AX: SEARCHING FOR DATABASE REGULARITIES USING CONCEPT NETWORKS

In many organizations, both business and scientific, we collect ever increasing amounts of data using information technology. Indeed, the technology for collecting data has outpaced our ability to analyze and interpret these very large databases. In this paper, we discuss the interaction of heuristic search and domain knowledge in the AX knowledge discovery tool. The search process rests on the use of rule quality measures and the organization of domain knowledge. A small loan application database from the machine learning repository is used to illustrate the process.Information Systems Working Papers Serie

Using Agent-Based Modelling to Address Malicious Behavior on Social Media

In this study we create a platform for evaluating social media policies through simulation. We argue that social media policies need to be tested and refined before they can be successfully applied. We propose agent-based modelling (ABM) as a method for representing both malicious and legitimate social media agents, along with their key behaviors. Our two main research questions are as follows. 1. How do we build an agent-based model of a social media platform to address social media regulation? 2. How can an agent-based simulation approach be used to assess the effectiveness of social media policies? A preliminary agent-based model has been implemented (in Python), using the five human user types (‘amplifier’, ‘broadcaster’, ‘commentator’, ‘influential user’ and ‘viewer’) and two bot types (‘simple’ and ‘sophisticated’). During the simulation, a social media network of 100 agents is created and the agents\u27 behaviors are captured in this paper

Navigating Workload Compatibility Between a Recommender System and a NoSQL Database: An Interactive Tutorial

In this tutorial, the issue of compatibility between a big data storage technology and an analytic workload is explored using a fictitious streaming company as an example. The tutorial offers an interactive approach to help students understand the importance of considering workload compatibility when adopting new technologies. We provide instructors with two Jupyter Notebooks that analyze the compatibility, a detailed instructor guide on how to execute these notebooks, lessons learned, and appendices containing solutions and explanations. This tutorial provides a valuable resource for instructors teaching courses in database systems, big data, and analytic concepts, helping students develop practical skills to navigate the complexities of big data technologies effectively

TECHNIQUES FOR THE COMPARATIVE ANALYSIS OF DATA FLOW DIAGRAMS

This paper presents an analytic framework for comparing data flow diagrams based on five dimensions: control points, process automation, data aggregation, resource usage, and raw counts. Our goal was to develop some simple quantitative metrics that are appropriate for computer-aided system development tools. In addition, we argue for computer-aided tools that support the tandem development of alternative system diagrams. Simultaneous development of competing system descriptions may allow for more accurate contrasts and insightful analysis. Finally, we use two case studies to illustrate the comparison techniques.Information Systems Working Papers Serie

AX: SEARCHING FOR DATABASE REGULARITIES USING CONCEPT NETWORKS

In many organizations, both business and scientific, we collect ever increasing amounts of data using information technology. Indeed, the technology for collecting data has outpaced our ability to analyze and interpret these very large databases. In this paper, we discuss the interaction of heuristic search and domain knowledge in the AX knowledge discovery tool. The search process rests on the use of rule quality measures and the organization of domain knowledge. A small loan application database from the machine learning repository is used to illustrate the process.Information Systems Working Papers Serie

TECHNIQUES FOR THE COMPARATIVE ANALYSIS OF DATA FLOW DIAGRAMS

This paper presents an analytic framework for comparing data flow diagrams based on five dimensions: control points, process automation, data aggregation, resource usage, and raw counts. Our goal was to develop some simple quantitative metrics that are appropriate for computer-aided system development tools. In addition, we argue for computer-aided tools that support the tandem development of alternative system diagrams. Simultaneous development of competing system descriptions may allow for more accurate contrasts and insightful analysis. Finally, we use two case studies to illustrate the comparison techniques.Information Systems Working Papers Serie

Community Healh Assessments: A Data Warehousing Approach

The measurement and assessment of health status in communities throughout the world is a massive information technology challenge. The Comprehensive Assessment for Tracking Community Health (CATCH) methodology provides a systematic framework for community-level assessment that can be a valuable tool for resource allocation and health care policy formulation. CATCH utilizes health status indicators from multiple data sources, using an innovative comparative framework and weighted evaluation process to produce a rank-ordered list of critical community health care challenges. The community-level focus is intended to empower local decision-makers and provide a clear methodology for organizing and interpreting relevant health care data. The effectiveness of the CATCH methodology is based on a data warehousing approach. The data warehouse allows a core set of reports to be produced at a reasonable cost for community use. In addition, online analytic processing (OLAP) functionality can be used to gain a deeper understanding of the health care issues. The data warehouse in conjunction with Internet-enabled dissemination methods will allow the information to be presented in a variety of formats and be distributed more widely in the decision-making community. On-going research directions in community health care decision making conclude the paper

Information Markets: A Research Landscape

Information markets are mechanisms that allow a group of geographically dispersed participants to reach and continuously reevaluate consensus by discovering the value of alternative outcomes. Evidence suggests that these markets can produce better quality decisions than a small subset of selected decision makers: a finding in direct opposition to the trust we place on expertise. In challenging and uncertain decision-making arenas, information markets offer an interesting, and somewhat counter-intuitive approach. In practice, information markets may be used in combination with other decision-making methods, but these market-based mechanisms offer many advantages. This paper presents an information market typology and explores some of the challenges raised by different market applications. Market types include event and estimation-based prediction markets, decision markets, and idea markets. An integrated research landscape model and research propositions are presented to help guide continuing research in this area

Focus Groups for Artifact Refinement and Evaluation in Design Research

Focus groups to investigate new ideas are widely used in many research fields. The use of focus groups in design research poses interesting opportunities and challenges. Traditional focus group methods must be adapted to meet two specific goals of design research. For the refinement of an artifact design, exploratory focus groups (EFGs) study the artifact to propose improvements in the design. The cycle of build and evaluate using EFGs continues until the artifact is released for field test in the application environment. Then, the field test of the design artifact may employ confirmatory focus groups (CFGs) to establish the utility of the artifact in field use. Rigorous investigation of the artifact requires multiple CFGs to be run with opportunities for quantitative and qualitative data collection and analyses across the multiple CFGs. In this paper, we discuss the adaptation of focus groups to design research projects. We demonstrate the use of both EFGs and CFGs in a design research project in the health care field