SPARQL basic graph pattern optimization using selectivity estimation

Stocker, Markus; Seaborne, Andy; Bernstein, Abraham; Kiefer, Christoph; Reynolds, Douglas H.

doi:10.1145/1367497.1367578

Cited by 231 publications

(220 citation statements)

References 14 publications

(21 reference statements)

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“…To identify the most suitable triple pattern to remove from a query, we utilize the variable counting heuristic introduced in [14]. Essentially, this heuristic is based on the assumption that unbound subjects are more selective than unbound objects which in turn are more selective than unbound predicates.…”

Section: Holistic Augmentationmentioning

confidence: 99%

Caching and Prefetching Strategies for SPARQL Queries

Lorey

Naumann

2013

Advanced Information Systems Engineering

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Linked Data repositories offer a wealth of structured facts, useful for a wide array of application scenarios. However, retrieving this data using Sparql queries yields a number of challenges, such as limited endpoint capabilities and availability, or high latency for connecting to it. To cope with these challenges, we argue that it is advantageous to cache data that is relevant for future information needs. However, instead of only retaining results of previously issued queries, we aim at retrieving data that is potentially interesting for subsequent requests in advance. To this end, we present different methods to modify the structure of a query so that the altered query can be used to retrieve additional related information. We evaluate these approaches by applying them to requests found in real-world Sparql query logs.

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Section: Holistic Augmentationmentioning

confidence: 99%

Caching and Prefetching Strategies for SPARQL Queries

Lorey

Naumann

2013

Advanced Information Systems Engineering

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“…Existing approaches focus on algebraic [11,13] or selectivity-based optimizations [14]. Despite an increasing need from practitioners [5,6], few works address top-k query optimization in SPARQL.…”

Section: Related Workmentioning

confidence: 99%

Efficient Execution of Top-K SPARQL Queries

Magliacane

Bozzon

Valle

2012

The Semantic Web – ISWC 2012

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Abstract. Top-k queries, i.e. queries returning the top k results ordered by a user-defined scoring function, are an important category of queries. Order is an important property of data that can be exploited to speed up query processing. State-of-the-art SPARQL engines underuse order, and top-k queries are mostly managed with a materialize-then-sort processing scheme that computes all the matching solutions (e.g. thousands) even if only a limited number k (e.g. ten) are requested. The SPARQL-RANK algebra is an extended SPARQL algebra that treats order as a first class citizen, enabling efficient split-and-interleave processing schemes that can be adopted to improve the performance of top-k SPARQL queries. In this paper we propose an incremental execution model for SPARQL-RANK queries, we compare the performance of alternative physical operators, and we propose a rank-aware join algorithm optimized for native RDF stores. Experiments conducted with an open source implementation of a SPARQL-RANK query engine based on ARQ show that the evaluation of top-k queries can be sped up by orders of magnitude.

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“…This means that optimisation must be done by the query authors, as writing extensive queries for a complex graph-matching is not yet the best solution regarding scalability. Further work should probably to be done to optimise complex SPARQL query processing and decomposition of patterns [23], so that developers could write single queries instead of relying on decomposition and recomposition of results through external scripts.…”

Section: Sparql: Be Quick or Be Neatmentioning

confidence: 99%

dbrec — Music Recommendations Using DBpedia

Passant

2010

Lecture Notes in Computer Science

135

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Abstract. This paper describes the theoretical background and the implementation of dbrec, a music recommendation system built on top of DBpedia, offering recommendations for more than 39,000 bands and solo artists. We discuss the various challenges and lessons learnt while building it, providing relevant insights for people developing applications consuming Linked Data. Furthermore, we provide a user-centric evaluation of the system, notably by comparing it to last.fm.

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SPARQL basic graph pattern optimization using selectivity estimation

Cited by 231 publications

References 14 publications

Caching and Prefetching Strategies for SPARQL Queries

Caching and Prefetching Strategies for SPARQL Queries

Efficient Execution of Top-K SPARQL Queries

dbrec — Music Recommendations Using DBpedia

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