Unstructured data extraction in distributed NoSQL

Author

Lomotey, Richard K. ; Deters, Ralph

Author_Institution

Dept. of Comput. Sci., Univ. of Saskatchewan, Saskatoon, SK, Canada

fYear

2013

fDate

24-26 July 2013

Firstpage

160

Lastpage

165

Abstract

While “Big data” has brought good tidings in terms of easy accessibility to voluminous data, we are faced with challenges too. The existing Knowledge Discovery in Database (KDD) processes which have been proposed for schema-oriented data sources are no longer applicable since todays data is unstructured. Previously, we deployed a tool called TouchR which relies on the Hidden Markov Model (HMM) to extract terms from unstructured data sources (specifically, NoSQL databases). This paper has advanced on the initially deployed version where we introduced re-usable dictionary and association rules to improve on the quality of the extracted terms. Also, the tool in its present stage is more adaptable to the user search based on the most frequently searched term.

Keywords

SQL; data mining; hidden Markov models; Big data; KDD; NoSQL databases; TouchR; association rules; distributed NoSQL; hidden Markov model; knowledge discovery in database; reusable dictionary; schema-oriented data sources; unstructured data extraction; Association rules; Databases; Dictionaries; Feature extraction; Hidden Markov models; Thesauri; Association rules; Hidden Markov Model (HMM); NoSQL; Re-usable dictionary; Unstructured data; big data; terms extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Ecosystems and Technologies (DEST), 2013 7th IEEE International Conference on

Conference_Location

Menlo Park, CA

ISSN

2150-4938

Print_ISBN

978-1-4799-0784-7

Type

conf

DOI

10.1109/DEST.2013.6611347

Filename

6611347