Bernhard Wolf

A planning method for component placement in smart item environments using heuristic search

Smart item environments consist of networked nodes with heterogeneous hardware equipment and intermittent network connections. Using a common component technology allows for flexible distribution of components for processing of smart item data. Finding a good deployment plan for a new set of components in an infrastructure is called Component Placement Problem. We propose an approach for finding suitable deployment plans for components with special regard to the characteristics of smart item environments. Our method evaluates deployment plans in terms of both resource consumption and availability. From the analysis of the solution space we conclude that the number of network link uses is an important criterion for the quality of a deployment plan regarding both cost and availability. Based on this finding, we have derived a heuristic that creates deployment plans, which have a low number of link uses and are thus more likely of high quality.

Implementing Gamification: Requirements and Gamification Platforms

To a large extent, gamification is a psychological- and design-oriented discipline, i.e., a lot of effort has to be spent already in the design phase of a gamification project. Once the design is finalized, the implementation phase takes place in information systems (IS) such as supply chain management, customer relationship management, portals, or enterprise resource planning which act as mediator to transport a gameful design to its users. However, in general the efforts for the subsequent development and integration process are often underestimated. In fact, most conceptual gamification designs are never implemented due to the high development costs. In this paper, we describe gamification as a software development process and present requirements for a successful introduction of gamification. Moreover, existing gamification solutions are analyzed with regards to these requirements.

Efficient persistency management in complex event processing: a hybrid approach for gamification systems

Complex Event Processing (CEP) has been successfully applied in various domains. As of today, the management of external, durable, and encapsulated state in such systems has received little attention in research. An emerging kind of rule and event-based systems are platforms for gamification. These systems require an efficient management of entities containing state. In this paper, we are proposing a hybrid system capable of fast event processing on the one hand and global state, entity, and persistency management on the other hand. Moreover, we present and evaluate different synchronization strategies between an event processor and a business entity provider. We demonstrate that our extensions outperform conventional CEP solutions in terms of state persistency and ex post analytics by adding just a marginal performance overhead.

Data stream processing in factory automation

Data stream processing is a valuable technique to solve demanding problems that also occur in factory automation, such as continuous data processing with high throughput and real-time output, and distributed data acquisition and processing. However, the intricacies of data stream processing techniques make its application difficult in real-life scenarios. One particularly challenging situation arises when changing conditions necessitate a modification in processing logic of system operators. This is especially difficult in the presence of streaming data and transient internal states of the system. Since downtimes are expensive, an efficient solution has to be provided for updating the processing logic. In this paper, strategies for on-the-fly adaptation of data stream queries are presented and experimentally evaluated with examples from the domain of condition-based maintenance. Techniques for state preservation allow for a fast transition to new processing logic. The results show that our strategies are well suited for demanding applications in factory environments.

Refining the window size of sliding window operations in running data processing systems

In spite of continuous improvements at running production systems, down times have to be reduced further. The challenge is to find means to implement changes online which denotes avoid stopping and restarting the system. As changes on running systems are difficult to handle and may result in lost states a solution is to replicate the running system, modify it in parallel and reintegrate the changes reusing the previous states. In this paper we present an approach of replicating a running data processing system with the special focus on the state retaining reintegration of the modified algorithms. Recovery mechanisms for sliding window operations are proposed and investigated by means of simulation.