DiAl: Distributed Streaming Analytics Anywhere, Anytime

Ivo Santos, Marcel Tilly, Badrish Chandramouli, and Jonathan Goldstein

Abstract

Connected devices are expected to grow to 50 billion in 2020. Through our industrial partners and their use cases, we validated the importance of inflight data processing to produce results with low latency, in particular local and global data analytics capabilities. In order to cope with the scalability challenges posed by distributed streaming analytics scenarios, we propose two new technologies: (1) JStreams, a low footprint and efficient JavaScript complex event processing engine supporting local analytics on heterogeneous devices and (2) DiAlM, a distributed analytics management service that leverages cloud-edge evolving topologies. In the demonstration, based on a real manufacturing use case, we walk through a situation where operators supervise manufacturing equipment through global analytics, and drill down into alarm cases on the factory floor by locally inspecting the data generated by the manufacturing equipment.

Details

Publication typeInproceedings
Published inInternational Conference on Very Large Databases (VLDB)
PublisherVery Large Data Bases Endowment Inc.
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