A comprehensive approach to horizontal class fragmentation in a Distributed Object Based System

Abstract

Optimal application performance on a Distributed Object Based System (DOBS) requires class fragmentation and the development of allocation schemes to place fragments at distributed sites so data transfer is minimized. Fragmentation enhances application performance by reducing the amount of irrelevant data accessed and the amount of data transferred unnecessarily between distributed sites. Algorithms for effecting horizontal and vertical fragmentation of relations exist, but fragmentation techniques for class objects in a distributed object based system are yet to appear in the literature. This paper first reviews a taxonomy of the fragmentation problem in a distributed object base. The paper then contributes by presenting a comprehensive set of algorithms for horizontally fragmenting the four realizable class models on the taxonomy. The fundamental approach is top-down, where the entity of fragmentation is the class object. Our approach consists of first generating primary horizontal fragments of a class based on only applications accessing this class, and secondly generating derived horizontal fragments of the class arising from primary fragments of its subclasses, its complex attributes (contained classes), and/or its complex methods classes. Finally, we combine the sets of primary and derived fragments of each class to produce the best possible fragments. Thus, these algorithms account for inheritance and class composition hierarchies as well as method nesting among objects, and are shown to be polynomial time.

An Analysis of Vertical Splitting Algorithm

“…This may be accomplished by eliminating irrelevant data accessed during the execution of queries and by reducing the data exchange among sites, which are the two main goals of the design of distributed databases [2]. The primary concern of distributed database systems is to design the fragmentation and allocation of the underlying database.…”

A Hybrid Fragmentation Approach for Distributed Deductive Database Systems

“…Shin and Irani (1991) utilize the user query expressions and certain knowledge about data for computing derived horizontal fragmentation. In Ezeife and Barker (1995) the authors present a number of horizontal fragmentation algorithms for four different class models in a distributed object-based system, and in Ezeife and Barker (1998) they propose different vertical fragmentation algorithms for four types of class object models. Zhou et al (1994) employ a parallel transitive closure algorithm to fragment data in deductive databases, whereas Pramanik et al (1992) identify two strategies for horizontal fragmentation of recursive relations based on the concept of partial-ordered sets which capture the relationships among two sets of attribute values in a recursive relation.…”

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

“…al. [12], Ezeife and Barker [4], Malinowski [13]. Mixed fragmentation is considered in [14] which starts from experimental results and propose a set of heuristics for mixed fragmentation.…”

“…Minimizing data transfer has been already considered by almost all distribution techniques [1], either supporting complex characteristics of the object oriented model, or just flat data models. Recently these issues have been considered in ( [4], [5], [6], [7], [2]). For fragmenting a class it is possible to use two basic techniques: vertical fragmentation and horizontal fragmentation.…”

“…Different approaches have been identified in solving issues regarding fragmentation, which, to a large extent, aim to extend and develop the relational fragmentation and allocation techniques to OODBs. Research papers in the OO area claim that relational fragmentation methods can be applied to the object oriented data model to some extent [8], while others state that starting from relational fragmentation techniques brings a handicap, which is difficult to cover.…”