Parameter Curation and Data Generation for Benchmarking Multi-model Queries

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Kuvaus

Unlike traditional database management systems which are organized around a single data model, a multi-model database is designed to support multiple data models against a single, integrated backend. For instance, document, graph, relational, and key-value models are examples of data models that may be supported by a multi-model database. As more and more platforms are proposed to deal with multi-model data, it becomes important to have benchmarks that can be used to evaluate performance and usability of the next generation of multi-model database systems. In this paper, we discuss the motivations and challenges for benchmarking multi-model databases, and then present our current research on the data generation and parameter curation for benchmarking multi-model queries. Our benchmark can
be found at http://udbms.cs.helsinki.fi/bench/.
Alkuperäiskielienglanti
OtsikkoProceedings of the VLDB 2018 PhD Workshop
Sivumäärä4
KustantajaCEUR-WS.org
Julkaisupäivä20 elokuuta 2018
TilaJulkaistu - 20 elokuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaVLDB 2018 PhD Workshop: Workshop co-located with the 44th International Conference on Very Large Databases - Rio de Janeiro, Brasilia
Kesto: 27 elokuuta 201831 elokuuta 2018
Konferenssinumero: VLDB-PhD 2018

Julkaisusarja

NimiCEUR Workshop Proceedings
Vuosikerta2175
ISSN (elektroninen)1613-0073

Tieteenalat

  • 113 Tietojenkäsittely- ja informaatiotieteet

Lainaa tätä

Zhang, C. (2018). Parameter Curation and Data Generation for Benchmarking Multi-model Queries. teoksessa Proceedings of the VLDB 2018 PhD Workshop (CEUR Workshop Proceedings; Vuosikerta 2175). CEUR-WS.org.
Zhang, Chao. / Parameter Curation and Data Generation for Benchmarking Multi-model Queries. Proceedings of the VLDB 2018 PhD Workshop. CEUR-WS.org, 2018. (CEUR Workshop Proceedings).
@inproceedings{fca268f443c646eca7102f692e9b9baf,
title = "Parameter Curation and Data Generation for Benchmarking Multi-model Queries",
abstract = "Unlike traditional database management systems which are organized around a single data model, a multi-model database is designed to support multiple data models against a single, integrated backend. For instance, document, graph, relational, and key-value models are examples of data models that may be supported by a multi-model database. As more and more platforms are proposed to deal with multi-model data, it becomes important to have benchmarks that can be used to evaluate performance and usability of the next generation of multi-model database systems. In this paper, we discuss the motivations and challenges for benchmarking multi-model databases, and then present our current research on the data generation and parameter curation for benchmarking multi-model queries. Our benchmark canbe found at http://udbms.cs.helsinki.fi/bench/.",
keywords = "113 Computer and information sciences",
author = "Chao Zhang",
year = "2018",
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day = "20",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "CEUR-WS.org",
booktitle = "Proceedings of the VLDB 2018 PhD Workshop",
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}

Zhang, C 2018, Parameter Curation and Data Generation for Benchmarking Multi-model Queries. julkaisussa Proceedings of the VLDB 2018 PhD Workshop. CEUR Workshop Proceedings, Vuosikerta 2175, CEUR-WS.org, VLDB 2018 PhD Workshop, Rio de Janeiro, Brasilia, 27/08/2018.

Parameter Curation and Data Generation for Benchmarking Multi-model Queries. / Zhang, Chao.

Proceedings of the VLDB 2018 PhD Workshop. CEUR-WS.org, 2018. (CEUR Workshop Proceedings; Vuosikerta 2175).

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

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N2 - Unlike traditional database management systems which are organized around a single data model, a multi-model database is designed to support multiple data models against a single, integrated backend. For instance, document, graph, relational, and key-value models are examples of data models that may be supported by a multi-model database. As more and more platforms are proposed to deal with multi-model data, it becomes important to have benchmarks that can be used to evaluate performance and usability of the next generation of multi-model database systems. In this paper, we discuss the motivations and challenges for benchmarking multi-model databases, and then present our current research on the data generation and parameter curation for benchmarking multi-model queries. Our benchmark canbe found at http://udbms.cs.helsinki.fi/bench/.

AB - Unlike traditional database management systems which are organized around a single data model, a multi-model database is designed to support multiple data models against a single, integrated backend. For instance, document, graph, relational, and key-value models are examples of data models that may be supported by a multi-model database. As more and more platforms are proposed to deal with multi-model data, it becomes important to have benchmarks that can be used to evaluate performance and usability of the next generation of multi-model database systems. In this paper, we discuss the motivations and challenges for benchmarking multi-model databases, and then present our current research on the data generation and parameter curation for benchmarking multi-model queries. Our benchmark canbe found at http://udbms.cs.helsinki.fi/bench/.

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Zhang C. Parameter Curation and Data Generation for Benchmarking Multi-model Queries. julkaisussa Proceedings of the VLDB 2018 PhD Workshop. CEUR-WS.org. 2018. (CEUR Workshop Proceedings).