Title :
Towards a Multi-cluster Analytical Engine for Transportation Data
Author :
Shtern, Mark ; Mian, Rizwan ; Litoiu, Marin ; Zareian, Saeed ; Abdelgawad, Hossam ; Tizghadam, Ali
Author_Institution :
York Univ., Toronto, ON, Canada
Abstract :
In the new digital age, the pace and volume of growing transportation related data is exceeding our ability to manage and analyze it. In this position paper, we present a data engine, Godzilla, to ingest real-time traffic data and support analytic and data mining over traffic data. Godzilla is a multi-cluster approach to handle large volumes of growing data, changing workloads and varying number of users. The data originates at multiple sources, and consists of multiple types. Meanwhile, the workloads belong to three camps, namely batch processing, interactive queries and graph analysis. Godzilla support multiple language abstractions from scripting to SQL-like language.
Keywords :
data handling; data mining; graph theory; pattern clustering; traffic engineering computing; transportation; Godzilla; SQL-like language; analytic mining; batch processing; data engine; data handling; data mining; graph analysis; interactive queries; language abstractions; multicluster analytical engine; multicluster approach; scripting language; traffic data; transportation data; Detectors; Elasticity; Engines; Media; Road transportation; Vehicles; Analytical Engine; Big Data; Godzilla; Intelligent Transportation System (ITS);
Conference_Titel :
Cloud and Autonomic Computing (ICCAC), 2014 International Conference on
Conference_Location :
London
DOI :
10.1109/ICCAC.2014.37