A Novel Query-Driven Clustering-Based Technique for Vertical Fragmentation and Allocation in Distributed Database Systems

Abstract

In this paper, heuristic query-driven clustering-based vertical fragmentation technique is efficiently developed. The intrinsic idea is to meet the idealistic case of DDBS design which comes to happen as each query attune “closely match” its relevant fragment. The proposed technique is mainly sought to breed clusters of queries in the first place. Consequently, these clusters would be tacitly used to generate intended disjoint fragments. Moreover, the allocation process has been considered so that replicated and non-replicated scenarios of data are applied. This technique basically meant to be efficaciously applicable at the initial stage of DDBS design without the need for data statistics or empirical results, in either dynamic or static DDBS environment. Many existing design-related techniques are being incorporated to make a promising work, particularly as communication costs being the foremost design objective. Throughout this work, the experimental results and internal evaluation are extensively illustrated to demonstrate the effectiveness and validity of proposed technique.

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

“…LDV seeks to cluster points based on their least distance/difference. Moreover, the LDV-based clustering was shown to outperform the threshold-based algorithm, which was applied to cluster sites in [7]. Threshold-based clustering has sometimes been observed to either shrink cluster numbers to an unpractical range or increase this number to an undesirable extent [2], which leads, in both cases, to an unavoidable negative impact on DDBS rendering, as described in the discussion section.…”

“…The intended technique is meant to further minimize TC while giving an obvious definition for TC. It is worth mentioning that the criteria involved in taxonomy [7] are also considered in this paper. TC is being lessened by increasing data locality and decreasing remote data access, while communication overhead is significantly reduced by adopting replication scenarios.…”

“…In this work, sites were clustered by applying the concept of least difference value (LDV). In its turn, LDV concept was used to group queries in [7], and, in fact, proved to be highly effective in improving DDBS performance. LDV seeks to cluster points based on their least distance/difference.…”

“…It is important to point out that, as opposed to a threshold-based algorithms [7,16], executing a clustering algorithm, as per steps (1-4), ensured that no outliers could be produced due to the fact that all sites were clustered. Consequently, this achievement of the proposed algorithm was observed to be a great contribution, as no site/cluster could be lost.…”

Empirical Evaluation of Threshold, Time, Volume, and Distance Constraint Algorithm for Redundant and Non-Redundant (TTVDCA-RNR) Dynamic Data Allocation in Distributed Database Systems

“…Over cloud environment [13] presented an data allocation and replication technique for distributed database, which can be applied at the initial stage of distributed database design where no information about the query execution is available. For vertical fragmentation and allocation in distributed database systems, [14] introduced a novel query-driven clusteringbased technique, which can be applicable to static as well as dynamic distributed database environment.…”