Towards a Multi-cluster Analytical Engine for Transportation Data

被引：10
|
作者：
Shtern, Mark [1 ]
Mian, Rizwan [1 ]
Litoiu, Marin [1 ]
Zareian, Saeed [1 ]
Abdelgawad, Hossam [2 ]
Tizghadam, Ali [2 ]
机构：
[1] York Univ, Toronto, ON M3J 2R7, Canada
[2] Univ Toronto, Toronto, ON, Canada
来源：
2014 INTERNATIONAL CONFERENCE ON CLOUD AND AUTONOMIC COMPUTING (ICCAC 2014) | 2014年
关键词：
Big Data; Analytical Engine; Intelligent Transportation System (ITS); Godzilla;
D O I：
10.1109/ICCAC.2014.37
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：249 / 257
页数：9
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