Extending the power of datalog recursion

Extending the power of datalog recursion Supporting aggregates in recursive logic rules represents a very important problem for Datalog. To solve this problem, we propose a simple extension, called Datalog $$^{FS}\,$$ (Datalog extended with frequency support goals), that supports queries and reasoning about the number of distinct variable assignments satisfying given goals, or conjunctions of goals, in rules. This monotonic extension greatly enhances the power of Datalog, while preserving (i) its declarative semantics and (ii) its amenability to efficient implementation via differential fixpoint and other optimization techniques presented in the paper. Thus, Datalog $$^{FS}\,$$ enables the efficient formulation of queries that could not be expressed efficiently or could not be expressed at all in Datalog with stratified negation and aggregates. In fact, using a generalized notion of multiplicity called frequency, we show that diffusion models and page rank computations can be easily expressed and efficiently implemented using Datalog $$^{FS}\,$$ . http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Extending the power of datalog recursion

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2013 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-012-0299-1
Publisher site
See Article on Publisher Site

References

  • Monotonic aggregation in deductive database
    Ross, KA; Sagiv, Y

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