A Multidimensional and Visual Exploration Approach to Project Portfolio Management

Managing projects in an organization, especially a project-oriented organization, is a challenging task. Project data has a large volume and is complex to manage. It is different from managing a single project, because one needs to integrate and synthesize information from multiple projects and multiple perspectives for high-level strategic business decisions, such as aligning projects with business objectives, balancing investment and expected return, and allocating resources. Current methods and tools either do not well integrate multiple aspects or are not intuitive and easy to use for managers and executives. In this dissertation project, a multidimensional and visual exploration approach was designed and evaluated to provide a unique and intuitive option to support decision making in project portfolio management. The research followed a general design science research methodology involving phases of awareness of problem, suggestion, development, evaluation and conclusion. The approach was implemented into a software system using a prototyping method and was evaluated through user interviews. The evaluation result demonstrates the utility and ease-of-use of the approach, and confirms design objectives. The research brings a new perspective and provides a new decision support tool for project portfolio management. It also contributes to the design knowledge of visual exploration systems for business portfolio management by theorizing the system

A Multidimensional and Visual Exploration Approach to Project Prioritization and Selection

In project management, many decisions are made based on multiple attributes (dimensions) of project data. However, these dimensions are usually condensed into one or two indicators in the analysis process. For example, projects are commonly prioritized using a scoring approach: they are evaluated according to predefined categories, which are then aggregated into one or two priority numbers. We argue that aggregated scores may only offer a limited view of project importance. Such scores tend to hide information that may effectively distinguish projects; this often leads decision makers to ignore the possible differences masked by aggregation. This paper presents a visual exploration approach that integrates human intuition and maintains the multidimensionality of project data as a decision basis for project prioritization and selection. The approach is based on the examination of portfolio perceptual maps, generated by a clustering technique. The research provides a useful and complementary approach for decision makers to analyze project portfolios

Bringing Business Intelligence to Health Information Technology Curriculum

Business intelligence (BI) and healthcare analytics are the emerging technologies that provide analytical capability to help healthcare industry improve service quality, reduce cost, and manage risks. However, such component on analytical healthcare data processing is largely missed from current healthcare information technology (HIT) or health informatics (HI) curricula. In this paper, we took an initial step to fill this gap. We investigated the current HIT educational programs, BI industry, and healthcare BI job listings, and students’ perceptions of BI and how BI could be incorporated into HIT programs. The student survey results showed strong interests from students in a HIT course containing BI components or a BI course specialized in the healthcare context. Based on the student survey and investigation of BI industry and job market, as well as HIT educational programs, we developed a general curriculum framework and exemplar implementation strategies to demonstrate how BI can be incorporated into an HI or HIT program. To the best of our knowledge, this research is the first of its kind. Our approach of integrating information from students, the HIT industry and other HIT programs can also be used as a model for general HIT curriculum development and improvement

A Multidimensional Perceptual Map Approach to Project Prioritization and Selection

When prioritizing projects, managers usually have to evaluate multiple attributes (dimensions) of project data. However, these dimensions are usually condensed into one or two indicators in many existing analysis processes. For example, projects are commonly prioritized using a scoring approach: they are evaluated according to predefined categories, which are then aggregated into one or two priority numbers. We argue that aggregated scores may only offer a limited view of project importance. This often leads decision makers to ignore the possible differences masked by the aggregation. Following the design science research paradigm, this paper presents a visual exploration approach based on multi-dimensional perceptual maps. It incorporates human intuition in the process and maintains the multidimensionality of project data as a decision basis for project prioritization and selection. A prototype system based on the approach was developed and qualitatively evaluated by a group of project managers. A qualitative analysis of the data collected shows its utility and usability

Developing Business Intelligence Competency In Health It: Perspectives From Health It Professionals

Business intelligence (BI) is a set of methods and technologies that can provide analytical power to help the healthcare industry to tackle the challenges brought by ever-growing and complex health data. To develop a successful Health Information Technology (HIT) or Health Informatics (HI) curriculum with the component of BI or health data analytics, it is critical to first identify the sets of important skills that a HIT student should possess upon graduation, especially from HIT professionals’ perspective. In this paper, we reported findings from a pilot study in which we surveyed a group of HIT practitioners. The implications of the pilot study are discussed

Promoting Information Systems Major to Undergraduate Students - A Comprehensive Investigation

Weak enrollment growth has been a concern for many Information Systems (IS) programs in recent years although the IT/IS job market remains strong. Stimulating undergraduate students’ interest to IS programs have been a challenge. In this paper, the researchers took a comprehensive approach to study how to effectively promote a Management Information Systems (MIS) program to undergraduate students at a medium-size public university in the southeastern US. Using a survey-based method, the researchers first investigated the factors that impact students’ selection of majors and identified students’ perceptions on an MIS program. In this paper, an MIS program promotion strategy was then developed and empirically validated. The research results showed that the promotion strategy can successfully stimulate participants’ positive perceptions on the MIS program. The approach presented in this study could serve as an exemplar to other IS programs or other major fields to tackle enrollment challenges

An Evaluation Framework for Selecting Collaboration Systems for Student Teamwork

Collaboration technologies play an increasingly important role in student teamwork in universities. With the proliferation of collaboration systems on the market and the wide range of features they offer, choosing an appropriate system can be an overwhelming task for college students. In this paper, the authors present an empirical study that aimed to help college instructors and students assess and select appropriate collaboration systems for their teamwork needs. They first identified and ranked the important features of collaboration systems for students through a web-based survey. Based on the survey results, the authors built an evaluation framework, in the form of weighted scoring tables, to help students systematically choose technologies that met their collaborative needs. They further demonstrated how to use those scoring tables for an undergraduate capstone class that had a term-long team project. The implications and future directions of the authors\u27 study are also discussed

Can Contextual Biasing Remain Effective with Whisper and GPT-2?

End-to-end automatic speech recognition (ASR) and large language models, such as Whisper and GPT-2, have recently been scaled to use vast amounts of training data. Despite the large amount of training data, infrequent content words that occur in a particular task may still exhibit poor ASR performance, with contextual biasing a possible remedy. This paper investigates the effectiveness of neural contextual biasing for Whisper combined with GPT-2. Specifically, this paper proposes integrating an adapted tree-constrained pointer generator (TCPGen) component for Whisper and a dedicated training scheme to dynamically adjust the final output without modifying any Whisper model parameters. Experiments across three datasets show a considerable reduction in errors on biasing words with a biasing list of 1000 words. Contextual biasing was more effective when applied to domain-specific data and can boost the performance of Whisper and GPT-2 without losing their generality.Comment: To appear in Interspeech 202