Information systems have become the nerve center of current computer-based engineering applications, which hereby put the requirements on engineering information modeling. Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling. It should be noted that, however, the current mainstream databases are mainly used for business applications. Some new engineering requirements challenge today’s database technologies and promote their evolvement. Database modeling can be classified into two levels: conceptual data modeling and logical database modeling. In this chapter, we try to identify the requirements for engineering information modeling and then investigate the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models. In addition, the relationships among the conceptual data models and the logical database models for engineering information modeling are presented in the chapter viewed from database conceptual design.
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Modern database technology involves processing a large volume of data in databases to discover new knowledge. Knowledge discovery is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data (Adriaans & Zantinge, 1996; Agrawal, Imielinski, & Swami, 1993; Berry & Linoff, 2000; Brachman & Anand, 1996; Brachman, Khabaza, Kloesgen, Piatetsky-Shapiro, & Simoudis, 1996; Bradley, Gehrke, Ramakrishnan, & Srikant, 2002; Fayad, 1996; Fayad, Piatetsky-Shapiro, & Symth, 1996a, 1996b, 1996c; Fayyad & Uthurusamy, 2002; Frawley, Piatetsky-Shapiro, & Matheus, 1992; Han & Kamber, 2000; Hand, Mannila, & Smyth, 2001; Inmon, 1996; Simoudis, 1996; Uthurusamy, 1996; Keyes, 1990).
Databases contain a variety of patterns, but few of them are of much interest. A pattern is interesting to the degree that it is not only accurate but that it is also useful with respect to the end user’s knowledge and objectives (Brachman et al., 1996; Bradley et al., 2002; Hand et al., 2001; Berry & Linoff, 2000; Piatetsky-Shapiro & Matheus, 1994; Silberschatz & Tuzhilin, 1995). A critical issue in knowledge discovery is how well the database is created and maintained. Real-world databases present difficulties as they tend to be dynamic, incomplete, redundant, inaccurate, and very large. Naturally, the efficiency of the discovery process and the quality of the discovered knowledge are strongly dependent on the quality of data.
To discover useful knowledge from the databases, we need to provide clean data to the discovery process. Most large databases have redundant and inconsistent data, missing data fields, and values, as well as data fields that are not logically related and are stored in the same data relations (Adriaans & Zantinge, 1996; Parsaye & Chingell, 1999; Piatetesky-Shapiro, 1991; Savasere et al. 1995). Subsequently, the databases have to be cleaned before the actual discovery process takes place in order to avoid discovering incomplete, inaccurate, redundant, inconsistent, and uninteresting knowledge. Different tools and techniques have been developed to improve the quality of the databases in recent years, leading to a better discovery environment. There are still problems associated with the discovery techniques/schemes which cause the discovered knowledge to be incorrect, inconsistent, incomplete, and uninteresting.