Streaming Similarity Self-Join

TL;DR

This paper addresses the challenge of computing similarity self-joins in streaming data by introducing time-dependent similarity and developing scalable algorithms, notably the STR framework with the L2 index, for real-time similarity detection.

Contribution

It proposes a novel streaming similarity self-join framework using time-dependent similarity, along with new algorithms and an optimized index for scalable real-time processing.

Findings

STR algorithm with L2 index outperforms other methods in scalability

Time-dependent similarity effectively manages unbounded streaming data

Extensive experiments validate the efficiency and scalability of the proposed approach

Abstract

We introduce and study the problem of computing the similarity 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 similarity 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 version 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.