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| Content Provider | ACM Digital Library |
|---|---|
| Author | Narayanan, Deepak Bailis, Peter Madden, Samuel Gan, Edward Suri, Sahaana Rong, Kexin |
| 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. |
| Starting Page | 541 |
| Ending Page | 556 |
| Page Count | 16 |
| File Format | |
| ISBN | 9781450341974 |
| DOI | 10.1145/3035918.3035928 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-05-09 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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