AI Clustering Techniques: a New Approach to Object Oriented Database Fragmentation

Abstract – Optimal application performance on a Distributed Object Based System requires class fragmentation and the development of allocation schemes to place fragments at distributed sites so data transfer is minimal. In this paper we present a horizontal fragmentation approach that uses the k-means centroid based clustering method for partitioning object instances into fragments. Our new method takes full advantage of existing data, where statistics are already present. We model fragmentation input data in a vector space and give different object similarity measures together with their geometrical interpretations. We provide quality and performance evaluations using a partition evaluator function. I

Semi-supervised learning techniques: k-means clustering in OODB Fragmentation

Abstract – Vertical and horizontal fragmentation are central issues in the design process of Distributed Object Based Systems. A good fragmentation scheme followed by an optimal allocation could greatly enhance performance in such systems, as data transfer between distributed sites is minimized. In this paper we present a horizontal fragmentation approach that uses the k-means AI clustering method for partitioning object instances into fragments. Our new method applies to existing databases, where statistics are already present. We model fragmentation input data in a vector space and give different object similarity measures together with their geometrical interpretations. We provide quality and performance evaluations using a partition evaluator function. I

Optimal Class Fragmentation Ordering in Object Oriented Databases

Abstract. Distributed Object Oriented Databases require class fragmentation, performed either horizontally or vertically. Complex class relationships like aggregation and/or association are often represented as two-way references or object-links between classes. In order to obtain a good quality horizontal fragmentation, an optimal class processing order is needed. We present in this paper a new technique for establishing an order for class fragmentation. We improve fragmentation quality by capturing the semantic of input queries in the context of the aggregation hierarchy. 1

A New Approach for Optimal Fragmentation Order in Distributed Object Oriented Databases

Class fragmentation is an important task in the design of Distributed OODBs and there are many algorithms handling it. Almost none of them deals however with the class fragmentation order details. We claim that class fragmentation order can induce severe performance penalties if not considered in the fragmentation phase. We propose here two variants of an algorithm for finding the optimal class fragmentation order in a DOODB. In both variants we capture all class relations (inheritance, aggregation) and we determine a class fragmentation order where precedence conflicts induced by relation cycles are eliminated in such way that the strongest links be maintained.

design of Distributed Object Oriented Databases (DOODB).

Abstract – Class fragmentation is an essential phase in th

ACTA UNIVERSITATIS APULENSIS No 10/2005 CLUSTERING TECHNIQUES FOR ADAPTIVE HORIZONTAL FRAGMENTATION IN OBJECT ORIENTED DATABASES

Abstract. Optimal application performance in a Distributed Object Oriented System requires class fragmentation and the development of allocation schemes to place fragments at distributed sites so data transfer is minimal. A horizontal fragmentation approach that uses data mining clustering methods for partitioning object instances into fragments has already been presented in [1, 2, 3, 4]. Essentially, our approach takes full advantage of existing data, where statistics are already present, and develops fragmentation around user applications (queries) that are to be optimized by the obtained fragmentation. But real databases applications evolve in time, and consequently require refragmentation in order to deal with new applications entering the system and other leaving. Obviously, for obtaining the fragmentation that fits the new user applications set, the original fragmentation scheme can be applied from scratch. However, this process can be inefficient. In this paper we extend our initial fragmentation approach and propose an incremental method to cope with the evolving user application set. Namely, we handle here the case when new user applications arrive in the system and the current fragments must be accordingly adapted. 2000 Mathematics Subject Classification: 62H30, 68P15. 1

USING FUZZY CLUSTERING FOR ADVANCED OODB HORIZONTAL FRAGMENTATION WITH FINE-GRAINED REPLICATION

In this paper we present a new approach for horizontal object oriented database fragmentation combined with fine-grained object level replication in one step. We build our fragmentation/replication method using AI probabilistic clustering (fuzzy clustering). Fragmentation quality evaluation is provided using an evaluator function. KEY WORDS Distributed object oriented databases (DOODB), fragmentation, replication, and fuzzy techniques 1

AI Clustering Techniques: A New Approach in Horizontal Fragmentation of Classes with Complex Attributes and Methods in Object Oriented Databases

Abstract – Horizontal fragmentation plays an important role in the design phase of Distributed Databases. Complex class relationships: associations, aggregations and complex methods, require fragmentation algorithms to take into account the new problem dimensions induced by these features of the object oriented models. We propose in this paper a new method for horizontal partitioning of classes with complex attributes and methods, using AI clustering techniques. We provide quality and performance evaluations using a partition evaluator function and we prove that fragmentation methods handling complex interclass links produce better results than those ignoring these aspects. 1