IS2020 A Competency Model for Undergraduate Programs in Information Systems: The Joint ACM/AIS IS2020 Task Force

The IS2020 report is the latest in a series of model curricula recommendations and guidelines for undergraduate degrees in Information Systems (IS). The report builds on the foundations developed in previous model curricula reports to develop a major revision of the model curriculum with the inclusion of significant new characteristics. Specifically, the IS2020 report does not directly prescribe a degree structure that targets a specific context or environment. Rather, the IS2020 report provides guidance regarding the core content of the curriculum that should be present but also provides flexibility to customize curricula according to local institutional needs

Verifying and Monitoring IoTs Network Behavior using MUD Profiles

IoT devices are increasingly being implicated in cyber-attacks, raising community concern about the risks they pose to critical infrastructure, corporations, and citizens. In order to reduce this risk, the IETF is pushing IoT vendors to develop formal specifications of the intended purpose of their IoT devices, in the form of a Manufacturer Usage Description (MUD), so that their network behavior in any operating environment can be locked down and verified rigorously. This paper aims to assist IoT manufacturers in developing and verifying MUD profiles, while also helping adopters of these devices to ensure they are compatible with their organizational policies and track devices network behavior based on their MUD profile. Our first contribution is to develop a tool that takes the traffic trace of an arbitrary IoT device as input and automatically generates the MUD profile for it. We contribute our tool as open source, apply it to 28 consumer IoT devices, and highlight insights and challenges encountered in the process. Our second contribution is to apply a formal semantic framework that not only validates a given MUD profile for consistency, but also checks its compatibility with a given organizational policy. We apply our framework to representative organizations and selected devices, to demonstrate how MUD can reduce the effort needed for IoT acceptance testing. Finally, we show how operators can dynamically identify IoT devices using known MUD profiles and monitor their behavioral changes on their network.Comment: 17 pages, 17 figures. arXiv admin note: text overlap with arXiv:1804.0435

Realizing networks of proactive smart products

The sheer complexity and number of functionalities embedded in many everyday devices already exceed the ability of most users to learn how to use them effectively. An approach to tackle this problem is to introduce ‘smart’ capabilities in technical products, to enable them to proactively assist and co-operate with humans and other products. In this paper we provide an overview of our approach to realizing networks of proactive and co-operating smart products, starting from the requirements imposed by real-world scenarios. In particular, we present an ontology-based approach to modeling proactive problem solving, which builds on and extends earlier work in the knowledge acquisition community on problem solving methods. We then move on to the technical design aspects of our work and illustrate the solutions, to do with semantic data management and co-operative problem solving, which are needed to realize our functional architecture for proactive problem solving in concrete networks of physical and resource-constrained devices. Finally, we evaluate our solution by showing that it satisfies the quality attributes and architectural design patterns, which are desirable in collaborative multi-agents systems

Proceedings of the 3rd IUI Workshop on Interacting with Smart Objects

These are the Proceedings of the 3rd IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

People in the E-Business: New Challenges, New Solutions

[Excerpt] Human Resource Planning Society’s (HRPS) annual State of the Art/Practice (SOTA/P) study has become an integral contributor to HRPS’s mission of providing leading edge thinking to its members. Past efforts conducted in 1995, 1996, 1997, 1998, and 1999 have focused on identifying the issues on the horizon that will have a significant impact on the field of Human Resources (HR). This year, in a divergence from past practice, the SOTA/P effort aimed at developing a deeper understanding of one critical issue having a profound impact on organizations and HR, the rise of e-business. The rise of e-business has been both rapid and dramatic. One estimate puts the rate of adoption of the internet at 4,000 new users each hour (eMarketer, 1999) resulting in the expectation of 250 million people on line by the end of 2000, and 350 million by 2005 (Nua, 1999). E-commerce is expected to reach $1.3 trillion by 2003, and of that, 87 percent will go to the business to business (B2B) and 13 percent to the business to consumer (B2C) segments, respectively (Plumely, 2000)

AI Affordances Perception for the Transformation of Mobility Ecosystems

Artificial Intelligence (AI) can transform organisations, industries, and ecosystems. However, how different organisations in a given ecosystem perceive the action potentials of AI (i.e., AI affordances) has not been researched. To advance the AI affordances research and develop a nomological net of organisational and ecosystem factors that influence the AI affordances perception, this paper contributes a conceptual framework with the context of the mobility ecosystem transformation. The framework draws from two theories: the affordances theory and the social cognitive theory. The paper presents an in-depth interpretation of these theories for the perception of AI affordances and develops propositions to explain two distinct types of affordances perceptions: vicarious and autonomous. Our conceptual work offers a foundation for developing models for prediction and opens new avenues of investigating AI affordances perception. Future research could further test and validate the framework