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Jennifer Reed

dblp:33/6391 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data integration and cleaning · 100%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data integration and cleaning
entity resolution
0.112012
Exploiting Evidence from Unstructured Data to Enhance Master Data Management · Proc. VLDB Endow. 2012
Data integration and cleaning › enterprise information integration
master data management
0.112012
Exploiting Evidence from Unstructured Data to Enhance Master Data Management · Proc. VLDB Endow. 2012
Natural language and speech › Information extraction and text analysis
named entity recognition
0.012012
Exploiting Evidence from Unstructured Data to Enhance Master Data Management · Proc. VLDB Endow. 2012

Methods — techniques the papers use, named apart from their topics

unstructured text correlation · 0.3
YearPublicationVenuePosition
2012 Exploiting Evidence from Unstructured Data to Enhance Master Data Management
abstract
Master data management (MDM) integrates data from multiple structured data sources and builds a consolidated 360-degree view of business entities such as customers and products. Today's MDM systems are not prepared to integrate information from unstructured data sources, such as news reports, emails, call-center transcripts, and chat logs. However, those unstructured data sources may contain valuable information about the same entities known to MDM from the structured data sources. Integrating information from unstructured data into MDM is challenging as textual references to existing MDM entities are often incomplete and imprecise and the additional entity information extracted from text should not impact the trustworthiness of MDM data. In this paper, we present an architecture for making MDM text-aware and showcase its implementation as IBM Info-Sphere MDM Extension for Unstructured Text Correlation, an add-on to IBM InfoSphere Master Data Management Standard Edition. We highlight how MDM benefits from additional evidence found in documents when doing entity resolution and relationship discovery. We experimentally demonstrate the feasibility of integrating information from unstructured data sources into MDM.
Karin Murthy, Prasad Deshpande, Atreyee Dey, Ramanujam Halasipuram, Mukesh K. Mohania, Deepak P 0001, Jennifer Reed, Scott Schumacher
Proc. VLDB Endow.7