%0 Conference Proceedings
%T T²K²: The Twitter Top-K Keywords Benchmark
%+ University Politehnica of Bucharest [Romania] (UPB)
%+ Entrepôts, Représentation et Ingénierie des Connaissances (ERIC)
%A Truică, Ciprian-Octavian
%A Darmont, Jérôme
%< avec comité de lecture
%( Communications in Computer and Information Science
%B 21st European Conference on Advances in Databases and Information Systems (ADBIS 2017)
%C Nicosie, Cyprus
%I Springer
%3 New Trends in Databases and Information Systems
%V 767
%P 21-28
%8 2017-09-24
%D 2017
%Z 1709.04747
%R 10.1007/978-3-319-67162-8
%K Top-k keywords
%K Benchmark
%K Term weighting
%K Database systems
%Z Computer Science [cs]/Databases [cs.DB]Conference papers
%X Information retrieval from textual data focuses on the construction of vocabularies that contain weighted term tuples. Such vocabularies can then be exploited by various text analysis algorithms to extract new knowledge, e.g., top-k keywords, top-k documents, etc. Top-k keywords are casually used for various purposes, are often computed on-the-fly, and thus must be efficiently computed. 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 a top-k keywords benchmark, T²K², which features a real tweet dataset and queries with various complexities and selectivities. T²K² helps evaluate weighting schemes and database implementations in terms of computing performance. To illustrate T²K²'s relevance and genericity, we successfully performed 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.
%G English
%2 https://hal.science/hal-01529581/document
%2 https://hal.science/hal-01529581/file/t2k2.pdf
%L hal-01529581
%U https://hal.science/hal-01529581
%~ UNIV-LYON1
%~ UNIV-LYON2
%~ ERIC
%~ LABEXIMU
%~ LYON2
%~ UDL
%~ UNIV-LYON