2014 IEEE JAVA DATA MINING PROJECT Data mining with big data

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Course in Big Data Analytics in association with IBM Everyday huge amount of data is created. This data comes from everywhere : sensors used to gather climate information, post to social media sites, digital pictures and videos, purchase transaction records and Cell phone GPS signals to name a few. This data is Big Data. Big data is a blanket term for any collection of data set so large and complex that it becomes difficult to process using on hand data management tools or traditional data processing applications. The challenges include capture, storage, search, sharing, transfer, analysis and visualization. Anyone who has knowledge on Java, basic UNIX and basic SQL can opt for Big Data training course.

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In any larger engineering setting, there is a huge number of documents that engineers and others need to use and be aware of in their daily work. To improve the handling of this amount of documents, we propose to view it under the angle of a new domain for professional search, thus incorporating search engine knowledge into the process. We examine the use of Information Retrieval (IR), Recommender Systems (RecSys), and Knowledge Management (KM) methods in the engineering domain of Knowledge-based Engineering (KBE). The KBE goal is to capture and reuse knowledge in product and process engineering with a systematic method. Based on previous work in professional search and enterprise search, we explore a combination of methods and aim to identify key issues in their application to KBE. We list detected challenges, discuss information needs and search tasks, then focus on issues to solve for a successful integration of the IR and KBE domain and give a system overview of our approach to build a search and recommendation tool to improve the daily information- seeking workflow of engineers in knowledge-intense disciplines. Our work contributes to bridging the gap between Information Retrieval and engineering support systems. Presentation of a paper at ICDIM. Full paper available from my homepage.

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A Seminar Presentation on Big Data for Students. Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.

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The International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.

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Advanced Computational Intelligence: An International Journal (ACII) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of computational intelligence. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced computational intelligence concepts and establishing new collaborations in these areas.

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The concern about national security has increased significantly since the 26/11 attacks at Mumbai, India. However, information and technology overload hinders the effective analysis of criminal and terrorist activities. Data mining applied in the context of law enforcement and intelligence analysis holds the promise of alleviating such problem. In this paper we use a clustering/classify based model to anticipate crime trends. The data mining techniques are used to analyze the city crime data from Tamil Nadu Police Department. The results of this data mining could potentially be used to lessen and even prevent crime for the forth coming years

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There are as many views and definitions of Data Mining as there are people working in and on the topic. Confusion reigns and people ask; what is it; why do we need it; and isn’t it just Data Mining rebranded? In this slide deck and presentation we set the scene an highlight the differences and need for Data Mining in order to give a framework for case studies and future projects. So - why do we need it? The economic, industrial, commercial, social, political and sustainability problems we face cannot be successfully addressed using the management techniques and models largely inherited from the Industrial Revolution. The world no longer appears infinite in resources, slow paced, linear and stable. We now see the limitations; feel the impact of rapid change; and we can conceptualize the non-linear and unstable nature of it all! We are also starting to comprehend the scale and the need for machine assistance. Modeling our situation ! Sophisticated computer models for weather systems are now complemented by ecological, economic, conflict and resource modeling of varying depth and accuracy. However, the key is always the accuracy and coverage of the primary data. We started with modest databases and data mining, but they mostly proved inadequate, and we are now amassing vast databases on every aspect of life - people, planet and machines. This ‘BIG DATA’ explosion demands a rethink of how, what, and where we gather data; the way we analyze and model; and the way we make decisions. So - what is the big difference? Data Mining was limited, planer, simple, linear and constrained to a few relationships amongst people: what they did, where they went, who they knew and so on. In contrast; Big Data is unbounded, spans all peoples and machines in all domains and activities with application to every aspect of life, business, industry, government and sustainability etc. It also takes into account the non-linear nature of relationships and events. “Big Data is an almost unconscious outcome of the desire and need to sustain all peoples on a rapidly smaller looking planet”

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Según Hal Varian (experto en microeconomía y economía de la información y, desde el año 2002, Chief Economist de Google) “En los próximos años, el trabajo más atractivo será el de los estadísticos: La capacidad de recoger datos, comprenderlos, procesarlos, extraer su valor, visualizarlos, comunicarlos serán todas habilidades importantes en las próximas décadas. Ahora disponemos de datos gratuitos y omnipresentes. Lo que aún falta es la capacidad de comprender estos datos“.

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At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also. We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas. MD MANIKANDAN-9037291113,04954021113 softroniics@gmail.com

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The software development process is complete for computer project analysis, and it is important to the evaluation of the random project. These practice guidelines are for those who manage big-data and big-data analytics projects or are responsible for the use of data analytics solutions. They are also intended for business leaders and program leaders that are responsible for developing agency capability in the area of big data and big data analytics . For those agencies currently not using big data or big data analytics, this document may assist strategic planners, business teams and data analysts to consider the value of big data to the current and future programs. This document is also of relevance to those in industry, research and academia who can work as partners with government on big data analytics projects. Technical APS personnel who manage big data and/or do big data analytics are invited to join the Data Analytics Centre of Excellence Community of Practice to share information of technical aspects of big data and big data analytics, including achieving best practice with modeling and related requirements. To join the community, send an email to the Data Analytics Centre of Excellence

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Abstract: Knowledge has played a significant role on human activities since his development. Data mining is the process of knowledge discovery where knowledge is gained by analyzing the data store in very large repositories, which are analyzed from various perspectives and the result is summarized it into useful information. Due to the importance of extracting knowledge/information from the large data repositories, data mining has become a very important and guaranteed branch of engineering affecting human life in various spheres directly or indirectly. The purpose of this paper is to survey many of the future trends in the field of data mining, with a focus on those which are thought to have the most promise and applicability to future data mining applications. Keywords: Current and Future of Data Mining, Data Mining, Data Mining Trends, Data mining Applications.

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1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com Data mining with big data Abstract Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution. Existing system Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. Proposed system This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.

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2014 IEEE JAVA DATA MINING PROJECT Data mining with big data

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