| dc.contributor.author | Bailis, Peter |  | 
| dc.contributor.author | Gan, Edward |  | 
| dc.contributor.author | Madden, Samuel |  | 
| dc.contributor.author | Narayanan, Deepak |  | 
| dc.contributor.author | Rong, Kexin |  | 
| dc.contributor.author | Suri, Sahaana |  | 
| dc.date.accessioned | 2021-11-08T20:13:09Z |  | 
| dc.date.available | 2021-11-08T20:13:09Z |  | 
| dc.date.issued | 2017-05 |  | 
| dc.identifier.uri | https://hdl.handle.net/1721.1/137811 |  | 
| dc.description.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, | en_US | 
| dc.language.iso | en |  | 
| dc.publisher | Association for Computing Machinery (ACM) | en_US | 
| dc.relation.isversionof | 10.1145/3035918.3035928 | en_US | 
| dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US | 
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US | 
| dc.source | website | en_US | 
| dc.title | MacroBase: Prioritizing Attention in Fast Data | en_US | 
| dc.type | Article | en_US | 
| dc.identifier.citation | Bailis, Peter, Gan, Edward, Madden, Samuel, Narayanan, Deepak, Rong, Kexin et al. 2017. "MacroBase: Prioritizing Attention in Fast Data." |  | 
| dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US | 
| dc.eprint.version | Author's final manuscript | en_US | 
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US | 
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US | 
| dc.date.updated | 2019-06-18T14:50:28Z |  | 
| dspace.date.submission | 2019-06-18T14:50:34Z |  | 
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |