Spatio Temporal Stream Reasoning With Adaptive State Stream Generation
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Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation
Author: Daniel de Leng
language: en
Publisher: Linköping University Electronic Press
Release Date: 2017-09-08
A lot of today's data is generated incrementally over time by a large variety of producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, making sense of these streams of data through reasoning is challenging. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in a physical environment. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and its refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this thesis, we integrate techniques for logic-based spatio-temporal stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over streaming data and the problem of robustly managing streaming data and its refinement. The main contributions of this thesis are (1) a logic-based spatio-temporal reasoning technique that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt in situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in the context of a case study on run-time adaptive reconfiguration. The results show that the proposed system – by combining reasoning over and reasoning about streams – can robustly perform spatio-temporal stream reasoning, even when the availability of streaming resources changes.
Robust Stream Reasoning Under Uncertainty
Author: Daniel de Leng
language: en
Publisher: Linköping University Electronic Press
Release Date: 2019-11-08
Vast amounts of data are continually being generated by a wide variety of data producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, the ability to make sense of these streams of data through reasoning is of great importance. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in physical environments. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and their refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this work, we integrate techniques for logic-based stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over uncertain streaming data and the problem of robustly managing streaming data and their refinement. The main contributions of this work are (1) a logic-based temporal reasoning technique based on path checking under uncertainty that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt to situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in a case study on run-time adaptive reconfiguration. The results show that the proposed system - by combining reasoning over and reasoning about streams - can robustly perform stream reasoning, even when the availability of streaming resources changes.
Exploring C2 Capability and Effectiveness in Challenging Situations
Author: Magdalena Granåsen
language: en
Publisher: Linköping University Electronic Press
Release Date: 2019-05-27
Modern societies are affected by various threats and hazards, including natural disasters, cyber-attacks, extreme weather events and inter-state conflicts. Managing these challenging situations requires immediate actions, suspension of ordinary procedures, decision making under uncertainty and coordinated action. In other words, challenging situations put high demands on the command and control (C2) capability. To strengthen the capability of C2, it is vital to identify the prerequisites for effective coordination and direction within the domain of interest. This thesis explores C2 capability and effectiveness in three domains: interorganizational crisis management, military command and control, and cyber defence operations. The thesis aims to answer three research questions: (1) What constitutes C2 capability? (2) What constitutes C2 effectiveness? and (3) How can C2 effectiveness be assessed? The work was carried out as two case studies and one systematic literature review. The main contributions of the thesis are the identification of perspectives of C2 capability in challenging situations and an overview of approaches to C2 effectiveness assessment. Based on the results of the three studies, six recurring perspectives of capability in the domains studied were identified: interaction (collaboration), direction and coordination, relationships, situation awareness, resilience and preparedness. In the domains there are differences concerning which perspectives that are most emphasized in order obtain C2 capability. C2 effectiveness is defined as the extent to which a C2 system is successful in achieving its intended result. The thesis discusses the interconnectedness of performance and effectiveness measures, and concludes that there is not a united view on the difference between measures of effectiveness and measures of performance. Different approaches to effectiveness assessment were identified, where assessment may be conducted based on one specific issue, in relation to a defined goal for a C2 function or using a more exploratory approach.