T ${}^2$ K ${}^2$：Twitter Top-K关键词基准测试
T${}^2$K${}^2$: The Twitter Top-K Keywords Benchmark
摘要：
从文本数据中进行信息检索的重点是构建包含加权词元组的词汇表。这样的词汇表然后可以被各种文本分析算法利用来提取新的知识，例如，前k个关键字、前k个文档等。前k个关键字被随意地用于各种目的，经常被即时计算，因此必须被有效地计算。为了比较相互竞争的加权方案和数据库实现，通常使用基准测试。据我们所知，目前没有基准解决这些问题。因此，在本文中，我们提出了一个top-k关键字基准测试，T ${}^2$ K ${}^2$，它具有一个真正的鸣叫数据集和各种复杂性和选择性的查询。T ${}^2$ K ${}^2$帮助评估加权方案和数据库实现的计算性能。为了说明T ${}^2$ K ${}^2$的相关性和通用性，我们一方面成功地对TF-IDF和Okapi BM 25加权方案进行了测试，另一方面成功地对不同的关系（Oracle，PostgreSQL）和面向文档（MongoDB）数据库实现进行了测试。
英文摘要：
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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
