Demo Data from EDBT 2026 / reviewers in the wild / expert
Jürgen Seib
dblp:41/5014
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1991
—ORCID · none
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 |
Database theory · 33% Query processing and optimization · 33% Data models and query languages · 33% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data models and query languages
datalog |
0.0 | 1 | 1991 | Parallelizing Datalog Programs by Generalized Pivoting · PODS 1991 |
Database theory
datalog evaluation |
0.0 | 1 | 1991 | Parallelizing Datalog Programs by Generalized Pivoting · PODS 1991 |
Query processing and optimization
parallel query processing |
0.0 | 1 | 1991 | Parallelizing Datalog Programs by Generalized Pivoting · PODS 1991 |
Parallel and multicore computing
parallel programming models |
0.0 | 1 | 1991 | Parallelizing Datalog Programs by Generalized Pivoting · PODS 1991 |
Methods — techniques the papers use, named apart from their topics
generalized pivoting · 0.0bottom-up evaluation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | Parallelizing Datalog Programs by Generalized PivotingabstractA Datalog program consists of function-free Horn clause rules.The main problem in evaluating Datalog programs is to achieve an acceptable performance.The reason is, that Datalog programs usually are evaluated bottom-up to compute in an iterative way the fixpoint of the program.Given a large amount of base facts, program evaluation can be a very lengthy process.In the paper parallel processing of decomposable Datalog programs is considered to overcome this problem.A decomposable program can be evaluated in parallel such that neither a communication nor a synchronization of the processors haa to be established [WS88].The concept of generalized pivoting is proposed, which is a sufficient condition for decomposability of arbitrary Datalog programs.Moreover, the clam of completely decomposable programs is introduced and it is shown, that generalized pivoting is also a necessary condition for complete decomposability. Jürgen Seib, Georg Lausen |
PODS | 1 |