Advanced Database Management & Systems

(JoADMS)

eISSN: 2393-8730

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Journal of Advanced Database Management & Systems

eISSN: 2393-8730

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Industrial Tribology Machine Dynamics & Maintenance

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Niusvel Acosta Mendoza Researcher at Advanced Technologies

Application Center (CENATAV)

Cuba.

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Science & EngineeringVeer Surendra Sai University of

Technology,Odisha, India.

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of technology tiruchirappalli.India.

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Jenq Haur WangAssistant ProfessorDepartment of

Computer Science and Information Engineering National Taipei University

of Technology Taiwan.

I take the privilege to present the print version for the Volume 1 Issue (2) of Journal of Advanced

Database Management & Systems. The intension of JoADMS is to create an atmosphere that

stimulates creativeness, research and growth in the area of advanced database.

The development and growth of the mankind is the consequence of brilliant Research done by

eminent Scientists and Engineers in every field. JoADMS provides an outlet for Research findings

and reviews in areas of advanced database found to be relevant for National and International recent

developments & research initiative.

The aim and scope of the Journal is to provide an academic medium and an important reference for

the advancement and dissemination of Research results that support high level learning, teaching and

research in the domain of advanced database.

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their continued support and invaluable contributions and suggestions in the form of authoring write

ups/ reviewing and providing constructive comments for the advancement of the journals. With

regards to their due continuous support and co-operation, we have been able to publish quality

Research/Reviews findings for our customers base.

I hope you will enjoy reading this issue and we welcome your feedback on any aspect of the Journal.

Dr. Archana Mehrotra

Director

STM Journals

Director's Desk

STM JOURNALS

1. An Improved SentiWordNet for Opinion Mining and Sentiment Analysis Soumya Vaidya, Mohamed Rafi 1

2. Comparison of Education in Europe on Specialty Information Technologies and Computer Science Zhangisina G., Murzalina G., Munalbaeva N. 8

3. NoSQL Databases- Analysis, Techniques, and ClassificationManoveg Saxena, Zakir Ali, Vinod Kumar Singh 13

4. Applying the XML-based Technique to Support Keyword Search in Graph DataYa-Hui Chang, Si-Yen Zhuang 25

5. An Empirical Study on Evaluating Graph Based Clustering for HD Data Using Attribute SelectionV. Hemapriya, K. P. N. V. Satya Sree, K. V. Narasimha Reddy 33

ContentsJournal of Advanced Database Management & Systems

JoADMS (2014)© STM Journals 2014. All Rights Reserved

Journal of Advanced Database Management & Systems ISSN: 2393-8730 (online)

Volume 1, Issue 2

www.stmjournals.com

An Improved SentiWordNet for Opinion Mining

and Sentiment Analysis

Soumya Vaidya*, Mohamed Rafi Computer Science and Engineering Department, University B D T College of Engineering,

Davanagere, Karnataka, India

Abstract Opinion Mining and Sentiment Analysis is an emergent research area, spanning over multiple disciplines such as data mining, text mining, etc. Opinion mining is an art of

extracting the opinions from the huge set of opinion set or reviews. Sentiment analysis is

a type of natural language processing for tracking the mood of the public about a particular product or topic. The existing works of opinion mining used Sentiwordnet as a

lexical resource. The major drawback of this existing Sentiwordnet is non-determination

of score count, i.e., it doesn’t provide the details of number of positively, negatively and neutrally scored words. This information is necessary because without the knowledge of

score count if the further data mining techniques are applied, it may give inaccurate results. To facilitate the opinion mining task, this work focus on design of Improved

Sentiwordnet so that it can produce the count of scored words by distinguishing them into

positive, negative and neutral words. Experiments are conducted on standard movie review and product review datasets. This work also make use of Stanford POS tagger for

tagging the dataset. The counted words can be used to improve the results comparatively

better.

Keywords: Opinion mining, sentiment analysis, POS tagging, scoring using improved

SentiWordNet

JoADMS (2014) © STM Journals 2014. All Rights Reserved

Journal of Advanced Database Management & Systems ISSN: 2393-8730 (online)

Volume 1, Issue 2

www.stmjournals.com

Comparison of Education in Europe on Specialty

Information Technologies and Computer Science

Zhangisina G.1*, Murzalina G.

1, Munalbaeva N.

2

1Department of Information Security, KazNTU named after K.I. Satpaeva,

Kazakhstan, Almaty 2Information Science, KazGosGenPU

Abstract In this paper the author works on the definition and comparison of computer science in

Europe. It is also important that the author writes about the change needed in teaching in

Kazakhstan, as computer science is considered very important in today's world. This article describes the modern European education in the field of computer science in high

schools considering the leading European universities (Oxford and Berlin Technical Universities) with different educations systems. Nevertheless, no matter how unique each

system, there are several positive features, which can be emulated by Kazakhstan

Universities and studied computer science and management bodies for the further improvement of higher education in the country and training graduates in Kazakhstan's

Universities. Comparisons of education in Europe on specialty information technologies and computer science are considered in this paper.

Keywords: European education, educational systems, Oxford, Berlin Technical

Universities, Computer science.

JoADMS (2014)© STM Journals 2014. All Rights Reserved

Journal of Advanced Database Management & Systems ISSN: 2393-8730 (online)

Volume 1, Issue 2

www.stmjournals.com

NoSQL Databases- Analysis, Techniques, and

Classification

Manoveg Saxena1, Zakir Ali

2, Vinod Kumar Singh

3* 1Heinz Nixdorf Institute, University of Paderborn, Germany

2Bundelkhand University, Jhansi, U.P., India

3S.R. Group of Institutions, Jhansi, U.P., India

Abstract In recent years in big data and real-time web applications, NoSQL database solutions

are gaining popularity in handling massive increase in size of data storage. The design of

traditional relational database management systems makes it difficult for them to take advantage of virtually unlimited processing power and economical storage available via

cloud computing. In contrast to this NoSQL databases support dynamic schemas, auto-sharding, automatic replication and integrated caching capabilities prominently among

many other features making it suitable to be used as storage mechanism in cloud

computing environments. Due to the big number of available storage solutions classification of NoSQL databases based on various criteria is analyzed. To cope with

this look, I discuss techniques common to NoSQL datastores and analyze four prominent solutions (MongoDB, SimpleDB, Riak, and Bigtable).

Keywords: NoSQL, RDBMS, MapReduce, BigTable, MongoDB

JoADMS (2014)© STM Journals 2014. All Rights Reserved

Journal of Advanced Database Management & Systems ISSN: 2393-8730 (online)

Volume 1, Issue 2

www.stmjournals.com

Applying the XML-based Technique to Support Keyword

Search in Graph Data

Ya-Hui Chang*, Si-Yen Zhuang Department of Computer Science and Engineering, National Taiwan Ocean University,

Keelung, Taiwan

Abstract Graphs have been important and proper structures to represent data with complex relationships in the Web, and keyword search provides a convenient way to querying

graphs. Some researchers considered to return r-cliques as answers for keyword search

in graph databases. Such concepts can retrieve meaningful information effectively, but the existing approach is computationally expensive. In this paper, we propose to utilize

the efficient technique originally designed for keyword search in XML data to help

identify answers. The main idea is first transforming the input graph into a tree, and then

applying the efficient ELCA technique on the transformed tree to find the subtrees which

consist of closely-related matches. The specially-designed SRE and SRE* algorithms are then applied on these subtrees to identify r-cliques. We have proposed several strategies

for transforming graphs into trees and designed a series of experiments to examine their

performance. Experimental results show that our approaches perform very well in terms

of efficiency and recall.

Keywords: Keyword search, Graph database, ELCA

JoADMS (2014) © STM Journals 2014. All Rights Reserved

Journal of Advanced Database Management & Systems ISSN: 2393-8730 (online)

Volume 1, Issue 2

www.stmjournals.com

An Empirical Study on Evaluating Graph Based

Clustering for HD Data Using Attribute Selection

V. Hemapriya*, K. P. N. V. Satya Sree, K. V. Narasimha Reddy Department of CSE, VNITSW, Guntur, AP, India

Abstract An attribute subset selection can be showed as a process of identifying and eliminating or

removing a number of irrelevant and surplus attributes (features) because irrelevant

attributes do not give predictive accuracy and the surplus attributes provide the information that is already present in the other attributes. Attribute selection involves

identifying a subset of the most useful attributes that produces the similar results as the

final set of results. An attribute (feature) selection algorithm may be evaluated from two points of view. First one concerns the time required to get the subset of attributes and the

second one concerns quality of the subset of attributes. Based on these criteria, graph-based clustering for attribute selection algorithm, GRACE is proposed. This algorithm

works in two steps. In the first step, attributes are divided into clusters by using graph-

theoretic clustering methods. In the second step, most similar attributes that are strongly related to the object classes are selected from each cluster from a subset of attributes.

Attributes in different clusters are relatively independent. To ensure the efficiency of this algorithm, the authors implemented the minimum spanning tree clustering method.

Keywords: Graph-based clustering, filter method, attribute subset selection