Distributed data management: unsolved problems and new issues

1996

The maturation of database management system (DBMS) technology has coincided with significant developments in distributed computing and parallel processing technologies. The end result is the emergence of distributed database management systems and parallel database management systems. These systems have started to become the dominant data-management tools for highly data-intensive applications.

Debu, 1982

Database Engineering Bulletin, 1982

Database Engineering Bulletin is a quarterly publication of the IEEE Computer Society Technical Committee on Database Engineering. Its scope of interest includes: data structures and models, access strategies, access control techniques, database architecture, database machines, intelligent front ends, mass storage for very large data bases, distributed database systems and techniques, database software design and implementation, database utilities, database security and related areas. Contribution to the Bulletin is hereby solicited. News items, letters, technical papers, book reviews, meeting previews, summaries, case studies, etc., should be sent to the Editor. All letters to the Editor will be considered for publication unless accompanied by a request to the con trary. Technical papers are unrefereed. Opinions expressed in contributions are those of the indi vidual author rather than the official position of the TC on Database Engineering, the IEEE Computer Society, or organizations with which the author may be affiliated.

International Journal of Advanced Computer Science and Applications

Distributed Databases Systems (DDBS) are a set of logically networked computer databases, managed by different sites, locations and accessible to the user as a single database. DDBS is an emerging technology that is useful in data storage and retrieval purposes. Still, there are some problems and issues that degrade the performance of distributed databases. The Aim of this paper is to provide a novel solution to distributed database problems that is based on distributed database challenges collected in one diagram and on the relationship among DDB challenges in another diagram. This solution presents two methodologies for Distributed Databases management Systems: deep learning-based fragmentation and allocation, and blockchain technology-based security provisioning. The contribution of this paper is twofold. First, it summarizes major issues and challenges in the distributed database. Additionally, it reviews the research efforts presented to resolve the issues. Secondly, this paper presents a distributed database solution that resolves the major issue of distributed database technology. This paper also highlights the future research directions that are appropriate for distributed database technology after the implementation in a large-scale environment and recommended the technologies that can be used to ensure the best implementation of the proposed solution.

Journal of Knowledge Management, Economics, and Information Technology, 2011

Distributed data - data, processed by a system, can be distributed among several computers, but it is accessible from any of them. A distributed database design problem is presented that involves the development of a global model, a fragmentation, and a data allocation. The student is given a conceptual entity-relationship model for the database and a description of the transactions and a generic network environment. A stepwise solution approach to this problem is shown, based on mean value assumptions about workload and service. A management system of a distributed database (SGBDD) is a software system that enables management and distributing BDD transparent to the user. A SGBDD consists of a single database which is decomposed into fragments, poassibly some fragments are multiplied, and each fragment or copy kept on one or more sites under the control of a local DBMS. Each site is capable of processing user queries in the local system, independently of the rest of the network, or ...

Debu, 1984

Reiner@CCA UUCP: decvax!cca!reiner Database Engineering Bulletin is a quarterly publication of the IEEE Computer Society Technical Committee on Database Engineering. Its scope of interest includes: data structures and models, access strategies, access control techniques, database architecture, database machines, intelligent front ends, mass storage for very large databases, distributed database systems and techniques, database software design and implementation, database utilities, database security and related areas. Contribution to the Bulletin is hereby solicited. News items, letters, technical papers, book reviews, meeting previews, summaries, case studies, etc., should be sent to the Editor. All letters to the Editor will be considered for publication unless accompanied by a request to the contrary. Technical papers are unretereed. Opinions expressed in contributions are those of the indi vidual author rather than the oflicial position of the TC on Database Engineering, the IEEE Computer Society, or orga nizations with which the author may be affiliated. Associate Editors, Database Engineering Dr. Haran Boral Microelectronics and Computer Technology Corporation (MCC) 9430 Research Blvd.

Database Engineering Bulletin is a quarterly publication of the IEEE Computer Society Technical Committee on Database Engineering. Its scope of interest includes: data structures and models, access strategies, access control techniques, database architecture, database machines, intelligent front ends, mass storage for very large data bases, distributed database systems and techniques, database software design and implementation, database utilities, database security and related areas. Contribution to the Bulletin is hereby solicited. News items, letters, technical papers, book reviews, meeting previews, summaries, case studies, etc., should be sent to the Editor. All letters to the Editor will be considered for publication unless accompanied by a request to the con trary. Technical papers are unrefereed. Opinions expressed in contributions are those of the indi vidual author rather than the official position of the TC on Database Engineering, the IEEE Computer Society, or organizations with which the author may be affiliated.

International Journal of Machine Learning and Computing

The distributed database system is the combination of two fully divergent approaches to data processing: database systems and computer network to deliver transparency of distributed and replicated data. The key determination of this paper is to achieve data integration and data distribution transparency, study and recognize the problems and approaches of the distributed database system. The distributed database is evolving technology to store and retrieve data from several location or sites with maintaining the dependability and obtainability of the data. In the paper we learn numerous problems in distributed database concurrency switch, design, transaction management problem etc. Distributed database allows to end worker to store and retrieve data anywhere in the network where database is located, during storing and accessing any data from distributed database through computer network faces numerous difficulties happens e.g. deadlock, concurrency and data allocation using fragmentation, clustering with multiple or single nodes, replication to overcome these difficulties it is essential to design the distributed database sensibly way.

2019

Distributed Databases Systems (DDBS) are a set of logically networked computer databases, managed by different sites and appearing to the user as a single database. This paper proposes a systematic review on distributed databases systems based on respectively three distribution strategies: Data fragmentation, Data allocation and Data replication. Some problems encountered when designing and using these strategies have been pointing out. Data fragmentation involves join optimization problem since when a query has to combine more than one fragment stored on different sites. This produces the high time response. Heuristic approaches have been examined to solve this problem as it is known as a NP-Hard problem. Data Allocation is also another particular problem which involves finding the optimal distribution of fragments to Sites. This has already been proved to be a NP-complete Problem. The review of some heuristics methods as solutions has been conducted. Finally, Data replication, wit...

Steen and Tanenbaum (2016) pronounced that computers were more ground-breaking and cost less than they did thirty or forty years ago. Local Area Networks (LANs) drove the charge, trailed by Wide Area Networks (WANs), followed by smartphones and a myriad of other innovative devices. Presently computers are capable of being networked to scale a large number of geographical territories thereby forming compelling dynamic distributed frameworks; dynamic implying that nodes are added or removed as required. Distributed systems are comprised of numerous clusters and nodes. As we delve into this research, the objective is to clarify how distributed systems contribute to enhanced performance, fault tolerance, and scalability. Distributed systems enable clients to leverage big data analytics in ways never before envisioned. As the proficiency and simplicity in connectivity expand, distributed systems will continue to scale up. As such, rather than focusing just on the interior and engineering components of distributed systems, significantly more attention will be placed on a survey of the framework overall and finding the best possible formalisms for defining the observed patterns (Steen, & Tanenbaum, 2016). A distributed system in this way becomes a method of study, much like examining and attempting to clarify current issues. This new method tracks ongoing research on understanding the structure and elements of processes like, large-scale distributed systems that store medical records and big data analytics that are often utilized to support bioinformatics. Keywords Big Data · Big Data Analytics · Distributed Database Management Systems (DDBMSs) · Electronic Patient Medical Records · Hadoop

2021

Distributed database (DDB) is one of the emerging fields of technology and market research. This article addresses different architectures of distributed databases, distributed database management systems (DDBMS), data dependency techniques, the importance and drawbacks of the DDB. The problem areas mentioned in the paper are extremely useful when implementing distributed databases to ensure easy management of competence, impasse, protection and privacy. In this paper we will also research distributed database design for integrating the business environment in a distributed database.

Implications of Globalization, 2010

2002

Although numerous database design models and solution algorithms have been developed, there h a s been little work that compares and evaluates these models. Lack of such work h a s left u s with several questions: Do the more comprehensive models actually result in better solutions than the simpler models? If so, what makes them better? Are they better under all conditions or only under certain conditions? Are there trade-offs between data redundancy and sophisticated operation allocation strategies? In this paper, we systematically compare and evaluate several distributed database design models in terms of total operating cost and average response time under various conditions. We vary the relative frequency of update queries and selectivities of queries. The results demonstrate that replication, join node selection, join order, and reduction by semijoin, all have significant impact on the efficiency of a distributed database system. Replication was most effective for retrieval intensive and high selectivity situations. Join node selection, join order, and reduction by semijoin were most effective for balanced retrieval/update and low selectivity situations. The results also suggest that there are trade-offs between total operating cost and average response time design criteria.

2002

Abstract. The current standard in governing distributed transaction termination is the so-called Two-Phase Commit protocol (2PC). The first phase of 2PC is a voting phase, where the participants in the transaction are given an ultimate right to abort that transaction. Giving up that veto right from all participants reduces the overhead of the atomic commitment protocol but also imposes some restrictions on the concurrency control and recovery protocols employed by the participants in the transaction. This paper gives, for the first time, a precise abstract specification of the Dictatorial Atomic Commitment (DAC) problem, resulting from removing veto rights from the traditional Atomic Commitment (AC) problem. We characterize transactional systems that are compatible with that specification in terms of necessary and sufficient conditions on concurrency control and recovery protocols, and discuss the practical impacts of those conditions. From this study, we capitalize on existing prot...