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- W4221145193 abstract "Big data, i.e. collecting, storing and processing of data at scale, has recently been possible due to the arrival of clusters of commodity computers powered by application-level distributed parallel operating systems like HDFS/Hadoop/Spark, and such infrastructures have revolutionized data mining at scale. For data mining project to succeed more consistently, some methodologies were developed (e.g. CRISP-DM, SEMMA, KDD), but these do not account for (1) very large scales of processing, (2) dealing with textual (unstructured) data (i.e. Natural Language Processing (NLP, text analytics), and (3) non-technical considerations (e.g. legal, ethical, project managerial aspects). To address these shortcomings, a new methodology, called Data to Value (D2V), is introduced, which is guided by a detailed catalog of questions in order to avoid a disconnect of big data text analytics project team with the topic when facing rather abstract box-and-arrow diagrams commonly associated with methodologies." @default.
- W4221145193 created "2022-04-03" @default.
- W4221145193 creator A5078661113 @default.
- W4221145193 date "2022-01-19" @default.
- W4221145193 modified "2023-09-24" @default.
- W4221145193 title "Data-to-Value: An Evaluation-First Methodology for Natural Language Projects" @default.
- W4221145193 doi "https://doi.org/10.48550/arxiv.2201.07725" @default.
- W4221145193 hasPublicationYear "2022" @default.
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