DataCalc: Ad-hoc Analyses on Heterogeneous Data Sources
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
Storing and processing data at different locations using a heterogeneous set of formats and data managements systems is state-of-the-art in many organizations. However, data analyses can often provide better insight when data from several sources is integrated into a combined perspective. In this paper we present an overview of our data integration system DataCalc. DataCalc is an extensible integration platform that executes adhoc analytical queries on a set of heterogeneous data processors. Our novel platform uses an expressive function shipping interface that promotes local computation and reduces data movement between processors. In this paper, we provide a discussion of the overall architecture and the main components of DataCalc. Moreover, we discuss the cost of integrating additional processors and evaluate the overall performance of the platform.
Details
Originalsprache | Englisch |
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Titel | Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 |
Redakteure/-innen | Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye |
Herausgeber (Verlag) | IEEE, New York [u. a.] |
Seiten | 463-468 |
Seitenumfang | 6 |
ISBN (elektronisch) | 9781728108582 |
Publikationsstatus | Veröffentlicht - Dez. 2019 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | 2019 IEEE International Conference on Big Data (Big Data) |
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Konferenz
Titel | 2019 IEEE International Conference on Big Data, Big Data 2019 |
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Dauer | 9 - 12 Dezember 2019 |
Stadt | Los Angeles |
Land | USA/Vereinigte Staaten |
Externe IDs
Scopus | 85081362423 |
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ORCID | /0000-0001-8107-2775/work/142253464 |