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Information retrieval from textual data focuses on the construction ofvocabularies that contain weighted term tuples. Such vocabularies can then beexploited by various text analysis algorithms to extract new knowledge, e.g.,top-k keywords, top-k documents, etc. Top-k keywords are casually used forvarious purposes, are often computed on-the-fly, and thus must be efficientlycomputed. To compare competing weighting schemes and database implementations,benchmarking is customary. To the best of our knowledge, no benchmark currentlyaddresses these problems. Hence, in this paper, we present a top-k keywordsbenchmark, T${}^2$K${}^2$, which features a real tweet dataset and queries withvarious complexities and selectivities. T${}^2$K${}^2$ helps evaluate weightingschemes and database implementations in terms of computing performance. Toillustrate T${}^2$K${}^2$'s relevance and genericity, we successfully performedtests on the TF-IDF and Okapi BM25 weighting schemes, on one hand, and ondifferent relational (Oracle, PostgreSQL) and document-oriented (MongoDB)database implementations, on the other hand.

T${}^2$K${}^2$: The Twitter Top-K Keywords Benchmark