Abstract: Disclosed herein is a computer-implemented method of monitoring a situation by generating an overall similarity score between a received data stream and a case in case-based reasoning (CBR) the method comprising: receiving a data stream comprising information on a monitored situation; generating a plurality of parallel data streams, wherein each of the generated plurality of data streams is dependent on the received data stream; generating, for each of the generated data streams, a similarity score for a feature of a case, wherein each similarity score is generated in dependence on a comparison between information in the generated data stream and stored information on the feature of a case, and each of the similarity scores is generated in dependence on a comparison with stored information on a different feature of the same case; and generating an overall similarity score between the received data stream and the case in dependence on the generated similarity scores.

Abstract: Disclosed herein is a computer-implemented method of monitoring a situation by determining a set of one or more cases in case-based reasoning (CBR) the method comprising: receiving a data stream comprising information on a monitored situation; generating a plurality of parallel data streams from the received data stream; generating, for each of the parallel data streams, an overall similarity score between the parallel data stream and one of a plurality of cases, wherein each overall similarity score is generated from a comparison between one of the plurality of data streams and a different case; and determining a set of one or more cases in dependence on the generated overall similarity scores.

Abstract: A case-based reasoning, CBR, platform for monitoring a situation includes: a CBR engine; a unified data cache; a case library; an agent application programming interface, API; a data source API; an application API; and a persistence API. The data source API provides an interface between live and/or static data sources, external from the CBR platform, and the unified data cache. The persistence API provides an interface between a persistence database, external from the CBR platform, and the unified data cache and the case library. The application API provides an interface between the systems of data analysts, platform administrators and/or operators, external from the CBR platform, and the unified data cache and the case library. The agent API receives information from data interpretation agents external from the CBR platform and sends the received information to the CBR engine to provide the CBR engine with information for generating the CBR results.

Abstract: Disclosed herein is a computer-implemented method of creating a new case in a case-based reasoning (CBR) system for monitoring a situation, the method comprising: determining a set of one or more cases from a plurality of cases in dependence on a received data stream comprising information on a monitored situation, wherein each case comprises information describing a problem and information describing a solution to the problem and the process of determining the set of one or more cases is performed without comparing the description of the problems of any of the plurality of cases with a previously generated current case comprising information describing the monitored situation; generating information describing a solution in dependence on information obtained from the determined set of one or more cases and/or in dependence on information received from a user interface; generating a current case comprising information describing the monitored situation in dependence on the received data stream; and generating

Abstract: Disclosed herein is a computer-implemented method of monitoring a situation by determining a set of one or more cases in case-based reasoning (CBR) the method comprising: receiving a data stream comprising information on a monitored situation; generating a plurality of parallel data streams from the received data stream; generating, for each of the parallel data streams, an overall similarity score between the parallel data stream and one of a plurality of cases, wherein each overall similarity score is generated from a comparison between one of the plurality of data streams and a different case; and determining a set of one or more cases in dependence on the generated overall similarity scores.

Abstract: Disclosed herein is a computer-implemented method in case-based reasoning, CBR, for creating components of a comparison agent for monitoring a situation, the method comprising: obtaining one or more parameter values and comparison information for each of a plurality of features of a case, wherein the comparison information of each feature defines a configuration of a computation unit; and creating, for each of the plurality of features, a computation unit in dependence on the obtained one or more parameter values and the comparison information of the feature, such that the created computation unit is configured to generate an output in dependence on the obtained one or more parameter values and the comparison information of the feature.

Abstract: Disclosed herein is a computer-implemented method of monitoring a situation by generating an overall similarity score between a received data stream and a case in case-based reasoning (CBR) the method comprising: receiving a data stream comprising information on a monitored situation; generating a plurality of parallel data streams, wherein each of the generated plurality of data streams is dependent on the received data stream; generating, for each of the generated data streams, a similarity score for a feature of a case, wherein each similarity score is generated in dependence on a comparison between information in the generated data stream and stored information on the feature of a case, and each of the similarity scores is generated in dependence on a comparison with stored information on a different feature of the same case; and generating an overall similarity score between the received data stream and the case in dependence on the generated similarity scores.