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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dey, L. Verma, I. Khurdiya, A. Sameera Bharadwaja, H. |
| Copyright Year | 2013 |
| Description | Author affiliation: TCS Innovation Labs., Tata Consultancy Services Ltd., New Delhi, India (Dey, L.; Verma, I.; Khurdiya, A.; Sameera Bharadwaja, H.) |
| Abstract | It is well-accepted that when information from structured and unstructured data sources is analyzed together, the potential of gaining meaningful insights increases manifolds. This paper provides a framework for integrating structured and unstructured data in the context of enterprise analytics. Structured data is assumed to be in the form of a time-series that encodes some aspect of enterprise performance over a specified period, like monthly or weekly sales figures or stock prices etc. Unstructured data may be gathered from news sources, internal repositories of consumer feedbacks, blogs and discussion forums or also from social-media like Twitter, Facebook etc. This paper focuses on intelligent methods of linking time-series data points to the unstructured content in an application-specific way such that the linked unstructured text creates a context for interpreting the time-series behavior. The aim is to generate new forms of data that can be employed in future to derive predictive models or perform causal analytics or also help in risk assessment for Enterprises. |
| Starting Page | 1988 |
| Ending Page | 1995 |
| File Size | 1940114 |
| Page Count | 8 |
| File Format | |
| ISBN | 9786058631113 |
| e-ISBN | 9781479902842 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-07-09 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | ISIF ( Intl Society of Information Fusi |
| Subject Keyword | Correlation Data mining Companies Semantics Context Indexing Time-series Analysis Information Fusion Data Association Fusion Enabled Decision Support |
| Content Type | Text |
| Resource Type | Article |
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