Study Of Data Mining Algorithm For Cloud Computing

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 increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

Abstract: Data mining algorithms determine how the cases for a data mining model are analyzed. Data mining model algorithms provide the decision-making capabilities needed to classify, segment, associate and analyze data for the processing of data mining columns that provide predictive, variance, or probability information about the case set. With an enormous amount of data stored in databases and data warehouses, it is increasingly important to develop powerful tools for analysis of such data and mining interesting knowledge from it. Data mining is a process of inferring knowledge from such huge data. Data Mining has three major components Clustering or Classification, Association Rules and Sequence Analysis.

Data mining is having such techniques and algorithms that helpful in finding analytics from given data set, and helps in finding association

The data stored in data warehouse will not provide any benefit to the organization until the hidden information is extracted from it. There are various way to extract the data, however data mining is the best method to get the meaningful trends and patterns from the data warehouse. Therefore, data mining can be defined as the method of extracting the valid, comprehensible and previously unknown information from the huge storage to use on the decision making process (P. 1233).

Data Mining technique is the result of a long process of studies and research in the area of databases and product development. This evolution began when business data and companies was stored for the first time on computer device, with continuous

“In recent 10 years, Internet has been developing very quickly. The cost of storage, the power consumed by computer and hardware is increasing. The storage space in data center can’t meet our needs and the system and service of original internet can’t solve above questions, so we need new solutions. At the same time, large enterprises have to study data source fully to support its business. The collection and analysis must be built on a new platform. Why we need Cloud Computing? It is to utilize the vacant resources of computer, increase the economic efficiency through improving utilization rate, and decrease the equipment energy consumption. Cloud Computing does not depend on special data center, but we can look it as the inevitable product of grid computing and efficiency computing. However, compared with general network service, Cloud Computing is easy to extend, and has a simple management style. Cloud is not only simply collecting the computer resource, but also provides a management mechanism and can provide services for millions of users simultaneously” [1].

With a significant development of the era of internet information, numerous data from vary field like science, engineer and business need to be handled quickly and accurately, which means the demand of computing power is far more than the ability of current technology. In order to enhance the computing system and save cost, ‘Cloud computing’ was emerged. People use cloud computing commonly in storage, which allow consumers use internet to share or store resources and information. The concept of this technology was proposed initially by John McCarthy in 1960s, while he predicted, computing will become a part of infrastructure instruction like gas, water and electricity everyone can get and use it easily and cheaply. The

In this proposed method, first we will be analyzing the performance of data processing individually on relational databases and Hadoop framework by taking a collection of sample datasets. After evaluating the performance of each system, we will be working on a new method of data processing by combinedly using both the computational powers of RDBMS and Hadoop frameworks. We will be using same experimental setup and configurations for analyzing data.

Big data analysis is one of the most important orientations in the area of information technology because of its importance in extracting information out of the available huge digital data and because of the increasingly growing use of internet services and digital transactions of the internet. In addition, data analysis plays a vital role in developing the future generation of products and services of intelligent systems for many different purposes. The National Center for Computation Technology and Applied Math CTAM deploys most of its research groups (areas of artificial intelligence, data mining, machine learning, high performance computing, language processing, software engineering) to lead national initiatives that focus on data science

Data mining consists of analysing huge sets of data and extracting relevant information and data patterns. Companies often have very large data sets that needs to be analysed for many different purposes. Initially this was a hard task to accomplish because of limitations in computing power. However, computer technology has accelerated so fast in recent past that analysing large volumes of data has become possible. Companies use these analysis results for

Data mining is the process of extracting useful knowledge from large databases or data warehouses. It can be also said as a set of mathematical functions and data manipulation techniques to extract useful data from databases. Data mining can also be said as knowledge discovery process in other words. It explores a large collection of data into a meaningful patterns and rules based on the queries provided by users using data mining query language. The meaningful patterns and rules are generated by analysing the database. Data mining makes use several techniques such as clustering, classification, association rule mining and so on to generate the meaningful patterns from the databases. The purpose of this report is to describe how data are prepared for data

Data mining is the extraction of knowledge from the various databases that was previously unknown (Musan & Hunyadi, 2010). Data mining consists of using software that conglomerates artificial intelligence, statistical analysis, and systems management in the act of extracting facts and understanding from data stored in data warehouses, data marts, and through metadata (Giudici, 2005). Through algorithms and learning capabilities data mining software can analyze large amounts of data and give the management team intellectual and effective information to help them form their decisions. The intention for data mining is to analyze prevailing data and form new truths and new associations that were unknown prior to the analysis (Musan & Hunyadi,

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.

Data, Data everywhere. It is a precious thing that will last longer than the systems. In this challenging world, there is a high demand to work efficiently without risk of losing any tiny information which might be very important in future. Hence there is need to create large volumes of data which needs to be stored and explored for future analysis. I am always fascinated to know how this large amount of data is handled, stored in databases and manipulated to extract useful information. A raw data is like an unpolished diamond, its value is known only after it is polished. Similarly, the value of data is understood only after a proper meaning is brought out of it, this is known as Data Mining.