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Main Title: STEAM: A Platform for Scalable Spatiotemporal Analytics
Author(s): Deva, Bersant
Raschke, Philip
Rodriguez Garzon, Sandro
Küpper, Axel
Type: Article
Language Code: en
Abstract: Spatiotemporal datasets have become increasingly available with the introduction of a various set of applications and services trac- ing the behavior of moving objects. Recently, there has been a high demand in understanding these datasets using spatiotemporal analytics. While being considered of high value, spatiotemporal analytics did not yet see a wide spreading into the actual business workflow or the direct configuration of services and applications. The computational complexity for spatiotemporal datasets and the heterogeneity of data sources are considered key factors for the current state. This paper introduces STEAM, a platform for distributed spatiotemporal analytics on heterogeneous spatiotemporal datasets. STEAM introduces a framework that abstracts the key components from incoming spatiotemporal datasets that originate from various positioning systems. This abstraction provides a common base for distributed and scalable analytics methods that is not bound to a specific underlying positioning technique. STEAM provides a distributed state-of-the-art implementation and is evaluated on a multi-machine testbed for linear scalability.
Issue Date: 2017
Date Available: 18-Sep-2019
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): big data applications
spatiotemporal analytics
service-oriented architectures
Sponsor/Funder: BMBF, 01IS12056, Software Campus (TU Berlin)
Journal Title: Procedia Computer Science
Publisher: Elsevier
Publisher Place: Amsterdam
Volume: 109
Publisher DOI: 10.1016/j.procs.2017.05.429
Page Start: 731
Page End: 736
EISSN: 1877-0509
Appears in Collections:FG Service-centric Networking » Publications

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