A Comprehensive Taxonomy of Fragmentation and Allocation Techniques in Distributed Database Design

Abstract

The need to design an optimally distributed database is increasingly important with the growth of information technology and computer networks. However, designing a distributed database is an extremely complex process due to a large number of geographically distributed sites and database relations. Moreover, decreasing communication costs and query response time should be taken into consideration. There are three main techniques applied to design a distributed database, namely Fragmentation, Data allocation, and Replication. It is notable that these techniques are often treated separately and rarely processed together. Some available allocation methods are applied regardless of how the fragmentation technique is performed or replication process is adopted. In contrast, other fragmentation techniques do not consider the allocation or the replication techniques. Therefore, the first and foremost step for designing an optimal database is to develop a comprehensive understanding of the current fragmentation, replication, and allocation techniques and their disadvantages. This article presents an attempt to fulfill this step by proposing a comprehensive taxonomy of the available fragmentation and allocation techniques in distributed database design. The article also discusses some case studies of these techniques for a deeper understanding of its achievements and limitations.

Towards an Efficient Data Fragmentation, Allocation, and Clustering Approach in a Distributed Environment

“…For the first problem, Figures 4-7 show the experiments that were carried out and which reflect the clear contribution of work of this paper. While Figures 4 and 5 display TC rate for both works as per site clustering of [8]; Figures 6 and 7 show results of TC rate when applying the new proposed site clustering of this paper, on both [7] and our work of this paper. According to the results obtained, our work proved to be highly efficient with respect to TC minimization.…”

“…In DDBS design, it is consensually agreed-upon that the more precise the data partitioning and allocation techniques, the better the performance and the lower the response time are likely to be obtained [8]. In [8], a comprehensive taxonomy was given. This taxonomy was fine-grained and comprehensively analyzed in both static and dynamic environments.…”

ASGOP: An aggregated similarity-based greedy-oriented approach for relational DDBSs design

“…Data fragmentation (Vertical, Horizontal or Mixed) has long seen to play the key role in DDBS performance enhancement. As a matter of fact, it is commonly agreed upon that the proper the data fragmentation and allocation (including replication) are, the highly likely that the overall performance of DDBS is sustainably satisfied (Nashat and Amer, 2018). In (Nashat and Amer, 2018), a fine-grained taxonomy was drawn.…”

“…As a matter of fact, it is commonly agreed upon that the proper the data fragmentation and allocation (including replication) are, the highly likely that the overall performance of DDBS is sustainably satisfied (Nashat and Amer, 2018). In (Nashat and Amer, 2018), a fine-grained taxonomy was drawn. This taxonomy was extensively examined that more than one hundred references (Chapters, Papers, Reports, Books, etc) were investigated in both static and dynamic environments.…”

SBBO Based Replicated Data Allocation Approach for Distributed Database Design

“…Fragmentation also helps in reduction of irrelevant data access and increases data localization [31]. Fragmentation of database has following three types [13,31,28]:…”