Volume-07 , Special Issue-04 , Feb - 2019 Go Back

Top-k denotes to the method which only returns the top K most important objects according to a given ranking function. To tackle the limitations of the existing Top-k query, we proposed a modified Top-k query algorithm. In this algorithm, we select the data elements which have higher ranking scores on each attribute, and then run a threshold controlling scheme on these data elements.This system reduces the manual and paper work. This proposed system will help the user to know the exact information and details of the facility that they are finding. This system is very much useful for all users. In the proposed system, the user can see the information’s of the facilities that are available in the various areas. The user can also see the top ten facilities that are available. And it is used to book travels ticket. And it’s contain the search button and corresponding textbox to search the particular information when the user click the search button it will be redirect to the related pages. The admin can add the additional information about the indexes of Kumbakonam. This system provides the addresses and information of the various facilities.

Massive data retrieval, Top-k query, I/O debugging, Accuracy

[1] Vega J., Murari A., Pereira A., Portas A., Ratta G. A., Castro R., Overview of intelligent data retrieval methods for waveforms and images in massive fusion databases, Fusion Engineering and Design, 2009, 84(7-11): 1916-1919.
[2] Vega J., Pereira, A., Portas A., et al., Data mining technique for fast retrieval of similar waveforms in Fusion massive databases, Fusion Engineering and Design, 2008, 83(1): 132-139.
[3] Czyzewski A., Bratoszewski P., Ciarkowski A., et al., Massive surveillance data processing with supercomputing cluster, Information Sciences, 2015, 296: 322-344.
[4] Neff Lucas P., Cannon Jeremy W., Morrison Jonathan J., Edwards Mary J., Spinella Philip C., Borgman Matthew A., Clearly defining pediatric massive transfusion: Cutting through the fog and friction with combat data, Journal of Trauma and Acute Care Surgery, 2015, 78(1): 22-28.
[5] Khan Mukhtaj, Ashton Phillip M., Li Maozhen, Taylor, Gareth A., PisicaIoana, Liu Junyong, Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data, IEEE Transactions on Smart Grid, 2015, 6(1): 360-368.
[6] Wang Debby D., Zhou Weiqiang, Yan Hong, Mining of proteinprotein interfacial residues from massive protein sequential and spatial data, Fuzzy Sets and Systems, 2015, 258: 101-116.
[7] Fang Cheng, Liu Jun, Lei Zhenming, Parallelized User Clicks Recognition from Massive HTTP Data Based on Dependency Graph Model, China Communications, 2014, 11(12): 13-25.
[8] Yu Ce, Wang Runtao, Xiao Jian, Sun Jizhou, High Performance Indexing for Massive Audio Fingerprint Data, IEEE Transactions on Consumer Electronics, 2014, 6(4): 690-695.
[9] Gog Simon, Petri Matthias, Optimized succinct data structures for massive data, Software-practice & Experience, 2014, 44(11): 1287-1314.
[10] Liu Dexi, Novel Semantics of the Top-k Queries on Uncertainly Fused Multi-Sensory, Journal of Information Science and Engineering, 2015, 31(1): 179-205.
[11] Guo Long, Shao Jie, AungHtooHtet, Tan Kian-Lee, Efficient continuous top-k spatial keyword queries on road networks, GEOINFORMATICA, 2015, 19(1): 29-60.
[12] DimitriouAggeliki, Theodoratos Dimitri, Sellis, Timos, Top-k-size keyword search on tree structured data, Information Systems, 2015, 47: 178-193.
[13] Rahul Saladi, Janardan Ravi, A General Technique for Top-k Geometric Intersection Query Problems, IEEE Transactions on Knowledge and Data Engineering, 2014, 26(12): 2859-2871.
[14] Zou Lei Chen Lei, Pareto-based dominant graph: An efficient indexing structure to answer top-K queries, IEEE Transactions on Knowledge and Data Engineering, 2011, 23(5): 727-741.
[15] DeNavas-Walt Carmen, Proctor Bernadette D, Smith Jessica C, US Census Bureau, current population reports, Income, poverty, and health insurance coverage in the United States, 2008, 60-236.

M. Nagalakshmi, M. Vaishnavi, "Index of Kumbakonam Using Top-K Query Retrieval Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.157-159, 2019.

Cloud Computing is a huge storage domain by which everyone can access the functions and utilities through the Internet, irrespective of the locations without any difficulties in maintenance and management. Security is a vital aspect in any business to sustain in the digitalized world forever. Cloud computing also enhances in the transfer of data without any deviations. The major security issues like breaches, inadequate access management, specter and meltdown, loss and DOS attacks of all business oriented data.

Cloud Computing, Security, Types, Business Terms

[1] Manoj Chopra, Jai Mungi, Kulbhushan Chopra, “A Survey on Use of Cloud Computing in various Fields”, International Journal of Science, Engineering and Technology Research, Volume 2, Issue 2, February 2013.
[2]National Seminar on “Cloud Computing and its Applications”, Organised by School Engineering Sciences and Technology (SEST), JamiaHamdrad, New Delhi, IJARCS, Special Issue Volume 8 Issue 2, March 2017.
[3]G. Kiryakova, N. Angelova, L. Yordanova, “Application of Cloud Computing Services In Business”, Trakia Journal of Sciences, Volume 13, Suppl. 1, pp 392 – 396, 2015.
[4] Briefly discuss the limitations of the research and Future Scope for improvement.

A. Fairosebanu, A. NishaJebaseeli, "Data Transmission Issues in Business while using CC – A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.160-161, 2019.

The field of data mining has evolved from its roots in databases, statistics, artificial intelligence, information theory and algorithms into a core set of techniques that have been applied to a range of problems. Computational simulation and data acquisition in scientific and engineering domains have made tremendous progress over the past two decades. A mix of advanced algorithms, exponentially increasing computing power and accurate sensing and measurement devices have resulted in more data repositories. Advanced technologies in networks have enabled the communication of large volumes of data across the world.This paper aims at further data mining study on scientific data. This paper highlights the data mining techniques applied to mine for surface changes over time (e.g. Earthquake rupture). The data mining techniques help researchers to predict the changes in the intensity of volcanos. This paper uses predictive statistical models that can be applied to areas such as seismic activity , the spreading of fire. The basic problem in this class of systems is unobservable dynamics with respect to earthquakes. The space-time patterns associated with time, location and magnitude of the sudden events from the force threshold are observable. This paper highlights the observable space time earthquake patterns from unobservable dynamics using data mining techniques, pattern recognition and ensemble forecasting. Thus this paper gives insight on how data mining can be applied in finding the consequences of earthquakes and hence alerting thepublic.

Earthquake, data mining techniques, space-time patterns

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A. Mary Subashini, "A Survey on Earth Quakes Prediction Techniques with Clustering Methods", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.162-166, 2019.

Network Security is assuming an imperative job in a wide range of systems. These days the system is actualized in all spots like workplaces, schools, banks and so on and every one of the people are participating in informal community media. Despite the fact that numerous sorts of system security frameworks are being used, the helpless exercises are occurring occasionally. This research exhibits a study about different sorts of system attacks for the most part web attack, and distinctive Intrusion Detection System which are being used. This may clear a way to outline another sort of Intrusion Detection System which may shield the system framework from different kinds of system attack. Essentially Intrusion Detection Systems is been utilized based on two major methodologies first the acknowledgment of odd exercises as it by and large happens on the abandons normal or abnormal conduct and second abuse location by watching unapproved "marks" of those perceived vindictive attacks and grouping vulnerabilities. Oddity or the unknown (conduct based) Intrusion Detection Systems assume the distinction of ordinary conduct underneath assaults and accomplish anomalous exercises assessed and perceived with predefined framework or client conduct reference show. Our novel approach will mainly focus on detect the intrusion in network communication and provide a security.

Network security, Intrusion detection system, Classification

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M. Jeyakarthic, A. Thirumalairaj, "A Brief Survey on Intrusion Detection System in Network Communication", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.167-172, 2019.

Cloud computing, also referred to as ‘on demand computing’, is a recent concept making waves in the IT industry. When compared to other specific, dedicated infrastructures in computing, cloud concept gives the advantage of reliability, quantifiability, cost- effectiveness and improved performance. It is easy and convenient to have network access to other shared configurable computing resources using this concept. Another advantage of the concept is that the resources can be utilized with increasing efficiency and minimum overhead efforts. Clouds can be used for services, solutions, and applications and also for storing large amount of data in different locations. Data is stored over a set of resources which are networked so that the data can be accessed through any other virtual system. The security and privacy issues associated with data management is reduced considerably using the cloud computing facility as the data centers are literally beyond the reach and control of users. Further, server breakdowns which normally affect data storage and use does not seem to affect the user in cloud computing. But this system has its own set of disadvantages. This work is an attempt discuss in detail cloud computing, its types and Network/security issues related to it. Networks structure faces some attacks that are denial off service attack, man in the middle attack, network sniffing, port scanning, SQL injection attack, cross site scripting. Security Issues that occur in Cloud Computing are XML signature element wrapping, Browser security, cloud malware injection attack, flooding attacks, data protection, insecure or incomplete data deletion, locks in.

Cloud Computing, Data Integrity, Data storage, Security, Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Interoperability, Denial of Service (DoS).

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P. Anusha, R. Maruthi, "A Survey on Security Threats in Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.173-176, 2019.

We are entering in a new era of computing technology i.e Internet of Things(IoT). IoT is a network in which all physical objects are connected to the internet through network devices or routers and exchange data. IoT allows objects to be controlled remotely across existing network infrastructure. This technique also has autonomous control feature by which any device can control without any human interaction. The enormous amount of data produced by IoT devices can be converted into knowledge using data mining techniques. Data mining will play important role in constructing smart system that provides convenient services. It is required to extract data and knowledge from the connected things. For this purpose, various data mining techniques are used. Various algorithms such as classification, clustering, association rule etc. helps to mine data. This paper represents the different Data mining techniques with IOT.

Internet of Things, Data mining

[1] H. Jiawei, M. Kamber, Data mining: concepts and techniques, Morgan Kaufmann Publishers, 2011.
[2] Feng Chen, Pan Deng, Jiafu Wan, Daqiang Zhang, Athanasios V. Vasilakos, and XiaohuiRong, “Data Mining for the Internet of Things: Literature Review and Challenges”, Hindawi Publishing Corporation, International Journal of Distributed Sensor Networks, 2015.
[3] Shweta Bhatia, Sweety Patel, “Analysis on different Data mining Techniques and algorithms used in IOT”, International Journal of Engineering Research and Applications, November 2015.
[4] Chun-Wei Tsai, Chin-Feng Lai, Ming-Chao Chiang, And Laurence T. Yang, “Data Mining For Internet Of Things: A Survey”, Ieee Communications Surveys & Tutorials, Vol. 16, 2014.
[5] AsmitaGorave, VrushaliKulkarni, “Discrimination Aware Data Mining In Internet Of Things”, Ijca, 0975 – 8887, Volume 159, February 2017.
[6] KrushikaTapedia, AnuragManoharWagh, “Data Mining For Various Internets Of Things Applications”, Ijrat, E-Issn: 2321-9637, Ncpci-2016.
[7] S. Haller, S. Karnouskos, and C. Schroth, “The Internet of Things in an enterprise context,” Future Internet Systems (FIS), LCNS, vol. 5468. Springer, 2008, pp. 14-8.

G. Saranya, M. Subashini, "A Review of Datamining Techniques in Internet of Things", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.177-180, 2019.

This technique is an ontology-based approach and a formal concept analysis (FCA) approach to integrating heterogeneous tourism information for online tour planning. Two ontologies, ontology for tourists and ontology for tourism information providers, are developed with respect to their own perspective. The ontology for tourists is developed from the tourism literature. The ontology for tourism information providers is developed by integrating heterogeneous online tourism information using a FCA approach. This HSOD Hybrid Semantic Ontology Data (HSOD) Analysis is then mapped using the FCA and Bayesian analysis to evaluate tourists` preferences against information published by tourism information providers. An example of planning a tour to Thanjvaur, Trichy is used to illustrate the proposed ontology approach. An analytic hierarchy process is used to rank the tourism attractions suggested by the ontology and FCA based approaches.

Ontology design, tourism, recommender system, formal concept analysis

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S. Subhalakshmi, C. Premila Rosy, "Hybrid Semantic Ontology Data (HSOD) Analysis Algorithm for Heterogeneous Tourism Information", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.181-183, 2019.

Internet of Things is a new pattern which provides a set of new services for the next form of technological development. IOT in the sense, it is a “universal global neural network” in the cloud which connects various things. The form of communication that is either human-device, or human-human but the Internet of Things (IoT) promises a great future for the internet where the type of communication is machine-machine (M2M). This paper aims to provide a future vision , IoT Architecture , Applications and its Challenges.

Vision, Challenges, Applications, Architecture

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[3] Bob Violino, ”Top IT Vendors reveal their IOT strategies”. It can be accessed at: http://www.networkworld.com/article/2604766/internet-ofthings/top-it-vendors-reveal-their-iot-strategies.html
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N.S. Sanguida, M. Sasikala, "A Study on Internet of Things(IoT)", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.184-186, 2019.

User’s access records are captured by implementing a data mining algorithm on the website. User mostly browses those products in which he is interested. This system will capture user’s browsing pattern using data mining algorithm. This system is a web application where user can view various resources on the website. User will register their profile in an exchange of a password. User will get user ID and password in order to access the system. Once the user login’s to the system user will gain access to certain resources on the website. The links to the resources on the website have been modified such that a record of information about the access would be recorded in the database when clicked. This way, data mining can be performed on a relatively clean set of access records about the users. When user clicks on certain resources on the website his access records will be captured by the system this can be achieved with the help of data mining algorithm used in this system. By using this application, product based organization will get to know the demand for certain products. This system will help organization to target right consumers. This system will help product based firms to maintain good customer relationship. Hence, a good deal of business intelligence about the users’ behavior’s, preferences and about the popularities of the resources (products) on the website can be gained.

User Access Record, Good Customer Relationship, Web Mining, User Behavior, Web Logs

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D. Kamalavadhanai, N. Aarthi, "User Web Access Record Mining for Business Intelligence", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.187-189, 2019.

WSN, which aims to exchange data wirelessly in a short distance using short-wavelength radio transmissions, is providing a necessary technology to create convenience, intelligence and controllability. Using RIFD technique to sense and monitoring the environment. Integrating RIFD with WSN not only provides identity and location of an object but also provides the information regarding the condition of the object carrying the sensors enabled RIFD tag. This paper discuss about the RIFD technology, which are used by the WSN through Zigbee device such as in Agriculture, Home Appliances and Health Care Monitoring. These applications are used for managing the user’s time and cost and also reduced human resources and improve productivity.

WSN, RIFD Applications, Zigbee, Arduino

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J.P. Keerthana, R. Chithirai Selvi, "A Study of WSN Based Application Using RFID", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.190-192, 2019.