Results 1  10
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180
GRIN: A graph based RDF index
 IN AAAI
, 2007
"... RDF (“Resource Description Framework”) is now a widely used World Wide Web Consortium standard. However, methods to index large volumes of RDF data are still in their infancy. In this paper, we focus on providing a very lightweight indexing mechanism for certain kinds of RDF queries, namely graphba ..."
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Cited by 41 (3 self)
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RDF (“Resource Description Framework”) is now a widely used World Wide Web Consortium standard. However, methods to index large volumes of RDF data are still in their infancy. In this paper, we focus on providing a very lightweight indexing mechanism for certain kinds of RDF queries, namely graphbased queries where there is a need to traverse edges in the graph determined by an RDF database. Our approach uses the idea of drawing circles around selected “center” vertices in the graph where the circle would encompass those vertices in the graph that are within a given distance of the “center” vertex. We come up with methods of finding such “center” vertices and identifying the radius of the circles and then leverage this to build an index called GRIN. We compare GRIN with three existing RDF indexex: Jena, Sesame, and RDFBroker. We compared (i) the time to answer graph based queries, (ii) memory needed to store the index, and (iii) the time to build the index. GRIN outperforms Jena, Sesame and RDFBroker on all three measures for graph based queries (for other types of queries, it may be worth building one of these other indexes and using it), at the expense of using a larger amount of memory when answering queries.
On Graph Query Optimization in Large Networks
"... The dramatic proliferation of sophisticated networks has resulted in a growing need for supporting effective querying and mining methods over such largescale graphstructured data. At the core of many advanced network operations lies a common and critical graph query primitive: how to search graph ..."
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Cited by 32 (3 self)
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The dramatic proliferation of sophisticated networks has resulted in a growing need for supporting effective querying and mining methods over such largescale graphstructured data. At the core of many advanced network operations lies a common and critical graph query primitive: how to search graph structures efficiently within a large network? Unfortunately, the graph query is hard due to the NPcomplete nature of subgraph isomorphism. It becomes even challenging when the network examined is large and diverse. In this paper, we present a high performance graph indexing mechanism, SPath, to address the graph query problem on large networks. SPath leverages decomposed shortest paths around vertex neighborhood as basic indexing units, which prove to be both effective in graph search space pruning and highly scalable in index construction and deployment. Via SPath, a graph query is processed and optimized beyond the traditional vertexatatime fashion to a more efficient pathatatime way: the query is first decomposed to a set of shortest paths, among which a subset of candidates with good selectivity is picked by a query plan optimizer; Candidate paths are further joined together to help recover the query graph to finalize the graph query processing. We evaluate SPath with the stateoftheart GraphQL on both real and synthetic data sets. Our experimental studies demonstrate the effectiveness and scalability of SPath, which proves to be a more practical and efficient indexing method in addressing graph queries on large networks. 1.
Energy and performancedriven NoC communication architectures synthesis using a decomposition approach
 in Proc. Design, Automation & Test in Europe Conf
, 2005
"... In this paper, we present a methodology for customized communication architecture synthesis that matches the communication requirements of the target application. This is an important problem, particularly for networkbased implementations of complex applications. Our approach is based on using fr ..."
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Cited by 31 (4 self)
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In this paper, we present a methodology for customized communication architecture synthesis that matches the communication requirements of the target application. This is an important problem, particularly for networkbased implementations of complex applications. Our approach is based on using frequently encountered generic communication primitives as an alphabet capable of characterizing any given communication pattern. The proposed algorithm searches through the entire design space for a solution that minimizes the system total energy consumption, while satisfying the other design constraints. Compared to the standard mesh architecture, the customized architecture generated by the newly proposed approach shows about 36 % throughput increase and 51 % reduction in the energy required to encrypt 128 bits of data with a standard encryption algorithm. 1.
Efficient subgraph matching on billion node graphs
 In PVLDB
, 2012
"... 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 pr ..."
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Cited by 30 (5 self)
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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 superlinear indexing time or superlinear indexing space. Unfortunately, for very large graphs, superlinear approaches are almost always infeasible. In this paper, we study the problem of subgraph matching on billionnode graphs. We present a novel algorithm that supports efficient subgraph matching for graphs deployed on a distributed memory store. Instead of relying on superlinear indices, we use efficient graph exploration and massive parallel computing for query processing. Our experimental results demonstrate the feasibility of performing subgraph matching on webscale graph data. 1.
GADDI: Distance index based subgraph matching in biological networks
 In Proceedings of the 12th international conference on extending database technology (EDBT’09
, 2009
"... Currently, a huge amount of biological data can be naturally represented by graphs, e.g., protein interaction networks, gene regulatory networks, etc. The need for indexing large graphs is an urgent research problem of great practical importance. The main challenge is size. Each graph may contain ..."
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Cited by 25 (2 self)
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Currently, a huge amount of biological data can be naturally represented by graphs, e.g., protein interaction networks, gene regulatory networks, etc. The need for indexing large graphs is an urgent research problem of great practical importance. The main challenge is size. Each graph may contain thousands (or more) vertices. Most of the previous work focuses on indexing a set of small or medium sized database graphs (with only tens of vertices) and finding whether a query graph occurs in any of these. In this paper, we are interested in finding all the matches of a query graph in a given large graph of thousands of vertices, which is a very important task in many biological applications. This increases the complexity significantly. We propose a novel distance measurement which reintroduces the idea of frequent substructures in a single large graph. We devise the novel structure distance based approach (GADDI) to efficiently find matches of the query graph. GADDI is further optimized by the use of a dynamic matching scheme to minimize redundant calculations. Last but not least, a number of real and synthetic data sets are used to evaluate the efficiency and scalability of our proposed method. 1.
Scaling RDF with Time, in
 Zhang (Eds.), Proc. of the 17th Int. Conf. on World Wide Web (WWW 2008), ACM
, 2008
"... The World Wide Web Consortium’s RDF standard primarily consists of (subject,property,object) triples that specify the value that a given subject has for a given property. However, it is frequently the case that even for a fixed subject and property, the value varies with time. As a consequence, effo ..."
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Cited by 25 (0 self)
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The World Wide Web Consortium’s RDF standard primarily consists of (subject,property,object) triples that specify the value that a given subject has for a given property. However, it is frequently the case that even for a fixed subject and property, the value varies with time. As a consequence, efforts have been made to annotate RDF triples with “valid time ” intervals. However, to date, no proposals exist for efficient indexing of such temporal RDF databases. It is clearly beneficial to store RDF data in a relational DB – however, standard relational indexes are inadequately equipped to handle RDF’s graph structure. In this paper, we propose the tGRIN index structure that builds a specialized index for temporal RDF that is physically stored in an RDBMS. Past efforts to store RDF in relational stores include Jena2 from HP, Sesame from OpenRDF.org, and 3store from the University of Southampton. We show that even when these efforts are augmented with well known temporal indexes like R+ trees, SRtrees, STindex, and MAP21, the tGRIN index exhibits superior performance. In terms of index build time, tGRIN takes two thirds or less of the time used by any other system, and it uses a comparable amount of memory and less disk space than Jena, Sesame and 3store. More importantly, tGRIN can answer queries three to six times faster for average query graph patterns and five to ten times faster for complex queries than these systems.
A Binary Linear Programming Formulation of the Graph Edit Distance
"... A binary linear programming formulation of the graph edit distance for unweighted, undirected graphs with vertex attributes is derived and applied to a graph recognition problem. A general formulation for editing graphs is used to derive a graph edit distance that is proven to be a metric provided t ..."
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Cited by 25 (3 self)
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A binary linear programming formulation of the graph edit distance for unweighted, undirected graphs with vertex attributes is derived and applied to a graph recognition problem. A general formulation for editing graphs is used to derive a graph edit distance that is proven to be a metric provided the cost function for individual edit operations is a metric. Then, a binary linear program is developed for computing this graph edit distance, and polynomial time methods for determining upper and lower bounds on the solution of the binary program are derived by applying solution methods for standard linear programming and the assignment problem. A recognition problem of comparing a sample input graph to a database of known prototype graphs in the context of a chemical information system is presented as an application of the new method. The costs associated with various edit operations are chosen by using a minimum normalized variance criterion applied to pairwise distances between nearest neighbors in the database of prototypes. The new metric is shown to perform quite well in comparison to existing metrics when applied to a database of chemical graphs.
SAPPER: Subgraph Indexing and Approximate Matching in Large Graphs
"... With the emergence of new applications, e.g., computational biology, new software engineering techniques, social networks, etc., more data is in the form of graphs. Locating occurrences of a query graph in a large database graph is an important research topic. Due to the existence of noise (e.g., mi ..."
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Cited by 22 (0 self)
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With the emergence of new applications, e.g., computational biology, new software engineering techniques, social networks, etc., more data is in the form of graphs. Locating occurrences of a query graph in a large database graph is an important research topic. Due to the existence of noise (e.g., missing edges) in the large database graph, we investigate the problem of approximate subgraph indexing, i.e., finding the occurrences of a query graph in a large database graph with (possible) missing edges. The SAPPER method is proposed to solve this problem. Utilizing the hybrid neighborhood unit structures in the index, SAPPER takes advantage of pregenerated random spanning trees and a carefully designed graph enumeration order. Real and synthetic data sets are employed to demonstrate the efficiency and scalability of our approximate subgraph indexing method.
Annotated rdf
 In ESWC. 487–501
, 2006
"... There are numerous extensions of RDF that support reasoning about uncertainty, reasoning about pedigree, reasoning about time, and so on. In this paper, we present Annotated RDF (or aRDF for short) in which RDF triples are annotated by members of a partially ordered set (with bottom element) that ca ..."
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Cited by 14 (1 self)
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There are numerous extensions of RDF that support reasoning about uncertainty, reasoning about pedigree, reasoning about time, and so on. In this paper, we present Annotated RDF (or aRDF for short) in which RDF triples are annotated by members of a partially ordered set (with bottom element) that can be selected in any way desired by the user. We present a formal declarative semantics (model theory) for annotated RDF and develop algorithms to check consistency of aRDF theories and to answer queries to aRDF theories. We show that annotated RDF captures versions of all the forms of reasoning mentioned above within a single unified framework. We develop a prototype aRDF implementation and show that our algorithms work efficiently even on real world data sets containing over 10 million triples.