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Deep Web Mining through Web Services

Deep Web Mining through Web Services

Monica Maceli, Min Song
Copyright: © 2009 |Pages: 7
ISBN13: 9781605660103|ISBN10: 1605660108|EISBN13: 9781605660110
DOI: 10.4018/978-1-60566-010-3.ch099
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MLA

Maceli, Monica, and Min Song. "Deep Web Mining through Web Services." Encyclopedia of Data Warehousing and Mining, Second Edition, edited by John Wang, IGI Global, 2009, pp. 631-637. https://doi.org/10.4018/978-1-60566-010-3.ch099

APA

Maceli, M. & Song, M. (2009). Deep Web Mining through Web Services. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining, Second Edition (pp. 631-637). IGI Global. https://doi.org/10.4018/978-1-60566-010-3.ch099

Chicago

Maceli, Monica, and Min Song. "Deep Web Mining through Web Services." In Encyclopedia of Data Warehousing and Mining, Second Edition, edited by John Wang, 631-637. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-010-3.ch099

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Abstract

With the increase in Web-based databases and dynamically- generated Web pages, the concept of the “deep Web” has arisen. The deep Web refers to Web content that, while it may be freely and publicly accessible, is stored, queried, and retrieved through a database and one or more search interfaces, rendering the Web content largely hidden from conventional search and spidering techniques. These methods are adapted to a more static model of the “surface Web”, or series of static, linked Web pages. The amount of deep Web data is truly staggering; a July 2000 study claimed 550 billion documents (Bergman, 2000), while a September 2004 study estimated 450,000 deep Web databases (Chang, He, Li, Patel, & Zhang, 2004). In pursuit of a truly searchable Web, it comes as no surprise that the deep Web is an important and increasingly studied area of research in the field of Web mining. The challenges include issues such as new crawling and Web mining techniques, query translation across multiple target databases, and the integration and discovery of often quite disparate interfaces and database structures (He, Chang, & Han, 2004; He, Zhang, & Chang, 2004; Liddle, Yau, & Embley, 2002; Zhang, He, & Chang, 2004). Similarly, as the Web platform continues to evolve to support applications more complex than the simple transfer of HTML documents over HTTP, there is a strong need for the interoperability of applications and data across a variety of platforms. From the client perspective, there is the need to encapsulate these interactions out of view of the end user (Balke & Wagner, 2004). Web services provide a robust, scalable and increasingly commonplace solution to these needs. As identified in earlier research efforts, due to the inherent nature of the deep Web, dynamic and ad hoc information retrieval becomes a requirement for mining such sources (Chang, He, & Zhang, 2004; Chang, He, Li, Patel, & Zhang, 2004). The platform and program-agnostic nature of Web services, combined with the power and simplicity of HTTP transport, makes Web services an ideal technique for application to the field of deep Web mining. We have identified, and will explore, specific areas in which Web services can offer solutions in the realm of deep Web mining, particularly when serving the need for dynamic, ad-hoc information gathering.

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