Streaming MASSIF : cascading reasoning for efficient processing of iot data streams

In the Internet of Things (IoT), multiple sensors and devices are generating heterogeneous streams of data. To perform meaningful analysis over multiple of these streams, stream processing needs to support expressive reasoning capabilities to infer implicit facts and temporal reasoning to capture temporal dependencies. However, current approaches cannot perform the required reasoning expressivity while detecting time dependencies over high frequency data streams. There is still a mismatch between the complexity of processing and the rate data is produced in volatile domains. Therefore, we introduce Streaming MASSIF, a Cascading Reasoning approach performing expressive reasoning and complex event processing over high velocity streams. Cascading Reasoning is a vision that solves the problem of expressive reasoning over high frequency streams by introducing a hierarchical approach consisting of multiple layers. Each layer minimizes the processed data and increases the complexity of the data processing. Cascading Reasoning is a vision that has not been fully realized. Streaming MASSIF is a layered approach allowing IoT service to subscribe to high-level and temporal dependent concepts in volatile data streams. We show that Streaming MASSIF is able to handle high velocity streams up to hundreds of events per second, in combination with expressive reasoning and complex event processing. Streaming MASSIF realizes the Cascading Reasoning vision and is able to combine high expressive reasoning with high throughput of processing. Furthermore, we formalize semantically how the different layers in our Cascading Reasoning Approach collaborate

Marrying Big Data with Smart Data in Sensor Stream Processing

Widespread deployments of spatially distributed sensors are continuously generating data that require advanced analytical processing and interpretation by machines. Devising machine-interpretable descriptions of sensor data is a key issue in building a semantic stream processing engine. This paper proposes a semantic sensor stream processing pipeline using Apache Kafka to publish and subscribe semantic data streams in a scalable way. We use the Kafka Consumer API to annotate the sensor data using the Semantic Sensor Network ontology, then store the annotated output in an RDF triplestore for further reasoning or semantic integration with legacy information systems. We follow a Design Science approach addressing a Smart Airport scenario with geolocated audio sensors to evaluate the viability of the proposed pipeline under various Kafka-based configurations. Our experimental evaluations show that the multi-broker Kafka cluster setup supports read scalability thus facilitating the parallelization of the semantic enrichment of the sensor data

Gameful Learning for a More Sustainable World

Municipal waste sorting is an important but neglected topic within sustainability-oriented Information Systems research. Most waste management systems depend on the quality of their citizens pre-sorting but lack teaching resources. Thus, it is important to raise awareness and knowledge on correct waste sorting to strengthen current efforts. Having shown promising results in raising learning outcomes and motivation in domains like health and economics, gamification is an auspicious approach to address this problem. The paper explores the effectiveness of gameful design on learning outcomes of waste sorting knowledge with a mobile game app that implements two different learning strategies: repetition and elaboration. In a laboratory experiment, the overall learning outcome of participants who trained with the game was compared to that of participants who trained with standard analogue non-game materials. Furthermore, the effects of two additional, learning-enhancing design elements – repetition and look-up – were analyzed. Learning outcome in terms of long-term retention and knowledge transfer were evaluated through three different testing measures two weeks after the training: in-game, through a multiple-choice test and real-life sorting. The results show that the game significantly enhanced the learning outcome of waste sorting knowledge for all measures, which is particularly remarkable for the real-life measure, as similar studies were not successful with regard to knowledge transfer to real life. Furthermore, look-up is found to be a promising game design element that is not yet established in IS literature and therefore should be considered more thoroughly in future research and practical implementations alike

Gameful Learning for a More Sustainable World – Measuring the Effect of Design Elements on Long-Term Learning Outcomes in Correct Waste Sorting

Municipal waste sorting is an important but neglected topic within sustainability-oriented Information Systems research. Most waste management systems depend on the quality of their citizens pre-sorting but lack teaching resources. Thus, it is important to raise awareness and knowledge on correct waste sorting to strengthen current efforts. Having shown promising results in raising learning outcomes and motivation in domains like health and economics, gamification is an auspicious approach to address this problem. The paper explores the effectiveness of gameful design on learning outcomes of waste sorting knowledge with a mobile game app that implements two different learning strategies: repetition and elaboration. In a laboratory experiment, the overall learning outcome of participants who trained with the game was compared to that of participants who trained with standard analogue non-game materials. Furthermore, the effects of two additional, learning-enhancing design elements – repetition and look-up – were analyzed. Learning outcome in terms of long-term retention and knowledge transfer were evaluated through three different testing measures two weeks after the training: in-game, through a multiple-choice test and real-life sorting. The results show that the game significantly enhanced the learning outcome of waste sorting knowledge for all measures, which is particularly remarkable for the real-life measure, as similar studies were not successful with regard to knowledge transfer to real life. Furthermore, look-up is found to be a promising game design element that is not yet established in IS literature and therefore should be considered more thoroughly in future research and practical implementations alike

Semantic Systems. The Power of AI and Knowledge Graphs

This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies

Semantic IoT for reasoning and BigData analytics

Recent developments in the IoT industries have led to an increase in data availability that is starting to weight heavily on the traditional idea of pushing data to the Cloud. This study focuses on identifying tasks that can be pulled from the Cloud in a semantic stream processing context

Special Topics in Information Technology

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2019-20 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists