Perspectives on the CAP Theorem

Gilbert, Seth; Lynch, Nancy

doi:10.1109/mc.2011.389

Cited by 171 publications

(93 citation statements)

References 25 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For this, the mechanism eventually causes better data response within optimised time span in presence of data consistency which has been shown through the query evaluation time comparison with existing SLDI approach over same set of queries. So, for the efficient data deliverability in the presence of data availability, data consistency and data partitioning, shows the CAP theorem [14] [15] support for the proposed MDIP mechanism. Like the ACID properties for relational databases, the desirable CAP properties support inversely explains the distributed heterogeneous database support for the proposed MDIP.…”

Section: Discussionmentioning

confidence: 99%

SaaS Level based Middleware Database Integrator Platform

Pal¹

2017

ijacsa

View full text Add to dashboard Cite

Abstract-In purpose of data searching acceleration, the fastest data response is the major concern for latest cloud environment. Regarding this, the intellectual decision is to enrich the SaaS level applications. Amongst the SaaS based applications, service level database integration is the recent trend to provide the integrated view of the heterogeneous cloud databases through shared services using DBaaS. But the generic limitations interacted during the database integration are dynamic adaptability of multiple databases structure, dynamic data location identification in the concern databases, data response using the data commonality. Data migration technique and single query approach are the two individual solutions for the proposed limitations. But the side effects during data migration technique are extra space utilisation and excess time consumption. Again, the single query approach suffers from worst case time complexity for data connectivity, data aggregation and query evaluation. So, to find a suitable data response solution by eliminating these combined major issues, a graph based Middleware Database Integrator Platform or MDIP model has been proposed. This integrator platform is actually the flexible metadata representation technique for the concerned heterogeneous cloud databases. The associativity and commonality among components of multiple databases would be further helpful for efficient data searching in an integrated way. For the incorporation within the service level but not in the services, MDIP is considered as the different platform. It is applicable over any service based database integration in purpose of data response efficiency. Finally, the quality assessment using evaluated query time compared with already proposed SLDI shows better data access quality. Thus, its expertise dedication in data response can overcome summarised challenges like data adaptation flexibility, dynamic identification of data location, wastage of data storage, data accessing within minimal time span and optimised cost in presence of data consistency, data partitioning and user side scalability.

show abstract

Section: Discussionmentioning

confidence: 99%

SaaS Level based Middleware Database Integrator Platform

Pal¹

2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…Сучасні сховища типу ключ-значення спроек-товані з урахуванням теореми Брюера [6]. За сучас-ною кваліфікацією такі сховища відносяться до BASE-систем [7].…”

Section: літературний оглядunclassified

Analysis of partial solution of the problem of the Brewer theorem in in-memory data grid

Dobrovolskyi¹

2017

ScienceRise

View full text Add to dashboard Cite

show abstract

“…GDAŃSK, 2016 and availability when it comes to managing huge volumes of data on different servers. Published by Eric Brewer in 2000, the CAP Theorem sets the basic requirements for a distributed system -Consistency, Availability, and Partition Tolerance [5]:…”

Section: A Nosql Versus Rdbmsmentioning

confidence: 99%

Big Data Techniques, Systems, Applications, and Platforms: Case Studies from Academia

Radenski

Gurov

Kaloyanova

et al. 2016

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-Big data is a broad term with numerous dimensions, most notably: big data characteristics, techniques, software systems, application domains, computing platforms, and big data milieu (industry, government, and academia). In this paper we briefly introduce fundamental big data characteristics and then present seven case studies of big data techniques, systems, applications, and platforms, as seen from academic perspective (industry and government perspectives are not subject of this publication). While we feel that it is difficult, if at all possible, to encapsulate all of the important big data dimensions in a strict and uniform, yet comprehendible language, we believe that a set of diverse case studies -like the one that is offered in this paper -a set that spreads over the principal big data dimensions can indeed be beneficial to the broad big data community by helping experts in one realm to better understand currents trends in the other realms.

show abstract

Perspectives on the CAP Theorem

Cited by 171 publications

References 25 publications

SaaS Level based Middleware Database Integrator Platform

SaaS Level based Middleware Database Integrator Platform

Analysis of partial solution of the problem of the Brewer theorem in in-memory data grid

Big Data Techniques, Systems, Applications, and Platforms: Case Studies from Academia

Contact Info

Product

Resources

About