Eichmann, Philipp ; Zgraggen, Emanuel ; Binnig, Carsten ; Kraska, Tim (2020)
IDEBench: A Benchmark for Interactive Data Exploration.
SIGMOD/PODS '20: International Conference on Management of Data. virtual Conference (14.06.2020-19.06.2020)
doi: 10.1145/3318464.3380574
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In recent years, many query processing techniques have been developed to better support interactive data exploration (IDE) of large structured datasets. To evaluate and compare database engines in terms of how well they support such workloads, experimenters have mostly used self-designed evaluation procedures rather than established benchmarks. In this paper we argue that this is due to the fact that the workloads and metrics of popular analytical benchmarks such as TPC-H or TPC-DS were designed for traditional performance reporting scenarios, and do not capture distinctive IDE characteristics. Guided by the findings of several user studies we present a new benchmark called IDEBench, designed to evaluate database engines based on common IDE workflows and metrics that matter to the end-user. We demonstrate the applicability of IDEBench through a number of experiments with five different database engines, and present and discuss our findings.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Eichmann, Philipp ; Zgraggen, Emanuel ; Binnig, Carsten ; Kraska, Tim |
| Art des Eintrags: | Bibliographie |
| Titel: | IDEBench: A Benchmark for Interactive Data Exploration |
| Sprache: | Englisch |
| Publikationsjahr: | 2020 |
| Verlag: | ACM |
| Buchtitel: | Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data |
| Veranstaltungstitel: | SIGMOD/PODS '20: International Conference on Management of Data |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 14.06.2020-19.06.2020 |
| DOI: | 10.1145/3318464.3380574 |
| Kurzbeschreibung (Abstract): | In recent years, many query processing techniques have been developed to better support interactive data exploration (IDE) of large structured datasets. To evaluate and compare database engines in terms of how well they support such workloads, experimenters have mostly used self-designed evaluation procedures rather than established benchmarks. In this paper we argue that this is due to the fact that the workloads and metrics of popular analytical benchmarks such as TPC-H or TPC-DS were designed for traditional performance reporting scenarios, and do not capture distinctive IDE characteristics. Guided by the findings of several user studies we present a new benchmark called IDEBench, designed to evaluate database engines based on common IDE workflows and metrics that matter to the end-user. We demonstrate the applicability of IDEBench through a number of experiments with five different database engines, and present and discuss our findings. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Data Management (2022 umbenannt in Data and AI Systems) |
| Hinterlegungsdatum: | 14 Dez 2020 09:23 |
| Letzte Änderung: | 21 Apr 2022 09:03 |
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