EDBT 2026 Demo / reviewers in the wild / expert
Sutedi Sutedi
dblp:255/2340
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0002-6341-1689ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 first-author
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% | |
| Theoretical computer science
1 paper |
Graph algorithms and graph theory · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 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
data transformation |
0.4 | 1 | 2020 | Enhanced Graph Transforming V2 Algorithm for Non-Simple Graph in Big Data Pre-Processing · IEEE Trans. Knowl. Data Eng. 2020 |
Graph algorithms and graph theory › graph theory
graph transformation |
0.4 | 1 | 2020 | Enhanced Graph Transforming V2 Algorithm for Non-Simple Graph in Big Data Pre-Processing · IEEE Trans. Knowl. Data Eng. 2020 |
Storage systems › key-value storage
NoSQL database |
0.1 | 1 | 2020 | Enhanced Graph Transforming V2 Algorithm for Non-Simple Graph in Big Data Pre-Processing · IEEE Trans. Knowl. Data Eng. 2020 |
Methods — techniques the papers use, named apart from their topics
graph transforming algorithm · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Enhanced Graph Transforming V2 Algorithm for Non-Simple Graph in Big Data Pre-ProcessingabstractIncapability of relational database in handling large-scale data triggers the development of NoSQL database that becomes part of a big data ecosystem. NoSQL database has different characteristics compared to the relational database. However, NoSQL database requires data from the relational database as one of the structured data sources. Therefore, data pre-processing is required to ensure proper data migration from a relational database to NoSQL database. This data pre-processing is normally called data transformation. One of the simple and understandable transformation algorithms is graph transforming algorithm. However, the algorithm has a problem in solving a non-simple graph (multigraph). This research proposes an algorithm to overcome several multigraph problems. The experimental work confirms that the algorithm proposed in this research is able to transform data from a relational database to NoSQL schema that has a minimum number of redundant attributes while the data completeness is still maintained. Sutedi Sutedi, Noor Akhmad Setiawan, Teguh Bharata Adji |
IEEE Trans. Knowl. Data Eng. | 1 |