Limitations of concurrency in transaction processing
ACM Transactions on Database Systems (TODS)
Simulated performance of a data-driven database machine
Journal of Parallel and Distributed Computing
A data-driven database model and its implementation on a highly-parallel architecture
A data-driven database model and its implementation on a highly-parallel architecture
Recovery architectures for multiprocessor database machines
SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
Extending the database relational model to capture more meaning
ACM Transactions on Database Systems (TODS)
The entity-relationship model—toward a unified view of data
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
Relational Data-Base Management Systems
ACM Computing Surveys (CSUR)
CODASYL Data-Base Management Systems
ACM Computing Surveys (CSUR)
Hierarchical Data-Base Management: A Survey
ACM Computing Surveys (CSUR)
Data-Driven and Demand-Driven Computer Architecture
ACM Computing Surveys (CSUR)
A relational model of data for large shared data banks
Communications of the ACM
RAP. 2 - an Associative Processor for data bases
ISCA '78 Proceedings of the 5th annual symposium on Computer architecture
Graph-Based Parallel Query Processingand Optimization Strategies for Object-Oriented Databases
Distributed and Parallel Databases
Distributed data flow computing system
ACM-SE 30 Proceedings of the 30th annual Southeast regional conference
Algorithms for Asynchronous Parallel Processing of Object-Oriented Databases
IEEE Transactions on Knowledge and Data Engineering
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In 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.