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Journal Article

MacroBase: Prioritizing Attention in Fast Data

Awarded “Best of SIGMOD 2017”

Abstract

As data volumes continue to rise, manual inspection is becoming increasingly untenable. In response, we present MacroBase, a data analytics engine that prioritizes end-user attention in high-volume fast data streams. MacroBase enables efficient, accurate, and modular analyses that highlight and aggregate important and unusual behavior, acting as a search engine for fast data. MacroBase is able to deliver order-of-magnitude speedups over alternatives by optimizing the combination of explanation and classification tasks and by leveraging a new reservoir sampler and heavy-hitters sketch specialized for fast data streams. As a result, MacroBase delivers accurate results at speeds of up to 2M events per second per query on a single core. The system has delivered meaningful results in production, including at a telematics company monitoring hundreds of thousands of vehicles.

Project page

MacroBase is a new analytic monitoring engine designed to prioritize human attention in large-scale datasets and data streams.
Author(s)
Peter Bailis
Edward Gan
Samuel Madden
Deepak Narayanan
Kexin Rong
Sahaana Suri
Journal Name
SIGMOD ’17: Proceedings of the 2017 ACM International Conference on Management of Data
Publication Date
May, 2017
DOI
10.1145/3035918.3035928
Publisher
ACM