Applying Fuzzy Ontologies to Implement the Social Semantic Web

Because the knowledge in the World Wide Web is continuously expanding, Web Knowledge Aggregation, Representation and Reasoning (abbreviated as KR) is becoming increasingly important. This article demonstrates how fuzzy ontologies can be used in KR to improve the interactions between humans and computers. The gap between the Social and Semantic Web can be reduced, and a Social Semantic Web may become possible. As an illustrative example, we demonstrate how fuzzy logic and KR can enhance technologies for cognitive cities. The underlying notion of these technologies is based on connectivism, which can be improved by incorporating the results of digital humanities research.

Dynamics of governance and control in inter-organizational software development

In recent years, development of information systems (IS) has rapidly changed towards increasing division of labor between firms. Two trends are emerging. First, client companies increasingly outsource software development to external service providers. Second, the formerly oligopolistic enterprise application software industry has started to disintegrate into focal partnership networks – so called platform ecosystems. Despite the increasing prominence of IS outsourcing and platform ecosystems, many of these inter-organizational partnerships fail to achieve expected benefits. Ineffective governance and control frequently plays a pivotal role in producing these failures. While designing effective governance and control mechanisms is always challenging, inter-organizational software development projects are often business-critical and exhibit additional dynamics and uncertainty. As a consequence governance and control have to be adapted over time. The three research projects included in this book provide a better understanding of how and why governance and control can be effectively adapted over time. The implications for successful management of inter-organizational software development projects are highly relevant for theory and practice.

Explaining Emergence and Consequences of Specific Formal Controls in IS Outsourcing – A Process-View

IS outsourcing projects often fail to achieve project goals. To inhibit this failure, managers need to design formal controls that are tailored to the specific contextual demands. However, the dynamic and uncertain nature of IS outsourcing projects makes the design of such specific formal controls at the outset of a project challenging. Hence, the process of translating high-level project goals into specific formal controls becomes crucial for success or failure of IS outsourcing projects. Based on a comparative case study of four IS outsourcing projects, our study enhances current understanding of such translation processes and their consequences by developing a process model that explains the success or failure to achieve high-level project goals as an outcome of two unique translation patterns. This novel process-based explanation for how and why IS outsourcing projects succeed or fail has important implications for control theory and IS project escalation literature.

The Software Prototype as Digital Boundary Object – A Revelatory Longitudinal Innovation Case

With the availability of lower cost but highly skilled software development labor from offshore regions, entrepreneurs from developed countries who do not have software development experience can utilize this workforce to develop innovative software products. In order to succeed in offshored innovation projects, the often extreme knowledge boundaries between the onsite entrepreneur and the offshore software development team have to be overcome. Prior research has proposed that boundary objects are critical for bridging such boundaries – if they are appropriately used. Our longitudinal, revelatory case study of a software innovation project is one of the first to explore the role of the software prototype as a digital boundary object. Our study empirically unpacks five use practices that transform the software prototype into a boundary object such that knowledge boundaries are bridged. Our findings provide new theoretical insights for literature on software innovation and boundary objects, and have implications for practice.

Sparse computation for large-scale binary classification

Well-known data mining algorithms rely on inputs in the form of pairwise similarities between objects. For large datasets it is computationally impossible to perform all pairwise comparisons. We therefore propose a novel approach that uses approximate Principal Component Analysis to efficiently identify groups of similar objects. The effectiveness of the approach is demonstrated in the context of binary classification using the supervised normalized cut as a classifier. For large datasets from the UCI repository, the approach significantly improves run times with minimal loss in accuracy.

An application of combinatorial optimization in the printing industry

Offset printing is a common method to produce large amounts of printed matter. We consider a real-world offset printing process that is used to imprint customer-specific designs on napkin pouches. The print- ing technology used yields a number of specific constraints. The planning problem consists of allocating designs to printing-plate slots such that the given customer demand for each design is fulfilled, all technologi- cal and organizational constraints are met and the total overproduction and setup costs are minimized. We formulate this planning problem as a mixed-binary linear program, and we develop a multi-pass matching-based savings heuristic. We report computational results for a set of problem instances devised from real-world data.

Portfolio-optimization models for small investors

Since 2010, the client base of online-trading service providers has grown significantly. Such companies enable small investors to access the stock market at advantageous rates. Because small investors buy and sell stocks in moderate amounts, they should consider fixed transaction costs, integral transaction units, and dividends when selecting their portfolio. In this paper, we consider the small investor’s problem of investing capital in stocks in a way that maximizes the expected portfolio return and guarantees that the portfolio risk does not exceed a prescribed risk level. Portfolio-optimization models known from the literature are in general designed for institutional investors and do not consider the specific constraints of small investors. We therefore extend four well-known portfolio-optimization models to make them applicable for small investors. We consider one nonlinear model that uses variance as a risk measure and three linear models that use the mean absolute deviation from the portfolio return, the maximum loss, and the conditional value-at-risk as risk measures. We extend all models to consider piecewise-constant transaction costs, integral transaction units, and dividends. In an out-of-sample experiment based on Swiss stock-market data and the cost structure of the online-trading service provider Swissquote, we apply both the basic models and the extended models; the former represent the perspective of an institutional investor, and the latter the perspective of a small investor. The basic models compute portfolios that yield on average a slightly higher return than the portfolios computed with the extended models. However, all generated portfolios yield on average a higher return than the Swiss performance index. There are considerable differences between the four risk measures with respect to the mean realized portfolio return and the standard deviation of the realized portfolio return.