There is ample evidence of deviations between the actual and planned interaction structures between a firm's IT department and business units. Such deviations can hinder senior managers from governing their IT organizations effectively because they do not know how
work really gets done. This paper develops an explanation for why actual structures differ from planned structures. Understanding this phenomenon is indispensable for managers to govern the real organization, to uphold compliance with important standards (e.g., ITIL, COBIT), to decide whether the formal or the actual organization is more effective, and, finally, to identify management actions that support the optimal structure. To develop this understanding, we analyze the interaction structures at the interface between firms' business units and IT units in four rich cases, using data from 56 interviews and 47 questionnaires, and applying qualitative methods and social network analysis, which give us deep insights into planned and actual interaction among employees. We test two different
explanations for deviations of actual from planned interaction structures and find that boundary-spanning theory provides the dominant explanation for such deviations: Inclined to span the business/IT boundary most effectively, the actors involved deviate from planned
structures especially when other structures offer better boundary-spanning potential, which is influenced primarily by cross-domain knowledge. In addition, relationships also play an important role. On the positive side, relationships provide opportunities for such deviations, while on the negative side, a conflict-laden relationship might hinder deviations even if they were advantageous.
Empathy measures that involve both cognitive and affective aspects of empathy are scarce. The aim of the work presented in this paper was to develop an instrument for the assessment of both aspects of dispositional empathy in children aged eight to fourteen, based on a variety of existing measures and results from the validation of a first preliminary version.
By using the remote functions of a modern IT service management system infrastructure, it is possible to analyze huge amounts of logfile data from complex technical equipment. This enables a service provider to predict failures of connected equipment before they happen. The problem most providers face in this context is finding a needle in a haystack - the obtained amount of data turns out to be too large to be analyzed manually. This report describes a process to find suitable predictive patterns in log files for the detection of upcoming critical situations. The identification process may serve as a hands-on guide. It describes how to connect statistical means, data mining algorithms and expert domain knowledge in the domain of service management. The process was developed in a research project which is currently being carried out within the Siemens Healthcare service organization. The project deals with two main aspects: First, the identification of predictive patterns in existing service data and second, the architecture of an autonomous agent which is able to correlate such patterns. This paper summarizes the results of the first project challenge. The identification process was tested successfully in a proof of concept for several Siemens Healthcare products.
We develop a transformation based on morphological filters
that measures the contrast of image texture. This ransforma
tion is proportional to texture contrast, but insensitive to its specific type. Though the transformation provides a high response in textured areas, it suppresses individual high contrast features that stand apart from textured areas. It can serve as an effective texture descriptor for unsupervised or supervised segmentation of textured regions, provides high accuracy of localization and does not involve heavy computations. The method is robust to variations of illumination and works on
different types of images without needing to be tuned. The only parameter is a scale related parameter. We illustrate the use of the proposed method on satellite and aerial images.
Recently, we introduced a morphological texture contrast (MTC) operator that allows detection of textural and non-texture regions in images. In this paper we provide comparison of the MTC with other available techniques. We show that, in contrast to other approaches, the MTC discriminates between texture details and isolated features, and does not extend borders of texture regions. Using the ideas underlying the MTC operator, we develop a complementary operator called morphological feature contrast (MFC) that allows extraction of isolated features while not being confused by texture details. We illustrate an application of the MFC operator for extraction of isolated bjects such as individual trees or buildings that should be distinguished from forests or urban centers. We furthermore provide an example of how this operator can be used for detection of isolated linear structures. We also derive an
extended version of the MFC that works with vector-valued images.