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» Supporting top-k join queries in relational databases
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DBVIS
1993
101views Database» more  DBVIS 1993»
15 years 1 months ago
Using Visualization to Support Data Mining of Large Existing Databases
In this paper, we present ideas how visualization technology can be used to improve the difficult process of querying very large databases. With our VisDB system, we try to provid...
Daniel A. Keim, Hans-Peter Kriegel
DAMON
2009
Springer
15 years 4 months ago
Spinning relations: high-speed networks for distributed join processing
By leveraging modern networking hardware (RDMA-enabled network cards), we can shift priorities in distributed database processing significantly. Complex and sophisticated mechani...
Philip Werner Frey, Romulo Goncalves, Martin L. Ke...
VLDB
2004
ACM
120views Database» more  VLDB 2004»
15 years 3 months ago
Merging the Results of Approximate Match Operations
Data Cleaning is an important process that has been at the center of research interest in recent years. An important end goal of effective data cleaning is to identify the relatio...
Sudipto Guha, Nick Koudas, Amit Marathe, Divesh Sr...
ICDE
2007
IEEE
137views Database» more  ICDE 2007»
15 years 11 months ago
SAO: A Stream Index for Answering Linear Optimization Queries
Linear optimization queries retrieve the top-K tuples in a sliding window of a data stream that maximize/minimize the linearly weighted sums of certain attribute values. To effici...
Gang Luo, Kun-Lung Wu, Philip S. Yu
95
Voted
CIKM
1994
Springer
15 years 1 months ago
TID Hash Joins
TID 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...
Robert Marek, Erhard Rahm