The role of management incentives in successful information systems development and implementation

Because organizations are making large investments in Information systems (IS), efficient IS project management has been found critical to success. This study examines how the use of incentives can improve the project success. Agency theory is used to: identify motivational factors of project success, help the IS owners to understand to what extent management incentives can improve IS development and implementation (ISD/I). The outcomes will help practitioners and researchers to build on theoretical model of project management elements which lead to project success. Given the principal-agent nature of most significant scale of IS development, insights that will allow for greater alignment of the agent’s goals with those of the principal through incentive contracts, will serve to make ISD/I both more efficient and more effective, leading to more successful IS projects.

Major issues in business process management: An Australian perspective

Business Process Management (BPM) is widely seen as the top priority in organizations wanting to survive competitive markets. However, the current academic research agenda does not seem to map with industry demands. In this paper, we address the need to identify the actual issues that organizations face in their efforts to manage business processes. To that end, we report a number of critical issues identified by industry in what we consider to be the first steps towards an industry-driven research agenda for the BPM area. The reported issues are derived from a series of focus groups conducted with Australian organizations. The findings point to, among others, a need for more consolidated efforts in the areas of business process governance, systematic change management, developing BPM methodologies, and introducing appropriate performance measures.

On aggregation issues in spatial data management

Large amounts of information can be overwhelming and costly to process, especially when transmitting data over a network. A typical modern Geographical Information System (GIS) brings all types of data together based on the geographic component of the data and provides simple point-and-click query capabilities as well as complex analysis tools. Querying a Geographical Information System, however, can be prohibitively expensive due to the large amounts of data which may need to be processed. Since the use of GIS technology has grown dramatically in the past few years, there is now a need more than ever, to provide users with the fastest and least expensive query capabilities, especially since an approximated 80 % of data stored in corporate databases has a geographical component. However, not every application requires the same, high quality data for its processing. In this paper we address the issues of reducing the cost and response time of GIS queries by preaggregating data by compromising the data accuracy and precision. We present computational issues in generation of multi-level resolutions of spatial data and show that the problem of finding the best approximation for the given region and a real value function on this region, under a predictable error, in general is "NP-complete.

Methods for conflict resolution in policy-based management systems

While developments in distributed object computing environments, such as the Common Object Request Broker Architecture (CORBA) [17] and the Telecommunication Intelligent Network Architecture (TINA) [16], have enabled interoperability between domains in large open distributed systems, managing the resources within such systems has become an increasingly complex task. This challenge has been considered for several years within the distributed systems management research community and policy-based management has recently emerged as a promising solution. Large evolving enterprises present a significant challenge for policy-based management partly due to the requirement to support both mutual transparency and individual autonomy between domains [2], but also because the fluidity and complexity of interactions occurring within such environments requires an ability to cope with the coexistence of multiple, potentially inconsistent policies. This paper discusses the need of providing both dynamic (run-time) and static (compile-time) conflict detection and resolution for policies in such systems and builds on our earlier conflict detection work [7, 8] to introduce the methods for conflict resolution in large open distributed systems.

Scalable location management for context-aware systems

Location information is commonly used in context-aware applications and pervasive systems. These applications and systems may require knowledge, of the location of users, devices and services. This paper presents a location management system able to gather, process and manage location information from a variety of physical and virtual location sensors. The system scales to the complexity of context-aware applications, to a variety of types and large number of location sensors and clients, and to geographical size of the system. The proposed location management system provides conflict resolution of location information and mechanisms to ensure privacy.

Efficient Data Management with Applications to IoT

The Internet of Things (IoT) consists of a worldwide “network of networks,” composed by billions of interconnected heterogeneous devices denoted as things or “Smart Objects” (SOs). Significant research efforts have been dedicated to port the experience gained in the design of the Internet to the IoT, with the goal of maximizing interoperability, using the Internet Protocol (IP) and designing specific protocols like the Constrained Application Protocol (CoAP), which have been widely accepted as drivers for the effective evolution of the IoT. This first wave of standardization can be considered successfully concluded and we can assume that communication with and between SOs is no longer an issue. At this time, to favor the widespread adoption of the IoT, it is crucial to provide mechanisms that facilitate IoT data management and the development of services enabling a real interaction with things. Several reference IoT scenarios have real-time or predictable latency requirements, dealing with billions of device collecting and sending an enormous quantity of data. These features create a new need for architectures specifically designed to handle this scenario, hear denoted as “Big Stream”. In this thesis a new Big Stream Listener-based Graph architecture is proposed. Another important step, is to build more applications around the Web model, bringing about the Web of Things (WoT). As several IoT testbeds have been focused on evaluating lower-layer communication aspects, this thesis proposes a new WoT Testbed aiming at allowing developers to work with a high level of abstraction, without worrying about low-level details. Finally, an innovative SOs-driven User Interface (UI) generation paradigm for mobile applications in heterogeneous IoT networks is proposed, to simplify interactions between users and things.