By Oris Friesen, Gilles Gauthier-Villars (auth.), Raghu Ramakrishnan (eds.)
The premise in the back of constructing strong declarative database languages is compelling: by means of allowing clients to specify their queries (and their integrity constraints) in a transparent, non-operational approach, they make the user's activity more uncomplicated, and supply the database procedure with extra possibilities for optimization. Relational database platforms provide a amazing evidence that this premise is certainly legitimate. the preferred relational question language, SQL, relies upon relational algebra and calculus, i.e., a small fragment of first-order good judgment, and the benefit of writing queries in SQL (in comparability to extra navigational languages) has been a huge consider the economic good fortune of relational databases. it's famous that SQL has a few vital barriers, inspite of its luck and recognition. particularly, the question language is non-recursive, and aid for integrity constraints is proscribed. certainly, spotting those difficulties, the newest ordinary, SQL-92, offers elevated help for integrity constraints, and it's expected that the successor to the SQL-92 usual, known as SQL3, RECURSIVE UNION operation . good judgment database platforms have will contain a focused on those extensions to the relational database paradigm, and a few platforms (e.g., Bull's DEL prototype) have even integrated object-oriented gains (another extension more likely to look in SQL3).
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Additional info for Applications of Logic Databases
We present our conclusions in Section 6. Q-Data: Improving Data Quality 2 27 RELATED WORK Our investigation in data validation and cleanup is driven by specific needs of our clients. These needs are similar to those discussed (albeit at a high-level) in  and .  discusses developing a (new) corporate subject database consisting of customer information by cleaning up data from multiple legacy systems. One application discussed in  involves verifying that related data in three legacy application systems are consistent.
Prolog does not guarentee that any particular query will terminate. • Prolog does not guarantee that any particular query will return all matching data items. Prolog for string matching: Advantages Another major sub-task in Q-Data was to determine whether two strings were duplicates of each other. A special kind of rule called a "soft string match" was needed which would match two strings even if there were misspellings, abbreviations, letter transpositions and/or different word order in the two strings.
The rest of this chapter is organized as follows. Section 2 briefly discusses some of the related work. Section 3 describes the overall data cleanup problem faced by a typical customer, and identifies several specific data cleanup tasks that are used as examples. Section 4 describes the software architecture for the Q-Data prototype system. Our experiences analyzing available technologies as components of Q-Data, and designing, building, and testing the prototype are discussed. We also describe in greater depth the strengths and weaknesses of LDL, Prolog, c++ and SQL languages and their implementations, for searching and control, database access, and rule specifications.