Distributed Database Management Systems

Rahimi, Saeed K.; Haug, Frank S.

doi:10.1002/9780470602379

Cited by 46 publications

(13 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Distributed Query Processing aims to quickly find a good plan for a query whose data are distributed in multiple machines [18,25,27]. In our problem the algorithm for selecting sharing plans doesn't need to have a very short response time, and thus for each sharing, we can often afford to explore a much larger space of possible plans or even enumerate all possible plans.…”

Section: Selecting Sharing Plansmentioning

confidence: 99%

Online optimization and fair costing for dynamic data sharing in a cloud data market

Liu¹,

Hacıgümüş²

2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

View full text Add to dashboard Cite

The data markets are emerging to address many organizations' need to find more useful external data sets for deeper insights. Existing data markets have two main limitations. First, they either sell a whole data set or some fixed views, but do not allow arbitrary adhoc queries. Second, they only sell static data, but not data that are frequently updated. While there exist proposed solutions for selling ad-hoc queries, it is an open question what mechanism should be used to sell ad-hoc queries on dynamic data. In this paper, we study a data market framework that enables the sale or sharing of dynamic data, where each sharing is specified by an ad-hoc query. To keep the shared data up-to-date, the service provider should create a view of the shared data and maintain the view for the data buyer. We provide solutions to two important problems in this framework: how to efficiently maintain the views, and how to fairly determine the cost each sharing incurs for its view to be created and maintained by the service provider. Both problems are challenging since in the first problem, different sharings with the same operations in their plans may reuse these operations, and each sharing plan must be generated online. In the second problem, there are several factors that impact the fairness of a costing function and a straightforward mechanism won't work. We propose an intuitive online algorithm for sharing plan selection, as well as a set of fair costing criteria and an algorithm that maximizes the fairness.

show abstract

Section: Selecting Sharing Plansmentioning

confidence: 99%

Online optimization and fair costing for dynamic data sharing in a cloud data market

Liu¹,

Hacıgümüş²

2014

Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

View full text Add to dashboard Cite

show abstract

“…the database servers) with the growth of data. However, it is challenging to allocate data in a distributed manner [159]. One of benefits of SQL databases is that most of SQL databases can guarantee ACID (Atomicity, Consistency, Isolation, Durability) properties, which are crucial to many commercial applications.…”

Section: Data Management Softwarementioning

confidence: 99%

“…They achieve the high scalability of NoSQL databases while guaranteeing data consistency (e.g., ACID properties) of SQL databases. (iii) Distributed computing models Though distributed databases [159] were proposed to improve the performance of DBMS through distributing operations over distributed data storages, they cannot fulfill the growing demands of BDA on the tremendous amount of data in large scale distributed systems. As a result, there are a number of distributed computing models proposed for BDA.…”

Section: Data Management Softwarementioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

View full text Add to dashboard Cite

The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large scale wireless networks. Big data of large scale wireless networks has the key features of wide variety, high volume, real-time velocity and huge value leading to the unique research challenges that are different from existing computing systems. In this paper, we present a survey of the state-of-art big data analytics (BDA) approaches for large scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.

show abstract

“…If the communication cost between two or more sites is less than, or equal to the threshold value (thv), then they will be grouped together in one cluster, otherwise the sites will be distributed in different clusters (table 9 shows the site grouping to form clusters). Moreover, 10), the pay of retrieval and update matrix (PRUM) will be formed. And using PRUM table, the cluster with the maximum cost, Max (PRUM(i)) for attribute A i will be selected to be the candidate cluster to store the attribute, given that there is no site constraints violation.…”

Section: • Sites Clusteringmentioning

confidence: 99%

“…In [9], a Stat Part static algorithm for VF is proposed. [10] Presented a heuristic method for specifying file and workload allocation simultaneously on a LAN to minimize the response time for processing transactions. In [11], the combined methods and class allocation problem was formulated and a model to calculate the total data transfer cost incurred was developed.…”

Section: Introductionmentioning

confidence: 99%

An integrated design scheme for performance optimization in distributed environments

Amer

Abdalla

2012

International Conference on Education and E-Learning Innovations

View full text Add to dashboard Cite

Among the techniques that the distributed database designer considers for performance improvement are fragmentation, replication and allocation techniques. Fragmentation is the process of dividing a relation into two or more relations called fragments. Usually two fragmentation techniques are considered, these are: vertical and horizontal fragmentation. A third rarely considered one is hybrid or mixed fragmentation. In this paper, vertical fragmentation along with a twophases allocation method in the context of relationalDDBSs is proposed to partition a relation vertically and allocate the resulting fragments/attributes across different sites. The proposed algorithm will greatly minimize communication cost and query response time in DDBSs. Empirical results have supported this hypothesis and shown that this method will present an efficient solution to vertical fragmentation and allocation problem in distributed environments.

show abstract

Distributed Database Management Systems

Cited by 46 publications

References 0 publications

Online optimization and fair costing for dynamic data sharing in a cloud data market

Online optimization and fair costing for dynamic data sharing in a cloud data market

Big Data Analytics for Large-scale Wireless Networks

An integrated design scheme for performance optimization in distributed environments

Contact Info

Product

Resources

About