Intelligent Data Analysis (IDA) And Visualization

In Brief

You will find the best dissertation research areas / topics for future researchers enrolled in Computer Science & Information.
In order to identify the future research topics, we have reviewed the computer science (recent peer-reviewed studies) on Data Analysis.
Process of finding and identifying the meaning of data.
Main advantage of visual representations is to discover, make sense of data and communicating data.

Data

Data is nothing but things known or anything that is assumed; facts from which conclusions can be gathered.

Data Analysis

Breaking up of any data into parts i.e., the examination of these parts to know about their nature, proportion, function, interrelationship, etc.
A process in which the analyst moves laterally and recursively between three modes: describing data (profiling, correlation, summarizing), assembling data (scrubbing, translating, synthesizing, filtering) and creating data (deriving, formulating, simulating).
It is a sense of making data. The process of finding and identifying the meaning of data.

Data Visualization

It is a process of revealing already existing data and/or its features (origin, metadata, allocation), which includes everything from the table to charts and multidimensional animation (Min Yao, 2014) .
To form an intellectual image of something not there to the sight.
Visual data analysis is another form of data analysis, in which some or all forms of data visualization may be used to give feedback sign to the analyst. Our product uses visual signs such as charts, interactive browsing, and workflow process cues to help the analyst in moving through the modes of data analysis.
The main advantage of visual representations is to discover, make sense of data and communicating data. Data visualization is a central part and an essential means to carry out data analysis and then, once the importance have been identified and understood, it is easy to communicate those meanings to others.

Importance of IDA:

Intelligent Data Analysis (IDA) is one of the major issues in artificial intelligence and information. Intelligent data analysis discloses hidden facts that are not known previously and provides potentially important information or facts from large quantities of data (White, 2008). It also helps in making a decision. Based on machine learning, artificial intelligence, recognition of pattern, and records and visualization technology mainly, IDA helps to obtain useful information, necessary data and interesting models from a lot of data available online in order to make the right choices.

Intelligent data analysis helps to solve a problem that is already solved as a matter of routine. If the data is collected for the past cases together with the result that was finally achieved, such data can be used to revise and optimize the presently used strategy to arrive at a conclusion.

In certain cases, if some questions arise for the first time, and have only a little knowledge about it, data from the related situations helps us to solve the new problem or any unknown relationships can be discovered from the data to gain knowledge in an unfamiliar area.

Steps Involved In IDA:

IDA, in general, includes three stages: (1) Preparation of data; (2) data mining; (3) data validation and explanation (Keim & Ward, 2007). The preparation of data involves opting for the required data from the related data source and incorporating it into a data set that can be used for data mining.

The main goal of intelligent data analysis is to obtain knowledge. Data analysis is the process of a combination of extracting data from data set, analyzing, classification of data, organizing, reasoning, and so on. It is challenging to choose suitable methods to resolve the complexity of the process.

Regarding the term visualization, we have moved away from visualization to use the term charting. The term analysis is used for the method of incorporating, influencing, filtering and scrubbing the data, which certainly contains, but is not limited to interrelating with their data through charts.

The Goal of Data Analysis:

Data analysis need not essentially involve arithmetic or statistics. While it is true that analysis often involves one or both, and that many analytical pursuits cannot be handled without them, much of the data analysis that people perform in the course of their work involves at most mathematics no more complicated than the calculation of the mean of a set of values. The essential activity of analysis is a comparison (of values, patterns, etc.), which can often be done by simply using our eyes.

The aim of the analysis is not to find out appealing information in the data. Rather, this is only a vital part of the process (Berthold & Hand, 2003). The aim is to make sense of data (i.e., to understand what it means) and then to make decisions based on the understanding that is achieved. Information in and of itself is not useful. Even understanding information in and of it is not useful. The aim of data analysis is to make better decisions.

The process of data analysis starts with the collection of data that can add to the solution of any given problem, and with the organization of that data in some regular form. It involves identifying and applying a statistical or deterministic schema or model of the data that can be manipulated for explanatory or predictive purposes. It then involves an interactive or automated solution that explores the structured data in order to extract information – a solution to the business problem – from the data.

The Goal of Visualization

The basic idea of visual data mining is to present the data in some visual form, allowing the user to gain insight into the data, draw conclusions, and directly interact with the data. Visual data analysis techniques have proven to be of high value in exploratory data analysis. Visual data mining is mainly helpful when the only little fact is known about the data and the exploration goals are indistinct.

The main uses of visual data examination over data analysis methods are:

Visual data examination can simply deal with highly non-homogeneous and noisy data.
Visual data exploration is spontaneous and requires no knowledge of complex mathematical or arithmetical algorithms or parameters.
Visualization can present a qualitative outline of the data, letting data phenomenon to be secluded for further quantitative analysis. Accordingly, visual data examination usually allows a quicker data investigation and often provides fascinating results, especially in cases where automatic algorithms fail.
Visual data examination techniques provide a much higher degree of assurance in the findings of the exploration.

Conclusion

The examination of large data sets is a significant but complicated problem. Information visualization techniques can be helpful in solving this problem. Visual data investigation is helpful for many purposes such as fraud detection system and data mining can make use of data visualization technology for improved data analysis.

References

Berthold, M. & Hand, D.J. (2003). Intelligent data analysis. [Online]. Springer. Available from: https://link.springer.com/content/pdf/bfm%253A978-3-540-48625-1%252F1.pdf.

Keim, D. & Ward, M. (2007). Visualization. In: Intelligent Data Analysis. [Online]. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 403–427. Available from: http://link.springer.com/10.1007/978-3-540-48625-1_11.

Min Yao (2014). Special Issue ‘Intelligent Data Analysis’. [Online]. Available from: https://www.mdpi.com/journal/information/special_issues/data-analysis?view=abstract&listby=type.

White, C. (2008). Business Intelligence Data Analysis and Visualization: What’s in a Name? Part 1. [Online]. Available from: http://www.b-eye-network.com/view/9336.