Demo Data from EDBT 2026 / reviewers in the wild / expert
Jennifer Reed
dblp:33/6391
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data integration and cleaning
entity resolution |
0.1 | 1 | 2012 | Exploiting Evidence from Unstructured Data to Enhance Master Data Management · Proc. VLDB Endow. 2012 |
Data integration and cleaning › enterprise information integration
master data management |
0.1 | 1 | 2012 | 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 |
0.0 | 1 | 2012 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Exploiting Evidence from Unstructured Data to Enhance Master Data ManagementabstractMaster 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 |