By Oris Friesen, Gilles Gauthier-Villars (auth.), Raghu Ramakrishnan (eds.)

The premise at the back of constructing strong declarative database languages is compelling: by means of permitting clients to specify their queries (and their integrity constraints) in a transparent, non-operational method, they make the user's job more uncomplicated, and supply the database approach with extra possibilities for optimization. Relational database structures provide a extraordinary evidence that this premise is certainly legitimate. the preferred relational question language, SQL, is predicated 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 big consider the industrial luck of relational databases. it truly is famous that SQL has a few very important boundaries, despite its good fortune and recognition. significantly, the question language is non-recursive, and aid for integrity constraints is restricted. certainly, spotting those difficulties, the newest common, SQL-92, presents elevated help for integrity constraints, and it truly is expected that the successor to the SQL-92 average, referred to as SQL3, RECURSIVE UNION operation [1]. good judgment database structures have will comprise 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 good points (another extension prone to seem in SQL3).

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Height integer); create basis predicate wall(height integer, support_beam ref(beam), floor ref(floor)); create basis predicate turbine(x_coord integer, y_coord integer); Now we define a global soft constraint to check that no columns exceed a height greater than twice the floor height. height CH) and 2 * H> CH; The literal "C:column(height H, floor F)" denotes the reference C of an object of class column, with value H for the "height" attribute and value F for the "floor" attribute. We can now create the following soft constraint: create soft constraint no_high_column not exists C: too_high_column(column C) notify (column C) with "The column" C "is too high"; 20 CHAPTER 1 Now check that the weight of all the beams and columns in the structural database is less than the capacity of some crane assigned to that floor.

The rules which specify the "fix" for "dirty" data are also discussed. 1 An Example Application In this application, "dirty" data from existing operational systems is "cleaned" and a new shared corporate database is populated with "clean" data. The data relates to the client's customers, and the telephone services that the client provides to its customers at different locations. The data is in several tables in a relational DBMS (some of these tables are populated by extracting data from non-relational databases of legacy applications).

And T. Imielinski, "Research Directions in Knowledge Discovery," SIGMOD Record, September 1991, pp. 76-78. , "Urban School Improvement Project Proposal," University of Illinois, May 8, 1992. , "Data Dredging," Data Engineering, Dec. 1990, pp. 58-63. , P. Bayer, A. Lefebvre and V. Kuechenhoff, "EKS, A Short Overview," AAAI-90 Workshop on Knowledge Management Systems. July 1990. edu ABSTRACT This chapter discusses an extended deductive database prototype system, Q-Data, developed by Bellcore to improve data quality through data validation and cleanup.

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