Demo Data from EDBT 2026 / reviewers in the wild / expert
Nick Kanellos
dblp:59/7259
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge graphs › ontology
domain ontology |
0.1 | 1 | 2009 | 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 |
0.1 | 1 | 2009 | SMDM: Enhancing Enterprise-Wide Master Data Management Using Semantic Web Technologies · Proc. VLDB Endow. 2009 |
Knowledge graphs
ontology |
0.1 | 1 | 2009 | SMDM: Enhancing Enterprise-Wide Master Data Management Using Semantic Web Technologies · Proc. VLDB Endow. 2009 |
Data models and query languages › semistructured data
RDF data |
0.0 | 1 | 2009 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Semantic Enhancement for Enterprise Data Management
Li Ma 0002, Xingzhi Sun 0001, Chen Wang 0020, Nick Kanellos, Daniel C. Wolfson |
ISWC | 6 |
| 2009 | SMDM: Enhancing Enterprise-Wide Master Data Management Using Semantic Web TechnologiesabstractMotivated 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 |