Query Optimization Techniques in Graph Databases

Ammar, Ali Ben

doi:10.5121/ijdms.2016.8401

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…Another area that is often used for graph databases is the Geographical sector, refer to publications [44,45] and [46]. Ten titles on studies, including [47,48,49] take works that could be implemented in all subjects or areas. It means that GDB is really sophisticated to not be taken technology in nowadays.…”

Section: Research Resultsmentioning

confidence: 99%

“…Neo4j product is comprehensive database, occupy to accommodate ranging from small-scale applications to applications with millions of users, Large Data processing to Big Data implementation. Additionally, most researcher implements SparkSee (DEX) [48][57] [61], ArangoDB [34,46] for RDF store, TitanGraph [14][41] [62], OrientDB [63], TigerGraph [36][64] and AllegroGraph discussed in [38][44][65], as alternative.…”

Section: Research Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Rizaldy¹,

Fahriah

Hartono³

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Planning, developing, and updating software cannot be separated from the role of the database. From various types of databases, graph databases are considered to have various advantages over their predecessor, relational databases. Graph databases then become the latest trend in the software and data science industry, apart from the development of graph theory itself. The proliferation of research on GDB in the last decade raises questions about what topics are associated with GDB, what industries use GDB in its data processing, what the GDB models are, and what types of GDB have been used most frequently by users in the last few years. This article aims to answer these questions through a Literature Review, which is carried out by determining objectives, determining the limits of review coverage, determining inclusion and exclusion criteria for data retrieval, data extraction, and quality assessment. Based on a review of 60 studies, several research topics related to GDB are Semantic Web, Big Data, and Parallel computing. A total of 19 (30%) studies used Neo4j as their database. Apart from Social Networks, the industries that implement GDB the most are the Transportation sector, Scientific Article Networks, and general sectors such as Enterprise Data, Biological data, and History data. This Literature Review concludes that research on the topic of the Graph Database is still developing in the future. This is shown by the breadth of application and the variety of new derivatives of GDB products offered by researchers to address existing problems.

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Section: Research Resultsmentioning

confidence: 99%

Section: Research Resultsmentioning

confidence: 99%

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Rizaldy¹,

Fahriah

Hartono³

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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“…There are works that improve performance of specific algorithms (e.g., delta stepping for SSSP [23]); however, our goal is to develop optimizations that apply to multiple iterative graph algorithms. There has been a great deal of work on graph based query languages (e.g., Gremlin [29]) and query support in graph databases (e.g., Neo4J and DEX [2,9,20]) that enable graph traversals and joins via lower-level graph primitives (e.g., vertices, edges, etc.). However, they are not efficient for iterative graph algorithms over large graphs.…”

Section: Related Workmentioning

confidence: 99%

“…There is prior work on graph based query languages (e.g., Gremlin [29]) and query support in graph databases (e.g., Neo4J and DEX [2,9,20]) that enable graph traversals and joins via lower-level graph primitives (e.g., vertices, edges, etc.). The strength of these systems is their versatility.…”

Section: Introductionmentioning

confidence: 99%

PnP

Vora

Gupta

2019

Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Syst

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Frequently used parallel iterative graph analytics algorithms are computationally expensive. However, researchers have observed that applications often require point-to-point versions of these analytics algorithms that are less demanding. In this paper we introduce the PnP parallel framework for iterative graph analytics that processes a stream of pointto-point queries with each involving a single source and destination vertex pair. The efficiency of our framework is derived from the following two novel features: online Pruning of graph exploration that eliminates propagation from vertices that are determined to not contribute to a query's final solution; and dynamic direction Prediction for solving the query in either forward (from source) or backward (from destination) direction as their costs can differ greatly. PnP employs a two-phase algorithm where, Phase 1 briefly traverses the graph in both directions to predict the faster direction and enable pruning; then Phase 2 completes query evaluation by running the algorithm for the chosen direction till it converges. Our experiments show that PnP responds to queries rapidly because of accurate direction selection and effective pruning that often offsets the runtime overhead of direction prediction. PnP substantially outperforms Quegel, the only other point-to-point query evaluation framework. Our experiments on multiple benchmarks and graphs show that PnP on a single machine is 8.2× to 3116× faster than Quegel on a cluster of four machines. CCS Concepts • Information systems → Social networks; • Computing methodologies → Shared memory algorithms.

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SimGQ+: Simultaneously evaluating iterative point-to-all and point-to-point graph queries

Mazloumi

Jiang

et al. 2022

Journal of Parallel and Distributed Computing

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Query Optimization Techniques in Graph Databases

Cited by 11 publications

References 19 publications

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

PnP

SimGQ+: Simultaneously evaluating iterative point-to-all and point-to-point graph queries

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