Enabling Deep Analytics in Stream Processing Systems

Milos Nikolic, Badrish Chandramouli, Jonathan Goldstein

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

Abstract / Description of output

Real-time applications often analyze data coming from sensor networks using relational and domain-specific operations such as signal processing and machine learning algorithms. To support such increasingly important scenarios, many data management systems integrate with numerical frameworks like R. Such solutions, however, incur significant performance penalties as relational engines and numerical tools operate on fundamentally different data models with expensive inter-communication mechanisms. In addition, none of these solutions supports efficient real-time and incremental analysis. In this work, we advocate a deep integration of domain-specific operations into general-purpose query processors with the goal of providing unified query and data models for both online and offline processing. Our proof-of-concept system tightly integrates relational and digital signal processing operations and achieves orders of magnitude better performance than existing loosely-coupled data management systems.
Original languageEnglish
Title of host publicationData Analytics
Subtitle of host publication31st British International Conference on Databases, BICOD 2017, London, UK, July 10–12, 2017, Proceedings
Number of pages5
ISBN (Electronic)978-3-319-60795-5
ISBN (Print)978-3-319-60794-8
Publication statusE-pub ahead of print - 14 Jun 2017
Event31st British International Conference on Databases - London, United Kingdom
Duration: 10 Jul 201712 Jul 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameInformation Systems and Applications, incl. Internet/Web, and HCI


Conference31st British International Conference on Databases
Abbreviated titleBICOD 2017
Country/TerritoryUnited Kingdom
Internet address


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