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Query optimization is considered as the most significant part in a model of distributed database. The optimizer tries to find an optimal join order, which reduces the query execution cost. Several factors may affect the cost of query execution, including number of relations, communication costs, resources, and access to large distributed data sets. The success of a processed query depends heavily on the search methodology that is implemented by the query optimizer. Query processing is considered as NP-hard problem and many researchers are focusing on this problem. Researches are trying to find an appropriate algorithm to seek an ideal solution especially when the size of the database increases. In case of large queries, classical heuristic methods such as ant colony and genetic algorithm can't cover all search space and may lead to falling in a local minimum. In this paper, quantum inspired ant colony algorithm (QIACO), as one of the hybrid strategy of probabilistic algorithms, is utilized to improve the query join cost in the distributed database model. The ability of quantum computing to diversify leads to cover query large search space, which helps in selecting the best trail and thus improves the slow convergence speed and avoid falling into a local optimum. Using this strategy, the algorithm aims to find an optimal join order which minimizes the total execution time. Experimental results show that the proposed model convergence faster with better goodness than the classic ant colony model for same number of ants used.

This article presents the evolution of databases from classical relational databases to distributed databases and data warehouses to fuzzy databases used in a production enterprise. This paper discusses characteristics of this kind of enterprise. The authors precisely define centralized and distributed databases, data warehouses and fuzzy databases. In the modern global world, many companies change their management strategy from the one based on a centralized database to an approach based on distributed database systems. Growing expectations regarding business intelligence encourage companies to deploy data warehouses. New solutions are sought as the demand for engineers' expertise continues to rise. The requested knowledge can be certain or uncertain. Certain knowledge does not any problems and is easy to obtain. However, uncertain knowledge requires new ways of obtaining, including the use of fuzzy logic. It is from where the fuzzy database approach takes its beginning. The above-mentioned strategies of a production enterprise were described herein as a case of special interest.

Two strategies for processing transactions during partition failures in distributed databases are reviewed: the optimistic protocol and conservative class conflict graph analysis. Both use graph techniques for detecting and resolving conflicts, although one is "optimistic," detecting and resolving conflict after the failure is repaired, while the other is "conservative," detecting and preventing potential conflicts when the failure occurs. A simulation comparing the two approaches with respect to the cost of missed opportunity, the cost of repair, and overhead cost is presented, along with sample results. The optimistic protocol generally minimizes missed opportunity, while conservative class conflict graph analysis requires less overhead and no repair. The applicability of these approaches to fractured networks involving more than two partitions is also discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Designing an efficient Distributed Database System (DDBS) is considered as one of the most challenging problems because of multiple interdependent factors which are affecting its performance. Allocation and fragmentation are two processes which their efficiency and correctness influence the performance of DDBS. Therefore, efficient data fragmentation and allocation of fragments across the network sites are considered as an important research area in distributed database design. This paper presents an approach which simultaneously fragments data vertically and allocates the fragments to appropriate sites across the network. Bond Energy Algorithm (BEA) is applied with a better affinity measure that improves the generated clusters of attributes. The algorithm simultaneously generates clusters of attributes, calculates the cost of allocating each cluster to each site and allocates each cluster to the most appropriate site. Results show more efficient clustering and allocation which gives better performance. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a secure and robust way to adopt Distributed Database System (DDBS). Distributed database is becoming very popular now a day. Today's business environment has an increasingly need for distributed database and client/server applications. We know that the aim of current technologies is to fulfil the desire for consistent, scalable, reliable and accessible information i.e. steadily growing information. In Distributed Database System, the data fragmentation and distributed database site's clustering problems are NP-Hard in nature and difficult to solve. One of the points considered that may cause loss of data means, while upgrading from Relational Database System, data distribution over different data-sites uses fragmentation. The database queries to access the applications on the data-sites and should be performed effectively. Therefore, the fragments that accessed by queries are needed to be allocated to the distributed database sites. Thus, fragmentation should be as secure as data be lossless during query execution. And, the second one may cause expensiveness of technology changes. Thus, we present a method for grouping the sites (clustering) of Distributed Database Systems in order to achieve not only secure fragmentation but also robust and cost effective congestion control. [ABSTRACT FROM AUTHOR]

Distributed database is very much popular in organizations which are distributed and operating from multiple geographic locations. The two important issues to improve the performance of distributed database systems (DDS) are - grouping of different sites into clusters, and dynamic reallocation of data fragment to these clusters. Clustering network sites and data allocation are still open research problems as their optimal solution is NP-hard. The prime contribution in this field is to develop a near optimal solution. In this paper a new algorithm is proposed for clustering distributed database sites based on the distance between sites to minimise the number of communications and network overhead between sites. Moreover, a Clustered Approach to Dynamic Data Allocation (CADDA) algorithm is proposed to dynamically reallocate data fragments to clusters in redundant and non-redundant distributed database system to reduce the remote data accesses and network overhead. The proposed approach is implemented on a sample distributed database system and compared with other cluster and non-cluster based data allocation algorithms. The comparison results show that proposed approach efficiency is better and it improves the overall performance of the distributed database system. [ABSTRACT FROM AUTHOR]

This research concerned with the designing and implementation of a distributed database system for the medical purposes. The system has been applied on dental clinic unit and statistics department at Al-Khansaa Educational Hospital at Mosul, and dental clinic unit at Woman Health Care Center at Mosul as a case study. Client/server model has been used to implement the proposed system's architecture. The computers have been connected together through a local area network (LAN). Horizontal fragmentation technique has been used to distribute the database which achieved a good level of local autonomy. Oracle software were used and utilized to implement the system. It played a dramatic role in protecting data using combination of passwords and user roles hierarchies in addition to achieving transparency and data integrity concepts in the system. The proposed system simplified saving and retrieving data of dental clinics. It also provides dental clinics units and statistics department with necessary reports and statistics. The proposed system has the ability to automatically perform daily backup for the database in addition to the manual options for database backup and recovery.

In this paper, an incremental data migration technique is evaluated in the multi-database system based on MySQL with SPIDER storage engine and the improvement of the turn-around times of other operations is proved in this systems. In this method, a large data migration is divided into small data migrations and other operations are inserted between these small data migrations. This technique is easy to implement in the multi-database system.

Distributed database systems provide a new data processing and storage technology for decentralized organizations of today. Query optimization, the process to generate an optimal execution plan for the posed query, is more challenging in such systems due to the huge search space of alternative plans incurred by distribution. As finding an optimal execution plan is computationally intractable, using stochastic-based algorithms has drawn the attention of most researchers. In this paper, for the first time, a multi-colony ant algorithm is proposed for optimizing join queries in a distributed environment where relations can be replicated but not fragmented. In the proposed algorithm, four types of ants collaborate to create an execution plan. Hence, there are four ant colonies in each iteration. Each type of ant makes an important decision to find the optimal plan. In order to evaluate the quality of the generated plan, two cost models are used-one based on the total time and the other on the response time. The proposed algorithm is compared with two previous genetic-based algorithms on chain, tree and cyclic queries. The experimental results show that the proposed algorithm saves up to about 80 % of optimization time with no significant difference in the quality of generated plans compared with the best existing genetic-based algorithm. [ABSTRACT FROM AUTHOR]

The developments in database and networking technologies and demand for increasing database sizes make distributed database systems (DDS) more important in modern society. The data allocation is a prominent issue in distributed database systems as the performance of the system is heavily depend on the data it accesses from different sites. Various algorithms have been proposed for data allocation in distributed database systems. Present paper explores the existing literature in which dynamic data allocation algorithm is used in distributed environment. A systematic study is applied to find as much literature as possible. A total of 31 papers were found suitable after defined search criteria. In order to derive useful findings from these papers, the data allocation approach presented in these papers are evaluated based on the various key parameters viz. performance efficiency, implementation technique, validation, usability, comparative analysis, and extendibility. This work is one of the first attempts to critically analyze such papers and suggests future research directions. The intension in the proposed work is to give a score for each data allocation algorithm proposed by the researchers based on the selected key parameters, but definitely not to criticize any research contribution by authors. [ABSTRACT FROM AUTHOR]

The Performance and the efficiency of a distributed database system depend highly on the way data are allocated to the sites. The NP-completeness of the data allocation problem and the large size of its real occurrence, call for employing a fast and scalable heuristic algorithm. In this paper, we address the data allocation problem in terms of minimizing two different types of data transmission across the network, i.e., data transmissions due to site-fragment dependencies and those caused by inter-fragment dependencies. We propose a new heuristic algorithm which is based on the ant colony optimization meta-heuristic, with regards to the applied strategies for query optimization and integrity enforcement. The goal is to design an efficient data allocation scheme to minimize the total transaction response time under memory capacity constraints of the sites. Experimental tests indicate that our algorithm is capable of producing near- optimal solutions within a reasonable time. The results also reveal the flexibility and scalability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we address the performance of distributed database systems with buffer constraints. Specifically, our objective is to design and analyze efficient data allocation and replication strategies to minimize the total servicing cost for an arbitrary read/write request sequence, under finite buffer constraints of the nodes in the system. When the available buffer space in a node is not enough to store a copy of an object, the decision has to be made on whether or not we should evict one or more objects in use to give room for the new object copy. In this paper, we design and analyze the data replication strategies with the model of Dynamic Window Mechanism (DWM) algorithm jointly implemented with different types of object replacement strategies (No Replacement, LRU, and LFU) commonly found in practice. We consider situations wherein the object sizes are identical as well as heterogeneous. We will show the impact on the performance of the allocation and replication strategies due to the limited local database buffer capacities. We analyze and quantify theoretically (using competitive analysis) the performances of all the proposed algorithms. Further, we perform rigorous simulation experiments to validate the findings with respect to several influencing parameters. Several useful conclusions are drawn based on the experimental results and we highlight the usefulness of the algorithms under different situations. [ABSTRACT FROM AUTHOR]

The efficient processing of distributed database queries is of great importance in a distributed database management system. The algorithm-S described in this research is very efficient and low in complexity. The algorithm assumes uniformly distributed data within the attributes of a relation and data independence across attributes. Natural data reductions which occur in nonjoining attributes with low data multiplicity are recognized. The method can give far more efficient solutions than methods which ignore these reductions. The processing strategies generated exhibit low response time as well as low total transmission time regardless of the cost objective employed. [ABSTRACT FROM AUTHOR]

A new resiliency scheme for a distributed database system with replicated data is presented. One salient feature of the scheme is that it does not employ a log subsystem; hence, it can be used for a highly reliable system that must tolerate a total crash of a site. In addition, the scheme supports system partitioning without any consistency problem. Tue new scheme is based on a precise treatment of logical data in particular, a precise definition of resilient system operation is given in terms of local data. [ABSTRACT FROM AUTHOR]

We present a methodology for structured database decomposition based on the relational data model. It is argued that in the distributed database environment, structured database decomposition is attractive both for efficiency and for database security considerations. Techniques for parallel processing and hashed access of structurally decomposed database are presented. Techniques for structured database decomposition to support multiple user views are also described. Structured database decomposition is most advantageous in a query only database environment with stable user views, although dynamic updates can also be handled using techniques described in this paper. [ABSTRACT FROM AUTHOR]

Many distributed database systems that guarantee high concurrency and scalability adopt read-write separation architecture. Simultaneously, these systems need to store massive amounts of data daily, requiring different mechanisms for storing and accessing data, such as hot and cold data access strategies. Unlike distributed storage systems, the distributed database splits a table into sub-tables or shards, and the request frequency of each sub-table is not the same within a specific time. Therefore, it is not only necessary to design hot-to-cold approaches to reduce storage overhead, but also cold-to-hot methods to ensure high concurrency of those systems. We present a new redundant strategy named CBase-EC, using erasure codes to trade the performances of transaction processing and storage efficiency for CBase database systems developed for financial scenarios of the Bank. Two algorithms are proposed: the hot-cold tablets (shards) recognition algorithm and the hot-cold dynamic conversion algorithm. Then we adopt two optimization approaches to improve CBase-EC performance. In the experiment, we compare CBase-EC with three-replicas in CBase. The experimental results show that although the transaction processing performance declined by no more than 6%, the storage efficiency increased by 18.4%. [ABSTRACT FROM AUTHOR]

A distributed database model can be effectively optimized through using query optimization. In such a model, the optimizer attempts to identify the most efficient join order, which minimizes the overall cost of the query plan. Successful query processing largely relies on the methodology implemented by the query optimizer. Many researches are concerned with the fact that query processing is considered an NP-hard problem especially when the query becomes bigger. Regarding large queries, it has been found that heuristic methods cannot cover all search spaces and may lead to falling in a local minimum. This paper examines how quantum-inspired ant colony algorithm, a hybrid strategy of probabilistic algorithms, can be devised to improve the cost of query joins in distributed databases. Quantum computing has the ability to diversify and expand, and thus covering large query search spaces. This enables the selection of the best trails, which speeds up convergence and helps avoid falling into a local optimum. With such a strategy, the algorithm aims to identify an optimal join order to reduce the total execution time. Experimental results show that the proposed quantum-inspired ant colony offers a faster convergence with better outcome when compared with the classic model. [ABSTRACT FROM AUTHOR]

In the control and supervision of smart grids, the objective is to handle any change in the system as fast as possible, with as few resources as possible. In this context, this paper proposes a new language, called Smartlog, designed with a declarative approach. This avoids collecting and analyzing data presenting no interest, and thus being less efficient in bandwidth usage and computational time. Smartlog is designed for operating smart grids defined as abstract structures of large and scalable distributed databases. From its definition, some major properties of this language, such as simplicity, incremental capacity, and scalability are highlighted. The language is tested on the application of the secondary control of an isolated microgrid using a real-time simulator connected to Raspberry Pis. The characteristics of Smartlog are illustrated thanks to a comparison with an imperative programming implementation of the same regulation. [ABSTRACT FROM AUTHOR]