Composite object support in an object-oriented database system
OOPSLA '87 Conference proceedings on Object-oriented programming systems, languages and applications
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
A stochastic approach for clustering in object bases
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Effective clustering of complex objects in object-oriented databases
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
On the performance of object clustering techniques
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Clustering strategies in O2: an overview
Building an object-oriented database system
A decomposition-based simulated annealing technique for data clustering
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Shoring up persistent applications
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Building a scaleable geo-spatial DBMS: technology, implementation, and evaluation
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A clustering algorithm for hierarchical structures
ACM Transactions on Database Systems (TODS)
A data model and data structures for moving objects databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient handling of tuples with embedded large objects
Data & Knowledge Engineering
A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
Concrete Mathematics: A Foundation for Computer Science
Concrete Mathematics: A Foundation for Computer Science
A Hybrid Object Clustering Strategy for Large Knowledge-Based Systems
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
The Case for Enhanced Abstract Data Types
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Object and File Management in the EXODUS Extensible Database System
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
Plug and Play with Query Algebras: SECONDO-A Generic DBMS Development Environment
IDEAS '00 Proceedings of the 2000 International Symposium on Database Engineering & Applications
SECONDO/QP: Implementation of a Generic Query Processor
DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
The Volcano Optimizer Generator: Extensibility and Efficient Search
Proceedings of the Ninth International Conference on Data Engineering
Supporting uncertainty in moving objects in network databases
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Representation of periodic moving objects in databases
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
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In implementations of non-standard database systems, large objects are often embedded within an aggregate of different types, i.e. a tuple. For a given size and access probability of a large object, query performance depends on its representation: either inlined within the aggregate or swapped out to a separate object. Furthermore, the implementation of complex data models often requires nested large objects, and access performance is highly influenced by the clustering strategy followed to store the resulting tree of large objects.In this paper, we describe a large object extension, which automatically clusters nested large objects. A rank function is developed which indicates the suitability of a large object being inserted into a given cluster. We present two clustering algorithms of different run-time complexity, both using the rank function, and a series of simulations is performed to compare them to each other as well as to two trivial ones. One of the algorithms proves to compute the most efficient clustering in all tests.