Streaming similarity self-join

Gianmarco De Francisci Morales, Aristides Gionis

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

20 Citations (Scopus)
152 Downloads (Pure)


We introduce and study the problem of computing the simi- larity self-join in a streaming context (sssj), where the input is an unbounded stream of items arriving continuously. The goal is to find all pairs of items in the stream whose similarity is greater than a given threshold. The simplest formulation of the problem requires unbounded memory, and thus, it is intractable. To make the problem feasible, we introduce the notion of time-dependent similarity: the similarity of two items decreases with the difference in their arrival time. By leveraging the properties of this time-dependent sim- ilarity function, we design two algorithmic frameworks to solve the sssj problem. The first one, MiniBatch (MB), uses existing index-based filtering techniques for the static ver- sion of the problem, and combines them in a pipeline. The second framework, Streaming (STR), adds time filtering to the existing indexes, and integrates new time-based bounds deeply in the working of the algorithms. We also introduce a new indexing technique (L2), which is based on an existing state-of-the-art indexing technique (L2AP), but is optimized for the streaming case. Extensive experiments show that the STR algorithm, when instantiated with the L2 index, is the most scalable option across a wide array of datasets and parameters.

Original languageEnglish
Title of host publicationProceedings of the VLDB Endowment
Number of pages12
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Very Large Databases - New Delhi, India
Duration: 5 Sep 20169 Sep 2016
Conference number: 42

Publication series

NameProceedings of the VLDB endowment
PublisherAssociation for Computing Machinery
ISSN (Print)2150-8097


ConferenceInternational Conference on Very Large Databases
CityNew Delhi


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