A Tutorial on Prototyping Internet of Things Devices and Systems: A Gentle Introduction to Technology that Shapes Our Lives

The Internet of Things, which has been quietly building and evolving over the past decade, now impacts many aspects of society, including homes, battlefields, and medical communities. Research in information systems, traditionally, has been concentrated on exploring the impacts of such technology, rather than how to actually create systems using it. Although research in design science could especially contribute to the Internet of Things, this type of research from the Information Systems community has been sparse. The most likely cause is the knowledge barriers to learning and understanding this kind of technology development. Recognizing the importance of the continued evolution of the Internet of Things, this paper provides a basic tutorial on how to construct Internet of Things prototypes. The paper is intended to educate Information Systems scholars on how to build their own Internet of Things so they can conduct technical research in this area and instruct their students on how to do the same

Signature-Based Detection of Notable Transitions in Numeric Data Streams

A major challenge in large-scale process monitoring is to recognise significant transitions in the process conditions and to distinguish them from random fluctuations that do not produce a notable change in the process dynamics. Such transitions should be recognised at the early stages of their development using a minimal "snapshot" of the observable process log. We consider a novel approach to detecting notable transitions based on analysis of coherent behavior of frequency components in the process log (coherency portraits). We have found that notable transitions in the process dynamics are characterised by unique coherency portraits, which are also invariant with respect to the random process fluctuations. Our experimental study demonstrates the significant efficiency of our approach as compared to traditional change detection techniques

EXPLORING BEHAVIORAL PATTERNS IN COMPLEX ADAPTIVE SYSTEMS

Many phenomenons in real world can be characterized as complex adaptive systems (CAS). We are surrounded with a huge number of communicating and interacting agents. Some of those agents may be capable of learning and adapting to new situation, trying to achieve their goals. E-commerce, social media, cloud computing, transportation network and real-time ride sharing, supply chain are a few examples of CAS. These are the systems which surround us in every day’s life, and naturally we want to make sense of those systems and optimize systems’ behavior or optimize our behavior around those systems. Given the complexity of these systems, we want to find a set of simplified patterns out of the seeming chaos of interactions in a CAS, and provide more manageable means of analysis for such systems. In my thesis I consider a few example problems from different domains: modeling human behavior during fire evacuation, detection of notable transitions in data streams, modeling finite resource sharing on a computational cluster with many clients, and predicting buyer behavior on the marketplace. These (and other) seemingly different problems demonstrate one important similarity: complex semi-repetitive or semi-similar behavior. This semi-repetitive behavior poses a challenge to model such processes. This challenge comes for two major reasons: 1 ) state-space explosion and sparsity of data 2 ) critical transitions and precision of process modeling I show, that the analysis of smilingly different CAS coming from different domains, can be performed by following the same recipe