Indexing Techniques for Advanced Database Systems

Bertino, Elisa; Ooi, Beng Chin; Sacks-Davis, Ron; Tan, Kian-Lee; Zobel, Justin; Shidlovsky, Boris; Andronico, Daniele

doi:10.1007/978-1-4615-6227-6

Cited by 90 publications

(56 citation statements)

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“…The study of path indexing has a long history, with a rich variety of strategies developed in the context of object-oriented [3] and XML [16] databases, and more recently in the indexing of graph data [17]. Related work includes approaches to creating structural summaries of semi-structured data, such as DataGuides [8], T-index [12], AK-index [11] and DK-index [4].…”

Section: Introductionmentioning

confidence: 99%

Investigations on Path Indexing for Graph Databases

Sumrall¹,

Fletcher

Poulovassilis

et al. 2017

Euro-Par 2016: Parallel Processing Workshops

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Abstract. Graph databases have become an increasingly popular choice for the management of the massive network data sets arising in many contemporary applications. We investigate the effectiveness of path indexing for accelerating query processing in graph database systems, using as an exemplar the widely used open-source Neo4j graph database. We present a novel path index design which supports efficient ordered access to paths in a graph dataset. Our index is fully persistent and designed for external memory storage and retrieval. We also describe a compression scheme that exploits the limited differences between consecutive keys in the index, as well as a workload-driven approach to indexing. We demonstrate empirically the speed-ups achieved by our implementation, showing that the path index yields query run-times from 2x up to 8000x faster than Neo4j. Empirical evaluation also shows that our scheme leads to smaller indexes than using general-purpose LZ4 compression. The complete stand-alone implementation of our index, as well as supporting tooling such as a bulk-loader, are provided as open source for further research and development.

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Section: Introductionmentioning

confidence: 99%

Investigations on Path Indexing for Graph Databases

Sumrall¹,

Fletcher

Poulovassilis

et al. 2017

Euro-Par 2016: Parallel Processing Workshops

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“…The distinguished properties of graph databases include graph storage and graph processing. Some Graph databases offer their native graph storage while others store graph data serially into general purpose database such as relational database [25], object-oriented database [26] and NoSQL store [27] (other than graph store). The approach used by graph database in which adjacent nodes directly point to each other is termed as index free adjacency.…”

Section: Graph Model Perspectivementioning

confidence: 99%

Designing Graphical Data Storage Model for Gene-Protein and Gene-Gene Interaction Networks

Farooq¹,

Ferzund²,

Mahmood³

et al. 2017

ijacsa

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Abstract-Graph is an expressive way to represent dynamic and complex relationships in highly connected data. In today's highly connected world, general purpose graph databases are providing opportunities to experience benefits of semantically significant networks without investing on the graph infrastructure. Examples of prominent graph databases are: Neo4j, Titan and OrientDB etc. In biological OMICS landscape, Interactomics is one of the new disciplines that focuses mainly on the data modeling, data storage and retrieval of biological interaction data. Biological experiments generate prodigious amount of data in various formats(semi-structured or unstructured). The large volume of such data posses challenges for data acquisition, data integration, multiple data modalities (either data model of storage model, storage, processing and visualization. This paper aims at designing a well suited graphical data storage model for biological information which is collected from major heterogeneous biological data repositories, by using graph database.

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“…Thus, the actual shape of a spatial object is often abstracted before being inserted to the index. To serve this purpose, there are two categories of techniques: object decomposition and object approximation [4,9]. In object decomposition, the shape of each object is represented as the geometric union of simpler shapes such as triangles and trapezoids.…”

Section: Review Of Existing Approachesmentioning

confidence: 99%