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Transcript of Journal of advanced database management & systems (vol1, issue2)
Advanced Database Management & Systems
(JoADMS)
eISSN: 2393-8730
May - August 2014
www.stmjournals.comSTM JOURNALSScientific Technical Medical
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Journal of Advanced Database Management & Systems
eISSN: 2393-8730
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Journal of Advanced Database Management & Systems is published (frequency: three times a year) in India by STM
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Systems Analysis and Design
Semantic Web and Ontology’s
Knowledge Modeling and Processing
Sensor data Management
Temporal, Spatial and High dimensional databases
Database System and applications
Data warehousing and data mining, OLAP
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Puneet Pandeya
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Dr. Bimlesh Lochab
Industrial Tribology Machine Dynamics & Maintenance
Engineering Centre (ITMMEC)
Indian Institute of Technology Delhi, India.
Prof. S. Ramaprabhu
Alternative Energy Technology Laboratory,
Department of Physics,
Indian Institute of Technology, Chennai, India.
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School of Materials Science and Technology,
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STM Journal (s) Advisory Board
Dr. Ashish RunthalaLecturer, Biological Sciences Group,
Birla Institute of Technology & Science, Pilani Rajasthan, India.
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Former Director, Indira Gandhi Centre for Atomic Research, India.
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of Civil Engineering National Institute of Technology Trichy, India.
Prof. Bankim Chandra RayProfessor and Head, Department of
Metallurgical and Materials Engineering National Institute of Technology,
Rourkela, India.
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AIIMS, New Delhi, India.
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Medicine, Maulana Azad Medical College, New Delhi, India.
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University, Baru Sahib, India.
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National Brain Research Centre, Manesar, India.
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Indian Institute of Technology Madras, India.
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Engineering Department, PEC University of Technology,
Chandigarh, India.
STM Journal (s) Advisory Board
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Niusvel Acosta Mendoza Researcher at Advanced Technologies
Application Center (CENATAV)
Cuba.
Dr. H.S. BeheraAsstistant Professor Dept. of Computer
Science & EngineeringVeer Surendra Sai University of
Technology,Odisha, India.
Dr. S. Nickolas Associate professor at National institute
of technology tiruchirappalli.India.
Shaoxu SongAssistant Professor
School of Software, Tsinghua UniversityBeijing, China.
Yahui changAssociate Professor
Department of Computer Science and Engineering, National Taiwan
Ocean University, Taiwan.
José María Luna ArizaDepartment of Computer Sciences and
Numerical Analysis Campus Universitario de Rabanales. University
of Córdoba, Spain.
Sebastián VenturaAssociate Professor Department
of Computer Science and Numerical Analysis. University of Córdoba.
Spain.
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.
Finally, I express my sincere gratitude and thanks to our Editorial/ Reviewer board and Authors for
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