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3.5 Databases, Knowledge Discovery, Information Retrieval, and Web Mining

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Hoche, Susanne, Ingolf Geist, Lourdes Peña Castillo, and Nadine Schulz. 2006. Section 3.5 Databases, Knowledge Discovery, Information Retrieval, and Web Mining, pp. 168-184 of Chapter 3 Methods, Algorithms, and Software, in CIGR Handbook of Agricultural Engineering Volume VI Information Technology. Edited by CIGR--The International Commission of Agricultural Engineering; Volume Editor, Axel Munack. St. Joseph, Michigan, USA: ASABE.  .(doi:10.13031/2013.21672)
Authors:   S. Hoche, I. Geist, L. Peña Castillo, and N. Schulz
Keywords:   Databases, Data mining, Information retrieval, Web mining

An abundance of digital information is now available, and large investments in data collection are being made in, for instance, the area of agribusiness. A successful exploitation of the gathered data, e.g., to extract valuable information, detect useful, frequent or extraordinary patterns, or to support complex decision processes, demands powerful means for storing, accessing and analyzing data. Database Management Systems (DBMSs) provide an efficient, integrated and standardized platform for data storage and access. Knowledge Discovery in Databases (KDD) aims at the semi-automatic discovery of useful information in large data collections usually stored in databases. Information Retrieval (IR) is concerned with gathering from unstructured and semantically fuzzy data, such as natural language texts, images, audio, or video, information relevant to a user-defined query. Web mining describes techniques to extract useful information from the World Wide Web. In this section, we present an overview of the state of the art of DBMSs and the emerging fields of KDD, IR, and web mining, and relate core methodologies to agricultural applications.

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