RCQ-GA: RDF Chain Query Optimization using Genetic Algorithms

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Alexander Hogenboom, Viorel Milea, Flavius Frasincar, and Uzay KaymakErasmus School of EconomicsErasmus University Rotterdam

{hogenboom, milea, frasincar, kaymak}@ese.eur.nl

October 30, 2009

Introduction

• The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools

• Fast query engines are needed for efficient querying of large amounts of data, usually represented using the Resource Description Framework (RDF)

• Problem: optimizing query paths (the order in which different parts of a query are evaluated)

• Two-phase optimization (2PO) has already been proposed (Stuckenschmidt et al. 2005) in a Semantic Web context, but a genetic algorithm (GA) appears to be a feasible alternative

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RDF and Query Paths (1)

• RDF model is a collection of facts declared using RDF• Facts are triples in the form of a node-arc-node link

consisting of a subject, a predicate, and an object• RDF sources can be queried using SPARQL• We consider a subset of SPARQL queries: chain

queries, where a query path is followed by performing joins between its subpaths of length 1

1. PREFIX c: <http://www.daml.org/2001/09/countries/fips#>2. PREFIX o: <http://www.daml.org/2003/09/factbook/factbook-ont#>3. SELECT ?partner4. WHERE { c:SouthAfrica o:importPartner ?impPartner .5. ?impPartner o:country ?partner .6. ?partner o:border ?border .7. ?border o:country ?neighbour .8. ?neighbour o:internationalDispute ?dispute .9. }

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RDF and Query Paths (2)

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Bushy query tree Right-deep query tree

RDF Query Path Optimization (1)

• Challenge: determine the right order in which the joins should be computed, hereby optimizing the overall response time

• Consider a solution space with query paths• Solutions are associated with data transmission and

processing costs• Data processing costs are the sum of all join costs,

which are influenced by the cardinalities of each operand and the join method used

• Neighbouring solutions in solution space can be identified using transformation rules introduced by Ioannidis and Kang (1990)

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RDF Query Path Optimization (2)

• Stuckenschmidt et al. (2005) propose to use 2PO for RDF chain query optimization:– Using Iterative Improvement (II), local optima are found by

walking through solution space (from random starting points), while only taking steps yielding improvement in solution quality

– The best local optimum thus found is used as starting point for Simulated Annealing (SA); a walk through solution space is performed, where moves not yielding improvement are accepted with a declining probability

• We propose to optimize RDF chain queries using a GA, RCQ-GA

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RDF Query Path Optimization (3)

• In a GA, a population of chromosomes (solutions) is exposed to evolution: selection, crossovers, and mutations

• A GA generally is aware of good solutions faster than 2PO, but tends to spend a lot of time optimizing these already good results before it terminates

• We adopt the BushyGenetic (BG) algorithm proposed by Steinbrunn et al. (1997) for traditional query path optimization, but stimulate quicker convergence through elitist selection, fitness-based selection, a decreased population size, and tighter stopping conditions

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RDF Query Path Optimization (4)

• Solutions are encoded using an efficient ordinal number encoding scheme, facilitating easy crossover and mutation operations

• The algorithm iteratively joins two concepts in an ordered list of concepts

• Result is saved on position of first appearing concept• Example:

– (c1, c2, c3, c4): join 3 and 4– (c1, c2, c3c4): join 1 and 2– (c1c2, c3c4): join 1 and 2– (c1c2c3c4)

• Encoded chromosome: ((3,4),(1,2),(1,2))

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RDF Query Path Optimization (5)

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Performance (1)

• We benchmark execution times and solution quality of BG and our adaptation to RDF query environments, RCQ-GA, against those of 2PO

• The effects of a time limit (1 second) on 2PO and RCQ-GA are also assessed

• The entire solution space is considered (i.e., bushy query trees are valid options)

• Each algorithm is tested on chain queries varying in length from 2 to 20 predicates

• Each experiment is iterated 100 times

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Performance (2)

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Relative deviation of average execution times from 2PO average

Performance (3)

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Relative deviation of average solution costs from 2PO average

Performance (4)

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Relative deviation of coefficients of variation of solution costs from 2PO average

Conclusions

• In optimizing the query path for chain queries in a single-source RDF query execution environment, the performance of a GA compared to 2PO is positively correlated with the complexity of the solution space and the restrictiveness of the environment

• An appropriately configured GA can outperform 2PO in solution quality, execution time needed, and consistency of solution quality

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Future Work

• Optimize parameters (e.g., using meta-algorithms)• Evaluate performance in a distributed setting• Experiment with other algorithms, such as ant colony

optimization or particle swarm optimization

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Questions?

• Feel free to contact:Alexander HogenboomErasmus School of EconomicsErasmus University RotterdamP.O. Box 1738, 3000 DR, The Netherlandshogenboom@ese.eur.nl

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