Matches in SemOpenAlex for { <https://semopenalex.org/work/W3028919994> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W3028919994 endingPage "32" @default.
- W3028919994 startingPage "13" @default.
- W3028919994 abstract "Abstract Purpose We propose InParTen2, a multi-aspect parallel factor analysis three-dimensional tensor decomposition algorithm based on the Apache Spark framework. The proposed method reduces re-decomposition cost and can handle large tensors. Design/methodology/approach Considering that tensor addition increases the size of a given tensor along all axes, the proposed method decomposes incoming tensors using existing decomposition results without generating sub-tensors. Additionally, InParTen2 avoids the calculation of Khari–Rao products and minimizes shuffling by using the Apache Spark platform. Findings The performance of InParTen2 is evaluated by comparing its execution time and accuracy with those of existing distributed tensor decomposition methods on various datasets. The results confirm that InParTen2 can process large tensors and reduce the re-calculation cost of tensor decomposition. Consequently, the proposed method is faster than existing tensor decomposition algorithms and can significantly reduce re-decomposition cost. Research limitations There are several Hadoop-based distributed tensor decomposition algorithms as well as MATLAB-based decomposition methods. However, the former require longer iteration time, and therefore their execution time cannot be compared with that of Spark-based algorithms, whereas the latter run on a single machine, thus limiting their ability to handle large data. Practical implications The proposed algorithm can reduce re-decomposition cost when tensors are added to a given tensor by decomposing them based on existing decomposition results without re-decomposing the entire tensor. Originality/value The proposed method can handle large tensors and is fast within the limited-memory framework of Apache Spark. Moreover, InParTen2 can handle static as well as incremental tensor decomposition." @default.
- W3028919994 created "2020-06-05" @default.
- W3028919994 creator A5060085721 @default.
- W3028919994 creator A5069421246 @default.
- W3028919994 date "2020-04-01" @default.
- W3028919994 modified "2023-10-16" @default.
- W3028919994 title "Multi-Aspect Incremental Tensor Decomposition Based on Distributed In-Memory Big Data Systems" @default.
- W3028919994 cites W111803032 @default.
- W3028919994 cites W1574504252 @default.
- W3028919994 cites W2103392911 @default.
- W3028919994 cites W2440837865 @default.
- W3028919994 cites W2538515886 @default.
- W3028919994 cites W2742340639 @default.
- W3028919994 cites W2795272597 @default.
- W3028919994 cites W2800338558 @default.
- W3028919994 cites W2907401972 @default.
- W3028919994 cites W2940605002 @default.
- W3028919994 cites W2941571614 @default.
- W3028919994 cites W2964103059 @default.
- W3028919994 cites W2965049319 @default.
- W3028919994 cites W3044727810 @default.
- W3028919994 cites W4230512203 @default.
- W3028919994 doi "https://doi.org/10.2478/jdis-2020-0010" @default.
- W3028919994 hasPublicationYear "2020" @default.
- W3028919994 type Work @default.
- W3028919994 sameAs 3028919994 @default.
- W3028919994 citedByCount "2" @default.
- W3028919994 countsByYear W30289199942022 @default.
- W3028919994 countsByYear W30289199942023 @default.
- W3028919994 crossrefType "journal-article" @default.
- W3028919994 hasAuthorship W3028919994A5060085721 @default.
- W3028919994 hasAuthorship W3028919994A5069421246 @default.
- W3028919994 hasBestOaLocation W30289199941 @default.
- W3028919994 hasConcept C11413529 @default.
- W3028919994 hasConcept C124681953 @default.
- W3028919994 hasConcept C126255220 @default.
- W3028919994 hasConcept C155281189 @default.
- W3028919994 hasConcept C18903297 @default.
- W3028919994 hasConcept C199360897 @default.
- W3028919994 hasConcept C22789450 @default.
- W3028919994 hasConcept C2524010 @default.
- W3028919994 hasConcept C2781215313 @default.
- W3028919994 hasConcept C2986737658 @default.
- W3028919994 hasConcept C33923547 @default.
- W3028919994 hasConcept C41008148 @default.
- W3028919994 hasConcept C459310 @default.
- W3028919994 hasConcept C86803240 @default.
- W3028919994 hasConceptScore W3028919994C11413529 @default.
- W3028919994 hasConceptScore W3028919994C124681953 @default.
- W3028919994 hasConceptScore W3028919994C126255220 @default.
- W3028919994 hasConceptScore W3028919994C155281189 @default.
- W3028919994 hasConceptScore W3028919994C18903297 @default.
- W3028919994 hasConceptScore W3028919994C199360897 @default.
- W3028919994 hasConceptScore W3028919994C22789450 @default.
- W3028919994 hasConceptScore W3028919994C2524010 @default.
- W3028919994 hasConceptScore W3028919994C2781215313 @default.
- W3028919994 hasConceptScore W3028919994C2986737658 @default.
- W3028919994 hasConceptScore W3028919994C33923547 @default.
- W3028919994 hasConceptScore W3028919994C41008148 @default.
- W3028919994 hasConceptScore W3028919994C459310 @default.
- W3028919994 hasConceptScore W3028919994C86803240 @default.
- W3028919994 hasIssue "2" @default.
- W3028919994 hasLocation W30289199941 @default.
- W3028919994 hasLocation W30289199942 @default.
- W3028919994 hasOpenAccess W3028919994 @default.
- W3028919994 hasPrimaryLocation W30289199941 @default.
- W3028919994 hasRelatedWork W2393920986 @default.
- W3028919994 hasRelatedWork W2567234673 @default.
- W3028919994 hasRelatedWork W2608089480 @default.
- W3028919994 hasRelatedWork W2920756479 @default.
- W3028919994 hasRelatedWork W2941571614 @default.
- W3028919994 hasRelatedWork W2963838862 @default.
- W3028919994 hasRelatedWork W3028919994 @default.
- W3028919994 hasRelatedWork W3104088881 @default.
- W3028919994 hasRelatedWork W4205920284 @default.
- W3028919994 hasRelatedWork W2765083400 @default.
- W3028919994 hasVolume "5" @default.
- W3028919994 isParatext "false" @default.
- W3028919994 isRetracted "false" @default.
- W3028919994 magId "3028919994" @default.
- W3028919994 workType "article" @default.