| 1. |
- Bonte, Pieter, et al.
(författare)
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Grounding Stream Reasoning Research
- 2024
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Ingår i: Transactions on Graph Data and Knowledge (TGDK). - Wadern, Germany : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH. - 2942-7517. ; 2:1, s. 1-47
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Tidskriftsartikel (refereegranskat)abstract
- In the last decade, there has been a growing interest in applying AI technologies to implement complex data analytics over data streams. To this end, researchers in various fields have been organising a yearly event called the "Stream Reasoning Workshop" to share perspectives, challenges, and experiences around this topic.In this paper, the previous organisers of the workshops and other community members provide a summary of the main research results that have been discussed during the first six editions of the event. These results can be categorised into four main research areas: The first is concerned with the technological challenges related to handling large data streams. The second area aims at adapting and extending existing semantic technologies to data streams. The third and fourth areas focus on how to implement reasoning techniques, either considering deductive or inductive techniques, to extract new and valuable knowledge from the data in the stream.This summary is written not only to provide a crystallisation of the field, but also to point out distinctive traits of the stream reasoning community. Moreover, it also provides a foundation for future research by enumerating a list of use cases and open challenges, to stimulate others to join this exciting research area.
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| 2. |
- de Leng, Daniel, 1988-, et al.
(författare)
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Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty
- 2019
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Ingår i: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI). - Palo Alto : AAAI Press. ; , s. 2760-2767
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Konferensbidrag (refereegranskat)abstract
- Stream reasoning can be defined as incremental reasoning over incrementally-available information. The formula progression procedure for Metric Temporal Logic (MTL) makes use of syntactic formula rewritings to incrementally evaluate formulas against incrementally-available states. Progression however assumes complete state information, which can be problematic when not all state information is available or can be observed, such as in qualitative spatial reasoning tasks or in robotics applications. In those cases, there may be uncertainty as to which state out of a set of possible states represents the ‘true’ state. The main contribution of this paper is therefore an extension of the progression procedure that efficiently keeps track of all consistent hypotheses. The resulting procedure is flexible, allowing a trade-off between faster but approximate and slower but precise progression under uncertainty. The proposed approach is empirically evaluated by considering the time and space requirements, as well as the impact of permitting varying degrees of uncertainty.
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| 3. |
- de Leng, Daniel, 1988-, et al.
(författare)
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DyKnow: A Dynamically Reconfigurable Stream Reasoning Framework as an Extension to the Robot Operating System
- 2016
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Ingår i: Proceedings of the Fifth IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR). - : IEEE conference proceedings. - 9781509046164 - 9781509046171 ; , s. 55-60
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Konferensbidrag (refereegranskat)abstract
- DyKnow is a framework for stream reasoning aimed at robot applications that need to reason over a wide and varying array of sensor data for e.g. situation awareness. The framework extends the Robot Operating System (ROS). This paper presents the architecture and services behind DyKnow's run-time reconfiguration capabilities and offers an analysis of the quantitative and qualitative overhead. Run-time reconfiguration offers interesting advantages, such as fault recovery and the handling of changes to the set of computational and information resources that are available to a robot system. Reconfiguration capabilities are becoming increasingly important with the advances in areas such as the Internet of Things (IoT). We show the effectiveness of the suggested reconfiguration support by considering practical case studies alongside an empirical evaluation of the minimal overhead introduced when compared to standard ROS.
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| 4. |
- de Leng, Daniel, 1988-, et al.
(författare)
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Last Night in Sweden: A Vision for Resource-Intelligent Stream Reasoning
- 2024
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Ingår i: Proceedings of the 18th ACM International Conference on Distributed and Event-based Systems (DEBS ’24).
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Even though data is continually being produced by a wide range of services, harnessing this data in an effective manner at different levels of abstraction is challenging. In this vision paper, we consider how data streams representing real-world observations produced by geographically-distributed Internet of Things (IoT) devices can be used to adaptively fulfill a user's information needs. To solve this information need in a generic fashion, it is up to the underlying system to configure itself, split up processing across available resources, discover data sources, integrate available data, deal with uncertainty, integrate domain knowledge, etc. This is an interdisciplinary problem, and while many fields have considered the subproblems involved, this challenge requires a novel and integrated approach when these subproblems are put together. We propose Resource-Intelligent Stream Reasoning as an overarching solution to tackle this interdisciplinary problem. We define a novel set of challenges that capture the need for an integrated approach, show the limitations in the the current state-of-the-art and define the future directions in order to realize Resource-Intelligent Stream Reasoning.
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| 5. |
- de Leng, Daniel, 1988-, et al.
(författare)
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Qualitative Spatio-Temporal Stream Reasoning With Unobservable Intertemporal Spatial Relations Using Landmarks
- 2016
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Ingår i: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI). - : AAAI Press. - 9781577357629 ; , s. 957-963
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Konferensbidrag (refereegranskat)abstract
- Qualitative spatio-temporal reasoning is an active research area in Artificial Intelligence. In many situations there is a need to reason about intertemporal qualitative spatial relations, i.e. qualitative relations between spatial regions at different time-points. However, these relations can never be explicitly observed since they are between regions at different time-points. In applications where the qualitative spatial relations are partly acquired by for example a robotic system it is therefore necessary to infer these relations. This problem has, to the best of our knowledge, not been explicitly studied before. The contribution presented in this paper is two-fold. First, we present a spatio-temporal logic MSTL, which allows for spatio-temporal stream reasoning. Second, we define the concept of a landmark as a region that does not change between time-points and use these landmarks to infer qualitative spatio-temporal relations between non-landmark regions at different time-points. The qualitative spatial reasoning is done in RCC-8, but the approach is general and can be applied to any similar qualitative spatial formalism.
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| 6. |
- de Leng, Daniel, 1988-
(författare)
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Querying Flying Robots and Other Things : Ontology-supported stream reasoning
- 2015
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Ingår i: XRDS: Crossroads, The ACM Magazine for Students - The Internet of Things. - New York, NY, USA : Association for Computing Machinery (ACM). - 1528-4972. ; 22:2, s. 44-47
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
- A discussion on the role of ontologies and stream reasoning in Internet of Things applications.
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| 7. |
- de Leng, Daniel, 1988-
(författare)
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Robust Stream Reasoning Under Uncertainty
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 8. |
- de Leng, Daniel, 1988-, et al.
(författare)
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Second Screen Journey to the Cup: Twitter Dynamics during the Stanley Cup Playoffs
- 2018
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Ingår i: Proceedings of the 2nd Network Traffic Measurement and Analysis Conference (TMA). - 9783903176096 - 9781538671528 ; , s. 1-8
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Konferensbidrag (refereegranskat)abstract
- With Twitter and other microblogging services, users can easily express their opinion and ideas in short text messages. A recent trend is that users use the real-time property of these services to share their opinions and thoughts as events unfold on TV or in the real world. In the context of TV broadcasts, Twitter (over a mobile device, for example) is referred to as a second screen. This paper presents the first characterization of the second screen usage over the playoffs of a major sports league. We present both temporal and spatial analysis of the Twitter usage during the end of the National Hockey League (NHL) regular season and the 2015 Stanley Cup playoffs. Our analysis provides insights into the usage patterns over the full 72-day period and with regards to in-game events such as goals, but also with regards to geographic biases. Quantifying these biases and the significance of specific events, we then discuss and provide insights into how the playoff dynamics may impact advertisers and third-party developers that try to provide increased personalization.
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| 9. |
- de Leng, Daniel, 1988-
(författare)
-
Spatio-Temporal Stream Reasoning with Adaptive State Stream Generation
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 10. |
- Olsson, Ella, et al.
(författare)
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Urdarbrunnen: Towards an AI-enabled mission system for Combat Search and Rescue operations
- 2023
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Ingår i: Proceedings of the 35th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS 2023). - : Linköping University Electronic Press. - 9789180752749 ; , s. 38-45
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Konferensbidrag (refereegranskat)abstract
- The Urdarbrunnen project is a Saab-led exploratory initiative that aims to develop an operator-assisted AI-enabled mission system for basic autonomous functions. In its first iteration, presented in this project paper, the system is designed to be capable of performing the search task of a combat search and rescue mission in a complex and dynamic environment, while providing basic human machine interaction support for remote operators. The system enables a team of agents to cooperatively plan and execute a search mission while also interfacing with the WARA-PS core system that allows human operators and other agents to monitor activities and interact with each other. The aim of the project is to develop the system iteratively, with each iteration incorporating feedback from simulations and real-world experiments. In future work, the capability of the system will be extended to incorporate additional tasks for other scenarios, making it a promising starting point for the integration of autonomous capabilities in a future air force.
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