TOWARDS CROSS-ORGANISATIONAL E-GOVERNMENT: AN INTEGRATED APPROACH

One of the most challenging issues in current e-Government initiatives is the seamless exchange of information and the efficient collaboration between public administrations, companies and the private sector. Either from an intra- or cross-organisational point of view spanning processes across multiple authorities leads to a collaboration of autonomous units under consideration of law and regulations. Despite the organisational dimension current approaches are mainly technical solutions – e.g. interoperability frameworks. Within this paper we present an integrated approach which incorporates organisational aspects of the public sector and which supports the correspondent implementation of solutions for cross-organisational e-Government by adopting Model-Driven-Development practices

Towards Compliance of Cross-Organizational Processes and their Changes

Businesses require the ability to rapidly implement new processes and to quickly adapt existing ones to environmental changes including the optimization of their interactions with partners and customers. However, changes of either intra- or cross-organizational processes must not be done in an uncontrolled manner. In particular, processes are increasingly subject to compliance rules that usually stem from security constraints, corporate guidelines, standards, and laws. These compliance rules have to be considered when modeling business processes and changing existing ones. While change and compliance have been extensively discussed for intra-organizational business processes, albeit only in an isolated manner, their combination in the context of cross-organizational processes remains an open issue. In this paper, we discuss requirements and challenges to be tackled in order to ensure that changes of cross-organizational business processes preserve compliance with imposed regulations, standards and laws

Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5.data mining,data clustering, higher education, decision trees, C4.5 algorithm, k-means, decision support, academic intelligence management

Change and Compliance in Collaborative Processes

During their lifecycle, business processes are keen to change. Changes either concern the process model structure or the accompanying rules; e.g. compliance rules (laws and regulations). In the context of business process collaborations, several process partners collaborate together, and changing one process might result in knock-on effects on the other processes; i.e., change propagation. Since business processes are often subject to restrictions that stem from laws, regulations or guidelines; i.e., compliance rules, changing them might lead to the violations of these rules (non-compliability). So far, only the impacts of process changes in choreographies have been studied. In this work, we propose an approach that analyzes and evaluates the impacts of process changes on the different compliance rules and inversely, the impacts of compliance rule changes on the process choreography

Virtual teams: A literature review

In the competitive market, virtual teams represent a growing response to the need for fasting time-to-market, low-cost and rapid solutions to complex organizational problems. Virtual teams enable organizations to pool the talents and expertise of employees and non-employees by eliminating time and space barriers. Nowadays, companies are heavily investing in virtual team to enhance their performance and competitiveness. Despite virtual teams growing prevalence, relatively little is known about this new form of team. Hence the study offers an extensive literature review with definitions of virtual teams and a structured analysis of the present body of knowledge of virtual teams. First, we distinguish virtual teams from conventional teams, different types of virtual teams to identify where current knowledge applies. Second, we distinguish what is needed for effective virtual team considering the people, process and technology point of view and underlying characteristics of virtual teams and challenges they entail. Finally, we have identified and extended 12 key factors that need to be considered, and describes a methodology focused on supporting virtual team working, with a new approach that has not been specifically addressed in the existing literature and some guide line for future research extracted.Virtual team, Literature review, Effective virtual team,

Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5

Caramba -- A Process-Aware Collaboration System Supporting Ad Hoc and Collaborative PROCESSES IN VIRTUAL TEAMS

Organizations increasingly define many business processes as projects executed by “virtual (project) teams”, where team members from within an organization cooperate with “outside ” experts. Virtual teams require and enable people to collaborate across geographical distance and professional (organizational) boundaries and have a somewhat stable team configuration with roles and responsibilities assigned to team members. Different people, coming from different organizations will have their own preferences and experiences and cannot be expected to undergo a long learning cycle before participating in team activities. Thus, efficient communication, coordination, and process-aware collaboration remain a fundamental challenge. In this paper we discuss the current shortcomings of approaches in the light of virtual teamwork (mainly Workflow, Groupware, and Project Management) based on models and underlying metaphors. Furthermore, we present a novel approach for virtual teamwork by tightly integrating all associations between processes, artifacts, and resources. In this paper we analyze (a) the relevant criteria for process-aware collaboration system metaphors, (b) coordination models and constructs for organizational structures of virtual teams as well as for ad hoc and collaborative processes composed out of tasks, and (c) architectural considerations as well as design and implementation issues for an integrated process-aware collaboration system for virtual teams on the Internet