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Erschienen in:
2017 | OriginalPaper | Buchkapitel
T\(^2\)K\(^2\): The Twitter Top-K Keywords Benchmark
verfasst von : Ciprian-Octavian Truică, Jérôme Darmont
Erschienen in: New Trends in Databases and Information Systems
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Abstract
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, T\(^2\)K\(^2\), which features a real tweet dataset and queries with various complexities and selectivities. T\(^2\)K\(^2\) helps evaluate weighting schemes and database implementations in terms of computing performance. To illustrate T\(^2\)K\(^2\)’s relevance and genericity, we show how to implement the TF-IDF and Okapi BM25 weighting schemes, on one hand, and relational and document-oriented database instantiations, on the other hand.