Kuvaus
| Alkuperäiskieli | englanti |
|---|---|
| Otsikko | Performance Evaluation and Benchmarking for the Era of Artificial Intelligence : 10th TPC Technology Conference, TPCTC 2018, Rio de Janeiro, Brazil, August 27–31, 2018, Revised Selected Papers |
| Toimittajat | Raghunath Nambiar, Meikel Poess |
| Sivumäärä | 16 |
| Julkaisupaikka | Cham |
| Kustantaja | Springer |
| Julkaisupäivä | 2019 |
| Sivut | 7-23 |
| ISBN (painettu) | 978-3-030-11403-9 |
| ISBN (elektroninen) | 978-3-030-11404-6 |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 2019 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisuussa |
| Tapahtuma | TPC Technology Conference on Performance Evaluation & Benchmarking - Rio De Janeiro, Brasilia Kesto: 27 elokuuta 2017 → 31 elokuuta 2017 Konferenssinumero: 10 |
Julkaisusarja
| Nimi | Lecture Notes in Computer Science |
|---|---|
| Kustantaja | Springer |
| Vuosikerta | 11135 |
| ISSN (painettu) | 0302-9743 |
| ISSN (elektroninen) | 1611-3349 |
Tieteenalat
- 113 Tietojenkäsittely- ja informaatiotieteet
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UniBench: A Benchmark for Multi-Model Database Management Systems. / Zhang, Chao; Lu, Jiaheng; Xu, Pengfei; Chen, Yuxing.
Performance Evaluation and Benchmarking for the Era of Artificial Intelligence: 10th TPC Technology Conference, TPCTC 2018, Rio de Janeiro, Brazil, August 27–31, 2018, Revised Selected Papers. toim. / Raghunath Nambiar; Meikel Poess. Cham : Springer, 2019. s. 7-23 (Lecture Notes in Computer Science; Vuosikerta 11135).Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu
TY - GEN
T1 - UniBench: A Benchmark for Multi-Model Database Management Systems
AU - Zhang, Chao
AU - Lu, Jiaheng
AU - Xu, Pengfei
AU - Chen, Yuxing
PY - 2019
Y1 - 2019
N2 - Unlike traditional database management systems which are organized around a single data model, a multi-model database (MMDB) utilizes a single, integrated back-end to support multiple data models, such as document, graph, relational, and key-value. As more and more platforms are proposed to deal with multi-model data, it becomes crucial to establish a benchmark for evaluating the performance and usability of MMDBs. Previous benchmarks, however, are inadequate for such scenario because they lack a comprehensive consideration for multiple models of data. In this paper, we present a benchmark, called UniBench, with the goal of facilitating a holistic and rigorous evaluation of MMDBs. UniBench consists of a mixed data model, a synthetic multi-model data generator, and a set of core workloads. Specifically, the data model simulates an emerging application: Social Commerce, a Web-based application combining E-commerce and social media. The data generator provides diverse data format including JSON, XML, key-value, tabular, and graph. The workloads are comprised of a set of multi-model queries and transactions, aiming to cover essential aspects of multi-model data management. We implemented all workloads on ArangoDB and OrientDB to illustrate the feasibility of our proposed benchmarking system and show the learned lessons through the evaluation of these two multi-model databases. The source code and data of this benchmark can be downloaded 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 (MMDB) utilizes a single, integrated back-end to support multiple data models, such as document, graph, relational, and key-value. As more and more platforms are proposed to deal with multi-model data, it becomes crucial to establish a benchmark for evaluating the performance and usability of MMDBs. Previous benchmarks, however, are inadequate for such scenario because they lack a comprehensive consideration for multiple models of data. In this paper, we present a benchmark, called UniBench, with the goal of facilitating a holistic and rigorous evaluation of MMDBs. UniBench consists of a mixed data model, a synthetic multi-model data generator, and a set of core workloads. Specifically, the data model simulates an emerging application: Social Commerce, a Web-based application combining E-commerce and social media. The data generator provides diverse data format including JSON, XML, key-value, tabular, and graph. The workloads are comprised of a set of multi-model queries and transactions, aiming to cover essential aspects of multi-model data management. We implemented all workloads on ArangoDB and OrientDB to illustrate the feasibility of our proposed benchmarking system and show the learned lessons through the evaluation of these two multi-model databases. The source code and data of this benchmark can be downloaded at http://udbms.cs.helsinki.fi/bench/.
KW - 113 Computer and information sciences
U2 - 10.1007/978-3-030-11404-6_2
DO - 10.1007/978-3-030-11404-6_2
M3 - Conference contribution
SN - 978-3-030-11403-9
T3 - Lecture Notes in Computer Science
SP - 7
EP - 23
BT - Performance Evaluation and Benchmarking for the Era of Artificial Intelligence
A2 - Nambiar, Raghunath
A2 - Poess, Meikel
PB - Springer
CY - Cham
ER -