1. Parisi, Francesco and Grant, John (2016) Knowledge {Representation} in {Probabilistic} {Spatio}-{Temporal} {Knowledge} {Bases}. Journal of Artificial Intelligence Research 55: 743--798 https://doi.org/10.1613/jair.4883, Parisi and Grant - 2016 - Knowledge Representation in Probabilistic Spatio-T.pdf:/Users/bau050/Zotero/storage/LLHPUVH7/Parisi and Grant - 2016 - Knowledge Representation in Probabilistic Spatio-T.pdf:application/pdf, March, 2022-01-28, en, We represent knowledge as integrity constraints in a formalization of probabilistic spatiotemporal knowledge bases. We start by de fining the syntax and semantics of a formalization called PST knowledge bases. This de finition generalizes an earlier version, called SPOT, which is a declarative framework for the representation and processing of probabilistic spatio-temporal data where probability is represented as an interval because the exact value is unknown. We augment the previous de finition by adding a type of non-atomic formula that expresses integrity constraints. The result is a highly expressive formalism for knowledge representation dealing with probabilistic spatio-temporal data. We obtain complexity results both for checking the consistency of PST knowledge bases and for answering queries in PST knowledge bases, and also specify tractable cases. All the domains in the PST framework are finite, but we extend our results also to arbitrarily large finite domains., https://jair.org/index.php/jair/article/view/10992, 1076-9757
2. Vald és, Fabio and G üting, Ralf Hartmut (2019) A framework for efficient multi-attribute movement data analysis. The VLDB Journal 28(4): 427--449 https://doi.org/10.1007/s00778-018-0525-6, PDF:/Users/bau050/Zotero/storage/SJDQY9B5/Vald à ©s-G à ¼ting2019_Article_AFrameworkForEfficientMulti-at.pdf:application/pdf;Vald és and G üting - 2019 - A framework for efficient multi-attribute movement.pdf:/Users/bau050/Zotero/storage/WRU5UM3F/Vald és and G üting - 2019 - A framework for efficient multi-attribute movement.pdf:application/pdf, Fundamental, Pattern matching, Indexing, Multi-attribute data, August, 2022-01-27, en, In the first two decades of this century, the amount of movement and movement-related data has increased massively, predominantly due to the proliferation of positioning features in ubiquitous devices such as cellphones and automobiles. At the same time, there is a vast number of requirements for managing and analyzing these records for economic, administrative, and private purposes. Since the growth of data quantity outpaces the ef ficiency development of hardware components, it is necessary to explore innovative methods of extracting information from large sets of movement data. Hence, the management and analysis of such data, also called trajectories, have become a very active research field. In this context, the time-dependent geographic position is only one of arbitrarily many recorded attributes. For several applications processing trajectory (and related) data, it is helpful or even necessary to trace or generate additional time-dependent information, according to the purpose of the evaluation. For example, in the field of aircraft traf fic analysis, besides the position of the monitored airplane, also its altitude, the remaining amount of fuel, the temperature, the name of the traversed country and many other parameters that change with time are relevant. Other application domains consider the names of streets, places of interest, or transportation modes which can be recorded during the movement of a person or another entity. In this paper, we present in detail a framework for analyzing large datasets having any number of time-dependent attributes of different types with the help of a pattern language based on regular expression structures. The corresponding matching algorithm uses a collection of different indexes and is divided into a filtering and an exact matching phase. Compared to the previous version of the framework, we have extended the flexibility and expressiveness of the language by changing its semantics. Due to storage adjustments concerning the applied index structures and further optimizations, the ef ficiency of the matching procedure has been signi ficantly improved. In addition, the user is no longer required to have a deep knowledge of the temporal distribution of the available attributes of the dataset. The expressiveness and ef ficiency of the novel approach are demonstrated by querying real and synthetic datasets. Our approach has been fully implemented in a DBMS querying environment and is freely available open source software., http://link.springer.com/10.1007/s00778-018-0525-6, 1066-8888, 0949-877X
3. Harder, Frederik and Besold, Tarek R. (2018) Learning Łukasiewicz logic. Cognitive Systems Research 47: 42--67 https://doi.org/10.1016/J.COGSYS.2017.07.004, PDF:/Users/bau050/Zotero/storage/CIGTRK78/full-text.pdf:application/pdf, Cognitive modelling, Fundamental, Integration, Logic programs, Neural networks, Neural-symbolic integration, Reasoning, Publisher: Elsevier, January, 2022-01-31, The integration between connectionist learning and logic-based reasoning is a longstanding foundational question in artificial intelligence, cognitive systems, and computer science in general. Research into neural-symbolic integration aims to tackle this challenge, developing approaches bridging the gap between sub-symbolic and symbolic representation and computation. In this line of work the core method has been suggested as a way of translating logic programs into a multilayer perceptron computing least models of the programs. In particular, a variant of the core method for three valued Łukasiewicz logic has proven to be applicable to cognitive modelling among others in the context of Byrne's suppression task. Building on the underlying formal results and the corresponding computational framework, the present article provides a modified core method suitable for the supervised learning of Łukasiewicz logic (and of a closely-related variant thereof), implements and executes the corresponding supervised learning with the backpropagation algorithm and, finally, constructs a rule extraction method in order to close the neural-symbolic cycle. The resulting system is then evaluated in several empirical test cases, and recommendations for future developments are derived., 1389-0417
4. {iMonitor}. Smart manufacturing platform. Systems, Commercial off-the-shelf, 2022, https://www.imonitor.net/?gclid=EAIaIQobChMI46iV8pnd9QIVlQsrCh2kbQDsEAAYBCAAEgLhu_D_BwE
5. {Data Dog}. Monitor your {IoT} devices and backend services in a single unified platform. Systems, Commercial off-the-shelf, 2022, https://www.datadoghq.com/dg/monitor/iot/