SliceNStitch: Continuous CP decomposition of sparse tensor streams

Cited 4 time in webofscience Cited 0 time in scopus
  • Hit : 156
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKwon, Taehyungko
dc.contributor.authorPark, Inkyuko
dc.contributor.authorLee, Dongjinko
dc.contributor.authorShin, Kijungko
dc.date.accessioned2021-09-02T02:30:25Z-
dc.date.available2021-09-02T02:30:25Z-
dc.date.created2021-09-02-
dc.date.created2021-09-02-
dc.date.created2021-09-02-
dc.date.issued2021-04-19-
dc.identifier.citation37th IEEE International Conference on Data Engineering, ICDE 2021, pp.816 - 827-
dc.identifier.issn1084-4627-
dc.identifier.urihttp://hdl.handle.net/10203/287563-
dc.description.abstractConsider traffic data (i.e., triplets in the form of source-destination-timestamp) that grow over time. Tensors (i.e., multi-dimensional arrays) with a time mode are widely used for modeling and analyzing such multi-aspect data streams. In such tensors, however, new entries are added only once per period, which is often an hour, a day, or even a year. This discreteness of tensors has limited their usage for real-time applications, where new data should be analyzed instantly as it arrives.How can we analyze time-evolving multi-aspect sparse data 'continuously' using tensors where time is 'discrete'? We propose SLICENSTITCH for continuous CANDECOMP/PARAFAC (CP) decomposition, which has numerous time-critical applications, including anomaly detection, recommender systems, and stock market prediction. SLICENSTITCH changes the starting point of each period adaptively, based on the current time, and updates factor matrices (i.e., outputs of CP decomposition) instantly as new data arrives. We show, theoretically and experimentally, that SLICENSTITCH is (1) 'Any time': updating factor matrices immediately without having to wait until the current time period ends, (2) Fast: with constant-time updates up to 464× faster than online methods, and (3) Accurate: with fitness comparable (specifically, 72 - 100%) to offline methods.-
dc.languageEnglish-
dc.publisherIEEE Computer Society-
dc.titleSliceNStitch: Continuous CP decomposition of sparse tensor streams-
dc.typeConference-
dc.identifier.wosid000687830800069-
dc.identifier.scopusid2-s2.0-85112868689-
dc.type.rimsCONF-
dc.citation.beginningpage816-
dc.citation.endingpage827-
dc.citation.publicationname37th IEEE International Conference on Data Engineering, ICDE 2021-
dc.identifier.conferencecountryGR-
dc.identifier.conferencelocationVirtual-
dc.identifier.doi10.1109/ICDE51399.2021.00076-
dc.contributor.localauthorShin, Kijung-
dc.contributor.nonIdAuthorKwon, Taehyung-
dc.contributor.nonIdAuthorPark, Inkyu-
dc.contributor.nonIdAuthorLee, Dongjin-
Appears in Collection
RIMS Conference Papers
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 4 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0