Generate Analytics from a Product based Company Web Log

The next generation of industries will be using Big Data to remedy the unsolved data difficulties within the physical global. Big Data analysis may be about constructing systems around the data that is generated. Every department of an organisation consisting of advertising and marketing, finance and HR are actually getting direct get admission to to their own statistics. This is developing a huge activity opportunity and there may be an pressing requirement for the experts to master Big Data Hadoop abilities. Nowadays most of the groups have became to Ecommerce which has grow to be a vital element for business approach and a catalyst for economic improvement. These groups need to predict the evaluation approximately their services and products to tune their commercial enterprise from the customers end. The response from the customers based totally on their sports on the web sites makes a decision the future modifications required to enhance the commercial enterprise values. These companies stores the statistics of all clients in element for destiny analysis which is commonly referred as large statistics, as it's far developing at high costs every day. One of the main programs of large statistics intelligence is Clickstream data which is ideal for e-commerce websites and websites that rely upon clicks. Clickstreams are records of consumer interactions with web sites and other packages. A common technique to load those facts and processing is through the use of traditional databases, however it involves many complexities even as appearing different operations. Here in this paper clickstream records is processed, analysed with the structure of Hadoop the usage of Hortonworks Data Platform (HDP) which offers massive scale processing overall performance and visualized thru strength

PROGRAMS WITH DATA MINING CAPABILITIES

The fact that the Internet has become a commodity in the world has created a framework for anew economy. Traditional businesses migrate to this new environment that offers many features and options atrelatively low prices. However competitiveness is fierce and successful Internet business is tied to rigorous use of allavailable information. The information is often hidden in data and for their retrieval is necessary to use softwarecapable of applying data mining algorithms and techniques. In this paper we want to review some of the programswith data mining capabilities currently available in this area.We also propose some classifications of this softwareto assist those who wish to use such software

Using bi-clustering algorithm for analyzing online users activity in a virtual campus

Data mining algorithms have been proved to be useful for the processing of large data sets in order to extract relevant information and knowledge. Such algorithms are also important for analyzing data collected from the users' activity users. One family of such data analysis is that of mining of log files of online applications that register the actions of online users during long periods of time. A relevant objective in this case is to study the behavior of online users and feedback the design processes of online applications to provide better usability and adaption to users' preferences. The context of this work is that of a virtual campus in which thousands of students and tutors carry out the learning and teaching activity using online applications. The information stored in log files of virtual campuses tend to be large, complex and heterogeneous in nature. Hence, their mining requires both efficient and intelligent processing and analysis of user interaction data during long-term learning activities. In this paper, we present a bi-clustering algorithm for processing large log data sets from the online daily activity of students in a real virtual campus. Our approach is useful to extract relevant knowledge about user activity such as navigation patterns, activities performed as well as to study time parameters related to such activities. The extracted information can be useful not only to students and tutors to stimulate and improve their experience when interacting with the system but also to the designers and developers of the virtual campus in order to better support the online teaching and learning.Peer ReviewedPostprint (published version

Big Data Analytics and Electronic Resource Usage in Academic Libraries: A Case Study of a Private University in Kenya

The purpose of the study was to apply Big Data analytics as a tool for evaluating electronic resources usage in the academic library setup in Kenya with reference to the library of one private university. Log files of postgraduate students were mined from the server where the offsite access platform (ezproxy) has been installed. Descriptive statistical techniques such as mean, standard deviation and percentages were computed. Data was transferred to the Statistical Package for Social Sciences (SPSS) software was aided in the analysis. Results revealed that in terms of usage intensity, total URL count was 2,352, the highest user made 283 downloads and the mean URL count was 49 downloads. Further findings revealed that no user utilized more than 5 databases over a period of one year. The mean usage intensity score for respondents who were trained or orientated on e-resource usage was above average at 69.0 while those who had not received training were below average at 29.8. It was concluded that big data analytics is a necessary and powerful tool for investigating electronic resources seeking and usage trends and patterns within Kenyan university libraries. Through big data analysis and data mining, usage patterns and trends such as usage intensity that might not have accurately been revealed through other tools are unearthed. Big data analytics has revealed user preferences and intensity of utilization of various databases and helped in detection of redundant databases. From the usage patterns, it was clear that the level of utilization of the University library’s e-resource platform was very low. Most of the databases accessible through the platform were redundant. Further, only two databases namely e-book central and ebscohost were the most popular among users while the rest were barely being utilized if at all. For most students, just one or two databases were sufficient in meeting their research needs. An integrated data analytics model for investigating university library’s e-resources usage is proposed

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

Guidelines for the analysis of student web usage in support of primary educational objectives

The Internet and World Wide Web provides huge amounts of information to individuals with access to it. Information is an important driving factor of education and higher education has experienced massive adoption rates of information and communication technologies, and accessing the Web is not an uncommon practice within a higher educational institution. The Web provides numerous benefits and many students rely on the Web for information, communication and technical support. However, the immense amount of information available on the Web has brought about some negative side effects associated with abundant information. Whether the Web is a positive influence on students’ academic well-being within higher education is a difficult question to answer. To understand how the Web is used by students within a higher education institution is not an easy task. However, there are ways to understand the Web usage behaviour of students. Using established methods for gathering useful information from data produced by an institution, Web usage behaviours of students within a higher education institution could be analysed and presented. This dissertation presents guidance for analysing Web traffic within a higher educational institution in order to gain insight into the Web usage behaviours of students. This insight can provide educators with valuable information to bolster their decision-making capacity towards achieving their educational goals