Adaptive Algorithms for Join Processing in Distributed Database Systems

Peter Scheuermann*, Eugene Inseok Chong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Distributed query processing algorithms usually perform data reduction by using a semijoin program, but the problem with these approaches is that they still require an explicit join of the reduced relations in the final phase. We introduce an efficient algorithm for join processing in distributed database systems that makes use of bipartite graphs in order to reduce data communication costs and local processing costs. The bipartite graphs represent the tuples that can be joined in two relations taking also into account the reduction state of the relations. This algorithm fully reduces the relations at each site. We then present an adaptive algorithm for response time optimization that takes into account the system configuration, i.e., the additional resources available and the data characteristics, in order to select the best strategy for response time minimization. We also report on the results of a set of experiments which show that our algorithms outperform a number of the recently proposed methods for total processing time and response time minimization.

Original languageEnglish (US)
Pages (from-to)233-269
Number of pages37
JournalDistributed and Parallel Databases
Issue number3
StatePublished - 1997


  • Adaptive algorithms
  • Bipartite graphs
  • Distributed query processing
  • Join algorithms

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Information Systems and Management


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