Knn Approach For Bigdata Classification Under Map Reduce Environment

The big data analytics deals with a large amount of data to work with and also the processing techniques to handle and manage large number of records with many attributes. The combination of big data and computing power with statistical analysis allows the designers to explore new behavioral data throughout the day at various websites. It represents a database that can’t be processed and managed by current data mining techniques due to large size and complexity of data. Big data analytic includes the representation of data in a suitable form and make use of data mining to extract useful information from these large dataset or stream of data. As stated above the big data analytics has recently emerged as a very popular research and practical-oriented framework that implements i) data mining, ii) predictive analysis forecasting, iii) text mining, iv) virtualization, v) optimization, vi) data security, vii) virtualization tools for processing very large data sets. In the implementation of big data applications, new data mining techniques and virtualization are required to be implemented due to the volume, variability, forms and velocity of the data to be processed. A set of machine learning techniques based on statistical analysis and neural networking technology for big data is still evolving but it shows a great potential for solving a big data business problems. Further, a new concept of in-memory database for enhancing the speed for analytic processing is further helping

The author points out that although there are existing algorithms and tools available to handle Big Data, they are not sufficient as the volume of data is exponentially increasing every day. To show the usefulness of Big Data mining, the author highlighted the work done by United Nations. In order to further enhance the reader’s perspective, the author provided research work of various professionals to educate its readers about the most recent updates in Big Data mining field. The author further describes the controversies surrounding Big Data. The author has first provided the context and exigence by elaborating on why we need new algorithm and tools to explore the Big Data. The author used the strategy of highlighting the logos by mentioning the research work of different industry professionals, workshops conducted on Big Data and was able to appeal to connect to the reader’s ethos. The author also used pathos by urging the budding Big Data researchers to further dig deep into the topic and explore this area

Big data is the present most-liked theme of today 's technology. These research goes through all description of techniques and technologies of extracting of the data, storing of data, distribution of data, analyzing of data, managing of data with high velocity and from the structured data and helps in the handling of the extreme data. Big data has the presentation the capacity to improve predictions, saving money and enhancing the decision making process in the fields of the traffic control, weather forecasting, disaster prevention, fraud control, business transaction, education system, health and the national security.

In order for business to harness big data, we must first look at how big data is created and stored. Computers throughout the world obtain data through their hardware and software. The end results of this collection of data as of 2014 is 11.2 zettabytes. Only one half percent of the 11.2 zettabytes of data is structured and utilized today. This means that most data is not valuable because it is not sorted. A business cannot utilize big data unless it is structured in a way to help a business reach a goal. A way for the data to become useless is through data mining. Data mining is the practice of examining large databases in order to generate new information. This new information is practical and structured.

Therefore, the consecutive sections discussed the definition of big data, tools for analyzing big data, data mining, knowledge discovery, visualization and collaborative

Today, the data consumption rate is tremendously expanding, the amount of data generated and stored is nearly imperceivable and highly growing. Big data that is nothing but a large volume of unstructured or structured data that runs in and out in to a business on daily basis. This big data is analyzed in order to achieve prominent business growth and improved business strategies [1]. Every year there is at least 40% increase in the amount of data growth on global level, leading to which companies have started adopting new data analytic techniques and tools and also have stepped ahead moving their data towards the cloud for their big data analytic requirements and for better analysis.[3][2] In big data analysis it is not the amount of data that is essential but how efficiently we handle, process and analyze it is the key factor. Big data analysis doesn’t revolve around how much data we occupy, it deals with how well you make use

The world is changing with respect to the growth in big data and to the way in which it is used. Growth in big data brings with it many challenges, but it also presents new opportunities. Figure 1, helps understand some of the big data related activities that are taking place in the world with respect to volume of data that is being consumed by these activities over the next 5 years.

Big data analytics methods and techniques (which is the application of predictive methods, pattern recognition techniques, cluster analysis, and other quantitative and qualitative methods in big data sets) can

Management of Big Data is useful with how one handles the information. Ways to use the stored information include, but not limited to, reduction of costs, time reductions, and making smart decisions based on data results. [1]

Today data is being flooded in all means as it is being collected in unprecedented ways. Decisions which were taken by way of guesswork and difficult models can now be made on the base of data itself. Big data analysis can be dream on every aspect of today’s society - Mobile services, manufacturing, retail, life sciences, financial services and physical sciences. Big Data has the potential to revolutionize scientific research, education, use of Information

This MapReduce basically divides the large tasks into smaller chunks typically (64 MB size) which will be distributed across a grid infrastructure of servers interconnected by secured communication network and runs the sub-jobs in different nodes, monitors their progress and handles the node failures with high fault tolerance and combines on accordance with user actions and reduces to a structured data set. Here, the interesting thing is the whole data processing is carried out with the metadata but not the actual information. So, this could save a lot of processing time and will increase the throughput. This new frameworks encouraged the IT firms to concentrate on the users behavioral study which is really helpful in making the predictions over the success probability of commercial products and their demand. Even this type of frameworks are welcomed into federal usage which is surprising as the large sets of historical or geological data can be carefully analyzed. Another important feature that has to be discussed about the MapReduce is the efficient use of the available resources, the Map and Reduce functions along with parallelizing the computations always runs keeps an eye on the resource and their and utilization thus making a good use of

The emerging data from the everywhere in the world make the birth of big data era. There exist potential values in those data, while the big volume, variety and velocity [2] of the data make it nearly impossible for humans to analyze resources manually so as to find the hidden treasures. Under this circumstance, the concept and technique

Abstract—Big data is a widespread term used to define the exponential progress and obtainability of data, both structured and unstructured. Big data may be as important to corporate society, more data may prime to more precise analyses. More truthful analyses may prime to, more assertive judgment creation and well judgments can mean greater functioning productivities, reduced cost and risk. In this paper we discuss about big data analysis using soft computing technique with the help of clustering approach and Differential Evolution algorithm.

The aim of this paper is to explore different aspects of the MapReduce framework. The primary focus will be given on how MapReduce framework follows the principles and techniques of distributed and parallel programming in the context of concurrent, parallel and distributed computing. In the following sections of the report, there will be a brief introduction of the MapReduce platform and how it is related to distributed and parallel computing. Following that, the discussion will be on the phases and job life cycle of MapReduce-based programming, the functionalities of the different components of a MapReduce job, implementation of MapReduce and the challenges in the implementations. Hence, the paper covers different aspects of the methodology, implementations, issues and examples of implementation of the MapReduce framework.

In the growing scenario, development of smart cities will be the most wanted area of research whose objective is to enhance the performance and well beings of people there by reducing the cost and consumption of resources. In a smart city, core fields like transport energy, health care, water industrial control, agriculture, waste management and soon are expected to function automatically and intelligently in a distributed manner with the help of internet. In the era of Information technology, concept like Internet of things, Grid, Cloud and big data computing and analysis plays a vital role in building smart cities. In this paper, different fields of smart cities and