EPOS : evolving personal to organizational knowledge spaces

EPOS will leverage the user´s personal workspace with its manyfold native information structures to his personal knowledge space and in cooperation with other personal workspaces contribute to the organizational knowledge space which is represented in the organizational memory. This first milestone presents results from the project´s first year in the areas of the personal informational model, user observation for context elicitation, collaborative information retrieval and information visualization

Personalized Web Search Techniques - A Review

Searching is one of the commonly used task on the Internet. Search engines are the basic tool of the internet, from which related information can be collected according to the specified query or keyword given by the user, and are extremely popular for recurrently used sites. With the remarkable development of the World Wide Web (WWW), the information search has grown to be a major business segment of a global, competitive and money-making market. A perfect search engine is the one which should travel through all the web pages inthe WWW and should list the related information based on the given user keyword. In spite of the recent developments on web search technologies, there are still many conditions in which search engine users obtains the non-relevant search results from the search engines. A personalized Web search has various levels of efficiency for different users, queries, and search contexts. Even though personalized search has been a major research area for many years and many personalization approaches have been examined, it is still uncertain whether personalization is always significant on different queries for diverse users and under different search contexts. This paper focusses on the survey of many efficient personalized Web search approaches which were proposed by many authors

Characteristics Description of Potential User Segments on the E-Commerce Website oriented to Precision Marketing

In the increasingly competitive environment between e-commerce companies, for more accurate implementation of marketing strategies, e-commerce websites often choose to subdivide the consumer market of the enterprise to identify site users’ characteristics to find their needs. In this paper, we subdivide consumer market from the four dimensions of behavior, geography, demography and psychology and propose a model to describe the characteristics of potential user market segments. Based on the web log data and user transaction data, a classification algorithm is used to analyze user behavior data in Web log to find the potential user segments and the user\u27s descriptive characteristics in user transaction data are clustered to obtain the distribution of consumer characteristics under various product categories, then we use the product categories in e-commerce website as an intermediary to give every single potential user in potential user market segments the descriptive characteristics, which can provide data support for the realization of precision marketing. The proposed model is applied to the actual data of a certain insurance e-commerce platform, and based on the results, we gain some implications for marketing of the e-commerce website

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Modeling usage of an online research community

Although online communities have been thought of as a new way for collaboration across geographic boundaries in the scientific world, they have a problem attracting people to keep visiting. The main purpose of this study is to understand how people behave in such communities, and to build and evaluate tools to stimulate engagement in a research community. These tools were designed based on a research framework of factors that influence online participation and relationship development. There are two main objectives for people to join an online community, information sharing and interpersonal relationship development, such as friends or colleagues. The tools designed in this study are to serve both information sharing and interpersonal relationship development needs. The awareness tool is designed to increase the sense of a community and increase the degree of social presence of members in the community. The recommender system is designed to help provide higher quality and personalized information to community members. It also helps to match community members into subgroups based on their interests. The designed tools were implemented in a field site - the Asynchronous Learning Networks (ALN) Research community. A longitudinal field study was used to evaluate the effectiveness of the designed tools. This research explored people\u27s behavior inside a research community by analyzing web server logs. The results show that although there are not many interactions in the community space, the WebCenter has been visited extensively by its members. There are over 2,000 hits per day on average and over 5,000 article accesses during the observation period. This research also provided a framework to identify factors that affect people\u27s engagement in an online community. The research framework was tested using the PLS modeling method with online survey responses. The results show that perceived usefulness performs a very significant role in members\u27 intention to continue using the system and their perceived preliminary networking. The results also show that the quality of the content of the system is a strong indicator for both perceived usefulness of the community space and perceived ease of use of the community system. Perceived ease of use did not show a strong correlation with intention to continue use which was consistent with other studies of Technology Acceptance Model (TAM). For the ALN research community, this online community helps its members to broaden their contacts, improve the quality and quantity of their research, and increase the dissemination of knowledge among community members

Utilizing a Restricted Access e-Learning Platform for Reform, Equity, and Self-development in Correctional Facilities

Objectives: The goal of this paper is to address the issues that arose because of the exclusion of law offenders in the Greek Correctional Institutions from second chance education during the COVID-19 pandemic. During this period, the offenders were deprived of their right to equal access to second-chance education since the pandemics blocked mobility and close contact with teaching personnel. Methods/Analysis: In this paper, we propose a framework based on the Technology Acceptance Model (TAM) that will be deployed to evaluate the acceptance of the CILMS by the learners in Correctional Institutions. We describe a methodology and a set of hypotheses that can reveal the intention of learners to use the system based on several factors, such as trust, perception of privacy, perception of usefulness, and perception of self-efficacy. Findings: We suggest that eLearning and limited Internet access should be added to the list of fundamental human rights for CI detainees as well, in order to counteract their separation from physical society. Inmates are still individuals. In fact, they should be placed in solitary confinement as prescribed by the law. Novelty/Improvement:This viewpoint has been demonstrated with the development and evaluation of acceptance by inmates through the TAM technology acceptance methodology, as well as the proposal of a generic privacy-preserving Web information and services access model for CIs that can, at the same time, provide sufficient information access freedom while respecting the restrictions that should be imposed on such an access for CI inmates. Doi: 10.28991/ESJ-2022-SIED-017 Full Text: PD