VLDB 2026 Research / reviewers in the wild / expert
Iis Afriyanti
dblp:200/2266
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
0009-0000-3234-691XORCID · reported
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 · 87% Database theory · 13% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › ontology
ontology engineering |
0.3 | 1 | 2017 | Ontology-Based Approach for Academic Evaluation System · ICDE 2017 |
Data integration and cleaning › web data integration
linked data integration |
0.3 | 1 | 2017 | Ontology-Based Approach for Academic Evaluation System · ICDE 2017 |
Data integration and cleaning
semantic integration |
0.3 | 1 | 2017 | Ontology-Based Approach for Academic Evaluation System · ICDE 2017 |
Database theory
ontology-mediated queries |
0.1 | 1 | 2017 | Ontology-Based Approach for Academic Evaluation System · ICDE 2017 |
Methods — techniques the papers use, named apart from their topics
semantic web technologies · 0.6SPARQL · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Ontology-Based Approach for Academic Evaluation SystemabstractAcademic evaluation is an important activity regularly conducted by every higher education institution to gauge the performance of education services and help the administrators of the institution improve the quality of those services. Implementation of academic evaluation almost always requires an integration of relevant data, which are often scattered in separate systems. Such an integration effort is often tedious and time consuming because the data must be gathered from different systems, manually integrated, and then presented in a format that meets some pre-determined academic evaluation criteria. One way to help reducing the amount of effort expended in the integration is to make the data available in a format that enables linking between any part of the data and allows various academic evaluation queries to be performed on them. Our work intends to realise this idea by using Semantic Web technologies, in particular ontology and linked data. In particular, our work is situated in a concrete use case based on the academic evaluation process as periodically conducted in Universitas Indonesia (UI). In this paper, we focus on the first step of this effort, namely the development of an ontology for academic evaluation with a particular emphasis on evaluation of undergraduate degree programs in UI. There has been a number of prior work focusing on the development of an ontology for a similar problem, yet none of them takes into account the fact that academic data evolve over time. Our ontology thus accounts for this aspect according to the UIs academic evaluation criteria. We also demonstrate that the ontology can be used as a basis in answering SPARQL queries that capture those criteria, which indicates the suitability and usability of the ontology. Siti Aminah, Iis Afriyanti, Adila Krisnadhi |
ICDE | 2 |