Alter ego, state of the art on user profiling: an overview of the most relevant organisational and behavioural aspects regarding User Profiling.

This report gives an overview of the most relevant organisational and\ud behavioural aspects regarding user profiling. It discusses not only the\ud most important aims of user profiling from both an organisation’s as\ud well as a user’s perspective, it will also discuss organisational motives\ud and barriers for user profiling and the most important conditions for\ud the success of user profiling. Finally recommendations are made and\ud suggestions for further research are given

A Survey on Web Usage Mining

Now a day World Wide Web become very popular and interactive for transferring of information. The web is huge, diverse and active and thus increases the scalability, multimedia data and temporal matters. The growth of the web has outcome in a huge amount of information that is now freely offered for user access. The several kinds of data have to be handled and organized in a manner that they can be accessed by several users effectively and efficiently. So the usage of data mining methods and knowledge discovery on the web is now on the spotlight of a boosting number of researchers. Web usage mining is a kind of data mining method that can be useful in recommending the web usage patterns with the help of users2019; session and behavior. Web usage mining includes three process, namely, preprocessing, pattern discovery and pattern analysis. There are different techniques already exists for web usage mining. Those existing techniques have their own advantages and disadvantages. This paper presents a survey on some of the existing web usage mining techniques

Knowledge Discovery from Web Logs - A Survey

Web usage mining is obtaining the interesting and constructive knowledge and implicit information from activities related to the WWW. Web servers trace and gather information about user interactions every time the user requests for particular resources. Evaluating the Web access logs would assist in predicting the user behavior and also assists in formulating the web structure. Based on the applications point of view, information extracted from the Web usage patterns possibly directly applied to competently manage activities related to e-business, e-services, e-education, on-line communities and so on. On the other hand, since the size and density of the data grows rapidly, the information provided by existing Web log file analysis tools may possibly provide insufficient information and hence more intelligent mining techniques are needed. There are several approaches previously available for web usage mining. The approaches available in the literature have their own merits and demerits. This paper focuses on the study and analysis of various existing web usage mining techniques

The Role of Internet in Marketing Strategies

The use of the Internet has increased in recent years remarkably. Conducting business in the digital economy means using Web- based systems on the Internet and other electronic networks to do some form of electronic commerce. Many research findings confirm and support being of positive effects of Internet on an enterprise's competitive advantage. In this paper, I will illustrate that enterprises can acquire relational and informational competency through Internet technology, and based on these competencies they can succeed in competitive cyber markets. According to the Internet competencies, Internet marketing strategies can be divided into five categories: Transactional, Profile, Customer-oriented, Relationship, and Knowledge strategies. Choosing and implementing any category of strategies depends on the degree of internet competencies (informational and relational) that a firm has. When both are high, proper internet marketing strategy seems to be knowledge strategies; and when both are low, transactional internet marketing strategies would be the suitable category.Internet marketing strategies; Information technologies; Network computing; Digital economy; Information system.

A decision-making framework for aligning business analytics with business objectives

Throughout this thesis, we discuss the impact of Business Analytics on the organizational decision-making process with the objective of designing a framework that provides the organization with extra-knowledge on how to implement and sustain their analytics. First, we develop the concept of capability using the resource-based view and the IT literature to define what is a Business Analytics capability. We then define the key capabilities that provide the organization with a competitive advantage. Moreover, we investigate the role of governance and alignment as well as the impact of the concepts on the decision making effectiveness. To provide an insight on the adjustment to be made in order to increase the organization Business Analytics performance, we emphasise the role of alignment between Information Technology governance, corporate governance, data governance and Business Analytics governance. Thereafter we create the framework based on academic and empirical research and apply this framework throughout a case study. Based on this case study we provide an academic recommendation to the investigated organization. This thesis highlights the importance of the creation of a Business Analytics governance. Also, the research provides a framework linking Business Analytics with decision making successfulness

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Technology in the 21st Century: New Challenges and Opportunities

Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research