PUMA publications for /tag/stream_processingWed Sep 23 15:22:17 CEST 2020PortoProceedings of the 7ᵗʰ International Conference on Data Science, Technology and Applicationsjun294–301DATA '18BRAID — A Hybrid Processing Architecture for Big Data12018Big_Data IoT Kappa_Architecture Lambda_Architecture batch_processing myown stream_processing The Internet of Things is applied in many domains and collects vast amounts of data. This data provides access to a lot of knowledge when analyzed comprehensively. However, advanced analysis techniques such as predictive or prescriptive analytics require access to both, history data, i.e., long-term persisted data, and real-time data as well as a joint view on both types of data. State-of-the-art hybrid processing architectures for big data—namely, the Lambda and the Kappa Architecture—support the processing of history data and real-time data. However, they lack of a tight coupling of the two processing modes. That is, the user has to do a lot of work manually in order to enable a comprehensive analysis of the data. For instance, the user has to combine the results of both processing modes or apply knowledge from one processing mode to the other. Therefore, we introduce a novel hybrid processing architecture for big data, called BRAID. BRAID intertwines the processing of history data and real-time data by adding communication channels between the batch engine and the stream engine. This enables to carry out comprehensive analyses automatically at a reasonable overhead.Mon Sep 21 11:45:55 CEST 2020PortoProceedings of the 15ᵗʰ International Joint Conference on e-Business and Telecommunicationsjul372–379SECRYPT '18The AVARE PATRON - A Holistic Privacy Approach for the Internet of Things22018IoT_apps Smart_Things privacy privacy_preferences_elicitation_&_verification stream_processing Applications for the Internet of Things are becoming increasingly popular. Due to the large amount of available context data, such applications can be used effectively in many domains. By interlinking these data and analyzing them, it is possible to gather a lot of knowledge about a user. Therefore, these applications pose a threat to privacy. In this paper, we illustrate this threat by looking at a real-world application scenario. Current state of the art focuses on privacy mechanisms either for Smart Things or for big data processing systems. However, our studies show that for a comprehensive privacy protection a holistic view on these applications is required. Therefore, we describe how to combine two promising privacy approaches from both categories, namely AVARE and PATRON. Evaluation results confirm the thereby achieved synergy effects.Mon Sep 21 11:45:55 CEST 2020HeraklionProceedings of the 4ᵗʰ International Conference on Internet of Things, Big Data and Securitymay57–68IoTBDS '19PSSST! The Privacy System for Smart Service Platforms: An Enabler for Confidable Smart Environments12019Internet_of_Things access_control actuators privacy sensors smart_service_platform stream_processing The Internet of Things and its applications are becoming increasingly popular. Especially Smart Service Platforms like Alexa are in high demand. Such a platform retrieves data from sensors, processes them in a back-end, and controls actuators in accordance with the results. Thereby, all aspects of our everyday life can be managed. In this paper, we reveal the downsides of this technology by identifying its privacy threats based on a real-world application. Our studies show that current privacy systems do not tackle these issues adequately. Therefore, we introduce PSSST!, a user-friendly and comprehensive privacy system for Smart Service Platforms limiting the amount of disclosed private information while maximizing the quality of service at the same time.Mon Sep 21 11:45:55 CEST 2020PortoProceedings of the 7ᵗʰ International Conference on Data Science, Technology and Applicationsjun294–301DATA '18BRAID — A Hybrid Processing Architecture for Big Data12018Big_Data IoT Kappa_Architecture Lambda_Architecture batch_processing stream_processing The Internet of Things is applied in many domains and collects vast amounts of data. This data provides access to a lot of knowledge when analyzed comprehensively. However, advanced analysis techniques such as predictive or prescriptive analytics require access to both, history data, i.e., long-term persisted data, and real-time data as well as a joint view on both types of data. State-of-the-art hybrid processing architectures for big data—namely, the Lambda and the Kappa Architecture—support the processing of history data and real-time data. However, they lack of a tight coupling of the two processing modes. That is, the user has to do a lot of work manually in order to enable a comprehensive analysis of the data. For instance, the user has to combine the results of both processing modes or apply knowledge from one processing mode to the other. Therefore, we introduce a novel hybrid processing architecture for big data, called BRAID. BRAID intertwines the processing of history data and real-time data by adding communication channels between the batch engine and the stream engine. This enables to carry out comprehensive analyses automatically at a reasonable overhead.Mon Sep 21 11:45:55 CEST 2020AthensProceedings of the 2018 IEEE International Conference on Pervasive Computing and Communications Workshopsmar238–243CoMoRea '18How a Pattern-based Privacy System Contributes to Improve Context Recognition2018access_control complex_event_processing databases pattern_concealing privacy stream_processing As Smart Devices have access to a lot of user-preferential data, they come in handy in any situation. Although such data—as well as the knowledge which can be derived from it—is highly beneficial as apps are able to adapt their services appropriate to the respective context, it also poses a privacy threat. Thus, a lot of research work is done regarding privacy. Yet, all approaches obfuscate certain attributes which has a negative impact on context recognition and thus service quality. Therefore, we introduce a novel access control mechanism called PATRON. The basic idea is to control access to information patterns. For instance, a person suffering from diabetes might not want to reveal his or her unhealthy eating habit, which can be derived from the pattern "rising blood sugar level" → "adding bread units". Such a pattern which must not be discoverable by some parties (e.g., insurance companies) is called private pattern whereas a pattern which improves an app's service quality is labeled as public pattern. PATRON employs different techniques to conceal private patterns and, in case of available alternatives, selects the one with the least negative impact on service quality, such that the recognition of public patterns is supported as good as possible.stream_processingCommunity for tag(s) stream_processing