Anytime Query Answering in RDF through Evolutionary Algorithms
Eyal Oren,Christophe Guéret,Stefan Schlobach +2 more
- 26 Oct 2008
- pp 98-113
TL;DR: A technique for answering queries over RDF data through an evolutionary search algorithm, using fingerprinting and Bloom filters for rapid approximate evaluation of generated solutions, and presents the prototype that evaluates basic SPARQL queries over arbitrary RDF graphs.
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Abstract: We present a technique for answering queries over RDF data through an evolutionary search algorithm, using fingerprinting and Bloom filters for rapid approximate evaluation of generated solutions. Our evolutionary approach has several advantages compared to traditional database-style query answering. First, the result quality increases monotonically and converges with each evolution, offering "anytime" behaviour with arbitrary trade-off between computation time and query results; in addition, the level of approximation can be tuned by varying the size of the Bloom filters. Secondly, through Bloom filter compression we can fit large graphs in main memory, reducing the need for disk I/O during query evaluation. Finally, since the individuals evolve independently, parallel execution is straightforward. We present our prototype that evaluates basic SPARQL queries over arbitrary RDF graphs and show initial results over large datasets.
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Citations
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References
Space/time trade-offs in hash coding with allowable errors
TL;DR: Analysis of the paradigm problem demonstrates that allowing a small number of test messages to be falsely identified as members of the given set will permit a much smaller hash area to be used without increasing reject time.
Introduction to Evolutionary Computing
Agoston E. Eiben,James C. Smith +1 more
- 01 Jan 2015
TL;DR: In the second edition, the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations as discussed by the authors.
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•Book
Introduction to evolutionary computing
Agoston E. Eiben,James C. Smith +1 more
- 01 Jan 2003
TL;DR: The authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations, and added a chapter on evolutionary robotics with an outlook on possible exciting developments in this field.