Some Other Applications in Community Graph under the Preview of Social Graph Using Graph-Mining Techniques

The retrieval of sub-graph from a large graph in structured data mining is one of the fundamental tasks for analyze. Visualization and analyze large community graph are challenging day by day. Since a large community graph is very difficult to visualize, so compression is essential. To study a large community graph, compression technique may be used for compression of community graph. There should not be any loss of information or knowledge while compressing the community graph. Similarly to extract desired knowledge of a particular sub-graph from a large community graph, then the large community graph needs to be partitioned into smaller sub-community graphs. The partition aims at the edges among the community members of dissimilar communities in a community graph. Sometimes it is essential to compare two community graphs for similarity which makes easier for mining the reliable knowledge from a large community graph. Once the similarity is done then the necessary mining of knowledge can be extracted from only one community graph rather than from both which leads saving of time.

Knowledge Representation Technologies Using Semantic Web

The World Wide Web (WWW) is global information medium, where users can read and write using computers over internet. Web is one of the services available on internet. The Web was created in 1989 by Sir Tim Berners-Lee. Since then a great refinement has done in the web usage and development of its applications. Semantic Web Technologies enable machines to interpret data published in a machine-interpretable form on the web. Semantic web is not a separate web it is an extension to the current web with additional semantics. Semantic technologies play a crucial role to provide data understandable to machines. To achieve machine understandable, we should add semantics to existing websites. With additional semantics, we can achieve next level web where knowledge repositories are available for better understanding of web data. This facilitates better search, accurate filtering and intelligent retrieval of data. This paper discusses about the Semantic Web and languages involved in describing documents in machine understandable format.

Mastering Intelligent Decision Support Systems in Enterprise Information Management

This chapter explains the overview of Intelligent Decision Support Systems (IDSSs); the overview of Enterprise Information Management (EIM); the IDSS techniques for EIM in terms of Expert System (ES), Multi-Agent System (MAS), Fuzzy Logic (FL), Artificial Neural Network (ANN), Evolutionary Computation (EC), and Hybrid System (HS); and the multifaceted applications of IDSSs in EIM. IDSS techniques are rapidly emerging as the modern tools in information management systems and include various techniques, such as ES, MAS, FL, ANN, EC, and HS. IDSS techniques can increase the sensitiveness, flexibility, and accuracy of information management systems. IDSS techniques should be implemented in modern enterprise in order to gain the benefits of using the decision-making process concerning EIM. The chapter argues that utilizing IDSS techniques for EIM has the potential to increase organizational performance and reach strategic goals in global operations.

DSS for Web Mining Using Recommendation System

Decision Supports Systems (DSS) are computer-based information systems designed to help managers to select one of the many alternative solutions to a problem. A DSS is an interactive computer based information system with an organized collection of models, people, procedures, software, databases, telecommunication, and devices, which helps decision makers to solve unstructured or semi-structured business problems. Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web mining can be divided into three different types – Web usage mining, Web content mining and Web structure mining. Recommender systems (RS) aim to capture the user behavior by suggesting/recommending users with relevant items or services that they find interesting in. Recommender systems have gained prominence in the field of information technology, e-commerce, etc., by inferring personalized recommendations by effectively pruning from a universal set of choices that directed users to identify content of interest.

Text Mining

This chapter reveals the overview of text mining; text mining, patent analysis, and keyword selection; text mining and sentiment analysis in modern marketing; text mining applications in the biomedical sciences; and the multifaceted applications of text mining. Text mining is an advanced technology utilized in business, marketing, biomedical sciences, education, and operations. Text mining offers a solution to many problems, drawing on techniques concerning information retrieval, natural language processing, information extraction, and knowledge management. Through text mining, information can be extracted to derive summaries for the words contained in the documents. Text mining has the potential to increase the research base available to business and society and to enable business to utilize the research base more effectively. Economic and societal benefits of text mining include cost savings, productivity gains, innovative new service development, new business models, and new medical treatments.

Discovering Knowledge Hidden in Big Data from Machine-Learning Techniques

As the speed of information growth exceeds in this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. Data analytic is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is about discovering knowledge from large volumes data and applying it to the business. Machine learning is ideal for exploiting the opportunities hidden in big data. This chapter able to discover and display the patterns buried in the data using machine learning.

Discover Patterns from Web-Based Dataset

The Web can be defined as a depot of varied range of information present in the form of millions of websites dispersed around us. Often users find it difficult to locate the appropriate information fulfilling their needs with the abundant number of websites in the Web. Hence multiple research work has been conducted in the field of Web Mining so as to present any information matching the user's needs. The application of data mining techniques on web usage, web content or web structure data to find out useful data like users' way in patterns and website utility statistics on a whole can be defined as Web mining. The main cause behind development of such websites was to personalize the substance of a website on user's preference. New methods are developed to deal with a Web site using a link hierarchy and a conceptual link hierarchy respectively on the basis of how users have used the Web site link structure.

Web Data Mining in Education

Web data mining for extracting meaningful information from large amount of web data has been explored over a decade. The concepts and techniques have been borrowed into the education sector and the new research discipline of learning analytics has emerged. With the development of web technologies, it has been a common practice to design online collaborative learning activities to enhance learning. To apply learning analytics techniques to monitor the online collaborative process enables a lecturer to make instant and informed pedagogical decisions. However, it is still a challenge to build strong connection between learning analytics and learning science for understanding cognitive progression in learning. In this connection, this chapter reports a study to apply learning analytics techniques in the aspect of web usage mining and clustering analysis with underpinning Bloom's taxonomy to analyze students' performance in the online collaborative learning process. The impacts of intermediate interventions are also elaborated.

Data Mining and Data Warehousing

It is generally observed throughout the world that in the last two decades, while the average speed of computers has almost doubled in a span of around eighteen months, the average speed of the network has doubled merely in a span of just eight months! In order to improve the performance, more and more researchers are focusing their research in the field of computers and its related technologies. Data Mining is one such research area. It extracts useful information the huge amount of data present in the database. The discovered knowledge can be applied in various application areas such as marketing, fraud detections and customer retention. It discovers implicit, previously unknown and potentially useful information out of datasets. Recent trend in data mining include web mining where it discover knowledge from web based information to improve the page layout, structure and its content.

Social Network Web Mining

Today, social networks are major part of everyone's lives. They provide means to communicate with people across the globe with ease. As of July 2016, there are over 1.71 billion monthly active Facebook users. They generate significant amount of data, which if analysed well will provide us with valuable information. This can be done by analysing the log data collected at the respective social networking service. This chapter focuses on extraction and analysis of Facebook data since it is presently the most used social network. The result of analysis can be used in building decision support systems for an organization to help with the decision making process.