Enabling Signal Processing over Data Streams

Milos Nikolic, Badrish Chandramouli, Jonathan Goldstein

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


Internet of Things applications analyze the data coming from large networks of sensor devices using relational and signal processing operations and running the same query logic over groups of sensor signals. 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 data processing 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 paper, we advocate a deep integration of signal processing operations and general-purpose query processors. We aim to reconcile the disparate data models and provide a common query language that allows users to seamlessly interleave tempo-relational and signal operations for both online and offline processing. Our approach is extensible and offers frameworks for quick and easy integration of user-defined operations while supporting incremental computation. Our system that deeply integrates relational and signal operations, called TRILLDSP, achieves up to two orders of magnitude better performance than popular loosely-coupled data management systems on grouped signal processing workflows.
Original languageEnglish
Title of host publicationSIGMOD '17: Proceedings of the 2017 ACM International Conference on Management of Data
Place of PublicationNew York, NY, USA
Number of pages14
ISBN (Print)978-1-4503-4197-4
Publication statusPublished - 9 May 2017
Event2017 ACM International Conference on Management of Data - Chicago, United States
Duration: 14 May 201719 May 2017


Conference2017 ACM International Conference on Management of Data
Abbreviated titleSIGMOD/PODS 2017
CountryUnited States
Internet address


  • array and relational data
  • data stream processing
  • digital signal processing
  • incremental computation
  • iot applications
  • realtime analysis
  • sensor networks
  • tight integration
  • trill
  • trilldsp

Fingerprint Dive into the research topics of 'Enabling Signal Processing over Data Streams'. Together they form a unique fingerprint.

Cite this