Demo Data from EDBT 2026 / reviewers in the wild / expert
Robert L. Hartmann
dblp:72/3376
· DBLP profile ↗
3ranked-venue papers
0as first author
0since 2021 · last 1989
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2Databases, 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 |
Database system architecture and tuning · 77% Transaction processing and concurrency control · 23% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Parallel and multicore computing · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Database system architecture and tuning
database machine |
0.0 | 1 | 1989 | AGM: A Dataflow Database Machine · ACM Trans. Database Syst. 1989 |
Parallel and multicore computing › parallel computation models
data-driven computation |
0.0 | 1 | 1989 | AGM: A Dataflow Database Machine · ACM Trans. Database Syst. 1989 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1989 | AGM: A Dataflow Database MachineabstractIn recent years, a number of database machines consisting of large numbers of parallel processing elements have been proposed. Unfortunately, there are two main limitations in database processing that prevent a high degree of parallelism; these are the available I/O bandwidth of the underlying storage devices and the concurrency control mechanisms necessary to guarantee data integrity. The main problem with conventional approaches is the lack of a computational model capable of utilizing the potential of any significant number of processing elements and storage devices and, at the same time, preserving the integrity of the database. This paper presents a database model and its associated architecture, which is based on the principles of data-driven computation. According to this model, the database is represented as a network in which each node is conceptually an independent, asynchronous processing element, capable of communicating with other nodes by exchanging messages along the network arcs. To answer a query, one or more such messages, called tokens, are created and injected into the network. These then propagate asynchronously through the network in search of results satisfying the given query. The asynchronous nature of processing permits the model to be mapped onto a computer architecture consisting of large numbers of independent disk units and processing elements. This increases both the available I/O bandwidth as well as the processing potential of the machine. At the same time, new concurrency control and error recovery mechanisms are necessary to cope with the resulting parallelism. Lubomir F. Bic, Robert L. Hartmann |
ACM Trans. Database Syst. | 2 |
| 1986 | Simulated Performance of a Data-Driven Database Machine
Lubomir F. Bic, Robert L. Hartmann |
J. Parallel Distributed Comput. | 2 |
| 1982 | The Active Graph Database Machine
Lubomir F. Bic, Robert L. Hartmann, J. Todhunter |
ICDCS | 2 |