Demo Data from EDBT 2026 / reviewers in the wild / expert
Robert Marek
dblp:84/1313
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 1995
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 3 · 1 first-authorArtificial intelligence and machine learning · 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.
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
Parallel and multicore computing · 88% Cloud and datacenter computing · 12% | |
| Databases, data mining, and information retrieval
1 paper |
Database system architecture and tuning · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Parallel and multicore computing › load balancing
dynamic load balancing |
0.0 | 2 | 1995 | Dynamic Multi-Resource Load Balancing in Parallel Database Systems · VLDB 1995 Analysis of Dynamic Load Balancing Strategies for Parallel Shared Nothing Database Systems · VLDB 1993 |
Parallel and multicore computing › parallel computing
parallel database systems |
0.0 | 1 | 1995 | Dynamic Multi-Resource Load Balancing in Parallel Database Systems · VLDB 1995 |
Database system architecture and tuning › parallel database system
shared-nothing parallel database |
0.0 | 1 | 1993 | Analysis of Dynamic Load Balancing Strategies for Parallel Shared Nothing Database Systems · VLDB 1993 |
Cloud and datacenter computing
cluster resource management and scheduling |
0.0 | 1 | 1995 | Dynamic Multi-Resource Load Balancing in Parallel Database Systems · VLDB 1995 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1995 | Dynamic Multi-Resource Load Balancing in Parallel Database Systems
Erhard Rahm, Robert Marek |
VLDB | 2 |
| 1994 | TID Hash JoinsabstractTID hash joins are a simple and memory-efficient method for processing large join queries.They are based on standard hash join algorithms but only store TID/key pairs in the hash table instead of entire ttrples.This typically reduces memory requirements by more than art order of magnitude bringing substantial benefits.In particular, performance for joins on Gigs-Byte relations can substantially be improved by reducing the amount of disk f/O to a large extent.Furthermore, efficient processing of mixed multi-user workloads consisting of both join queries and OLTP transactions is supported.We present a detailed simulation study to analyze the performance of TID hash joins.In particular, we identify the conditions under which TID hash joins are most beneficial.Furthermore, we compare TID hash join with adaptive hash join algorithms that have been proposed to deal with mixed workloads. Robert Marek, Erhard Rahm |
CIKM | 1 |
| 1993 | Analysis of Dynamic Load Balancing Strategies for Parallel Shared Nothing Database Systems
Erhard Rahm, Robert Marek |
VLDB | 2 |