Bozzon, Alessandro

Dell'Aglio, Daniele

Della Valle, Emanuele

Zahmatkesh, Shima

ZAHMATKESH, SHIMA

DELLA VALLE, EMANUELE

DELL'AGLIO, DANIELE

BOZZON, ALESSANDRO

Archivio istituzionale della ricerca - Politecnico di Milano

Towards a Top-K SPARQL Query BenchmarkShima Zahmatkesh, Emanuele Della Valle, Daniele Dell’Aglio, and AlessandroBozzonDEIB - Politecnico of Milano, Delft University of Technologyshima.zahmatkesh@polimi.it, emanuele.dellavalle@polimi.it,daniele.dellaglio@polimi.it, a.bozzon@tudelft.nlAbstract. The research on optimization of top-k SPARQL query wouldlargely benefit from the establishment of a benchmark that allows com-paring different approaches. For such a benchmark to be meaningful, atleast two requirements should hold: 1) the benchmark should resemblereality as much as possible, and 2) it should stress the features of the top-k SPARQL queries both from a syntactic and performance perspective.In this paper we propose Top-k DBPSB: an extension of the DBpediaSPARQL benchmark (DBPSB), a benchmark known to resemble real-ity, with the capabilities required to compare SPARQL engines on top-kqueries.Keywords: Top-k Query, SPARQL, Benchmark1 Problem StatementTop-k queries – queries returning the top k results ordered according to a user-defined scoring function – are gaining attention in the Database [1] and SemanticWeb communities [2–6]. Order is an important property that can be exploitedto speed up query processing, but state-of-the-art SPARQL engines such asVirtuoso [7], Sesame [8], and Jena [9], do no exploit order for query optimisa-tion purposes. Top-k SPARQL queries are managed with a materialize-then-sortprocessing schema [1] that computes all the matching solutions (e.g., thousands)even if only a limited number k (e.g., ten) are requested.Recent works [2–5] have shown that an efficient split-and-interleave process-ing schema [1] could be adopted to improve the performance of top-k SPARQLqueries. To the best of our knowledge, a consistent comparison of those worksdoes not exist. As often occurs, the main cause for this fragmentation resides inthe partial lack of a SPARQL benchmark covering top-k SPARQL queries. Tofoster the work on top-k query processing within the Semantic Web community,we believe that it is the right time to define a top-k SPARQL benchmark.Following well known principles of benchmarking [10], we formulate the re-search question of this work as: is it possible to set up a benchmark for top-kSPARQL queries, which resembles reality as much as possible and stresses thefeatures of top-k queries both from a syntactic (i.e., queries should contain rank-related clauses) and performance (i.e., the query mix should insist on character-istics of top-k queries which stress SPARQL engine) perspective?2 MetodologyIn this poster, we describe our ongoing work on Top-k DBpedia SPARQL Bench-mark (Top-k DBPSB). It extends the methodology, proposed in DBPSB [11], tobuild SPARQL benchmarks that resemble reality. It uses the same dataset, per-formance metrics, and test driver of DBPSB, but it extends the query variabil-ity feature of DBPSB by proposing an algorithm to automatically create top-kqueries from the 25 auxiliary queries of the DBPSB and its datasets.In order to present Top-k DBPSB, we need to introduce some terminology:– Definition 1: Rankable Data Property is a RDF property whose range isin xsd:int, xsd:long, xsd:float, xsd:integer, xsd:decimal, xsd:double, xsd:date,xsd:dateTime, xsd:time, or xsd:duration.– Definition 2: Rankable Triple Pattern is a triple pattern that has a Rank-able Data Property in the property position of the pattern.– Definition 3: When a variable, in the object position of a Rankable TriplePattern, appears in a scoring criteria of the scoring function, we call it Scor-ing Variable and we call Rankable Variable the one appearing in the subjectposition.Figure 1 shows an overview of the process followed by Top-k DBPSB to gener-ate top-k SPARQL queries from the Auxiliary Queries and datasets of DBSBM.The process consists of four steps: 1) finding rankable variables, 2) computingmaximum and minimum values for each rankable variable, 3) generating scoringfunctions, and 4) generating top-k queries.Fig. 1: Top-k DBPSB generates top-k SPARQL queries starting from the Auxiliary queries and datasetsof DBPSB (a SPARQL benchmark known to resemble reality).In the first step, Top-k DBPSB looks for all rankable variables, in each aux-iliary query of each BDPSB query template, which could be used as part of aranking criterion in a scoring function. For each DBPSB auxiliary query, Top-kDBPSB first checks if the variables in the query fit the definition of scoring vari-able. To find additional rankable variable Top-k DBPSB considers all variablesin the query and tries also to find rankable triple pattern related to them.For the sake of simplicity, Top-k DBPSB generates scoring function as aweighted sum of normalized ranking criteria, therefore, for each rankable vari-able, Top-k DBPSB needs to compute its maximum and minimum value. So, foreach DBPSB auxiliary query and each rankable triple pattern identified in theprevious step, Top-k DBPSB generates a query to find those values.After having collected those values, for each rankable triple pattern, Top-kDBPSB creates a new top-k query template. For instance, Query 2 of Figure1 shows such a query as generated by the Top-K DBPSB. In order to obtainan executable query, for each scoring variable that appears in scoring functionTop-K DBPSB adds the related rankable triple pattern to the body of the query.1.3 ExperimentsGiven that we extended DBPSB we give for positively answered the first part ofour research question (i.e., Top-k DBPSB resembles reality). In this section, weprovide preliminary evidence that the query variability feature of Top-k DBPSBpositively answers also the second part of our research question. We do so byoperationalising our research question in three hypotheses:H.1 The more are the number of the Rankable Variables, the longer is the averageexecution time.H.2 The more are the number of the Scoring Variables in the scoring function,the longer is the average execution time.H.3 The value of the LIMIT clause has not any significant impact on the averageexecution time.In order to evaluate our hypothesis, we carry out our experiments by usingthe SPARQL engines Virtuoso, and Jena. After preparing the experimental en-vironment, we load the DBpedia dataset with the scale factors of 10% on theSPARQL engines. In order to evaluate the performance of SPARQL engines,we use the DBpedia SPARQL Benchmark test driver and modify it for Top-kqueries. We execute the generated Top-k SPARQL queries against these twoSPARQL engines. The information gains from the execution of randomly gener-ated query against the SPARQL engines is used to evaluate our hypotheses.As a result we got that most of the queries templates generated by Top-kDBPSB are compatible with our hypotheses H.2 and H.3. On the contrary, Top-k DBPSB does not generate adequate query templates to test the hypothesis H.1:only 6 queries templates out of the 25 in DBPSB can be use in validating H.1while the others have only one rankable variable. Further investigation is neededto refine the hypothesis H.1 and better qualify the cases that stress SPARQLengines when answering top-k queries.1 The implementation code is available online at https ://bitbucket.org/sh_zahmatkesh/top− k − dbpsb4 ConclusionIn this work, we presented Top-k DBPSB, an extension of DBPSB [11] that addsthe possibility to automatically generate top-k queries. Top-k DBPSB satisfiesthe requirement of resembling the reality extending DBPSB which automaticallyderives datasets and queries from DBpedia and it query logs. Moreover, weprovide experimental evidence that Top-k DBPSB also satisfies the requirementto stress the distinguish features of top-k queries.In order to support the claim that we positively answered to the researchquestion presented in Section 1, we experimentally showed that the query vari-ability provided by Top-k DBPSB stresses the SPARQL engines. To this end,we formulated three hypothesis and we empirically demonstrated that when thenumber of scoring variables increases the average execution time also does (hy-pothesis H.2) and that the average execution time is independent from the valueused in the LIMIT clause (hypothesis H.3). Counterintuitively, hypothesis H.1 isnot confirmed by our experiments.References1. Ilyas, I.F., Beskales, G., Soliman, M.A.: A survey of top-k query processing tech-niques in relational database systems. ACM Computing Surveys (CSUR) 40(4)(2008) 112. Magliacane, S., Bozzon, A., Della Valle, E.: Efficient execution of top-k sparqlqueries. In: The Semantic Web–ISWC 2012. Springer (2012) 344–3603. Wagner, A., Duc, T.T., Ladwig, G., Harth, A., Studer, R.: Top-k linked data queryprocessing. In: The Semantic Web: Research and Applications. Springer (2012)56–714. Cheng, J., Ma, Z., Yan, L.: f-sparql: a flexible extension of sparql. In: Databaseand Expert Systems Applications, Springer (2010) 487–4945. Cedeno, J.P.: A Framework for Top-K Queries over Weighted RDF Graphs. PhDthesis, Arizona State University (2010)6. Siberski, W., Pan, J.Z., Thaden, U.: Querying the semantic web with preferences.In: ISWC. (2006) 612–6247. Erling, O., Mikhailov, I.: Rdf support in the virtuoso dbms. In Auer, S., Bizer, C.,Müller, C., Zhdanova, A.V., eds.: CSSW. Volume 113 of LNI., GI (2007) 59–688. Broekstra, J., Kampman, A., van Harmelen, F.: Sesame: A generic architecture forstoring and querying rdf and rdf schema. In Horrocks, I., Hendler, J.A., eds.: In-ternational Semantic Web Conference. Volume 2342 of Lecture Notes in ComputerScience., Springer (2002) 54–689. Owens, A., Seaborne, A., Gibbins, N., mc schraefel: Clustered tdb: A clusteredtriple store for jena. Technical report, Electronics and Computer Science, Univer-sity of Southampton (2008)10. Gray, J., ed.: The Benchmark Handbook for Database and Transaction Systems(2nd Edition). Morgan Kaufmann (1993)11. Morsey, M., Lehmann, J., Auer, S., Ngomo, A.C.N.: Dbpedia sparql benchmark- performance assessment with real queries on real data. In Aroyo, L., Welty, C.,Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N.F., Blomqvist, E., eds.: Inter-national Semantic Web Conference (1). Volume 7031 of Lecture Notes in ComputerScience., Springer (2011) 454–469

Towards a Top-K SPARQL Query Benchmark

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Towards a Top-K SPARQL Query Benchmark

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