A framework for interfacing unstructured data into business process from enterprise social networks

Research output: Contribution to journalArticle

Abstract

The increased number of Enterprise Social Networks (ESN) business applications has had a major impact on organizations' business processes improvements by allowing the involvement of human interactions to these process. However, these applications generate unstructured data which create barriers and challenges to offering the data in the form of web services in a SOA environment, which again impacts negatively the business process. In this context, the authors propose a framework to interface ESN unstructured data into BP using text mining techniques. The Term frequency-inverse document frequency is used as a weighting schema in this framework. After that, the cosine similarity and k-mean are utilized to find similar values from different documents and cluster documents into groups respectively. The result of the evaluation of the framework shows promising results for retrieving social unstructured data. These results can be published into the SOA enterprise service bus using the RESTful web services.

Original languageEnglish
Pages (from-to)15-30
Number of pages16
JournalInternational Journal of Enterprise Information Systems
Volume13
Issue number4
DOIs
Publication statusPublished - Oct 1 2017

Fingerprint

Industry
Service oriented architecture (SOA)
Web services
Business process
Social networks
Process improvement
Bus
Weighting
Evaluation
Interaction
K-means
Text mining

Keywords

  • Business processes
  • Enterprise social networks
  • SOA
  • Text mining
  • TF-IDF
  • Unstructured data
  • Web service

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science Applications
  • Information Systems and Management

Cite this

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abstract = "The increased number of Enterprise Social Networks (ESN) business applications has had a major impact on organizations' business processes improvements by allowing the involvement of human interactions to these process. However, these applications generate unstructured data which create barriers and challenges to offering the data in the form of web services in a SOA environment, which again impacts negatively the business process. In this context, the authors propose a framework to interface ESN unstructured data into BP using text mining techniques. The Term frequency-inverse document frequency is used as a weighting schema in this framework. After that, the cosine similarity and k-mean are utilized to find similar values from different documents and cluster documents into groups respectively. The result of the evaluation of the framework shows promising results for retrieving social unstructured data. These results can be published into the SOA enterprise service bus using the RESTful web services.",
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