Efficient query processing on graph databases

Cheng, James

doi:10.14711/thesis-b1029224

Cited by 71 publications

(189 citation statements)

References 21 publications

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“…A popular benchmark dataset, the AIDS antiviral database, is used in our performance evaluation. The [6,19,23], edge labels are ignored for a tough evaluation. The default dataset consists of randomly chosen 10K graphs from AIDS.…”

Section: Methodsmentioning

confidence: 99%

“…Query Set. To thoroughly evaluate our techniques, we download the five benchmark query sets, Q8, Q12, Q16, Q20 and Q24 from the web-site as pointed and used by [6,19,23]. Each query graph in Qi has exactly i edges.…”

Section: Methodsmentioning

confidence: 99%

“…Due to NP-completeness, techniques for processing a large number of data graphs follow the filtering-verification paradigm. Most work in subgraph containment search focuses on developing effective and efficient indexing techniques, including, GraphGrep [10], gIndex [23], Closure-Tree [11], TreePi [26], gString [13], a graph decomposition based index [21], FGIndex [6], Tree+δ [27], GCoding [28], etc. In [5], cIndex is developed for the reverse version of subgraph containment search, namely super-graph containment search .…”

Section: Related Workmentioning

confidence: 99%

“…UML and ER diagrams are other examples. There has been a considerable effort, from both database and data mining communities, in developing techniques for managing, processing, and analyzing graph databases, including graph pattern discovery [12,15,18,22], structurebased graph queries [5,6,10,11,19,23,27,26], etc. The substructure search problem, also called subgraph containment query, is that for a graph database and a given query graph, we want to find all data graphs which contain the query graph.…”

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confidence: 99%

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Connected substructure similarity search

Shang

Lin

Zhang

et al. 2010

Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data

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Substructure similarity search is to retrieve graphs that approximately contain a given query graph. It has many applications, e.g., detecting similar functions among chemical compounds. The problem is challenging as even testing subgraph containment between two graphs is NP-complete. Hence, existing techniques adopt the filtering-and-verification framework with the focus on developing effective and efficient techniques to remove non-promising graphs.Nevertheless, existing filtering techniques may be still unable to effectively remove many "low" quality candidates.To resolve this, in this paper we propose a novel indexing technique, GrafD-Index, to index graphs according to their "distances" to features. We characterize a tight condition under which the distance-based triangular inequality holds. We then develop lower and upper bounding techniques that exploit the GrafD-Index to (1) prune non-promising graphs and (2) include graphs whose similarities are guaranteed to exceed the given similarity threshold. Considering that the verification phase is not well studied and plays the dominant role in the whole process, we devise efficient algorithms to verify candidates. A comprehensive experiment using real datasets demonstrates that our proposed methods significantly outperform existing methods.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Connected substructure similarity search

Shang

Lin

Zhang

et al. 2010

Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data

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show abstract

“…For instance, utilization of graph can be seen in areas such as food chain, UML diagram, software engineering, social networks [1,2,3], ERD diagrams in database [4], gas, water, electricity, and communication, telephone, technology transportation networks, and XML documents [5,6,7]. Graph database system is a database system used to manage graph data.…”

Section: Introductionmentioning

confidence: 99%

A New Method for Graph Queries Processing without Index Reconstruction on Dynamic Graph Databases

Dinari¹

2017

IJMECS

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Abstract-Graphs play notable role in daily life. For instance, they are used in variety fields such as social networks, malware detection, and biological networks. Graph data processing performed to extract useful information is known as graph mining. A critical field of graph mining is graph containment problem, in which all graphs containing the query are returned by a graph query q. Scanning the whole database (graph query as a subgraph) for a query is a time consuming process. To improve query performance, an inverted index is constructed on the graph database and then the query is performed based on the query. The problem in this process is that when a graph is added to or removed from a database, the inverted index must be reconstructed. The present study proposes a method in which index updating is not needed upon a change in the database. This feature enables simultaneous inverted index updating and querying. The assessment results showed optimum and satisfactory performance of the proposed method.

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Efficient subgraph search on large anonymized graphs

Ding

Jia

et al. 2018

Concurrency and Computation

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Summary Graph is one of the most important data structures to model social networks and becomes popular to find interesting relationships between individuals. Since graphs may contain sensitive information, data curators usually need to anonymize the graph before publication to prevent individual re‐identification, which thus leads to plenty of anonymized graphs for data sharing and exploration. However, the new structures and properties of anonymized graphs make the traditional graph indexing method inefficient or even invalid for query processing. To address the subgraph query problem over anonymized graph database, in this paper, we first introduce basic concepts about anonymized graphs and subgraph queries, then propose an index structure named Closure+‐tree to process the subgraph query efficiently. In particular, graphs were organized hierarchically that each node is an union of its child nodes under some specified mapping functions. During the processing of subgraph queries, the whole graph descendants will be pruned if their union does not contain the query graph. To evaluate the performance of our proposed Closure+‐tree, extensive experiments are performed on both real and synthetic graph data sets. The experimental results revealed that our index structure can prune up to 80% unqualified graphs with variable size of queries. Furthermore, the size of our index structure is only around a quarter of the entire anonymized graph data set, which indicates good scalability over large data sets.

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Efficient query processing on graph databases

Cited by 71 publications

References 21 publications

Connected substructure similarity search

Connected substructure similarity search

A New Method for Graph Queries Processing without Index Reconstruction on Dynamic Graph Databases

Efficient subgraph search on large anonymized graphs

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