TextBenDS: a Generic Textual Data Benchmark for Distributed Systems

Extracting top- k keywords and documents using weighting schemes are popular techniques employed in text mining and machine learning for different analysis and retrieval tasks. The weights are usually computed in the data preprocessing step, as they are costly to update and keep track of all the mod...

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Vydáno v:Information systems frontiers Ročník 23; číslo 1; s. 81 - 100
Hlavní autoři: Truică, Ciprian-Octavian, Apostol, Elena-Simona, Darmont, Jérôme, Assent, Ira
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.02.2021
Springer Nature B.V
Springer Verlag
Edice:Breakthroughs on Cross-Cutting Data Management, Data Analytics and Applied Data Science
Témata:
Algorithms
Benchmarks
Big Data
Business and Management
Computation
Computer networks
Computer Science
Control
Data base management systems
Data mining
Datasets
Document and Text Processing
Ecosystems
Errors
Information systems
IT in Business
Keywords
Machine learning
Management of Computing and Information Systems
Multidimensional approach
Operations Research/Decision Theory
Performance evaluation
Queries
Retrieval
Scores
Subsets
Systems Theory
Weighting
Weighting schemes
Distributed DBMSs
Top
keywords
Distributed frameworks
documents
Benchmark
Top-k documents
Top-k keywords
ISSN:1387-3326, 1572-9419
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Shrnutí:Extracting top- k keywords and documents using weighting schemes are popular techniques employed in text mining and machine learning for different analysis and retrieval tasks. The weights are usually computed in the data preprocessing step, as they are costly to update and keep track of all the modifications performed on the dataset. Furthermore, calculation errors are introduced when analyzing only subsets of the dataset, i.e., wrong weighting are computed as weighting schemes use the number of documents for scoring keywords and documents. Therefore, in a Big Data context, it is crucial to lower the runtime of computing weighting schemes, without hindering the analysis process and the accuracy of the machine learning algorithms. To address this requirement for the task of computing top- k keywords and documents (which largely relies on weighting schemes), it is customary to design benchmarks that compare weighting schemes within various configurations of distributedframeworks and database management systems. Thus, we propose T ext B en DS - a generic document-oriented benchmark for storing textual data and constructing weighting schemes. Our benchmark offers a generic data model designed with a multidimensional approach for storing text documents. We also propose using aggregation queries with various complexities and selectivities for constructing term weighting schemes, that are utilized in extracting top- k keywords and documents. We evaluate the computing performance of the queries on several distributed environments set within the Apache Hadoop ecosystem. Our experimental results provide interesting insights. As an example, MongoDB shows the best overall performance, while Spark’s execution time remains almost constant regardless of weighting schemes.
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ISSN:1387-3326
1572-9419
DOI:10.1007/s10796-020-09999-y