Designing a Business Intelligence and Analytics Maturity Model for higher education: A design science approach

Business Intelligence and Analytics (BIA) systems play an essential role in organizations, providing actionable insights that enable business users to make more informed, data-driven decisions. However, many Higher Education (HE) institutions do not have accessible and usable models to guide them through the incremental development of BIA solutions to realize the full potential value of BIA. The situation is becoming ever more acute as HE operates today in a complex and dynamic environment brought forward by globalization and the rapid development of information technologies. This paper proposes a domain-specific BIA maturity model (MM) for HE–the HE-BIA Maturity Model. Following a design science approach, this paper details the design, development, and evaluation of two artifacts: the MM and the maturity assessment method. The evaluation phase comprised three case studies with universities from different countries and two workshops with practitioners from more than ten countries. HE institutions reported that the assessment with the HE-BIA model was (i) useful and adequate for their needs; (ii) and contributed to a better understanding of the current status of their BIA landscape, making it explicit that a BIA program is a technology endeavor as well as an organizational development.info:eu-repo/semantics/publishedVersio

Building An Integrated Digital Transformation System Framework: A Design Science Research, The Case Of Feduni

The purpose of this paper is to propose an integrated digital transformation system framework (IDTSF) to help support business leaders and teams in making their products, services, and operations more streamlined and competitive. The framework will help organizations to best meet user/customer needs with minimum waste and time and enables businesses to achieve efficiency compared with island and traditional sequential approaches. The proposed framework can also provide insights to help organizations to avoid common failures when deploying digital transformation initiatives. The paper follows the design science research (DSR) and the information systems design science research (ISDSR) methodologies to develop the IDTSF model and a practical design artifact. The main problems were the initiation, execution, and governance challenges associated with digital transformation. After identifying the problems and the objectives, a relevant IDTSF model was synthesized and tested as a design artifact. The results of the test of the proposed artifact showed its effectiveness and efficiency in facilitating the components of the model in creating a cohesive framework

Performance-preserving event log sampling for predictive monitoring

Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy

System of Systems Lifecycle Management: A New Concept Based on Process Engineering Methodologies

In order to tackle interoperability issues of large-scale automation systems, SOA (Service-Oriented Architecture) principles, where information exchange is manifested by systems providing and consuming services, have already been introduced. However, the deployment, operation, and maintenance of an extensive SoS (System of Systems) mean enormous challenges for system integrators as well as network and service operators. The existing lifecycle management approaches do not cover all aspects of SoS management; therefore, an integrated solution is required. The purpose of this paper is to introduce a new lifecycle approach, namely the SoSLM (System of Systems Lifecycle Management). This paper first provides an in-depth description and comparison of the most relevant process engineering methodologies and ITSM (Information Technology Service Management) frameworks, and how they affect various lifecycle management strategies. The paper’s novelty strives to introduce an Industry 4.0-compatible PLM (Product Lifecycle Management) model and to extend it to cover SoS management-related issues on well-known process engineering methodologies. The presented methodologies are adapted to the PLM model, thus creating the recommended SoSLM model. This is supported by demonstrations of how the IIoT (Industrial Internet of Things) applications and services can be developed and handled. Accordingly, complete implementation and integration are presented based on the proposed SoSLM model, using the Arrowhead framework that is available for IIoT SoS. View Full-Tex

Image recognition-based architecture to enhance inclusive mobility of visually impaired people in smart and urban environments

The demographic growth that we have witnessed in recent years, which is expected to increase in the years to come, raises emerging challenges worldwide regarding urban mobility, both in transport and pedestrian movement. The sustainable development of cities is also intrinsically linked to urban planning and mobility strategies. The tasks of navigation and orientation in cities are something that we resort to today with great frequency, especially in unknown cities and places. Current navigation solutions refer to the precision aspect as a big challenge, especially between buildings in city centers. In this paper, we focus on the segment of visually impaired people and how they can obtain information about where they are when, for some reason, they have lost their orientation. Of course, the challenges are different and much more challenging in this situation and with this population segment. GPS, a technique widely used for navigation in outdoor environments, does not have the precision we need or the most beneficial type of content because the information that a visually impaired person needs when lost is not the name of the street or the coordinates but a reference point. Therefore, this paper includes the proposal of a conceptual architecture for outdoor positioning of visually impaired people using the Landmark Positioning approach.5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira PaivaN/

A critical evaluation of competitive intelligence and insight management practice.

This thesis forms part of a PhD by Publication based on the research and scholarly work of Sheila Wright. It explores her contribution to the field of Competitive Intelligence & Insight Management (CI&IM) made through ten articles published between 2002 and 2010. The research projects that involved the collection of primary data were carried out within a qualitative research methodology using a semi-structured interview or case study method and typically adopting a pragmatic paradigm. Through this work, it has been possible to present a best-practice framework for CI&IM. Similarly, it has been possible to develop an operational framework for CI&IM, which identifies the inter-connectivity of the CI&IM tasks as well as the organisational influencing factors which can aid or hinder successful implementation. As well as engaging in a critical evaluation of current CI&IM practice the thesis indicates how the articles offer solutions which can aid the delivery of improved performance to practitioners as well as academics who teach and research the subject. The thesis also identifies the place of CI&IM in the business discipline and draws attention to the cross-boundary, inter-disciplinary nature of its reach. The development of bibliometric software and citation identification programmes has enabled the inclusion of a citation analysis for each article which also identifies the context within which that citation was made. This is presented as supporting evidence for the contribution to knowledge and value of the body of work. Through this mechanism it has also been possible, not only to identify the frequency with which the articles have been cited, but more importantly, the impact and contribution this has had on scholars who have subsequently used the output and frameworks as a basis for their own research.This is a PhD by Publication. Due to copyright restrictions, copies of the articles have been omitted in this version of the thesis. Full bibliographic details including DOI's and URL's for the articles can be found in Appendix 2

Air Force Institute of Technology Research Report 2010

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physic

Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles

Recent papers have introduced a novel approach to explain why a Predictive Process Monitoring (PPM) model for outcome-oriented predictions provides wrong predictions. Moreover, they have shown how to exploit the explanations, obtained using state-of-the art post-hoc explainers, to identify the most common features that induce a predictor to make mistakes in a semi-automated way, and, in turn, to reduce the impact of those features and increase the accuracy of the predictive model. This work starts from the assumption that frequent control flow patterns in event logs may represent important features that characterize, and therefore explain, a certain prediction. Therefore, in this paper, we (i) employ a novel encoding able to leverage DECLARE constraints in Predictive Process Monitoring and compare the effectiveness of this encoding with Predictive Process Monitoring state-of-the art encodings, in particular for the task of outcome-oriented predictions; (ii) introduce a completely automated pipeline for the identification of the most common features inducing a predictor to make mistakes; and (iii) show the effectiveness of the proposed pipeline in increasing the accuracy of the predictive model by validating it on different real-life datasets