Data-to-Value: An Evaluation-First Methodology for Natural Language Projects

  title={Data-to-Value: An Evaluation-First Methodology for Natural Language Projects},
  author={Jochen L. Leidner},
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… 



The KDD process for extracting useful knowledge from volumes of data

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Knowledge discovery standards

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Data Mining Using SAS Enterprise Miner – A Case Study Approach

Enterprise Miner add-ins to SAS/Warehouse Administrator are defined as advanced methods for exploring and modeling relationships in large amounts of data.

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Knowledge discovery in databases (KDD) is an iterative multi-stage process for extracting useful, non-trivial information from large databases. Each stage of the process presents numerous choices to

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Software engineering: What is it?

  • B. McMillin
  • Computer Science
    2018 IEEE Aerospace Conference
  • 2018
This paper will make an admittedly bold and brash attempt to boil it all down into something anyone can understand, hopefully resulting in a brief reference — a type of lens through which existing standards can be more practically viewed.

CRISP-DM 1.0: Step-by-step data mining guide

The CRISP-DM process model is described, including an introduction to the CRISp-DM methodology, the CRisP- DM reference model, theCRISP -DM user guide and the CR ISP-DL reports, as well as an appendix with additional useful and related information.

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A survey of Knowledge Discovery and Data Mining process models

This research presents a novel approach to data mining called "Smart Mining for Knowledge Discovery and Data Mining", which automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and cataloging data and extracting insights from it.


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