Article On Data Mining With Big Data

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

With the wide spread internet and improvement in technology the big data field have expanded at various fields like banking, finance, social network etc. This paper reviews how the data is being exposed on the internet and gives scope for the infringement of privacy; also I will review a variety of electronic tools/methods that helps in protecting users’ privacy as well reflect upon how much less people know about these infringements but how much more is happening. Also, I will review some of the rules that exist to protect the privacy of users online.

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

This article can be regarded as current since it was published in 2013. What is more, the authors of this text both work for the department of business in the universities. They may have specific expertise or knowledge in the field of big data as it is an essential factor in business. Furthermore, Business Intelligence Journal contains a professional data warehouse for business. As a result, this article is also authoritative and reliable. Besides, as a journal article, not only does it follow the usual academic conventions like in-text citations and references, but also its language is impersonal and formal, which seems to be objective. Big data has become a useful tool to help companies make decisions and turn to customer-centred

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.

Big data is certainly one of the biggest buzz phrases in it today. The term ’Big Data’ appeared for first time in 1998 in a Silicon Graphics (SGI) slide deck by John Mashey with the title of ”Big Data and the Next Wave of InfraStress” [9]. -Combined with virtualization and cloud computing, big data is a technological capability that will force data centers to significantly transform and evolve within the next five years. Similar to virtualization, big data infrastructure is unique and can create an architectural upheaval in the way systems, storage, and software infrastructure are connected and managed. Big data is an amalgam of large and varieties of data sets including structured data, semi structured data and unstructured data so it’s beyond the capability of traditional tools to capture, store, process and analysis of big data. It is true that big data have capability of unlocking new sources of development in many fields but at the same time researchers are being confronted challenges with big data. This paper reveals the various challenges faced with big data and opportunities realized with big data. Keywords: Big data, Challenges, Opportunities, Security Issues.

Understanding and applying the use of big data and data analysis creates advantages for businesses. As such, this paper discusses big data and analysis regarding differences between big data and small data, characterization according to 3 Vs, big data applications, data analysis, types of data structures, obtaining sources, tools and processes used for analysis, and advantages of data analysis.

The purpose of this paper is to give an insight of Big Data, its background and future opportunities.

Provide an approach for research efforts towards developing highly scalable and autonomic data management systems associated with programming models for processing Big Data. Aspects of such systems should address challenges related to data analysis algorithms, real-time processing and visualisation, context awareness, data management and performance and scalability, correlation and causality and to some extent, distributed storage [1]. Provide an approach for framework for evaluating big data initiatives [2]. Provide an approach for summarize opportunities and challenges with big data. Recent technological advances and novel applications, such as sensors, cyber-physical systems, smart mobile devices ,cloud systems, data analytics, and social networks, are making possible to capture, process, and share huge amounts of data – referred to as big data - and to extract useful knowledge, such as patterns, from this data and predict trends and events. Big data is making possible tasks that before were impossible, like preventing disease spreading and crime, personalizing healthcare, quickly identifying business opportunities, managing emergencies, protecting the homeland, and so on [3]. Provide an approach for sources of structured and unstructured big data. Unstructured data is everywhere. In fact, most individuals and organizations conduct their lives around unstructured data [4]. Successful decision-making will increasingly be driven by analytics-generated

Big Data is a useful tool that helps in collecting, analyzing and disseminating any form of information that may start from simple phones and ranging up to enormous super computers to analyze the movement of pattern, behavior of people towards data and patterns that are developed out of data analysis (LaValle, 2013). Data mining techniques are also aided by the concepts of big data to scoop out some of the useful information in various fields and researches of study. The pattern analysis can be aided with the help of text messages via cell phones. It is almost next to impossible to trap the data generated by millions of users but with the invention of this new technique and plethora of tools aiding the process of data analysis, the cell phone data generated by approximately six billion of

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.

Big data is a popular term used to describe the improvement and availability of data in both structured and unstructured formats. Structure data is located in a fixed field within a record or file and it is present in the relational data bases and spreadsheets whereas an unstructured data file includes text and multimedia contents. The primary objective of this big data concept is to describe the extreme volume of data sets i.e. both structured and unstructured. It is further defined with three “V” dimensions namely Volume, Velocity and Variety, and two more “V” also added Value and Veracity. Volume refers to the amount of data, Velocity depends upon the speed of the data processing, Variety is described with the types of the