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i-manager's Journal on Computer Science (JCOM)

Current Issue Vol. 11 Issue 3

Evaluating Hybrid Model Performance in Predicting the Impact of Lockdown on Controlling Covid-19 Cases Progression in Middle Income Settings
A Method for the Identification of Denial of Service (DoS) Attack in Network Traffic using Machine Learning Techniques
Agriculture Management System
Unveiling Sentiment Analysis: A Comparative Study of LSTM and Logistic Regression Models with XAI Insights
Resume Screener System

Most Cited

Volume 3 Issue 2 June - August 2015

Research Paper

UNIFI: A Protocol for Association Rule Mining InVertically Distributed Databases

M. Suresh Babu* , K.F.Bharati**

* PG Scholar, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapur, India.

** Assistant Professor, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapur, India.

Babu, M.S., and Bharati, K.F. (2015). UNIFI: A Protocol for Association Rule Mining in Vertically Distributed Databases. i-manager’s Journal on Computer Science, 3(2), 1-9. https://doi.org/10.26634/jcom.3.2.3544

Abstract

Data warehouses or databases may store large amount of data. In such databases, much processing power is needed for mining association rules. Therefore the solution used is a distributed system. In data mining, association rules are useful for analyzing and predicting customer behavior. They play an important part in shopping basket data analysis, product clustering, catalogue design and store layout. In this paper, the authors have used a protocol Unifying Lists of Locally Frequent Item sets (UNIFI) [1] for mining association rules in vertically partitioned data. In this Proposed system, the authors have aimed to implement the UNIFI protocol for Association Rule Mining in vertically distributed database. This protocol depends on the Fast Distributed Mining (FDM) algorithm like UNIFI-KC (Kantarcioglu and Clifton) in [6]. FDM is an unsecured version of the apriori algorithm.

References

[1]. Tamir Tassa, (2014). “Secure Mining of Association Rules in Horizontally Distributed Databases”, IEEE Transactions on Knowledge and Data Engineering, Vol. 26(4), pp.970-983.

[2]. T. Tassa and D. Cohen, (2012). “Anonymization of Centralized and Distributed Social Networks by Sequential Clustering”, IEEE Transactions on Knowledge and Data Engineering, Vol. 25(2), pp.311-324.

[3]. T. Tassa and E. Gudes, (2012). “Secure Distributed Computation of Anonymized Views of Shared Databases”, ACM Transactions on Database Systems, Vol. 37 (2), pp.1-44.

[4]. Ben David, N. Nisan, and B. Pinkas, (2008). “Fair play MP - A system for secure multi-party computation”, th Proceedings of 15 ACM Conference on Computer and Communications Security, pp.257–266.

[5]. S. Zhong, Z. Yang, and R. N. Wright, (2005). “Privacy Enhancing K-Anonymization of Customer Data”, th Proceedings of 25 ACM Symposium on Principles of Database Systems, pp.139– 147.

[6]. M. Kantarcioglu and C. Clifton, (2004). “Privacy Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data”, IEEE Transactions on Knowledge and Data Engineering, Vol.16(4), pp.1026–1037.

[7]. Chris Clifton, Murat Kantarcioglu, Jaideep Vaidya, Xiaodong Lin, and Michael Y. Zhu, (2003). “Tools for Privacy Preserving Distributed Data Mining,” SIGKDD Explorations, Vol. 4(2), pp.1-7.

[8]. J. Vaidya and C. Clifton, (2002). “Privacy Preserving Association Rule Mining in Vertically Partitioned Data”, th Proceedings of the 8 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 639-644.

[9]. D. W. L. Cheung, J. Han, V. T. Y. ng, A.W.C. Fu. A, (1996). “Fast Distributed Algorithm for Mining Association th Rules”, Proceedings of 4 IEEE International Conference on Parallel and Distributed Information Systems, pp. 31- 42.

[10]. D. Beaver, S. Micali, and P. Rogaway, (1990). “The Round Complexity of Secure Protocols”, Proceedings of nd the 22 Annual ACM Symposium on Theory of Computing, pp. 503–513.

[11]. A. C. Yao, (1982). “Protocols for Secure rd Computations”, Proceedings of the 23 Annual IEEE Symposium on Foundations of Computer Science, pp. 160 – 164.

[12]. R. Agrawal and R. Srikant, (1994). “Fast Algorithms for th Mining Association Rules”, Proceedings of the 20 International Conference on Very Large Data Bases. pp. 487–499. http://www.vldb.org/dblp/db/conf/vldb/vldb94- 487.html

[13]. Y. Lindell and B. Pinkas, (2004).“Privacy Preserving Data Mining,” Advances in Cryptology – CRYPTO 2000, pp. 36–54. http://link.springer.de/link/service/series/0558/ bibs/1880/18800036.htm

[14] . I. Ioannidis and A. Grama, (2003). “An Efficient Protocol for Yao's Millionaires Problem”, Hawaii International Conference on System Sciences (HICSS-36).

[15] . C. Clifton, M. Kantarcioglu, and J. Vaidya, (2002). “Defining Privacy for Data Mining”, National Science Foundation Workshop on Next Generation Data Mining, H. Kargupta, A. Joshi, and K. Sivakumar, Eds., Baltimore, MD, pp. 126–133.

[16]. R. Srikant and R. Agrawal, (1995). “Mining Generalized Association Rules”, International Conference Very Large Data Bases, pp. 487-499.

[17]. J. Zhan, S. Matwin, and L. Chang (2005). “Privacy Preserving Collaborative Association Rule Mining”, Data and Applications Security, pp.153–165.

[18]. S. C. Pohlig and M. E. Hellman,(1978). “An Improved Algorithm for Computing Logarithms Over GF(p) and its Cryptographic Significance”, IEEE Transactions on Information Theory, Vol. 24(1), pp. 106–110.

[19] . Maheshkumar Ramrao Gangasagare and Rafik Juber Thekiya, (2015). “Survey on Secure Mining of Association Rules in Horizontally Distributed Databases”, International Journal of Advance Research in Computer Science and Management Studies, Vol.3(3), pp.138-145.

[20] . Kishori Pawar and Y. B. Gurav, (2014). “Overview of Privacy in Horizontally Distributed Databases ”, International Journal of Innovative Research in Advanced Engineering, Vol.1(4), pp. 82-87

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Research Paper

Dynamic Coalition Pattern for Distributed CoordinationOf Multi-Agent Networks

K. Ankitha Priyadarshini* , lalitha.B**

* PG Scholar, Department of Computer Science and Engineering, JNTUA College of Engineering, Andhra Pradesh, India.

** Assistant Professor and Research Scholar, Department of Computer Science and Engineering, JNTUA College of Engineering, Andhra

Priyadarshini, K.A., and Lalitha, B. (2015). Dynamic Coalition Pattern for Distributed Coordination of Multi-Agent Networks. i-manager’s Journal on Computer Science, 3(2), 10-15. https://doi.org/10.26634/jcom.3.2.3545

Abstract

Self-organization presents a suitable model for building complex distributed systems which are self-managed. Here, the self-organizing behavior in multi-agent system is one of the most interesting aspect, where, an integrative selforganization mechanism is employed which associates the three principles of self-organization such as cloning, resource exchange and relation adaptation. Estimating the performance of this technique, it defeats when examined with three distinct assessments here and the scenario pretends to be agents with identical capacity. In this research paper the authors have considered agents with distinct capacities and the authors have aimed to design a paradigm to associate dynamic coalition pattern formation technique with self-organization, in an agent network which is well structured. Depending on self-organization rules, coalition formation is incorporated and it facilitates agents to dynamically adjust their degrees of entanglement in distinct coalitions and to unite new coalitions at any time if necessary. To attain the self-adaptation concept, a covenant protocol is employed.

References

[1]. Dayong Ye, Minjie Zhang, and Danny Sutanto, (2014). “Cloning, Resource Exchange, and Relation Adaptation: An Integrative Self-Organization Mechanism in a Distributed Agent Network”, IEEE Transactions on Parallel and Distributed Systems, Vol. 25(4), pp. 887 - 897

[2]. Dayong Ye, Minjie Zhang, and Danny Sutanto, (2012). "Integrating Self-organization into Dnamic Coalition Formation”, Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, Vol. 3, pp.1253-1254.

[3]. O. Shehory and S. Kraus, (1998). “Methods for Task Allocation via Agent Coalition Formation”, Journal on Artificial Intelligence, Vol. 101(1-2), pp. 165-200.

[4]. T. Rahwan, S.D. Ramchurn, A. Giovannucci, and N.R. Jennings, (2009). “An Anytime Algorithm for Optimal Coalition Structure Generation”, Journal on Artificial Intelligence Research, Vol. 34, pp. 521-567.

[5]. T. Michalak, J. Sroka, T. Rahwan, M. Wooldridge, P. McBurney, and N.R. Jennings, (2010). “A Distributed Algorithm for Anytime Coalition Structure Generation”, Proceedings of International Joint Conference on Autonomous Agents and Multi-agent Systems, pp. 1007- 1014.

[6]. S.D. Ramchurn, M. Polukarov, A. Farinelli, C. Truong, and N.R. Jennings, (2010). “Coalition Formation with Spatial and Temporal Constraints”, Proceedings of International Joint Conference on Autonomous Agents and Multi-agent Systems, pp. 1181-1188.

[7]. M. E. Gaston and M. Des Jardins, (2005). “Agent- Organized Networks for Dynamic Team Formation”, Proceedings of International Joint Conference on Autonomous Agents and Multi-agent Systems, pp. 230- 237,

[8]. R. Glinton, K. Sycara, and P. Scerri, (2008). “Agent Organized Networks Redux”, Proceedings of International Joint Conference on Autonomous Agents and Multiagent Systems , pp. 83-88.

[9]. M. E. Gaston and M. Des Jardins, (2005). “Agent- Organized Networks for Dynamic Team Formation”, Proceedings of International Joint Conference on Autonomous Agents and Multi-agent Systems, pp. 230- 237.

[10]. M. E. Gaston and M. Des Jardins, (2008). “The Effect of Network Structure on Dynamic Team Formation in Multi- Agent Systems”, Computational Intelligence, Vol. 24(2), pp. 122–157.

[11]. Shalini Kapoor, Poonam Kashyap, (2011).“Resource Management in Distributed System”, International Journal of Computer Technology Application, Vol. 2(5), pp.1722- 1728.

[12]. Dayong Ye, Minjie Zhang, and Danny Sutanto, (2013). “Self-Adaptation-Based Dynamic Coalition Formation in a Circulated Agent Network: A Mechanism and a Brief Survey", IEEE Transactions on Parallel and Distributed Systems, Vol. 24(10), pp.1-10.

[13].Jaeho Lee and Edmund H. Durfee, (1995). “A Microeconomic Approach to Intelligent Resource Sharing in Multi-agent Frameworks”, Standford University Laboratories.

[14]. Chongjie Zhang, Victor Lesser, Prashant Shenoy, (2009). "A Multi-Agent Learning Approach to Online Distributed Resource Allocation", Proceedings of Twenty- First International Joint Conference on Artificial Intelligence, Vol.1, pp.361-366

[15]. E.Costa-Montenegro, J.C. Burguillo-Rial, F. J. Gonzalez-Casta, and J. Vales- Alonso,(2007). "Agent Controlled Sharing of Distributed Resources in User Networks", Studies in Computational Intelligence, Vol. 72, pp. 29-60.

[16]. Wee Siong Ng1 Beng Chin Ooi1 Kian-Lee Tan1 Aoying Zhou, (2003). “PeerDB: A P2P-based System for th Distributed Data Sharing”, Proceedings 19 IEEE International Conference on Data Engineering, pp. 633 – 644.

[17]. Karl Czajkowski Steven Fitzgeraldy Ian Fosterzx Carl Kesselman, (2001). “Grid-Information Services for th Distributed Resource Sharing”, Proceedings of 10 IEEE International Symposium on High Performance Distributed Computing, pp. 181 – 194.

[18]. A. Rubinstein, (1982). "Perfect Equilibrium in a Bargaining Model”, Econometrica, Vol. 50(1), pp.97-109.

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Research Paper

Android App for ATM Location and Service Tracking

B. Soundhariya Lakshmi* , M.Ramya, D. Jagadeesan*

- Graduate, Department of Computer Science and Engineering, Adhiparasakthi College of Engineering, Kalavai, Vellore District, TamilNadu, India.

Professor, Department of Computer Science and Engineering, Sreenivasa Institute of Technology and Management Studies, Chittoor, India.

Lakshmi, B.S., Ramya, K., and Jagadeesan, K. (2015). Android App For ATM Location And Service Tracking. i-manager’s Journal on Computer Science, 3(2), 16-20. https://doi.org/10.26634/jcom.3.2.3546

Abstract

ATM (Automated Teller Machine) is important in our day-today lives for withdrawing money from our bank accounts. Android application for ATM finder is used to find ATMs around the user’s place, and it will reduce the time of searching the ATM. This application is a advanced and simple application in which ATM locator helps to find not only ATM's around us but also their service availability such as its working condition. This quick click series app is built for speed and ease of use. Types of ATM locations are preloaded to assist us with the optional name search, and it can also be done with the use of GPS. The ATM will periodically update its current status to the Bank, and in the proposed system the updated information from the Bank is obtained and utilized in the app to track the ATMs current service (Active or De-active). This will reduce the user's valuable time by making available the current working status of the ATM.

References

[1]. Seema Vanjire, Unmesh Kanchan, Ganesh Shitole and Pradnyesh Patil, (2014). “Location Based Services on Smart Phone through the Android Application”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3,No.1, pp.4982- 4987.

[2]. Sabyasachi Patra, Karishma Velisetty and Prathamesh Patel, (2014). “Location Based Tracking”, International Journal of Engineering Research and Development, Vol. 9, No.12, pp.5-12.

[3]. Malathi Veeraraghavan, Mark J. Karol, and Kai Y. Eng, “IEEE Mobility and Connection Management in a Wireless ATM LAN”, IEEE International Journal on Selected Areas in Communication, Vol.15, No.1,pp.50-68.

[4]. Amit Kushwaha and Vineet Kushwaha, (2011). “Location Based Services using Android Mobile Operating System”, International Journal of Advances in Engineeringand Technology, Vol. 1, No. 1, pp.14-20.

[5]. Vedang Moholkar, Prathamesh Hule, Mandar Khule and Sumit Sourabh, (2014). “Automated Location Based Services”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, No.2, pp.27-32.

[6]. Muthumurugesan, Nalini and Vinodini, (2013). “Smart Way to Track the Location in Android Operating System”, IOSR Journal of Computer Engineering, Vol. 12, Issue 4, pp. 27-32.

[7]. Garima Pandey and Diksha Dani, (2014). “Android Mobile Application Build on Eclipse”, International Journal of Scientific and Research Publications, Vol. 4, No. 2, pp.1-5.

[8]. http://developer.android.com/guide/index.html

[9].http://www.codeproject.com/Articles/102065/ Android-A-beginner-s-guide

[10].http://mobile.dzone.com/articles/fundamentalsandroid- tutorial

[11].http://wiki.openstreetmap.org/wiki/Accuracy_of_GPS _data

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Research Paper

A Hybrid Approach for Replica Placement-Replacement (Harp-R Algo) Algorithm In Data-Grid

Ashish Kumar Singh* , Udai Shanker**

* Lecturer, Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

** Professor, Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Singh, A.K., and Shanker, U. (2015). A Hybrid Approach for Replica Placement-Replacement (Harp-R Algo) Algorithm In Data-Grid. i-manager’s Journal on Computer Science, 3(2), 21-32. https://doi.org/10.26634/jcom.3.2.3547

Abstract

Distributed database system is a network in which multiple clients are connected logically, but physically they are distributed and each client has their own database. Replication process in such a environment plays a vital role for reducing response time. The process of creating exact copy of file is known as replication. Replication process can be executed in two ways, one is static replication and another is dynamic replication. In a static replication, created replica exist in the system till user deletes it manually or it’s time expired. In dynamic replication, it will behave with change in user behavior and it will automatically create new replicas or delete replicas to improve performance. Data replication, as one of the popular services in a distributed database system, is used to increase the data availability and scalability. In this paper, the authors have proposed a data replication protocol in which they replicate those file which are popular in all, and replication process for those file whose success rate is high. In this paper proposed system uses the advantages of both static and dynamic replication.

References

[1]. A. Chervenak, I. Foster, C. Kesselman, C. Salisbury, and S. Tuecke, (2006). "The Data Grid: Towards an Architecture for the Distributed Management and Analysis of Large Scientific Datasets", Journal of Network and Computer Application, Vol. 23, pp. 187-200.

[2]. Yixiu Huang, Ouri Wolfson, (1993). "A Competitive th Dynamic Data Replication Algorithm", Proceedings of 9 IEEE International Conference on Data Engineering, pp. 310-317.

[3]. Kavitha Ranganathan, and Ian T. Foster, (2001). "Identifying Dynamic Replication Strategies for a High nd Performance Data Grid", Proceedings of the 2 International Workshop on Grid Computing, pp. 75-86.

[4]. M. Tang, B.-S. Lee, X. Tang, and C.-K. Yeo, February (2006). "The Impact of Data Replication of Job Scheduling Performance in the Data Grid", Future Generation Computer Systems, Vol. 22, pp. 254-268.

[5]. Ruay-Shiung Chang, Hui-Ping Chang, Yun-Ting Wang (2008). " A Dynamic Weighted Data Replication Strategy in Data Grids", IEEE International Conference. on Computer and Applications, pp. 414-421.

[6]. K. Sashi and Dr. Antony Selvadoss Thanamani, August (2010). "Dynamic Replica Management for Data Grid", International Journal of Engineering and Technology (IACSIT), Vol. 2, No. 4, pp. 329-333.

[7]. Ming Lei, Susan V. Vrbsky, Xiaoyan Hong, (2007). “an On-line Replication Strategy to Increase Availability in Data Grids”, Future Generation Computer Systems, Vol. 24, No. 2, pp. 85-98.

[8]. S.M. Park, J.H. Kim, Y.B. Ko, and W.S. Yoon (2003). "Dynamic Data Grid Replication Strategy based on nd Internet Hierarchy", 2 International Workshop on Grid and Cooperative Computing (GCC'2003), pp. 838-846.

[9]. Ghalem Belalem and Bakhta Meroufel, (2006). "Management and Placement of Replicas in a Hierarchical Data Grid", International Journal of Distributed and Parallel Systems (IJDPS), Vol.2, No.6, pp. 23-30.

[10]. W. Allcock, I. Foster, V. Nefedova, A. Chervenak, E. Deelman, C. Kesselman, J. Lee, A. Sim, A. Shoshani, B. Drach, and D. Williams, (2001). "High-performance Remote Access to Climate Simulation Data: A Challenge Problem for Data Grid Technologies", In Proceedings of ACM/IEEE Conference on Superconductivity, pp. 20.

[11]. D.T. Nukarapu, B. Tang, L. Wang, and S. Lu, (2011). "Data Replication in Data Intensive Scientific Applications with Performance Guarantee", IEEE Transactions on Parallel and Distributed Systems, Vol. 22, No. 8, pp. 1299- 1306.

[12]. A. Chervenak, R. Schuler, M. Ripeanu, M.A. Amer, S. Bharathi, I Foster, and C. Kesselman, (2009). "The Globus Replica Location Service: Design and Experience", IEEE Transactions on Parallel and Distributed Systems, Vol. 20, NO. 9, pp. 1260–1272.

[13]. M. Lei, S. Vrbsky, (2006). “A Data Replication Strategy to Increase Availability in Data Grids”, Grid Computing and Applications, pp.221-227.

[14]. T.Huu, M-T.Segarra, J-M.Gilliot (2008). “Unsystème Adaptatif De Placement De Données”, In CFSE’6 : Conference Français sur les Systemes d’Exploitation, Fribourg, Suisse, du 11 au 13 février.

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Research Paper

Investigation of Validity Metrics for Modified K-MeansClustering Algorithm

S. Govinda Rao* , Dr. A. Govardhan**

* Associate Professor, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology, India.

** Director, School of Information Technology, JNTU Hyderabad, India.

Rao, S.G., and Govardhan, A. (2015). Investigation of Validity Metrics for Modified K-Means Clustering Algorithm. i-manager’s Journal on Computer Science, 3(2), 33-36. https://doi.org/10.26634/jcom.3.2.3548

Abstract

Clustering analysis is used to partition data set based on objects within a group and the clustering results are influenced by choice of distance measure and the clustering algorithm. Clustering analysis has been applied to group of author's hindex and g-index with similar or dissimilar features. Validity measure is calculated to determine which is the best clustering by finding the minimum value for our measure. In this paper, the authors have presented the effective validations possible with Davies-Bouldin index, Silhouette index and quantization error

References

[1]. Jain A. R., Murthy M. N. and Flynn P. J. (1999). “Data Clustering: A Review”, ACM Computing Surveys, Vol. 31(3), pp. 265-323.

[2]. Halkidi M., Batistakis Y., and Vazirgiannis M. (2001). “On Clustering Validation Techniques”, Journal of Intelligent Information Systems, Vol. 17(2-3), pp. 107–145.

[3]. Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, and Prabhakar Raghavan. (1998). “Automatic Subspace Clustering of High-dimensional Data for Data Mining Applications”, In ACM SIGMOD Conference on Management of Data.

[4]. Pablo A Jaskowiak et al. (2014). “On the Selection of Appropriate Distances for Gene Expression Data Clustering”, BMC Bioinformatics, Vol. 15(2).

[5]. www.divaportal.org/smash/get/diva2:348099/FULLTEXT01.pdf - accessed on February 2nd 2015.

[6]. Margaret H. Dunham (2002). “Data Mining”, Introductory and Advanced Topics. Prentice Hall.

[7]. Jiawei Han and Micheline Kamber (2001). “Data Mining: Concepts and Techniques”, Academic Press.

[8]. James C. Bezdak and Nikhil R. Pal. (1998). “Some New Indexes of Cluster Validity”, IEEE transactions on Systems, Vol.28(3) .

[9]. Milligan, G. W. and Cooper, M. C. (1985). “An Examination of Procedures for Determining the Number of Clusters in a Data Set”, Psychometrika, Vol. 50(2), pp.159–179.

[10]. Vendramin, L.; Campello, R. J. G. B.; and Hruschka, E. R. (2010). “Relative Clustering Validity Criteria: A Comparative Overview”. Statistical Analysis and Data Mining, Vol. 3(4), pp. 209–235.

[11]. SergiosTheodoridis and Konstantinos Koutroumbas. (1999). “Pattern Recognition”, Academic Press.

[12]. James C. Bezdak and Nikhil R. Pal (1998). “Some New Indexes of Cluster Validity”, IEEE Transactions on Systems, Vol. 28(3).

[13]. Davies, D. L.; Bouldin, D. W. A (1979). “Cluster Separation Measure”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 2, pp. 224-226.

[14]. Rousseeuw, P. J. (1987). “Silhouettes: A Graphic Aid to the Interpretation and Validation of Cluster Analysis”, Journal of Computational and Applied Mathematics, Vol. 20(1), pp. 53–65.

[15]. Rana S., Jasola Kumar R.,(2010). “A Hybrid Sequential Approach for Data Clustering Using K-means and Particle Swarm Optimization Algorithm”, International Journal of Engineering, Science and Technology, Vol. 2(6), pp. 167-176.

[16]. Jonathan Baarsch and M. EmreCelebi (2012). "Investigation of Internal Validity Measures for K-Means Clustering", In Proceedings of the International Multiconference of Engineers and Computer Scientists, Vol. 1.

[17]. S. Govinda Rao, Dr A Govardhan. (2014). “Assessing h- and g- indices of Scientific Papers using k-means Clustering”, International Journal of Computer Applications (0975-8887), Vol.100(11).

[18]. Kaijun Wang, Baijie Wang , and Liuqing Peng (2009). “cvap: Validation For Cluster Analyses”, Data Science Journal, Vol. 8(20).

i-manager's Journal on Computer Science (JCOM)

Current Issue Vol. 11 Issue 3

Most Read

Most Cited

Volume 3 Issue 2 June - August 2015

UNIFI: A Protocol for Association Rule Mining InVertically Distributed Databases

M. Suresh Babu* , K.F.Bharati**

* PG Scholar, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapur, India. ** Assistant Professor, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapur, India.

Babu, M.S., and Bharati, K.F. (2015). UNIFI: A Protocol for Association Rule Mining in Vertically Distributed Databases. i-manager’s Journal on Computer Science, 3(2), 1-9. https://doi.org/10.26634/jcom.3.2.3544

Abstract

References

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Dynamic Coalition Pattern for Distributed CoordinationOf Multi-Agent Networks

K. Ankitha Priyadarshini* , lalitha.B**

* PG Scholar, Department of Computer Science and Engineering, JNTUA College of Engineering, Andhra Pradesh, India. ** Assistant Professor and Research Scholar, Department of Computer Science and Engineering, JNTUA College of Engineering, Andhra

Priyadarshini, K.A., and Lalitha, B. (2015). Dynamic Coalition Pattern for Distributed Coordination of Multi-Agent Networks. i-manager’s Journal on Computer Science, 3(2), 10-15. https://doi.org/10.26634/jcom.3.2.3545

Abstract

References

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Android App for ATM Location and Service Tracking

B. Soundhariya Lakshmi* , M.Ramya**, D. Jagadeesan***

*-** Graduate, Department of Computer Science and Engineering, Adhiparasakthi College of Engineering, Kalavai, Vellore District, TamilNadu, India. *** Professor, Department of Computer Science and Engineering, Sreenivasa Institute of Technology and Management Studies, Chittoor, India.

Lakshmi, B.S., Ramya, K., and Jagadeesan, K. (2015). Android App For ATM Location And Service Tracking. i-manager’s Journal on Computer Science, 3(2), 16-20. https://doi.org/10.26634/jcom.3.2.3546

Abstract

References

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A Hybrid Approach for Replica Placement-Replacement (Harp-R Algo) Algorithm In Data-Grid

Ashish Kumar Singh* , Udai Shanker**

* Lecturer, Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India. ** Professor, Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

Singh, A.K., and Shanker, U. (2015). A Hybrid Approach for Replica Placement-Replacement (Harp-R Algo) Algorithm In Data-Grid. i-manager’s Journal on Computer Science, 3(2), 21-32. https://doi.org/10.26634/jcom.3.2.3547

Abstract

References

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Investigation of Validity Metrics for Modified K-MeansClustering Algorithm

S. Govinda Rao* , Dr. A. Govardhan**

* Associate Professor, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology, India. ** Director, School of Information Technology, JNTU Hyderabad, India.

Rao, S.G., and Govardhan, A. (2015). Investigation of Validity Metrics for Modified K-Means Clustering Algorithm. i-manager’s Journal on Computer Science, 3(2), 33-36. https://doi.org/10.26634/jcom.3.2.3548

Abstract

References

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* PG Scholar, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapur, India.

** Assistant Professor, Department of Computer Science and Engineering, JNTUA College of Engineering, Anantapur, India.

* PG Scholar, Department of Computer Science and Engineering, JNTUA College of Engineering, Andhra Pradesh, India.

** Assistant Professor and Research Scholar, Department of Computer Science and Engineering, JNTUA College of Engineering, Andhra

B. Soundhariya Lakshmi* , M.Ramya, D. Jagadeesan*

- Graduate, Department of Computer Science and Engineering, Adhiparasakthi College of Engineering, Kalavai, Vellore District, TamilNadu, India.

Professor, Department of Computer Science and Engineering, Sreenivasa Institute of Technology and Management Studies, Chittoor, India.

* Lecturer, Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

** Professor, Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.

* Associate Professor, Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology, India.

** Director, School of Information Technology, JNTU Hyderabad, India.