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

Problem and Solution Frameworks for Software Development Process Modeling,

This paper presents and discusses two frameworks for use in software development process modeling. These frameworks organize in one place many of the problems and successful solution approaches identified over the past several years in the process modeling communityi, identifying general dimensions of the process modeling problem, and general dimensions that should be considered in any potential process modeling solution approach. In addition, several important problems and potential solution approaches that, to date, have received minimal focus are proposed and included in these frameworks

A Framework for Theory Development in Design Science Research: Multiple Perspectives

One point of convergence in the many recent discussions on design science research in information systems (DSRIS) has been the desirability of a directive design theory (ISDT) as one of the outputs from a DSRIS project. However, the literature on theory development in DSRIS is very sparse. In this paper, we develop a framework to support theory development in DSRIS and explore its potential from multiple perspectives. The framework positions ISDT in a hierarchy of theories in IS design that includes a type of theory for describing how and why the design functions: Design-relevant explanatory/predictive theory (DREPT). DREPT formally captures the translation of general theory constructs from outside IS to the design realm. We introduce the framework from a knowledge representation perspective and then provide typological and epistemological perspectives. We begin by motivating the desirability of both directive-prescriptive theory (ISDT) and explanatory-predictive theory (DREPT) for IS design science research and practice. Since ISDT and DREPT are both, by definition, mid-range theories, we examine the notion of mid-range theory in other fields and then in the specific context of DSRIS. We position both types of theory in Gregor’s (2006) taxonomy of IS theory in our typological view of the framework. We then discuss design theory semantics from an epistemological view of the framework, relating it to an idealized design science research cycle. To demonstrate the potential of the framework for DSRIS, we use it to derive ISDT and DREPT from two published examples of DSRIS

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

Formalizing Theory Development in IS Design Science Research: Learning from Qualitative Research

The parallels between design science research and various types of qualitative research as well as the synergies between the two research paradigms have been pointed out in many recent design science research in IS (DSRIS) papers. Commonly, for example, a qualitative research method, action research, has been used or proposed for validation of a DSRIS artifact. Building on insights into the similarities of the two methodologies, we have surveyed the qualitative research literature in search of techniques from that area that could be applicable to theory construction and refinement in DSRIS. We have found four techniques widely used in theory construction in qualitative research that are immediately applicable to DSRIS, thus leveraging the work in an older discipline for the benefit of DSRIS. In addition we explicate the similarity between qualitative research and DSRIS in a more detailed manner than has been done previously

IS Bibliographic Repository (ISBIB): A Central Repository of Research Information for the IS Community

The IS Bibliographic Repository (ISBIB), a central repository of Information Systems citations and author information, is a shared resource for research and researcher assessment that can support multiple streams of research. The goal of the repository is to capture research citations and other valuable information from all sub-cultures and disciplines within the international IS community, thereby providing a balanced perspective on the state of art in IS research. This repository should lead to a better understanding on the scope and objectives of IS research in general. The repository also aims to be an unbiased data source for bibliometric research, and studies on IS research methods and processes. It currently holds systematic information about 82 journals. In the spirit of community development, the repository is available to the entire IS community, free of charge. This article describes the current state of the repository and invites readers to use it both for their own research and for bibliometric analysis. Because the repository is intended to be a reflection of the global IS community, the authors, who are also its maintainers, encourage IS researchers and journal editors to provide bibliographic information to extend the repository\u27s usefulness

k-Parameter Approach for False In-Season Anomaly Suppression in Daily Time Series Anomaly Detection

Detecting anomalies in a daily time series with a weekly pattern is a common task with a wide range of applications. A typical way of performing the task is by using decomposition method. However, the method often generates false positive results where a data point falls within its weekly range but is just off from its weekday position. We refer to this type of anomalies as "in-season anomalies", and propose a k-parameter approach to address the issue. The approach provides configurable extra tolerance for in-season anomalies to suppress misleading alerts while preserving real positives. It yields favorable result.Comment: 5 pages, 7 figure