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 @math K @math , which features a real tweet dataset and queries with various complexities and selectivities. T @math K @math helps evaluate weighting schemes and database implementations in terms of computing performance. To illustrate T @math K @math '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.
Term weighting schemes are extensively benchmarked in sentiment analysis @cite_5 , semantic similarity @cite_11 , text classification and categorization @cite_14 @cite_12 @cite_11 @cite_17 , and textual corpus generation @cite_2 . Benchmarks for text analysis focus mainly on algorithm accuracy, while either term weights are known before the algorithm is applied, or their computation is incorporated with preprocessing. Thus, such benchmarks do not evaluate weighting scheme construction efficiency as we do. Other benchmarks evaluate parallel text processing in big data applications in the cloud @cite_6 @cite_13 . PRIMEBALL notably specifies several relevant properties characterizing cloud platforms @cite_6 , such as scale-up, elastic speedup, horizontal scalability, latency, durability, consistency and version handling, availability, concurrency and other data and information retrieval properties. However, PRIMEBALL is only a specification; it is not implemented.
@cite_14 @cite_11 @cite_6 @cite_2 @cite_5 @cite_13 @cite_12 @cite_17
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Short text semantic similarity measurement is a new and rapidly growing field of research. 'Short texts' are typically sentence length but are not required to be grammatically correct. There is great potential for applying these measures in fields such as information retrieval, dialogue management and question answering. A dataset of 65 sentence pairs, with similarity ratings, produced in 2006 has become adopted as a de facto gold standard benchmark. This paper discusses the adoption of the 2006 dataset, lays down a number of criteria that can be used to determine whether a dataset should be awarded a 'gold standard' accolade and illustrates its use as a benchmark. Procedures for the generation of further gold standard datasets in this field are recommended. In this paper, we draw the specifications of a novel benchmark for comparing parallel processing frameworks in the context of big data applications hosted in the cloud. We aim at filling several gaps in already existing cloud data processing benchmarks, which lack a real-life context for their processes, thus losing relevance when trying to assess performance for real applications. Hence, we propose a fictitious news site hosted in the cloud that is to be managed by the framework under analysis, together with several objective use case scenarios and measures for evaluating system performance. The main strengths of our benchmark are parallelization capabilities supporting cloud features and big data properties. Modern storage systems incorporate data compressors to improve their performance and capacity. As a result, data content can significantly influence the result of a storage system benchmark. Because real-world proprietary datasets are too large to be copied onto a test storage system, and most data cannot be shared due to privacy issues, a benchmark needs to generate data synthetically. To ensure that the result is accurate, it is necessary to generate data content based on the characterization of real-world data properties that influence the storage system performance during the execution of a benchmark. The existing approach, called SDGen, cannot guarantee that the benchmark result is accurate in storage systems that have built-in word-based compressors. The reason is that SDGen characterizes the properties that influence compression performance only at the byte level, and no properties are characterized at the word level. To address this problem, we present TextGen, a realistic text data content generation method for modern storage system benchmarks. TextGen builds the word corpus by segmenting real-world text datasets, and creates a word-frequency distribution by counting each word in the corpus. To improve data generation performance, the word-frequency distribution is fitted to a lognormal distribution by maximum likelihood estimation. The Monte Carlo approach is used to generate synthetic data. The running time of TextGen generation depends only on the expected data size, which means that the time complexity of TextGen is O(n). To evaluate TextGen, four real-world datasets were used to perform an experiment. The experimental results show that, compared with SDGen, the compression performance and compression ratio of the datasets generated by TextGen deviate less from real-world datasets when end-tagged dense code, a representative of word-based compressors, is evaluated. The emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, bearing profound implications for our understanding of human behavior. Given the growing assortment of sentiment measuring instruments, comparisons between them are evidently required. Here, we perform detailed tests of 6 dictionary-based methods applied to 4 different corpora, and briefly examine a further 20 methods. We show that a dictionary-based method will only perform both reliably and meaningfully if (1) the dictionary covers a sufficiently large enough portion of a given text's lexicon when weighted by word usage frequency; and (2) words are scored on a continuous scale. Massive-scale Big Data analytics is representative of a new class of workloads that justifies a rethinking of how computing systems should be optimized. This paper addresses the need for a set of benchmarks that system designers can use to measure the quality of their designs and that customers can use to evaluate competing systems offerings with respect to commonly performed text-oriented workflows in Hadoop™. Additions are needed to existing benchmarks such as HiBench in terms of both scale and relevance. We describe a methodology for creating a petascale data-size text-oriented benchmark that includes representative Big Data workflows and can be used to test total system performance, with demands balanced across storage, network, and computation. Creating such a benchmark requires meeting unique challenges associated with the data size and its often unstructured nature. To be useful, the benchmark also needs to be sufficiently generic to be accepted by the community at large. Here, we focus on a text-oriented Hadoop workflow that consists of three common tasks: categorizing text documents, identifying significant documents within each category, and analyzing significant documents for new topic creation. Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories recently made available by Reuters, Ltd. for research purposes. Use of this data for research on text categorization requires a detailed understanding of the real world constraints under which the data was produced. Drawing on interviews with Reuters personnel and access to Reuters documentation, we describe the coding policy and quality control procedures used in producing the RCV1 data, the intended semantics of the hierarchical category taxonomies, and the corrections necessary to remove errorful data. We refer to the original data as RCV1-v1, and the corrected data as RCV1-v2. We benchmark several widely used supervised learning methods on RCV1-v2, illustrating the collection's properties, suggesting new directions for research, and providing baseline results for future studies. We make available detailed, per-category experimental results, as well as corrected versions of the category assignments and taxonomy structures, via online appendices.