Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Nick Kanellos

dblp:59/7259 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

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

Databases, data management, data science and information retrieval · 2

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
Knowledge graphs · 61% Data integration and cleaning · 30% Data models and query languages · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge graphs › ontology
domain ontology
0.112009
SMDM: Enhancing Enterprise-Wide Master Data Management Using Semantic Web Technologies · Proc. VLDB Endow. 2009
Data integration and cleaning › enterprise information integration
master data management
0.112009
SMDM: Enhancing Enterprise-Wide Master Data Management Using Semantic Web Technologies · Proc. VLDB Endow. 2009
Knowledge graphs
ontology
0.112009
SMDM: Enhancing Enterprise-Wide Master Data Management Using Semantic Web Technologies · Proc. VLDB Endow. 2009
Data models and query languages › semistructured data
RDF data
0.012009
SMDM: Enhancing Enterprise-Wide Master Data Management Using Semantic Web Technologies · Proc. VLDB Endow. 2009

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

semantic web technologies · 0.1reasoning · 0.1ontology · 0.1
YearPublicationVenuePosition
2009 Semantic Enhancement for Enterprise Data Management
Li Ma 0002, Xingzhi Sun 0001, Chen Wang 0020, Nick Kanellos, Daniel C. Wolfson
ISWC6
2009 SMDM: Enhancing Enterprise-Wide Master Data Management Using Semantic Web Technologies
abstract
Motivated by evolving business requirements and novel enterprise applications, we propose and implement the Semantic Master Data Management (SMDM), a semantics-level enhancement to the existing MDM solutions. The SMDM system publishes relational-based master data as virtual RDF store, and injects instantaneous reasoning capabilities into semantic queries. Two kinds of ontologies are introduced to the system, the core MDM ontology and the external imported domain ontology. SMDM enables data linking among multi-domains, implicit relationship discovery, and declarative definition and extension of business policies and entities. Based on these functions, modern companies can customize their applications and services on demand within the MDM hub. In the demonstration, we build the system environment based on IBM's MDM solution, and run the use cases on the master data of an insurance company.
Xingzhi Sun 0001, Li Ma 0002, Nick Kanellos, Yong Yu 0001
Proc. VLDB Endow.5