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A Parallel Platform for Big Data Analytics: A Design Science Approach

Loebbecke, Claudia; Bienert, Joerg; Sunyaev, Ali


Following a Design Science approach, at the core of this paper we propose a technically innovative parallel platform for Big Data analytics. The design of the proposed platform allows for analyzing and filtering billions of records, querying data structures with 1,000s of columns, getting answers in milliseconds without cubes, continuously importing data with low latency, and executing 1,000s of concurrent queries. Deploying the platform has empowered organizations across many industries to capture new business opportunities from better analytic quality of very large, close to real-time data. With our single platform design project, we hope to provide an interim attempt at theorizing [1] about achieving data quality and business opportunities from Big Data analytics.

Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2013
Sprache Englisch
Identifikator ISSN: 2231-0711
KITopen-ID: 1000091319
Erschienen in International Journal of Computer Science Engineering and Technology (IJCSET)
Band 3
Heft 5
Seiten 152-156
Externe Relationen Abstract/Volltext
Schlagwörter Big Data Analytics, Parallel Platform Design, Design Science
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