asin} and scrape the data?”

This question impelled me to investigate this matter. I contacted several web scraper developers, and they kindly provided me with detailed answers that allowed me to bring the following summary to your attention:
Visual Web Ripper

An input data source can be used to provide a list of input values to a data extraction project. A data extraction project will be run once for each row of input values. You can find the additional information here.
Web Content Extractor

You can use the -at”filename” command line option to add new URLs from TXT or CSV file:

    WCExtractor.exe projectfile -at”filename” -s

projectfile: the file name of the project (*.wcepr) to open.
filename – the file name of the CSV or TXT file that contains URLs separated by newlines.
-s – starts the extraction process

You can find some options and examples here.
Mozenda

Since Mozenda is cloud-based, the external data needs to be loaded up into the user’s Mozenda account. That data can then be easily used as part of the data extracting process. You can construct URLs, search for strings that match your inputs, or carry through several data fields from an input collection and add data to it as part of your output. The easiest way to get input data from an external source is to use the API to populate data into a Mozenda collection (in the user’s account). You can also input data in the Mozenda web console by importing a .csv file or importing one through our agent building tool.

Once the data is loaded into the cloud, you simply initiate building a Mozenda web agent and refer to that Data list. By using the Load page action and the variable from the inputs, you can construct a URL like http://www.amazon.com/gp/product/%asin%.
Helium Scraper

Here is a video showing how to do this with Helium Scraper:

The video shows how to use the input data as URLs and as search terms. There are many other ways you could use this data, way too many to fit in a video. Also, if you know SQL, you could run a query to get the data directly from an external MS Access database like
SELECT * FROM [MyTable] IN "C:\MyDatabase.mdb"

Note that the database needs to be a “.mdb” file.
WebSundew Data Extractor
Basically this allows using input data from external data sources. This may be CSV, Excel file or a Database (MySQL, MSSQL, etc). Here you can see how to do this in the case of an external file, but you can do it with a database in a similar way (you just need to write an SQL script that returns the necessary data).
In addition to passing URLs from the external sources you can pass other input parameters as well (input fields, for example).
Screen Scraper

Screen Scraper is really designed to be interoperable with all sorts of databases. We have composed a separate article where you can find a tutorial and a sample project about scraping Amazon products based on a list of their ASINs.

Source: http://extract-web-data.com/using-external-input-data-in-off-the-shelf-web-scrapers/

How to scrape Yellow Pages with ScreenScraper Chrome Extension

Recently I was asked to help with the job of scraping company information from the Yellow Pages website using the ScreenScraper Chrome Extension. After working with this

simple scraper, I decided to create a tutorial on how to use this Google Chrome Extension for scraping pages similar to this one. Hopefully, it will be useful to many of you.
1. Install the Chrome Extension

You can get the extension here. After installation you should see a small monitor icon in the top right corner of your Chrome browser.
2. Open the source page

Let’s open the page from which you want to scrape the company information:

3. Determine the parent element (row)

The first thing you need to do for the scraping is to determine which HTML element will be the parent element. A parent element is the smallest HTML element that contains all

the information items you need to scrape (in our case they are Company Name, Company Address and Contact Phone).  To some extent a parent element defines a data row in the

resulting table.

To determine it, open Google Chrome Developer Tools (by pressing Ctrl+Shift+I), click the magnifying class (at the bottom of the window) and select the parent element on the

page. I selected this one:

As soon as you have selected it, look into the developer tools window and you will see the HTML code related to this element:

As is seen from the highlighted HTML line, you can easily define a parent element by its class: listingInfoAndLogo.
5. Determine the information elements (columns)

After you have learned how to determine the parent element, it should be easy to specify the information elements that contain the information you want to scrape (they

represent columns in the resultant table).

Just do this in the same way that you did it for the parent element -  by selecting it on the page:

and looking at the highlighted HTML code below:
As you can see, the company name is defined by businessName class.
6. Tune the ScreenScraper itself

After all the data elements you want to scrape are found, open the ScreenScraper by clicking the small monitor icon in the top-right corner of your browser. Then do the

following:

    Enter the parent element class name (listingInfoAndLogo in our case) into the Selector field, preceding it with a dot (*see below for why)
    Click the Add Column button
    Enter a field’s name (any) into the Field text box
    Enter the information item class into the Selector text box, preceding it with a dot
    Repeat steps 2-4 for each information item element you want to be scraped

*You need to put a dot before the class name because the ScreenScraper requires element definition in CSS Selector format only (with a dot before it)

After you enter all these definitions you should see the preview of the scraped data at the bottom of the extension’s window:

If the result is satisfactory you can download it in JSON or CSV format by pressing the corresponding button.


Source: http://extract-web-data.com/how-to-scrape-yellow-pages-with-screenscraper-chrome-extension/

A simple way to turn a website into JSON

Recently, while surfing the web I stumbled upon an simple web scraping service named Web Scrape Master. It is a kind of RESTful web service that extracts data from a specified web site and returns it to you in JSON format.
How it works

Though I don’t know what this service may be useful for, I still like its simplicity: all you need to do is to make an HTTP GET request, passing all necessary parameters in the query string:
http://webscrapemaster.com/api/?url={url}&xpath={xpath}&attr={attr}&callback={callback}

    url  - the URL of the website you want to scrape
    xpath – xpath determining the data you need to extract
    attr - attribute the name you need to get the value of (optional)
    callback - JSON callback function (optional)

For example, for the following request to our testing ground:

http://webscrapemaster.com/api/?url=http://testing-ground.extract-web-data.com/blocks&xpath=//div[@id=case1]/div[1]/span[1]/div

You will get the following response:

[{"text":"<div class='name'>Dell Latitude D610-1.73 Laptop Wireless Computer</div>","attrs":{"class":"name"}}]
Visual Web Scraper

Also, this service offers a special visual tool for building such requests. All you need to do is to enter the URL of the website and click to the element you need to scrape:
Visual Web Scraper
Conclusion

Though I understand that the developer of this service is attempting to create a simple web scraping service, it is still hard to imagine where it can be useful. The task that the service does can be easily accomplished by means of any language.

Probably if you already have software receiving JSON from the web, and you want to feed it with data from some website, then you may find this service useful. The other possible application is to hide your IP when you do web scraping. If you have other ideas, it would be great if you shared them with us.



Source: http://extract-web-data.com/a-simple-way-to-turn-a-website-into-json/

Selenium IDE and Web Scraping

Selenium is a browser automation framework that includes IDE, Remote Control server and bindings of various flavors including Java, .Net, Ruby, Python and other. In this post we touch on the basic structure of the framework and its application to  Web Scraping.
What is Selenium IDE


Selenium IDE is an integrated development environment for Selenium scripts. It is implemented as a Firefox plugin, and it allows recording browsers’ interactions in order to edit them. This works well for software tests, composing and debugging. The Selenium Remote Control is a server specific for a particular environment; it causes custom scripts to be implemented for controlled browsers. Selenium deploys on Windows, Linux, and iOS. How various Selenium components are supported with major browsers read here.
What does Selenium do and Web Scraping

Basically Selenium automates browsers. This ability is no doubt to be applied to web scraping. Since browsers (and Selenium) support JavaScript, jQuery and other methods working with dynamic content why not use this mix for benefit in web scraping, rather than to try to catch Ajax events with plain code? The second reason for this kind of scrape automation is browser-fasion data access (though today this is emulated with most libraries).

Yes, Selenium works to automate browsers, but how to control Selenium from a custom script to automate a browser for web scraping? There are Selenium PHP and other language libraries (bindings) providing for scripts to call and use Selenium. It is possible to write Selenium clients (using the libraries) in almost any language we prefer, for example Perl, Python, Java, PHP etc. Those libraries (API), along with a server, the Java written server that invokes browsers for actions, constitute the Selenum RC (Remote Control). Remote Control automatically loads the Selenium Core into the browser to control it. For more details in Selenium components refer to here.



A tough scrape task for programmer

“…cURL is good, but it is very basic.  I need to handle everything manually; I am creating HTTP requests by hand.
This gets difficult – I need to do a lot of work to make sure that the requests that I send are exactly the same as the requests that a browser would
send, both for my sake and for the website’s sake. (For my sake
because I want to get the right data, and for the website’s sake
because I don’t want to cause error messages or other problems on their site because I sent a bad request that messed with their web application).  And if there is any important javascript, I need to imitate it with PHP.
It would be a great benefit to me to be able to control a browser like Firefox with my code. It would solve all my problems regarding the emulation of a real browser…
it seems that Selenium will allow me to do this…” -Ryan S

Yes, that’s what we will consider below.
Scrape with Selenium

In order to create scripts that interact with the Selenium Server (Selenium RC, Selenium Remote Webdriver) or create local Selenium WebDriver script, there is the need to make use of language-specific client drivers (also called Formatters, they are included in the selenium-ide-1.10.0.xpi package). The Selenium servers, drivers and bindings are available at Selenium download page.
The basic recipe for scrape with Selenium:

    Use Chrome or Firefox browsers
    Get Firebug or Chrome Dev Tools (Cntl+Shift+I) in action.
    Install requirements (Remote control or WebDriver, libraries and other)
    Selenium IDE : Record a ‘test’ run thru a site, adding some assertions.
    Export as a Python (other language) script.
    Edit it (loops, data extraction, db input/output)
    Run script for the Remote Control

The short intro Slides for the scraping of tough websites with Python & Selenium are here (as Google Docs slides) and here (Slide Share).
Selenium components for Firefox installation guide

For how to install the Selenium IDE to Firefox see  here starting at slide 21. The Selenium Core and Remote Control installation instructions are there too.
Extracting for dynamic content using jQuery/JavaScript with Selenium

One programmer is doing a similar thing …

1. launch a selenium RC (remote control) server
2. load a page
3. inject the jQuery script
4. select the interested contents using jQuery/JavaScript
5. send back to the PHP client using JSON.

He particularly finds it quite easy and convenient to use jQuery for
screen scraping, rather than using PHP/XPath.
Conclusion

The Selenium IDE is the popular tool for browser automation, mostly for its software testing application, yet also in that Web Scraping techniques for tough dynamic websites may be implemented with IDE along with the Selenium Remote Control server. These are the basic steps for it:

    Record the ‘test‘ browser behavior in IDE and export it as the custom programming language script
    Formatted language script runs on the Remote Control server that forces browser to send HTTP requests and then script catches the Ajax powered responses to extract content.

Selenium based Web Scraping is an easy task for small scale projects, but it consumes a lot of memory resources, since for each request it will launch a new browser instance.



Source: http://extract-web-data.com/selenium-ide-and-web-scraping/

Basics of Web Data Mining and Challenges in Web Data Mining Process

Today World Wide Web is flooded with billions of static and dynamic web pages created with programming languages such as HTML, PHP and ASP. Web is great source of information offering a lush playground for data mining. Since the data stored on web is in various formats and are dynamic in nature, it's a significant challenge to search, process and present the unstructured information available on the web.

Complexity of a Web page far exceeds the complexity of any conventional text document. Web pages on the internet lack uniformity and standardization while traditional books and text documents are much simpler in their consistency. Further, search engines with their limited capacity can not index all the web pages which makes data mining extremely inefficient.

Moreover, Internet is a highly dynamic knowledge resource and grows at a rapid pace. Sports, News, Finance and Corporate sites update their websites on hourly or daily basis. Today Web reaches to millions of users having different profiles, interests and usage purposes. Every one of these requires good information but don't know how to retrieve relevant data efficiently and with least efforts.

It is important to note that only a small section of the web possesses really useful information. There are three usual methods that a user adopts when accessing information stored on the internet:

• Random surfing i.e. following large numbers of hyperlinks available on the web page.
• Query based search on Search Engines - use Google or Yahoo to find relevant documents (entering specific keywords queries of interest in search box)
• Deep query searches i.e. fetching searchable database from eBay.com's product search engines or Business.com's service directory, etc.

To use the web as an effective resource and knowledge discovery researchers have developed efficient data mining techniques to extract relevant data easily, smoothly and cost-effectively.




Source: http://ezinearticles.com/?Basics-of-Web-Data-Mining-and-Challenges-in-Web-Data-Mining-Process&id=4937441

Data Mining and Financial Data Analysis

Introduction:

Most marketers understand the value of collecting financial data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining - technologies and techniques for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs as well as financial need. In this accessible introduction, we provides a business and technological overview of data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis.

Objective:

1. The main objective of mining techniques is to discuss how customized data mining tools should be developed for financial data analysis.

2. Usage pattern, in terms of the purpose can be categories as per the need for financial analysis.

3. Develop a tool for financial analysis through data mining techniques.

Data mining:

Data mining is the procedure for extracting or mining knowledge for the large quantity of data or we can say data mining is "knowledge mining for data" or also we can say Knowledge Discovery in Database (KDD). Means data mining is : data collection , database creation, data management, data analysis and understanding.

There are some steps in the process of knowledge discovery in database, such as

1. Data cleaning. (To remove nose and inconsistent data)

2. Data integration. (Where multiple data source may be combined.)

3. Data selection. (Where data relevant to the analysis task are retrieved from the database.)

4. Data transformation. (Where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance)

5. Data mining. (An essential process where intelligent methods are applied in order to extract data patterns.)

6. Pattern evaluation. (To identify the truly interesting patterns representing knowledge based on some interesting measures.)

7. Knowledge presentation.(Where visualization and knowledge representation techniques are used to present the mined knowledge to the user.)

Data Warehouse:

A data warehouse is a repository of information collected from multiple sources, stored under a unified schema and which usually resides at a single site.

Text:

Most of the banks and financial institutions offer a wide verity of banking services such as checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some also offer insurance services and stock investment services.

There are different types of analysis available, but in this case we want to give one analysis known as "Evolution Analysis".

Data evolution analysis is used for the object whose behavior changes over time. Although this may include characterization, discrimination, association, classification, or clustering of time related data, means we can say this evolution analysis is done through the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.

Data collect from banking and financial sectors are often relatively complete, reliable and high quality, which gives the facility for analysis and data mining. Here we discuss few cases such as,

Eg, 1. Suppose we have stock market data of the last few years available. And we would like to invest in shares of best companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing our decision making regarding stock investments.

Eg, 2. One may like to view the debt and revenue change by month, by region and by other factors along with minimum, maximum, total, average, and other statistical information. Data ware houses, give the facility for comparative analysis and outlier analysis all are play important roles in financial data analysis and mining.

Eg, 3. Loan payment prediction and customer credit analysis are critical to the business of the bank. There are many factors can strongly influence loan payment performance and customer credit rating. Data mining may help identify important factors and eliminate irrelevant one.

Factors related to the risk of loan payments like term of the loan, debt ratio, payment to income ratio, credit history and many more. The banks than decide whose profile shows relatively low risks according to the critical factor analysis.

We can perform the task faster and create a more sophisticated presentation with financial analysis software. These products condense complex data analyses into easy-to-understand graphic presentations. And there's a bonus: Such software can vault our practice to a more advanced business consulting level and help we attract new clients.

To help us find a program that best fits our needs-and our budget-we examined some of the leading packages that represent, by vendors' estimates, more than 90% of the market. Although all the packages are marketed as financial analysis software, they don't all perform every function needed for full-spectrum analyses. It should allow us to provide a unique service to clients.

The Products:

ACCPAC CFO (Comprehensive Financial Optimizer) is designed for small and medium-size enterprises and can help make business-planning decisions by modeling the impact of various options. This is accomplished by demonstrating the what-if outcomes of small changes. A roll forward feature prepares budgets or forecast reports in minutes. The program also generates a financial scorecard of key financial information and indicators.

Customized Financial Analysis by BizBench provides financial benchmarking to determine how a company compares to others in its industry by using the Risk Management Association (RMA) database. It also highlights key ratios that need improvement and year-to-year trend analysis. A unique function, Back Calculation, calculates the profit targets or the appropriate asset base to support existing sales and profitability. Its DuPont Model Analysis demonstrates how each ratio affects return on equity.

Financial Analysis CS reviews and compares a client's financial position with business peers or industry standards. It also can compare multiple locations of a single business to determine which are most profitable. Users who subscribe to the RMA option can integrate with Financial Analysis CS, which then lets them provide aggregated financial indicators of peers or industry standards, showing clients how their businesses compare.

iLumen regularly collects a client's financial information to provide ongoing analysis. It also provides benchmarking information, comparing the client's financial performance with industry peers. The system is Web-based and can monitor a client's performance on a monthly, quarterly and annual basis. The network can upload a trial balance file directly from any accounting software program and provide charts, graphs and ratios that demonstrate a company's performance for the period. Analysis tools are viewed through customized dashboards.

PlanGuru by New Horizon Technologies can generate client-ready integrated balance sheets, income statements and cash-flow statements. The program includes tools for analyzing data, making projections, forecasting and budgeting. It also supports multiple resulting scenarios. The system can calculate up to 21 financial ratios as well as the breakeven point. PlanGuru uses a spreadsheet-style interface and wizards that guide users through data entry. It can import from Excel, QuickBooks, Peachtree and plain text files. It comes in professional and consultant editions. An add-on, called the Business Analyzer, calculates benchmarks.

ProfitCents by Sageworks is Web-based, so it requires no software or updates. It integrates with QuickBooks, CCH, Caseware, Creative Solutions and Best Software applications. It also provides a wide variety of businesses analyses for nonprofits and sole proprietorships. The company offers free consulting, training and customer support. It's also available in Spanish.

ProfitSystem fx Profit Driver by CCH Tax and Accounting provides a wide range of financial diagnostics and analytics. It provides data in spreadsheet form and can calculate benchmarking against industry standards. The program can track up to 40 periods.




Source: http://ezinearticles.com/?Data-Mining-and-Financial-Data-Analysis&id=2752017

Digitize Data With Data Processing Services

Unorganized data might cost you your numero UNO position in your domain. If you have well-organized data, it will not only be helpful in decision-making but will also guarantee a smooth flow of your business. If you are stuck with heaps of documents to be converted into electronic format. Then, outsourcing your files to a company providing Large Volume Data Processing Services is the most accurate and efficient option.

Data processing is the process in which computer programs and other processing systems are used to analyze, summarize and convert the data into an electronic format.

It involves a series of process which are: -

    Validation - This process checks that whether the entries are correct or not.
    Sorting - In this process, sorting is done either sequentially or in various sets.
    Summarize data - This process summarizes the data into main points.
    Aggregation - Combination of different fragments of records takes place in this process.
    Analysis - This process involves the analysis, interpretation and presentation of the collected and organized data.

Data processing companies have comprehensive knowledge about all the above mentioned steps and will provide a complete package of Large volume data processing services which includes: -

    Manual data entry
    Forms based data capture
    Full text data capture
    Digitization
    Document conversion
    Word Processing
    e-Book conversion
    Data extraction from web
    OCR- Optical character recognition

By outsourcing, you can get rid of large volumes of data pretty quickly and can lay more stress on core business activities.

You will have access to many other benefits like: -

    Heaps of cluttered and unorganized work will be organized, sorted and digitized.
    You can make use of neatly organized data to make informed business decisions.
    Chances of losing data will be scarce once it is digitized.
    You can do away with unwanted data and get access to relevant data.
    You can cut down the operating costs and need not incur any expenses in setting up infrastructure.
    You can get the data converted into a form of your choice.

Companies that deal with Large volume data processing services have the experience, expertise, manpower and technology to deliver results as per your expectations. They can handle your bulk of data easily and process it in your desired format within the deadline.

If you want your large volume of data to be digitized with accuracy and at cost-effective rates, choose an outsourcing company which has years of experience in providing Large volume data processing services. You just need to spend a few hours browsing on the net and then short-listing the prospectives. Once you are done with going through the portfolio of these firms and are contented with their information, you can negotiate the rate with them and stipulate the time.

This article about large volume data Processing services has been authored by Sam Efron. He is an experienced technical content writer from data-entry-india.com. With several years of experience and expertise of writing about Data Processing Services, he brings a seasoned maturity and knowledge to his articles.




Source: http://ezinearticles.com/?Digitize-Data-With-Data-Processing-Services&id=7963690