Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Brown, P.G.: Overview of SciDB: large scale array storage, processing and analysis. In: SIGMOD, pp. 963–968 (2010)
Chandramouli, B., Goldstein, J., Barnett, M., DeLine, R., Platt, J.C., Terwilliger, J.F., Wernsing, J.: Trill: a high-performance incremental query processor for diverse analytics. PVLDB 8(4), 401–412 (2014)
Nikolic, M., Chandramouli, B., Goldstein, J.: Enabling signal processing over data streams. In: SIGMOD, pp. 95–108 (2017)
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauly, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: NSDI, pp. 15–28 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Nikolic, M., Chandramouli, B., Goldstein, J. (2017). Enabling Deep Analytics in Stream Processing Systems. In: Calì, A., Wood, P., Martin, N., Poulovassilis, A. (eds) Data Analytics. BICOD 2017. Lecture Notes in Computer Science(), vol 10365. Springer, Cham. https://doi.org/10.1007/978-3-319-60795-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-60795-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60794-8
Online ISBN: 978-3-319-60795-5
eBook Packages: Computer ScienceComputer Science (R0)