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K. Bernhard Schiefer

dblp:95/5216 · DBLP profile ↗
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7ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 7 · 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
4 papers
Query processing and optimization · 51% Data integration and cleaning · 26% Database system architecture and tuning · 15%
Computer architecture, parallel and distributed computing, and storage systems
2 papers
Storage systems · 79% Memory systems · 21%

Topics — the 7 heaviest of 10, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data integration and cleaning
integrity constraint discovery
0.012001
Discovery and Application of Check Constraints in DB2 · ICDE 2001
Query processing and optimization › semantic query processing
semantic query optimization
0.011999
Implementation of Two Semantic Query Optimization Techniques in DB2 Universal Database · VLDB 1999
Query processing and optimization › query execution › scan processing
index scan
0.011995
Estimating Page Fetches for Index Scans with Finite LRU Buffers · VLDB J. 1995
Storage systems
buffer management
0.011995
Estimating Page Fetches for Index Scans with Finite LRU Buffers · VLDB J. 1995
Indexing and storage engines
b+-tree
0.011994
Estimating Page Fetches for Index Scans with Finite LRU Buffers · SIGMOD Conference 1994
Query processing and optimization
cardinality estimation
0.011994
Estimating Page Fetches for Index Scans with Finite LRU Buffers · SIGMOD Conference 1994
Memory systems
cache management
0.011994
Estimating Page Fetches for Index Scans with Finite LRU Buffers · SIGMOD Conference 1994

Methods — techniques the papers use, named apart from their topics

semantic rewriting · 0.0empirical modeling · 0.0constraint propagation · 0.0
YearPublicationVenuePosition
2001 Discovery and Application of Check Constraints in DB2
abstract
The traditional role of integrity constraints is to protect the integrity of data, but integrity constraints can and do play other roles in databases; for example, they can be used for query optimization. In this role, they do not need to model the domain; it is sufficient that they describe regularities that are true about the data currently stored in a database. In this paper, we describe two algorithms for finding such regularities (in the syntactic form of check constraints) and discuss some of their applications in DB2. In particular, we show their use in query optimization.
Jarek Gryz, K. Bernhard Schiefer, Calisto Zuzarte
ICDE2
2000 Performance of DB2 Enterprise-Extended Edition on NT with Virtual Interface Architecture
Sivakumar Harinath, Robert L. Grossman, K. Bernhard Schiefer, Xun Xue, Sadique Syed
EDBT3
1999 Implementation of Two Semantic Query Optimization Techniques in DB2 Universal Database
Jarek Gryz, Fred Koo, T. Y. Cliff Leung, Linqi Liu, K. Bernhard Schiefer
VLDB7
1998 IBM's DB2 Universal Database demonstrations at VLDB'98
K. Bernhard Schiefer, Jim Kleewein, Karen Brannon, Guy M. Lohman, Gene Fuh
VLDB1
1995 Estimating Page Fetches for Index Scans with Finite LRU Buffers
Arun N. Swami, K. Bernhard Schiefer
VLDB J.2
1994 On the Estimation of Join Result Sizes
Arun N. Swami, K. Bernhard Schiefer
EDBT2
1994 Estimating Page Fetches for Index Scans with Finite LRU Buffers
abstract
We describe an algorithm for estimating the number of page fetches for a partial or complete scan of a B-tree index. The algorithm obtains estimates for the number of page fetches for an index scan when given the number of tuples selected and the number of LRU buffers currently available. The algorithm has an initial phase that is performed exactly once before any estimates are calculated. This initial phase, involving LRU buffer modeling, requires a scan of all the index entries and calculates the number of page fetches for different buffer sizes. An approximate empirical model is obtained from this data. Subsequently, an inexpensive estimation procedure is called by the query optimizer whenever it needs an estimate of the page fetches for the index scan. This procedure utilizes the empirical model obtained in the initial phase.
Arun N. Swami, K. Bernhard Schiefer
SIGMOD Conference2