LFTF: A Framework for Efficient Tensor Analytics at Scale

Summary: LFTF is a distributed, lock-free tensor factorization framework using asynchronous execution on a re-formulated problem for sparse, massive tensors. It delivers higher CPU/network throughput, ~17× faster convergence, and scales to very large datasets for tensor analytics (classification, recommendation, anomaly detection). (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 11569
Venue: VLDB
Year: 2017
Pagerank: 4.613363e-05
Overall Rank: 7,951 | 44.69%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,440	FlexPS: Flexible Parallelism Control in Parameter Server Architecture	2018	VLDB	8.8119143e-05
4,609	A General and Efficient Querying Method for Learning to Hash	2018	SIGMOD	6.0528541e-05
11,135	TUCKET: A Tensor Time Series Data Structure for Efficient and Accurate Factor Analysis over Time Ranges	2024	VLDB	4.1945683e-05
11,782	The Best of Both Worlds: Big Data Programming with Both Productivity and Performance	2017	SIGMOD	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2,033	NOMAD: Non-locking, stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion	2014	VLDB	9.7172731e-05
4,120	Husky: Towards a More Efficient and Expressive Distributed Computing Framework	2016	VLDB	6.4364588e-05

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