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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  2/2017 - 8

A New Optimized Model to Handle Temporal Data using Open Source Database

KUMAR, S. See more information about KUMAR, S. on SCOPUS See more information about KUMAR, S. on IEEExplore See more information about KUMAR, S. on Web of Science, RISHI, R. See more information about RISHI, R. on SCOPUS See more information about RISHI, R. on SCOPUS See more information about RISHI, R. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,184 KB) | Citation | Downloads: 358 | Views: 919

Author keywords
indexing, object oriented databases, open source software, query processing, runtime

References keywords
temporal(17), data(15), relational(7), databases(7), systems(6), information(6), database(6), object(5), time(4), science(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-05-31
Volume 17, Issue 2, Year 2017, On page(s): 55 - 60
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.02008
Web of Science Accession Number: 000405378100008
SCOPUS ID: 85020110475

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The majority of the database applications now a days deal with temporal data. Temporal records are known to change during the course of time and facilities to manage the multiple snapshots of these records are generally missing in conventional databases. Consequently, different temporal data models have been proposed and implemented as an extension of the temporal less database systems. In the single relation model, the present and past instances are stored in a single relation that makes its handling cumbersome and inefficient. This paper emphasize upon storing the past instances of the records in the multiple historical relations. The current relations will manage the recent snapshot of data. The tuple time stamping approach is used to timestamp the temporal records. This paper proposes a temporal model for the management of time varying data built on the top of conventional open source database. Indexing is used to enhance the performance of the model. The proposed model is also compared with the single relation model.

References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Record]

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[CrossRef] [SCOPUS Times Cited 2]

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[CrossRef] [SCOPUS Times Cited 9]

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[CrossRef] [Full Text] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]

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[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 5]

References Weight

Web of Science® Citations for all references: 247 TCR
SCOPUS® Citations for all references: 355 TCR

Web of Science® Average Citations per reference: 12 ACR
SCOPUS® Average Citations per reference: 17 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2019-06-19 13:30 in 131 seconds.

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