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ABSTRACT
Title |
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Text Analytics to Data Warehousing |
Authors |
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Kalli Srinivasa Nageswara Prasad, S. Ramakrishna |
Keywords |
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Information Extraction (IE); Entity;
Semantics; Natural Language Processing (NLP); Parsing. |
Issue Date |
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September 2010 |
Abstract |
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Information hidden or stored in unstructured
data can play a critical role in making decisions,
understanding and conducting other business functions.
Integrating data stored in both structured and
unstructured formats can add significant value to an
organization. With the extent of development happening in
Text Mining and technologies to deal with unstructured
and semi structured data like XML and MML(Mining
Markup Language) to extract and analyze data, text
analytics has evolved to handle unstructured data to helps
unlock and predict business results via Business
Intelligence and Data Warehousing. Text mining involves
dealing with texts in documents and discovering hidden
patterns, but Text Analytics enhances Information
Retrieval in form of search and enabling clustering of
results and more over Text Analytics is text mining and
visualization. In this paper we would discuss on handling
unstructured data that are in documents so that they fit
into business applications like Data Warehouses for further
analysis and it helps in the framework we have used for the
solution.
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Page(s) |
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2201-2207 |
ISSN |
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0975–3397 |
Source |
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Vol. 2, Issue.6 |
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