Abstract: In an example computer-implemented method, a dataset and a query including an expression to be matched to the dataset is received via a processor. A false positive rate (FPR) and a false negative rate (FNR) is calculated via the processor for each possible value assignment of a plurality of possible value assignments in response to detecting a missing value in the dataset. A value assignment is selected, via the processor, from the plurality of possible value assignments based on the FPR and the FNR. A response to the query is generated via the processor based on the selected value assignment.

Abstract: A method for diagnosing and troubleshooting failures of components of a physical system with low troubleshooting cost, according to which for each component in the system, a Model-Based Diagnosis (MBD) is used for computing the probability of causing a system failure, based on currently observed system behavior or on knowledge about the system's structure. Then the probability of causing a system failure is computed, based on its age and its survival curves. Then, it is determined whether a faulty component C should be fixed or replaced by minimizing future troubleshooting costs, being the costs of the process of diagnosing and repairing an observed failure.

Abstract: In an example computer-implemented method, a dataset and a query comprising an expression to be matched to the dataset is received via a processor. A false positive rate (FPR) and a false negative rate (FNR) is calculated via the processor for each possible value assignment of a plurality of possible value assignments in response to detecting a missing value in the dataset. A value assignment is selected, via the processor, from the plurality of possible value assignments based on the FPR and the FNR. A response to the query is generated via the processor based on the selected value assignment.

Abstract: The present invention is directed to a method for diagnosing faults in a software system, according to which, predictions of the probability of each software component of the software system to be faulty are obtained from a diagnoser, and the probability predictions are input to a fault diagnosis software module, executed by the diagnoser.

Abstract: A method for diagnosing and troubleshooting failures of components of a physical system with low troubleshooting cost, according to which for each component in the system, a Model-Based Diagnosis (MBD) is used for computing the probability of causing a system failure, based on currently observed system behavior or on knowledge about the system's structure. Then the probability of causing a system failure is computed, based on its age and its survival curves. Then, it is determined whether a faulty component C should be fixed or replaced by minimizing future troubleshooting costs, being the costs of the process of diagnosing and repairing an observed failure.

Abstract: An artificial intelligence based method for improving a software testing process, according to which upon finding a bug, a set of candidate diagnoses is proposed to the tester, based on a Model-Based Diagnosis (MBD) process. A planning process is used for automatically suggesting further test steps to be performed by the tester, to identify the correct diagnosis for the developer in the form of faulty software component that caused the bug, while minimizing the tests steps performed by the tester. Additional information is provided to the MBD process, based on the outputs of the further test steps, thereby pruning incorrect candidate diagnoses. These steps are iteratively repeated while in each time, minimizing the set of candidate diagnoses, until a single diagnosis remains in the set.

Abstract: The present invention is directed to a method for diagnosing faults in a software system, according to which, predictions of the probability of each software component of the software system to be faulty are obtained from a diagnoser, and the probability predictions are input to a fault diagnosis software module, executed by the diagnoser.

Abstract: A method for detecting and diagnosing sensor faults in an autonomous system that includes sensors and hardware components, according to which sensors are related to hardware components and correlations between data readings are recognized online and correlation between sensors is determined. Predefined suspicious patterns are identified by online and continuously tracking the data readings from each sensor and detecting correlation breaks over time. The readings from sensors that match at least one of the patterns are marked as uncertain. For each online reading of the sensors, whenever sensors that used to be correlated show a different behavior, reporting that the reading indicates a fault. Upon identifying fault detection, diagnosing which of the internal components or sensors caused the fault, based on a function that returns the state of the sensor which is associated with the fault detection.

Abstract: An artificial intelligence based method for improving a software testing process, according to which upon finding a bug, a set of candidate diagnoses is proposed to the tester, based on a Model-Based Diagnosis (MBD) process. A planning process is used for automatically suggesting further test steps to be performed by the tester, to identify the correct diagnosis for the developer in the form of faulty software component that caused the bug, while minimizing the tests steps performed by the tester. Additional information is provided to the MBD process, based on the outputs of the further test steps, thereby pruning incorrect candidate diagnoses. These steps are iteratively repeated while in each time, minimizing the set of candidate diagnoses, until a single diagnosis remains in the set.

Abstract: A method for detecting and diagnosing sensor faults in an autonomous system that includes sensors and hardware components, according to which sensors are related to hardware components and correlations between data readings are recognized online and correlation between sensors is determined. Predefined suspicious patterns are identified by online and continuously tracking the data readings from each sensor and detecting correlation breaks over time. The readings from sensors that match at least one of the patterns are marked as uncertain. For each online reading of the sensors, whenever sensors that used to be correlated show a different behavior, reporting that the reading indicates a fault. Upon identifying fault detection, diagnosing which of the internal components or sensors caused the fault, based on a function that returns the state of the sensor which is associated with the fault detection.

Abstract: A method for detecting an anomaly in operation of a data analysis device, comprising: receiving present real-time readings of multiple sensors associated with the data analysis device, and maintaining a history of past real-time readings; determining which of said multiple sensors are correlated; computing a deviation between at least some of said present and at least some of said past real-time readings of said correlated sensors; and declaring an anomaly when said deviation exceeds a predetermined threshold.

Abstract: A method for detecting an anomaly in operation of a data analysis device, comprising: receiving present real-time readings of multiple sensors associated with the data analysis device, and maintaining a history of past real-time readings; determining which of said multiple sensors are correlated; computing a deviation between at least some of said present and at least some of said past real-time readings of said correlated sensors; and declaring an anomaly when said deviation exceeds a predetermined threshold.

Abstract: A method for voting by peers via mobile devices operating in a network on behalf of their owners, comprising iteratively sending by a plurality of peers votes for candidates to a peer and determining by the peer, according to the votes, at least one winner candidate.

Abstract: A method for voting by peers via mobile devices operating in a network on behalf of their owners, comprising iteratively sending by a plurality of peers votes for candidates to a peer and determining by the peer, according to the votes, at least one winner candidate.