Data And Data Of Data

Knowledge is the framework for understanding information and using it to inform judgements, opinions, predictions and decisions – a pyramidal relationship (See diagram 1)

From doing this assignment I thought that data and information was the same but now when I do this assignment I see that is not that.it true they go hand to hand but there not the same at all.

Information: A collection of facts organized and processed so that they have additional value beyond the value of the individual

With the advent of machine learning and its potential in getting best out of any application, even the data mining played the game of harnessing the power of machine learning. Needless to say, SVM is one of the very powerful and revolutionary algorithms in the field of machine learning due to its efficiency in classifying. In this report, my concentration mostly lies in discussing the applications of SVM in Data mining and analyzing the performance. Data mining is very important and essential technique in the field of analytics. The principle being extracting use full information from a massive data source and using it as an input for improvement or development. When we have a huge amount of data and equally less amount of information, data mining is one technique that enables to get better information out of the data. However, it 's not very easy to do the analysis part on huge datasets, and hence machine intelligence is introduced into the field of data mining.

Data stream mining is a stimulating field of study that has raised challenges and research issues to be addressed by the database and data mining communities. The following is a discussion of both addressed and open research issues [19].

A data stream is a real time, continuous, structured sequence of data items. Mining data stream is the process of extracting knowledge from continuous, rapid data records. Data arrives faster, so it is a very difficult task to mine that data. Stream mining algorithms typically need to be designed so that the algorithm works with one pass of the data. Data streams are a computational challenge to data mining problems because of the additional algorithmic constraints created by the large volume of data. In addition, the problem of temporal locality leads to a number of unique mining challenges in the data stream case. The data mining techniques namely clustering, classification and frequent pattern mining are applied to extract the knowledge

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.

Owing to rapid developments in digital technologies, the use of electronic media to capture, process and accumulate the information is witnessing an extraordinary development [1]. The stored information is reaching zeta-bytes [2], whereas our capability to scrutinize such large amount of data lags far behind the growth. One of the impediments is the high dimensionality of the datasets. This includes information in different application areas, such as in electronic health records (EHRs), biology, astronomy, medical imaging, video archiving, and web data. Different data mining techniques have been used to extract knowledge available in some of these data sets, albeit with limited success [3].

Mohamed Medhat Gaber, Arkady Zaslavsky and Shonali Krishnaswamy. Illustrated that the theoretical foundations of data stream analysis discussed. Mining data stream systems, techniques are critically reviewed. Finally, the research problems in streaming mining field of study are discussed. These research issues should be addressed in order to realize robust systems that are capable of fulfilling the needs of data stream mining applications. The main aim is to explore the data for testing a specific hypothesis. The machine learning field came into existence with advancement in computing power. So, the goal is to achieve efficient solutions to data analysis problems. There are some issues regarding data stream mining discussed such as ‘Handling the continuous flow of data streams.’, ‘Unbounded

Secondly, Information is probably one of the most used terminologies, however it is one of the least understood terms. The term Information can be defined as fragmented knowledge, knowledge packaged for a user and even the pattern of organisation of matter and energy, given meaning by a living being (Bawden, 2012, page 65-67).

In the later part of the paper the author discussed about the challenges faced by Big Data in the three tiers as difficulty in accessing the data in terms of storage and computing, gaining knowledge by understanding semantics and how privacy can be achieved and how the overall optimization can be obtained. These challenges made the authors to develop many data mining methods which explains the convoluted relationships of evolving data.

Data Mining in Cloud Computing applications is data retrieving from huge collection of data sets. The process of converting a huge set of data

Many people actually think that data and information can be used interchangeably but this is not true. Data is raw material that has not been processed and has been extracted from the source the way it is. Data is also unorganized therefore cannot be used for a meaningful purpose. On the other hand, information is processed data therefore the latter has to be organized into a meaningful manner for the former to exist. A good example of how data and information interact is through the use of computers; the computers need data and they process it into information that is usable by humans. In the case of a computer, data is a series of 1s and 0s which means the data has not been organized or interpreted therefore the human cannot understand it, but when these 1s and 0s are organized by the computer they give out information that can be used in decision making. Data forms the foundation for information and knowledge. Data exists in the form of pictures, words, numbers, character, sound, and video that have little or no meaning to the user.

|Meaning: |Data is raw, unorganized facts that need to |When data is processed, organized, structured|

Data is facts of the world. For example statistical facts on yourself – your height, weight, hair and eye colour. All of these facts are data. Data is perceived through our senses and then the brain can process it into knowledge.