Big data and business

None, None; None, None; None, None; None, None; None, None; None, None

Big data and business

Tech mining to capture business interests and activities around big data

Conference Paper (2016)

Author(s)

Ying Huang

Jan Youtie

Alan L. Porter

Douglas K R Robinson

Scott W. Cunningham (TU Delft - Technology, Policy and Management)

Donghua Zhu

Research Group

Policy Analysis

Network analysis Big data Tech mining Commercial database ProQuest

DOI related publication

https://doi.org/10.1109/BDCloud-SocialCom-SustainCom.2016.32 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:84cb8ea0-c4ed-4622-bd0b-703343f3d051

More Info

expand_more

Publication Year

2016

Language

English

Research Group

Policy Analysis

Article number

7723686

Pages (from-to)

145-150

ISBN (electronic)

9781509039364

Event

6th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2016, 9th IEEE International Conference on Social Computing and Networking, SocialCom 2016 and 2016 IEEE International Conference on Sustainable Computing and Communications, SustainCom 2016 (2016-10-08 - 2016-10-10), Atlanta, United States

Downloads counter

151

Abstract

Innovations around "Big Data" can be characterized in terms of rapid technology development and deployment dynamics. For this purpose, combining "tech mining" (extraction of usable intelligence) from publication and patent databases with tech mining of business-related databases can elucidate activities and interests of business communities regarding Big Data innovation pathways. In this paper, we focus on commercially oriented databases - ABI/INFORM as a source from which to extract business intents. We select the database to help gauge "hot topics" in industry with regard to Big Data. Our results show that certain types of firms can be clustered into thematic groups relating to Big Data discussions and activities. In the paper we demonstrate that such analyses can illuminate themes being pursued by businesses. Like social media analyses, this text mining can provide useful intelligence to inform more in-depth investigation mobilizing other data sources and techniques.