Conjoint Mining of Data and Content with Applications in Business, Bio-medicine, Transport Logistics and Electrical Power Systems

Abstract : Digital information within an enterprise consists of (1) structured data and (2) unstructured content. The structured data includes enterprise and business data like sales, customers, products, accounts, inventory and enterprise assets, etc. while the content includes contracts, reports, emails, customer opinions, transcribed calls, on-line inquires, complements and complaints. Further, cutting edge businesses also using GPS tracking or surveillance monitors as well as sensor technologies for productivity, performance and efficiency measures, and these are provided by outsourcers etc. Similarly in the Biomedical area, resources can be structured data say in Swiss- Prot or unstructured text information in journal articles stored in content repositories such as PubMed. The structured data and the unstructured content generally reside in entirely separate repositories with the former being managed by a DBMS and the latter by a content manager frequently provided by an outsourcer or vendor [76]. This separation is undesirable since the information content of these sources is complementary. Further, each outsourcer or vendor keep the data on their own Cloud, and data are not sharable between the vendor systems, and most vendor system were not integrated with the enterprise systems, and leaves the organization to consolidate the data and information manually for data analytics. Effective knowledge and information use requires seamless access and intelligent analysis of information in its totality to allow enterprises to gain enhanced critical insights. This is becoming even more important, as the proportion of structured to unstructured information has shifted from 50-50 in the 1960s to 5-95 today [1]. Unless we can effectively utilize the unstructured content conjointly with the structured data, we will only obtain very limited and shallow knowledge discovery from an increasingly narrow slice of information. The techniques developed in our research will then be used to address significant issues in three application areas, but potential applications with significant impact are much more extensive.
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Tharam Dillon, Yi-Ping Chen, Elizabeth Chang, Mukesh Mohania. Conjoint Mining of Data and Content with Applications in Business, Bio-medicine, Transport Logistics and Electrical Power Systems. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. pp.1-19, ⟨10.1007/978-3-662-44654-6_1⟩. ⟨hal-01391287⟩

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