Databases

Constraints and Databases by Raghu Ramakrishnan, Peter Stuckey

By Raghu Ramakrishnan, Peter Stuckey

Constraints and Databases comprises seven contributions at the quickly evolving learn zone of constraints and databases. This number of unique study articles has been compiled as a tribute to Paris C. Kanellakis, one of many pioneers within the box.
Constraints have lengthy been used for preserving the integrity of databases. extra lately, constraint databases have emerged the place databases shop and manage facts within the kind of constraints. The generality of constraint databases makes them hugely appealing for plenty of functions. Constraints offer a uniform mechanism for describing heterogenous facts, and complex constraint fixing tools can be utilized for effective manipulation of constraint facts.
The articles integrated during this ebook disguise the diversity of subject matters regarding constraints and databases; subscribe to algorithms, overview equipment, purposes (e.g. information mining) and implementations of constraint databases, in addition to extra conventional issues akin to integrity constraint upkeep.
Constraints and Databases is an edited quantity of unique learn comprising invited contributions by means of top researchers.

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Comprehension syntax. SIGMOD Record. 16. -H. Byon & P. (1995). Disco: A constraint database system with sets. In CONTESSA Workshop on Constraint Databases and Applications. 17. S. Chauduri & K. (l993). Query optimization in the presence of foreign functions. In Proc. 19th International Conference on Very Large Data Bases. 18. J. Chomicki & T. (1989). Relational specifications of infinite query answers. In Proc. ACM SIGMOD International Conference on Management ofData, pages 174-183. 19. A. (l990).

In the first phase, we use samples that are relatively small, so that we can spend the gambling time on considering many potential plans. However, instead of keeping just the best estimated plan we keep a small set of the best plans. Then, in the second phase we concentrate on a more accurate sampling, spending the remaining amount of the gambling time to try to find the best plan. The algorithm has several parameters, including the degrees of confidence used in statistical tests, some number constants (such as the maximum number of "best" plans that will be output), and some fractional constants (such as the proportion € that represents a significant difference in cost).

Kim & Y. (1992). Querying object-oriented databases. In Proc. ACM SIGMOD Inti. Con! on Management ofData, pages 393-402. 38. A. (I988). On conjunctive queries containing inequalities. Journal ofACM 35: 146-160. 39. G. M. (1993) Aggregation in constraint databases. [n Proc. Workshop on Principles and Practice of Constraint Programming. 40. (1991). C. Lassez & J-L. Lassez. Quantifier elimination for conjunctions of linear constraints via a convex hull algorithm. Technical Report RC[6779, IBM TJ. Watson Research Center.

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