Vol.35, No.5, 2020, pp.321-334, doi:
OPEN ACCESS
ARTICLE
Conjoint Knowledge Discovery Utilizing Data and Content with Applications in Business, Bio-medicine, Transport Logistics and Electrical Power Systems
  • Tharam S. Dillon1,2,∗, Yi-Ping Phoebe Chen1,†, Elizabeth Chang2,‡, Mukesh Mohania3,§, Vish Ramakonar4
1 Department of Computer Science and Computer Engineering„ La Trobe University, Melbourne, Victoria 3086, Australia
2 School of Business, Australian Defence Force Academy, University of New South Wales, Canberra, Australia
3 IBM India Research Lab
4 Alsys MSC Sdn Bhd, Kuala Lumpur, Malaysia
∗ Tharam.dillon7@gmail.com
† Phoebe.Chen@latrobe.edu.au
‡ Elizabeth.chang@unsw.edu.au
§ mkmukesh@in.ibm.com
Abstract
In Digital Enterprises Structured Data and Semi/Unstructured Content are normally stored in two different repositories, with the first often being stored in relational Databases and the second in a content manager which is frequently at an external outsourcer. This storage of complementary information in two different silos has led to the information being processed and data mined separately which is undesirable. Effective knowledge and information use requires seamless access and intelligent analysis of information in its totality to allow enterprises to gain enhanced insights. In this paper, we develop techniques to carry out correlation of the information across different sources and then carryout out knowledge discovery across these complementary sources in a conjoint manner. The techniques developed in our research will then be used to address significant issues in four application areas namely Business, Logistics, Bioinformatics and Electric Power Systems but potential applications with significant impact are much more extensive.
Cite This Article
T. S. Dillon, Y. P. Chen, E. Chang, M. Mohania and V. Ramakonar, "Conjoint knowledge discovery utilizing data and content with applications in business, bio-medicine, transport logistics and electrical power systems," Computer Systems Science and Engineering, vol. 35, no.5, pp. 321–334, 2020.
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