Harvesting Deep Web Data through Produser Involvement

Acquiring the data from the deep Web is a complex process, which requires understanding of Website navigation issues, data extraction, and integration techniques. Currently existing solutions to automate it are not ready to cover the whole deep Web and require skills and knowledge to be applied in practice. However, several systems were created, which approach the problem by involving end users who are able to bring the data from the deep Web to the surface while creating solutions for their own information needs. The authors study these systems in the chapter from the end user perspective, investigating their interfaces, languages that they expose to end users, and the platforms that accompany the systems to involve end users and allow them to share the results of their work.

Download Full-text

Evaluating E-Learning from an End User Perspective

Advances in Business Information Systems and Analytics - Frameworks of IT Prosumption for Business Development ◽

10.4018/978-1-4666-4313-0.ch017 ◽

2014 ◽

pp. 259-283 ◽

Cited By ~ 2

Author(s):

Robert Costello

Keyword(s):

Quality Control ◽

Learning Environment ◽

End Users ◽

User Modelling ◽

End User ◽

User Perspective ◽

Personalised Learning ◽

E Learning ◽

On Line ◽

Evaluation Techniques

Evaluating e-learning is an important measure for quality control, which aims to improve the whole e-learning environment through taking into consideration users’ perceptions and needs, as well as participants, stakeholders, and institutions. However, literature does indicate that institutions are only using e-learning as a repository for uploading academic materials, instead of taking into consideration of features and the learner. This chapter examines a variety of evaluation techniques adopted from e-learning, personalised learning, and User Modelling to suggest improvements within the industry to challenge the end users’ perceptions of on-line education.

Download Full-text

Evaluating E-Learning from an End User Perspective

Student Engagement and Participation ◽

10.4018/978-1-5225-2584-4.ch041 ◽

2018 ◽

pp. 818-844

Author(s):

Robert Costello

Keyword(s):

Quality Control ◽

Learning Environment ◽

End Users ◽

User Modelling ◽

End User ◽

User Perspective ◽

Personalised Learning ◽

E Learning ◽

On Line ◽

Evaluation Techniques

Evaluating e-learning is an important measure for quality control, which aims to improve the whole e-learning environment through taking into consideration users' perceptions and needs, as well as participants, stakeholders, and institutions. However, literature does indicate that institutions are only using e-learning as a repository for uploading academic materials, instead of taking into consideration of features and the learner. This chapter examines a variety of evaluation techniques adopted from e-learning, personalised learning, and User Modelling to suggest improvements within the industry to challenge the end users' perceptions of on-line education.

Download Full-text

The Collaborative Use of Information Technology

Advances in Information Resources Management - Advanced Topics in Information Resources Management, Volume 1 ◽

10.4018/978-1-930708-44-0.ch011 ◽

2002 ◽

pp. 184-203

Author(s):

William J. Doll ◽

Xiaodong Deng

Keyword(s):

Systems Analysis ◽

User Satisfaction ◽

Information Needs ◽

Flow Analysis ◽

User Participation ◽

End Users ◽

End User ◽

Information Flow Analysis ◽

Collaborative Applications ◽

Research Questions

Building upon the work of behavioral scientists who study participative decision making, Doll and Torkzadeh (1991) present a congruence construct of participation that measures whether end users participate as much as they want in key systems analysis decisions. Using a sample of 163 collaborative and 239 non-collaborative applications, this research focuses on three research questions: (1) Is user participation more effective in collaborative applications? (2) What specific decision issues enhance user satisfaction and productivity? and (3) Can permitting end users to participate as much as they want on some issues be ineffective or even dysfunctional? The results indicate that user participation is more effective in collaborative applications. Of the four decision issues tested, only participation in information needs analysis predicts end-user satisfaction and task productivity. Encouraging end users to participate as much as they want on a broad range of systems analysis issues such as project initiation, information flow analysis, and format design appears to be, at best, a waste of time and, perhaps, even harmful. These findings should help managers and analysts make better decisions about how to focus participatory efforts and whether end users should participate as much as they want in the design of collaborative systems.

Download Full-text

A Deep Web Data Extraction and Application System Based on Cloud Technology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.2583 ◽

2013 ◽

Vol 756-759 ◽

pp. 2583-2587 ◽

Cited By ~ 1

Author(s):

Zi Yang Han ◽

Feng Ying Wang ◽

Ping Sun ◽

Zheng Yu Li

Keyword(s):

Parallel Computing ◽

Data Extraction ◽

Scheduling Algorithm ◽

Computing System ◽

Extraction Process ◽

Deep Web ◽

System Structure ◽

Web Data ◽

Cloud Technology ◽

Web Data Extraction

There are so many Deep Webs in Internet, which contains a large amount of valuable data, This paper proposes a Deep Web data extraction and service system based on the principle of cloud technology. We adopt a kind of multi-node parallel computing system structure and design a task scheduling algorithm in the data extraction process, in above foundation, balance the task load of among nodes to accomplish data extraction rapidly; The experimental results show that cloud parallel computing and dispersed network resources are used to extract data in Deep Web system is valid and improves the data extraction efficiency of Deep Web and service quality.

Download Full-text