783 research outputs found
Distributed Processing of Generalized Graph-Pattern Queries in SPARQL 1.1
We propose an efficient and scalable architecture for processing generalized
graph-pattern queries as they are specified by the current W3C recommendation
of the SPARQL 1.1 "Query Language" component. Specifically, the class of
queries we consider consists of sets of SPARQL triple patterns with labeled
property paths. From a relational perspective, this class resolves to
conjunctive queries of relational joins with additional graph-reachability
predicates. For the scalable, i.e., distributed, processing of this kind of
queries over very large RDF collections, we develop a suitable partitioning and
indexing scheme, which allows us to shard the RDF triples over an entire
cluster of compute nodes and to process an incoming SPARQL query over all of
the relevant graph partitions (and thus compute nodes) in parallel. Unlike most
prior works in this field, we specifically aim at the unified optimization and
distributed processing of queries consisting of both relational joins and
graph-reachability predicates. All communication among the compute nodes is
established via a proprietary, asynchronous communication protocol based on the
Message Passing Interface
Adding Logical Operators to Tree Pattern Queries on Graph-Structured Data
As data are increasingly modeled as graphs for expressing complex
relationships, the tree pattern query on graph-structured data becomes an
important type of queries in real-world applications. Most practical query
languages, such as XQuery and SPARQL, support logical expressions using
logical-AND/OR/NOT operators to define structural constraints of tree patterns.
In this paper, (1) we propose generalized tree pattern queries (GTPQs) over
graph-structured data, which fully support propositional logic of structural
constraints. (2) We make a thorough study of fundamental problems including
satisfiability, containment and minimization, and analyze the computational
complexity and the decision procedures of these problems. (3) We propose a
compact graph representation of intermediate results and a pruning approach to
reduce the size of intermediate results and the number of join operations --
two factors that often impair the efficiency of traditional algorithms for
evaluating tree pattern queries. (4) We present an efficient algorithm for
evaluating GTPQs using 3-hop as the underlying reachability index. (5)
Experiments on both real-life and synthetic data sets demonstrate the
effectiveness and efficiency of our algorithm, from several times to orders of
magnitude faster than state-of-the-art algorithms in terms of evaluation time,
even for traditional tree pattern queries with only conjunctive operations.Comment: 16 page
Answering Regular Path Queries on Workflow Provenance
This paper proposes a novel approach for efficiently evaluating regular path
queries over provenance graphs of workflows that may include recursion. The
approach assumes that an execution g of a workflow G is labeled with
query-agnostic reachability labels using an existing technique. At query time,
given g, G and a regular path query R, the approach decomposes R into a set of
subqueries R1, ..., Rk that are safe for G. For each safe subquery Ri, G is
rewritten so that, using the reachability labels of nodes in g, whether or not
there is a path which matches Ri between two nodes can be decided in constant
time. The results of each safe subquery are then composed, possibly with some
small unsafe remainder, to produce an answer to R. The approach results in an
algorithm that significantly reduces the number of subqueries k over existing
techniques by increasing their size and complexity, and that evaluates each
subquery in time bounded by its input and output size. Experimental results
demonstrate the benefit of this approach
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
Efficient Subgraph Matching on Billion Node Graphs
The ability to handle large scale graph data is crucial to an increasing
number of applications. Much work has been dedicated to supporting basic graph
operations such as subgraph matching, reachability, regular expression
matching, etc. In many cases, graph indices are employed to speed up query
processing. Typically, most indices require either super-linear indexing time
or super-linear indexing space. Unfortunately, for very large graphs,
super-linear approaches are almost always infeasible. In this paper, we study
the problem of subgraph matching on billion-node graphs. We present a novel
algorithm that supports efficient subgraph matching for graphs deployed on a
distributed memory store. Instead of relying on super-linear indices, we use
efficient graph exploration and massive parallel computing for query
processing. Our experimental results demonstrate the feasibility of performing
subgraph matching on web-scale graph data.Comment: VLDB201
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