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Das Forschungscluster Smart Data Analytics stellt in dem vorliegenden Band seine Forschung aus den Jahren 2019 und 2020 vor. In der ersten Hälfte des Bandes geben 20 Kurzporträts von laufenden oder kürzlich abgeschlossenen Projekten einen Überblick über die Forschungsthemen im Cluster. Enthalten in den Kurzporträts ist eine vollständige, kommentierte Liste der wissenschaftlichen Veröffentlichungen aus den Jahren 2019 und 2020. In der zweiten Hälfte dieses Bandes geben vier längere Beiträge exemplarisch einen tieferen Einblick in die Forschung des Clusters und behandeln Themen wie Fehlererkennung in Datenbanken, Analyse und Visualisierung von Sicherheitsvorfällen in Netzwerken, Wissensmodellierung und Datenintegration in der Medizin, sowie die Frage ob ein Computerprogramm Urheber eines Kunstwerkes im Sinne des Urheberrechts sein kann.
In recent years, multiple efforts for reducing energy usage have been proposed. Especially buildings offer high potentials for energy savings. In this paper, we present a novel approach for intelligent energy control that combines a simple infrastructure using low cost sensors with the reasoning capabilities of Complex Event Processing. The key issues of the approach are a sophisticated semantic domain model and a multi-staged event processing architecture leading to an intelligent, situation-aware energy management system.
Complex Event Processing (CEP) has been established as a well-suited software technology for processing high-frequent data streams. However, intelligent stream based systems must integrate stream data with semantical background knowledge. In this work, we investigate different approaches on integrating stream data and semantic domain knowledge. In particular, we discuss from a software engineering per- spective two different architectures: an approach adding an ontology access mechanism to a common Continuous Query Language (CQL) is compared with C-SPARQL, a streaming extension of the RDF query language SPARQL.
In huge warehouses or stockrooms, it is often very difficult to find a certain item, because it has been misplaced and is therefore not at its assumed position. This position paper presents an approach on how to coordinate mobile RFID agents using a blackboard architecture based on Complex Event Processing.
In this article, we present the software architecture of a new generation of advisory systems using Intelligent Agent and Semantic Web technologies. Multi-agent systems provide a well-suited paradigm to implement negotiation processes in a consultancy situation. Software agents act as clients and advisors, using their knowledge to assist human users. In the presented architecture, the domain knowledge is modeled semantically by means of XML-based ontology languages such as OWL. Using an inference engine, the agents reason, based on their knowledge to make decisions or proposals. The agent knowledge consists of different types of data: on the one hand, private data, which has to be protected against unauthorized access; and on the other hand, publicly accessible knowledge spread over different Web sites. As in a real consultancy, an agent only reveals sensitive private data, if they are indispensable for finding a solution. In addition, depending on the actual consultancy situation, each agent dynamically expands its knowledge base by accessing OWL knowledge sources from the Internet. Due to the standardization of OWL, knowledge models easily can be shared and accessed via the Internet. The usefulness of our approach is proved by the implementation of an advisory system in the Semantic E-learning Agent (SEA) project, whose objective is to develop virtual student advisers that render support to university students in order to successfully organize and perform their studies.
In parcel delivery, the “last mile” from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes. In crowdshipping, private citizens (“the crowd”) perform short detours in their daily lives to contribute to parcel delivery in exchange for small incentives. However, achieving desirable crowd behavior is challenging as the crowd is highly dynamic and consists of autonomous, self-interested individuals. Leveraging crowdshipping for time-sensitive deliveries remains an open challenge. In this paper, we present an agent-based approach to on-time parcel delivery with crowds. Our system performs data stream processing on the couriers’ smartphone sensor data to predict delivery delays. Whenever a delay is predicted, the system attempts to forge an agreement for transferring the parcel from the current deliverer to a more promising courier nearby. Our experiments show that through accurate delay predictions and purposeful task transfers many delays can be prevented that would occur without our approach.
Complex Event Processing (CEP) is a modern software technology for the dynamic analysis of continuous data streams. CEP is able of searching extremely large data streams in real time for the presence of event patterns. So far, specifying event patterns of CEP rules is still a manual task based on the expertise of domain experts. This paper presents a novel batinspired swarm algorithm for automatically mining CEP rule patterns that express the relevant causal and temporal relations hidden in data streams. The basic suitability and performance of the approach is proven by extensive evaluation with both synthetically generated data and real data from the traffic domain.
M2M (machine-to-machine) systems use various communication technologies for automatically monitoring and controlling machines. In M2M systems, each machine emits a continuous stream of data records, which must be analyzed in real-time. Intelligent M2M systems should be able to diagnose their actual states and to trigger appropriate actions as soon as critical situations occur. In this paper, we show how complex event processing (CEP) can be used as the key technology for intelligent M2M systems. We provide an event-driven architecture that is adapted to the M2M domain. In particular, we define different models for the M2M domain, M2M machine states and M2M events. Furthermore, we present a general reference architecture defining the main stages of processing machine data. To prove the usefulness of our approach, we consider two real-world examples ‘solar power plants’ and ‘printers’, which show how easily the general architecture can be extended to concrete M2M scenarios.
Mobile crowdsourcing refers to systems where the completion of tasks necessarily requires physical movement of crowdworkers in an on-demand workforce. Evidence suggests that in such systems, tasks often get assigned to crowdworkers who struggle to complete those tasks successfully, resulting in high failure rates and low service quality. A promising solution to ensure higher quality of service is to continuously adapt the assignment and respond to failure-causing events by transferring tasks to better-suited workers who use different routes or vehicles. However, implementing task transfers in mobile crowdsourcing is difficult because workers are autonomous and may reject transfer requests. Moreover, task outcomes are uncertain and need to be predicted. In this paper, we propose different mechanisms to achieve outcome prediction and task coordination in mobile crowdsourcing. First, we analyze different data stream learning approaches for the prediction of task outcomes. Second, based on the suggested prediction model, we propose and evaluate two different approaches for task coordination with different degrees of autonomy: an opportunistic approach for crowdshipping with collaborative, but non-autonomous workers, and a market-based model with autonomous workers for crowdsensing.
The Gravitational Search Algorithm is a swarm-based optimization metaheuristic that has been successfully applied to many problems. However, to date little analytical work has been done on this topic.
This paper performs a mathematical analysis of the formulae underlying the Gravitational Search Algorithm. From this analysis, it derives key properties of the algorithm's expected behavior and recommendations for parameter selection. It then confirms through empirical examination that these recommendations are sound.
