Benchmarking Top-K Keyword and Top-K Document Processing with T${}^2$K${}^2$ and T${}^2$K${}^2$D${}^2$

TL;DR

This paper introduces T2K2 and T2K2D2, benchmarks designed to evaluate the performance of top-k keyword and document extraction methods across different weighting schemes and database systems using real tweet data.

Contribution

It presents the first dedicated benchmarks for top-k keyword and document extraction, enabling comparison of weighting schemes and database implementations.

Findings

TF-IDF and Okapi BM25 weighting schemes tested

Performance evaluated on Oracle, PostgreSQL, and MongoDB

Benchmarks demonstrate versatility and relevance

Abstract

Top-k keyword and top-k document extraction are very popular text analysis techniques. Top-k keywords and documents are often computed on-the-fly, but they exploit weighted vocabularies that are costly to build. To compare competing weighting schemes and database implementations, benchmarking is customary. To the best of our knowledge, no benchmark currently addresses these problems. Hence, in this paper, we present T${}^2$K${}^2$, a top-k keywords and documents benchmark, and its decision support-oriented evolution T${}^2$K${}^2$D${}^2$. Both benchmarks feature a real tweet dataset and queries with various complexities and selectivities. They help evaluate weighting schemes and database implementations in terms of computing performance. To illustrate our bench-marks' relevance and genericity, we successfully ran performance tests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and on different relational (Oracle, PostgreSQL) and document-oriented (MongoDB) database implementations, on the other hand.