Statistical Analysis : The Big Data Analytics

The research proposal can be developed from the topic of big data to match up the demands of enormous data flow in the dynamic world. Data visualization tools at significant cost can help up in analytics of big data and can form an innovative research proposal for analysis and extensive research. The real time data and the use of techniques of big data in this domain can form an excellent topic of research calling for formulating methodologies and strategies to tap the untapped potential of this field and to experiment more in the field of research.

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 analytics is the process of checking and handling large data sets which normally contains variety of data types. Through handling these data it helps to know the patterns and even the correlations which are not known. Handling of the large data sets also helps in knowing the market trends and even in knowing what the customers prefer. The information which these data analytics gives are important information for business. Most of the surveys which have been done have shown that big data analytics have revolutionized supply chain. Most companies and

In this current era, many organizations are using the technology to store and analyze petabytes of data that are related to their company, business and their customers for future requirement. Because of this classification of data becomes even more important. Techniques such as encryption, logging, and security measures are required for securing this big data. Usage of the Big data for fraud detection looks very interesting and profit making for many organizations. Big data style analyzing of data can solve the problems like advanced threats, cyber security related issues and even malicious intruders. With the use of more sophisticated pattern analysis and with the use of multiple data sources it is easy to detect the threats in early stages of the project itself. Many organizations are fighting with the remaining issues like private issues with the usage of big data. Data privacy is a liability; thus companies must be on privacy defensive. When compared to security, Privacy should consider as profit making asset because it results in the selling of unique product to customers which results in making money. We need to maintain balance between data privacy and national security. Visualization, controlling and inspection of the network links and ports are required to ensure security. Thus there is a need to put ones in insight the loop

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.

Big data analytics can help enterprises to better explore and understand the information contained within the data and will also help to recognize the data which becomes critical for future business decisions. Big data analysts basically depends on the knowledge of the analyzed data

Data analytics and analysis are often used in conjunction with one another, and can be applied in variety of situations, enterprises, and domains. Data analytics and analysis often fall under the umbrella of data science, which is the discipline associated with structured and unstructured data [1]. However, there are altering views of what each term represents, as well as how they are interrelated. One source describes analytics as a subset of analysis, with analysis being the larger entity [2]. They describe analysis as the sum of human activities driven to gain insight into the given dataset, with or without exceptional data processing techniques applied [2]. The exceptional data processing techniques falls under the analytics portion of analysis, which encompass many advanced statistical tools and machine learning algorithms.

On the one hand, Big Data bring many attractive opportunities. On the other hand, we are also facing a lot of challenges [4] when handle Big Data problems. If we cannot overcome those challenges, Big Data will become a gold ore but we do not have the capabilities to explore it.One challenge is existing in computer architecture for several decades, that is, CPU-heavy but I/O-poor [2]. This system imbalance still restraint the development of the discovery from Big Data. The following challenges have to be surmounted by Big Data in order to perform effectively :

arouse mainly because data is asset to Organization , analyzing data is inexpensive and data

Due to the rapid growth in the use of Internet and its connected tools, an enormous amount of data are being produced on a daily basis. The concept of big data arrives when we were unable to manage this huge data with traditional methods. Big data is a mechanism of capturing, storing and analyzing the big datasets and also an idea of extracting some value from it. It is very handful while determining the root causes of failures, issues and defects in near-real time, creating coupons and other sales offers according to the customers shopping patterns, detecting any suspicious and fraudulent activities in real-time. As it is very advantageous, it also has some issues. Some of the common issues can be characterized into heterogeneity, complexity, timeless, scalability and privacy. The most important and significant challenge in the big data is to preserve privacy information of the customers, employees, and the organizations. It is very sensitive and includes conceptual, technical as well as legal significance.

Abstract— The Data which is structured and unstructured and is so large with massive volume that it is not possible by traditional database system to process this data is termed as Big Data. The governance, organization and administration of the big data is known as Big Data Management. For reporting and analysis purposes we use data warehouse techniques to process data. These are the central repositories from disparate data sources. Now Big Data Management also requires the data warehousing techniques for future predictions and reporting. So in this paper we touched certain issues of data warehousing usage in Big Data management, its applications as well as limitations also and tried to give the ways data warehousing is useful in Big Data Management.

As Big Data problems evolve, each application have its own characteristics with respect to their data and analysis process. Firstly, besides the huge amount of historical data, streaming data plays an important role. For instance, GPS ground stations do monitor and predict geological events on earthquakes generates lots of real time data which needs streaming data processing. Automatic trading systems in stock market needs dynamic

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

Abstract— Big Data is a new term used to describe a massive volume of both structured and unstructured data that is so large that it is difficult to process using traditional database and software techniques. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variability, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. We present in this issue, a broad overview of the topic, its current status, and forecast to the future. We also introduce some articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining.

Data is a powerful weapon as well as a resource. Having data does not make you powerful but what you do with it makes all the difference. Companies like Amazon, eBay and Netflix are already using data to predict user behavior and utilizing that to increase their revenue. But processing data in real time is not an easy task. The data today has great volume, is veracious in nature and is increasing at an enormous rate and hence has been given the term Big Data. There is a constant research going on to find a solution to process such huge amount of data in real time.