The complexity of evaluating path expressions in SPARQL

Losemann, Katja; Martens, Wim

doi:10.1145/2213556.2213573

Cited by 56 publications

(50 citation statements)

References 43 publications

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“…A number of systems have focused on executing and optimizing SPARQL queries [8,21], but we find SPARQL to be neither necessary nor sufficient for graph manipulation: Datalog is strictly more expressive than SPARQL without v1.1 path expressions [3], and the semantics of path expressions make query processing intractable [16].…”

Section: Related Workmentioning

confidence: 99%

Compiled Plans for In-Memory Path-Counting Queries

Myers

Hyrkas

Halperin

et al. 2015

In Memory Data Management and Analysis

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Abstract. Dissatisfaction with relational databases for large-scale graph processing has motivated a new class of graph databases that offer fast graph processing but sacrifice the ability to express basic relational idioms. However, we hypothesize that the performance benefits amount to implementation details, not a fundamental limitation of the relational model. To evaluate this hypothesis, we are exploring code-generation to produce fast in-memory algorithms and data structures for graph patterns that are inaccessible to conventional relational optimizers. In this paper, we present preliminary results for this approach on path-counting queries, which includes triangle counting as a special case. We compile Datalog queries into main-memory pipelined hash-join plans in C++, and show that the resulting programs easily outperform PostgreSQL on real graphs with different degrees of skew. We then produce analogous parallel programs for Grappa, a runtime system for distributed memory architectures. Grappa is a good target for building a parallel query system as its shared memory programming model and communication mechanisms provide productivity and performance when building communication-intensive applications. Our experiments suggest that Grappa programs using hash joins have competitive performance with queries executed on Greenplum, a commercial parallel database. We find preliminary evidence that a code generation approach simplifies the design of a query engine for graph analysis and improves performance over conventional relational databases.

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Section: Related Workmentioning

confidence: 99%

Compiled Plans for In-Memory Path-Counting Queries

Myers

Hyrkas

Halperin

et al. 2015

In Memory Data Management and Analysis

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“…Only recently, with the addition of property paths (PPs) in version 1.1 [15], SPARQL has been enhanced officially with such features. The final definition of PPs has been influenced by research that studied the computational complexity of an early draft version of PPs [3,27]. There also already exists a proposal to extend the expressive power of PPs [11].…”

Section: Related Workmentioning

confidence: 99%

SPARQL with property paths on the Web

Hartig

Pirrò

2017

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Abstract. Linked Data on the Web represents an immense source of knowledge suitable to be automatically processed and queried. In this respect, there are different approaches for Linked Data querying that differ on the degree of centralization adopted. On one hand, the SPARQL query language, originally defined for querying single datasets, has been enhanced with features to query federations of datasets; however, this attempt is not sufficient to cope with the distributed nature of data sources available as Linked Data. On the other hand, extensions or variations of SPARQL aim to find trade-offs between centralized and fully distributed querying. The idea is to partially move the computational load from the servers to the clients. Despite the variety and the relative merits of these approaches, as of today, there is no standard language for querying Linked Data on the Web. A specific requirement for such a language to capture the distributed, graph-like nature of Linked Data sources on the Web is a support of graph navigation. Recently, SPARQL has been extended with a navigational feature called property paths (PPs). However, the semantics of SPARQL restricts the scope of navigation via PPs to single RDF graphs. This restriction limits the applicability of PPs for querying distributed Linked Data sources on the Web. To fill this gap, in this paper we provide formal foundations for evaluating PPs on the Web, thus contributing to the definition of a query language for Linked Data. We first introduce a family of reachability-based query semantics for PPs that distinguish between navigation on the Web and navigation at the data level. Thereafter, we consider another, alternative query semantics that couples Web graph navigation and data level navigation; we call it context-based semantics. Given these semantics, we find that for some PP-based SPARQL queries a complete evaluation on the Web is not possible. To study this phenomenon we introduce a notion of Web-safeness of queries, and prove a decidable syntactic property that enables systems to identify queries that are Web-safe. In addition to establishing these formal foundations, we conducted an experimental comparison of the context-based semantics and a reachability-based semantics. Our experiments show that when evaluating a PP-based query under the context-based semantics one experiences a significantly smaller number of dereferencing operations, but the computed query result may contain less solutions.

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“…Three surveys summarize the recent progress on this topic [12], [14], [31]. Graph pattern matching is particularly well-explored in the context of the Resource Description Framework (RDF) [28], [22] and [5]. The majority of the work in this area assumes that the whole graph dataset can fit into the memory of a single machine, and proposes efficient centralized pattern matching algorithms.…”

Section: F Summary Of Experimental Findingsmentioning

confidence: 99%

Query optimization of distributed pattern matching

Huang

Venkatraman

Abadi

2014

2014 IEEE 30th International Conference on Data Engineering

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Abstract-Greedy algorithms for subgraph pattern matching operations are often sufficient when the graph data set can be held in memory on a single machine. However, as graph data sets increasingly expand and require external storage and partitioning across a cluster of machines, more sophisticated query optimization techniques become critical to avoid explosions in query latency. In this paper, we introduce several query optimization techniques for distributed graph pattern matching. These techniques include (1) a System-R style dynamic programming-based optimization algorithm that considers both linear and bushy plans, (2) a cycle detection-based algorithm that leverages cycles to reduce intermediate result set sizes, and (3) a computation reusing technique that eliminates redundant query execution and data transfer over the network. Experimental results show that these algorithms can lead to an order of magnitude improvement in query performance.

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The complexity of evaluating path expressions in SPARQL

Cited by 56 publications

References 43 publications

Compiled Plans for In-Memory Path-Counting Queries

Compiled Plans for In-Memory Path-Counting Queries

SPARQL with property paths on the Web

Query optimization of distributed pattern matching

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