In the mobility industry collaborative processes are often described in natural language and stored in Word and PDF handbooks and logbooks. This unstructured information is complemented by emails and meeting minutes resulting from the communication between project stakeholders (customers, managers, engineers). Execution logs of past processes also contribute to this unstructured repository of process information. In the railway domain, non-functional requirements, such as safety, reliability, certifiability, and standard compliance of both the systems and the business processes used in creating them are key to the success of products and projects. As the fulfillment of these non-functional requirements is extremely costly and time-consuming, the automation and optimization of business processes for developing railway systems are constantly sought after in large business organizations.
To enable automation and optimization of a business process for configuring railway interlocking systems, a BPMN (Business Process Model and Notation) workflow was implemented using the Camunda Suite, which supports visual semantics and executable code generation from BPMN models. In the proposed solution, semantic technologies are used to infer semantic process models, which refine existing models at runtime. The proposed solution helps reduce the process execution time and costs through process automation and optimization. This is facilitated by semantic technologies and a strict separation of concerns using a 3-process approach: (1) a productive process monitored by (2) a mining process and dynamically refined by (3) an adaptation process.
Information overload on news data is a known problem these days. People and organizations have an increasing demand for extraction of relevant information from massive amounts of news data arriving in real-time as news streams. In this paper, we present a novel approach for real-time extraction of news, based on user specifications and by using background knowledge from specific news domains. We create a powerful filtering service which limits the news data to the concrete and essential preferences of a user. In our approach, enrichment of real-time news with background knowledge is a preprocessing step. We use a Complex Event Processor to detect complex events from the enriched articles and match them to the user specified query. Each time a news article is matched, its result is notified to the user immediately. Our experimental evaluation shows that our approach is feasible for detecting news in real-time with high precision and recall.
Recently, many approaches have been proposed to manage sensor data using Semantic Web technologies for effective heterogeneous data integration. However, our research survey revealed that these solutions primarily focused on semantic relationships and still paid less attention to its temporal-spatial correlation. Most semantic approaches do not have spatiotemporal support. Some of them have served limitations on providing full spatiotemporal support but have poor performance for complex spatiotemporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, a challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this paper, we propose a spatiotemporal query engine for sensor data based on Linked Data model. The ultimate goal of our approach is to provide an elastic and scalable system which allows fast searching and analysis on the relationships of space, time and semantic in sensor data. We also introduce a set of new query operators in order to support spatiotemporal computing in linked sensor data context.
