Show simple item record

dc.contributor.authorAbuzaid, Firas
dc.contributor.authorBailis, Peter
dc.contributor.authorDing, Jialin
dc.contributor.authorGan, Edward
dc.contributor.authorMadden, Samuel
dc.contributor.authorNarayanan, Deepak
dc.contributor.authorRong, Kexin
dc.contributor.authorSuri, Sahaana
dc.date.accessioned2021-10-27T20:10:35Z
dc.date.available2021-10-27T20:10:35Z
dc.date.issued2018
dc.identifier.urihttps://hdl.handle.net/1721.1/135069
dc.description.abstract© 2018 Association for Computing Machinery. 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 eficient, 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 (i.e., feature selection) 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.
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.isversionof10.1145/3276463
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceOther repository
dc.titleMacroBase: Prioritizing Attention in Fast Data
dc.typeArticle
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalACM Transactions on Database Systems
dc.eprint.versionAuthor's final manuscript
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2019-06-18T17:06:52Z
dspace.orderedauthorsAbuzaid, F; Bailis, P; Ding, J; Gan, E; Madden, S; Narayanan, D; Rong, K; Suri, S
dspace.date.submission2019-06-18T17:06:53Z
mit.journal.volume43
mit.journal.issue4
mit.metadata.statusAuthority Work and Publication Information Needed


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record