Distributed In-Memory Analytics for Big Temporal Data

Bin Yao, Wei Zhang, Zhi-Jie Wang, Zhongpu Chen, Shuo Shang, Kai Zheng, Minyi Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


The temporal data is ubiquitous, and massive amount of temporal data is generated nowadays. Management of big temporal data is important yet challenging. Processing big temporal data using a distributed system is a desired choice. However, existing distributed systems/methods either cannot support native queries, or are disk-based solutions, which could not well satisfy the requirements of high throughput and low latency. To alleviate this issue, this paper proposes an In-memory based Two-level Index Solution in Spark (ITISS) for processing big temporal data. The framework of our system is easy to understand and implement, but without loss of efficiency. We conduct extensive experiments to verify the performance of our solution. Experimental results based on both real and synthetic datasets consistently demonstrate that our solution is efficient and competitive.
Original languageEnglish (US)
Title of host publicationDatabase Systems for Advanced Applications
PublisherSpringer Nature
Number of pages17
ISBN (Print)9783319914510
StatePublished - May 13 2018


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