What is Data Harvesting?
As we know that the verb harvest is used to indicate the analogy with agriculture where the fruits have to be harvested before they fall from the plants, in the same way one can harvest data from various websites. Data harvesting is the process of extracting representing and analyzing, trends and patterns from raw social media data. Social media data harvesting requires expert data analysts and automated software programs to filter through massive amounts of raw social media data (e.g., on social media usage, online behaviors, connections between individuals, online buying behavior, sharing of content etc.) in order to detect patterns and trends. Web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol (http), or through a web browser easily.
While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a bot or spider (web crawler). It is a form of copying, in which specific data is gathered and copied from the web, typically into a spreadsheet or central local database, for later analysis.
Web scraping a web page involves extracting it and fetching from it. Fetching is the downloading of a page. Web crawling is a main element of web scraping, to fetch pages for later processing. Once fetched, then extraction can be easily take place. The content of a page may be searched, parsed, reformatted, its data copied into a spreadsheet, and so on. Web scrapers typically take something out of a page; make use of it for another purpose somewhere else. For example, find and copy names and phone numbers, or companies and their URLs to a list. This is well-known as contact scraping.
Process of web harvesting
The process involves in web harvesting is mainly divides into three tasks:
- Retrieving data, which involves finding useful information on the Web and storing it locally. This requires knowledge of tools for searching and navigating the Web,
- Extracting data, which involves identifying useful data on retrieved content pages and extracting it into a structured format. The important tools that allow access to the data for further analysis are content spotters, parsers and adaptive wrappers
- However, Integrating data, which involves filtering, cleaning transforming, combining and refining the data extracted from one or more web sources, and structuring the results according to a desired output. The important aspect of this task is organizing the extracted data in such a way as to allow data mining tasks and unified access for further analysis.
The ultimate goal of web harvesting is to assemble as much information as possible from the Web on one or more domains and to create a huge, structured knowledge base. This knowledge base should then allow querying for information similar to a conventional database system.
Different software’s used for data harvesting
- Rapid Miner
- Orange
- Weka
- KNIME
- Sisense
- SSDT (SQL Server Data Tools)
- Apache Mahout
- Oracle Data Mining
- Rattle
- DataMelt
- IBM Cognos
- IBM SPSS Modele
- SAS Data Mining
- Teradata
- Board
- Dundas BI
Methods to prevent Web Harvesting
The term data harvesting or web scraping, has always been a concern for website operators, developers and data publishers. Data harvesting is a process to extract large amount of data from websites automatically with the help of a small script. This process is familiar as a malicious bot. As a cheap and easy way to collect online data, the technique can often use without permission to steal website information such as contact lists, photos, text email addresses, etc.
Aside from obvious consequence of data loss, data harvesting can also be harmful to businesses in other ways:
- Poor SEO Ranking. If your website content is reproduced, scraped and used on other sites. This will significantly affect the SEO ranking and performance of your website on search engines.
- Decreased Website Speed. When used repeatedly, data scraping attacks can lower the performance of website and also affect the user experience.
- Lost Market Advantages. Your competitors may use data harvesting to take valuable information such as customer lists to gather rough idea, intelligence about your business.
Website builders opts various available methods to protect different types of online data from scrapping.
Tools for prevention of Data Scraping
For the protection of databases, Caspio provides various tools to help prevent our data from being targeted by malicious bots.
- CAPTCHA — One of the most effective and successful methods to fight data harvesting or data scraping is CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Human Apart). It protects data against bots by displaying a code and tests that only humans can verify to ensure the user is not a bot.
- Access Control — Firstly, Caspio provide a built-in feature to create search criteria for allow access to database records. To be specific, only records that match the search criteria can be accessed. Therefore, data harvesting can be prevented, a bot is unable to gain access to records that do not match the search criteria through the report.
- Complex IDs — many databases use an auto-number or other equivalent ID forms as database keys. If you have an Update Form or a pre-defined criteria Report based on a sequential ID, it’s very easy for a bot to cycle through all your records using the sequential IDs. Using a much more complex ID such as a GUID is one way to address this. In Caspio, you can easily generate random unique IDs in a hidden text field format.
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