Big Data Analysis Using Soft Computing Techniques

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

With the growth of technology, there is a substantial growth of data by volume, variety and velocity satisfying the criteria of Big Data . Volume of data already exceeded 100 EB at the end of 1990s, reached 1.8 ZB at 2011, and we have already entered in the age of ZB. By 2020, it was forecasted that the volume of data will be 50 times bigger than the one at 2011 . Big data is used to describe the huge data sets (terabyte to Exabyte) and Big Data analytics are the techniques applied on them.

Big Data Analytics is the process of analyzing large amounts of raw information generated and stored. In today 's fast paced technologies, we are inundated with in a tsunami of data before us. All applications, in a broader range are depending on data in a remarkable way. BDA is driving almost every field in our society from Retail, Manufacturing and Mobile applications to life and physical sciences. The Data Analytics techniques are performed to uncover hidden patterns, unknown correlations and other useful information. Earlier, Data Analytics were based on guessing and inaccurate data models but currently this can be done directly. Big Data has truly revolutionized scientific research (Computing Research Association 2014).

There are large amounts of information available in the world through various media outlets, such as the internet, mobile conversations, emails, chats, messages, etc. Nowadays, everything is a big data and with the emergence of the whole new breed of big data software which is distributed all over in the world, whatever data is available can be used for analyzing.

Every year the data transmitted over the internet is growing exponentially. By the end of 2016, Cisco estimates that the annual global data traffic will reach 6.6 zettabytes. The challenge will be not only to “speed up” the internet connections, but also to develop software systems that will be able to handle large data requests in optimal time. For the definition of the Big Data, there are various different

With 3.2 billion internet users [1] and 6.4 billion internet-connected devices in 2016 alone [2], unprecedented amount of data is being generated and processed daily and increasingly every year. With the advent of web 2.0, the growth and creation of new and more complex types of data has created a natural demand for analysis of new data sources in order to gain knowledge. This new data volume and complexity is being called Big Data, famously characterised by Volume, Variety and Velocity and has created data management and processing challenges due to technological limitations, efficiency or cost to store and process in a timely fashion. The large volume and complexity of data cannot be handled and/or processed by most current information systems in a timely manner, while traditional data mining and analytics methods developed for a centralized data system may not be practical for Big Data.

Big data is an extensive collection of structured and unstructured data. It is a modern day technology which is applied to store, manage and analyze data that are not possible to manage, store and analyze by using the commonly used software or tools. Since all of our daily tasks are overtaken by the modern technologies and all the businesses and organizations are using internet system to operate, the production of data has increased significantly in past

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.

Big Data Management (BDM) is the governance and management of huge volumes of all types of data. Big data management is the huge change to technology that will help to make a better society and the industrial sector. The integration, manipulation, quality and governance are the things that big data management has to deal with and management of Big Data including the key factors- Volume, Velocity and Variety of Big Data. Big data is all about size of data. Big data is very large databases. So these ample amount of data needs to be managed in order to use this data at any time. This is known as Big Data Management (BDM). Big Data management is around two things—big information and information management. Big Data Management serves as the essential step for overseeing and administrating huge amount of information called as Big Data in the organizations.

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

Big data when mined properly can give accurate trends of what products will sell better at what times and to whom these products will sell better to. Big data and data mining when used together properly can significantly boost the decision making and business processes in a business by using management of information systems. Big data can be used for many different things but after it is used and patterns are drawn out of it, the data can be stored in a data warehouse. A data warehouse is a database that stores current and historical data of potential interest to decision makers throughout the company (Laudon, 2013, pp. 225). Databases need to be implemented in companies so they can manage their different customers, sales, suppliers, products, and employees and keep all of their data separate. Databases contain lots of big data but to a human all this data is useless so they need to mine it. Data mining is one of the many complimentary services to big data because it is needed to find patterns and relationships in large databases and

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 —Data Clustering is key point used in data processing algorithms for Data Mining. Clustering is a data mining technique used to place data elements into related groups without advance knowledge of the group definitions. Popular clustering techniques include k-means clustering. Clustering is imperative idea in data investigation and data mining applications. In last decade, K-means has been popular clustering algorithm because of its ease of use and simplicity. Now days, as data size is continuously increasing, some researchers started working over distributed environment such as MapReduce to get high performance for big data clustering.

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