Data Allocation in Distributed Data Base in the Network Using Modularization Algorithm

In recent past, the bookstores generate more data such as customer details, transaction details, book details, etc. for analyzing them to make data-driven decisions. These data are stored in a centralized database. In centralized database system, the data are stored in a single site or location and that are managed. The major drawback is that if the centralized system gets crashed the entire data are lost and also it makes access bottleneck when more users need to access the database. In order to overcome these limitations, the distributed database system is used to store the business data by locating them in different databases that are located in different sites in different location. The distributed database maintains the replication and duplication that ensure the availability and fault tolerance on the data. The distributed system also provides the high accessibility to the users. Thus, this paper presents a distributed database for a bookstore to improve availability, fault tolerance, and accessibility. This distribution also maintains the transparency and supports the database properties such as atomicity, consistency, inheritance and durability (ACID). Furthermore, this distributed database is designed with four sites and each site keeps the bookstore data for access and analyses. This distributed database is constructed and accessed using structured query language (SQL) with MySQL relational database management system (RDBMS). It also provides the security and work even in offline mode. Keywords: distributed databases; query processing; relational database management; structured query language; bookstore database;

Data access in distributed data base management systems

Distributed databases (DDBs) provide smart processing of large databases, the problems of fragmentation and allocation are vital design problems in addition to the centralized design. The majority of performance degradation in DDBs is due to the communication cost by query remote access and retrieval of data. This can be optimized through an efficient data allocation approach that will provide flexible retrieval of a query by low cost accessible sites. In this paper, a novel high performance data allocation approach is designed using Chicken Swarm Optimization (CSO) algorithm. Data allocation problem (DAP) is a NP-Hard problem modelled as optimization problem. The proposed data allocation approach initially characterizes the DAP into optimal problem of choosing the appropriate and minimal communication cost provoking sites for the data fragments. Then the CSO algorithm optimally chooses the sites for each of the data fragments without creating much overhead and data route diversions. This enhances the overall distributed database design and subsequently ensures quality replication. The experimental results illustrate that the proposed CSO based intelligent data fragment allocation approach has better performance than most existing approaches and thus signifies the impact of efficient data allocation in DDBs.

In cloud computing environments, database systems have to serve a large number of tenants instantaneously and handle applications with different load characteristics. To provide a high quality of services, scalable distributed database systems with self-provisioning are required. The number of working nodes is adjusted dynamically based on user demand. Data fragments are reallocated frequently for node number adjustment and load balancing. The problem of data allocation is different from that in traditional distributed database systems, and therefore existing algorithms may not be applicable. In this paper, we first formally define the problem of data allocation in scalable distributed database systems. Then, we propose an algorithm for the problem. The algorithm makes use of time series models to perform short-term load forecasting such that node number adjustment and fragment reallocation can be performed in advance to avoid node over loadings and performance degradation due to fragment migrations. In addition, excessive working nodes can be minimized for resource-saving.

Caching frequently asked queries is an effective way to improve the performance of both centralized and distributed database systems. Intensive works have been done in this area to propose different query caching techniques and to evaluate their performance. However, most of these works were confined to caching previous query results in a single-level caching architecture. Evaluations of these works were based on simulations. In [D.S. Elzanfally et al., 2003], we proposed a new query caching technique for caching both query results and execution plans in a multilevel caching architecture. The centralized version of this technique was evaluated and the results were reported in [D.S. Elzanfally et al., 2003]. In this paper, we present an analytical model to evaluate the performance of the proposed technique in distributed database systems.

Distributed heterogeneous integrates asset of autonomous database systems to provide global database functions. The client/server-based distributed heterogeneous database system has a limited concurrency and scalability. In this paper, we proposed a new distributed heterogeneous database system model based on intelligent agent approach. In this model, we implement the functions of the system using agent approach. We defined several agents, such as, UIagent, Transagent, GIagent, parti-agent, coord-agent, etc. We discussed the transaction processing process and discussed the ACID properties of the transaction. This model can farthest develop the concurrency of the transaction and it is suitable for the large scale distributed heterogeneous database system.

With the growth of information technology and computer networks, there is a vital need for optimal design of distributed databases with the aim of performance improvement in terms of minimizing the round-trip response time and query transmission and processing costs. To address this issue, new fragmentation, data allocation, and replication techniques are required. In this paper, we propose enhanced vertical fragmentation, allocation, and replication schemes to improve the performance of distributed database systems. The proposed fragmentation scheme clusters highly-bonded attributes (i.e., normally accessed together) into a single fragment in order to minimize the query processing cost. The allocation scheme is proposed to find an optimized allocation to minimize the round-trip response time. The replication scheme partially replicates the fragments to increase the local execution of queries in a way that minimizes the cost of transmitting replicas to the sites. Experimental results show that, on average, the proposed schemes reduce the round-trip response time of queries by 23% and query processing cost by 15%, as compared to the related work.

For better protection of distributed systems, two well-known techniques are: checkpointing and rollback recovery. While failure protection is often considered a separate issue, it is crucial for establishing better secure distributed systems. This article is dedicated to the proposal of a new checkpointing algorithm for saving a consistent-transaction state in distributed databases ensuring that database management systems are able, after a failure, to recover the state of the database. The proposed communication-induced algorithm does not hamper the normal transaction processing and saves a global consistent-transaction state that records only the fully completed transactions. Analysis and experimental results of our proposal show that the proposed scheme saves a minimum number of forced checkpoints and has some performance gains compared to the alternative approaches.

Many algorithms have been devised for minimizing the costs associated with obtaining the answer to a single, isolated query in a distributed database system. However, if more than one query may be processed by the system at the same time and if the arrival times of the queries are unknown, the determination of optimal query-processing strategies becomes a stochastic optimization problem. In order to cope with such problems, a theoretical state-transition model is presented that treats the system as one operating under a stochastic load. Query-processing strategies may then be distributed over the processors of a network as probability distributions, in a manner which accommodates many queries over time. It is then shown that the model leads to the determination of optimal query-processing strategies as the solution of mathematical programming problems, and analytical results for several examples are presented. Furthermore, a divide-and-conquer approach is introduced for decomposing stochastic query optimization problems into distinct subproblems for processing queries sequentially and in parallel.

This paper proposes an enhancement for the ADRW algorithm to achieve dynamic fragmentation and object allocation in distributed databases. The algorithm adapts to the changing patterns of object requests with the objective to dynamically adjust the allocation schemes of objects in order to minimize the total servicing cost of all requests. Objects may be replicated or fragmented depending on patterns of reads and writes. Qualitative analysis was used to characterize the performance of the enhanced algorithm.

Consider the class of distributed database systems consisting of a set of nodes connected by a high bandwidth network. Each node consists of a processor, a random access memory, and a slower but much larger memory such as a disk. There is no shared memory among the nodes. The data are horizontally partitioned often using a hash function. Such a description characterizes many parallel or distributed database systems that have recently been proposed, both commercial and academic. We study the optimization problem that arises when the query processor must repartition the relations and intermediate results participating in a multijoin query. Using estimates of the sizes of intermediate relations, we show (1) optimum solutions for closed chain queries; (2) the NP-completeness of the optimization problem for star, tree, and general graph queries; and (3) effective heuristics for these hard cases. Our general approach and many of our results extend to other attribute partitioning schemes, for example, sort-partitioning on attributes, and to partitioned object databases.

In this paper, semantic query optimization in distributed database systems is translated into a multilevel search process. The overall search process is decomposed in two main stages: (1) guided by the syntactic complexity of a query expression, search for an appropriate optimization strategy, (2) given this strategy, transform the query expression into an efficient distributed query evaluation plan. During the second stage, properties of the application being modeled are used to attack a number of problems: detecting inconsistent and redundant selection and join conditions, estimating intermediate and final results, defining and using fragmentation knowledge. An extensible knowledge-based architecture is described to accommodate a variety of existing and future optimization techniques.

An optimistic scheme, called ODL, that uses dummy locks to test the validity of a transaction for concurrency control in distributed database systems is suggested. The dummy locks are long-term locks; however, they do not conflict with any other lock. By the use of long-term dummy locks, the need for the information about the write sets of validated transactions is eliminated, and during the validation test only the related sites are checked. Also, the transactions to be aborted are immediately recognized before the validation test, and therefore the cost of restarts is reduced. The usual read and write locks are used as short-term locks during the validation test. This use of short-term locks eliminates the need for the systemwide critical section and results in a distributed and parallel validation test. >

This paper investigates some of the problems encountered in the management of a data base that is logically distributed. The term logical distribution is used to connote a situation wherein users in various locales are responsible for creation and maintenance of their own portions of a data base, but a user in one locale can access data maintained by a user in another locale. An example of this situation is drawn from the realm of water quality management. A generalised mapping language is proposed as a mechanism for information transferral within a distributed data base, and a general data structure for supporting the mapping function is illustrated. The presented method accommodates a variety of user views, is independent of whether the data base is geographically distributed or centralised, furnishes a straightforward security mechanism and provides a basis for treating the contingency of uninformed or non-programming users.

Distributed database system (DDBS) design is still an open challenge even after decades of research, especially in a dynamic network setting. Hence, to meet the demands of high-speed data gathering and for the management and preservation of huge systems, it is important to construct a distributed database for real-time data storage. Incidentally, some fragmentation schemes, such as horizontal, vertical, and hybrid, are widely used for DDBS design. At the same time, data allocation could not be done without first physically fragmenting the data because the fragmentation process is the foundation of the DDBS design. Extensive research have been conducted to develop effective solutions for DDBS design problems. But the great majority of them barely consider the RDDBS's initial design. Therefore, this work aims at proposing a clustering-based horizontal fragmentation and allocation technique to handle both the early and late stages of the DDBS design. To ensure that each operation flows into the next without any increase in complexity, fragmentation and allocation are done simultaneously. With this approach, the main goals are to minimize communication expenses, response time, and irrelevant data access. Most importantly, it has been observed that the proposed approach may effectively expand RDDBS performance by simultaneously fragmenting and assigning various relations. Through simulations and experiments on synthetic and real databases, we demonstrate the viability of our strategy and how it considerably lowers communication costs for typical access patterns at both the early and late stages of design.