Abstract: This disclosure relates to a system for livestock monitoring. Animal monitoring devices, mounted on animals, collect accelerometer data of parts of the animals. The devices classify the accelerometer data into one of multiple animal behaviours, to create behaviour classification data. Gateway devices receive the behaviour classification data from the animal monitoring devices, responsive to the one or more of the multiple animal monitoring devices being within a communication range of the gateway device. The gateway devices also obtain sensor data in relation to the animals that is measured independent from the animal behaviours. A rules engine, located remotely from the animals, determines compliance of the animal behaviours with predetermined rules, wherein the behaviours and sensor data are used as variable values in determining the compliance.

Abstract: This disclosure relates to a computer analysing log data. The computer receives log data comprising traces having log events from respective process executions. The computer creates a stream of log events, wherein the stream is sorted by the event time. The computer iterates over the stream of log events, and for each log event, executes update functions that define updates of a set of variables based on the log events. The set of variables comprises at least one cross-trace variable to calculate an updated value of the set of variables. The update functions define updates of the cross-trace variable in response to the log events of the traces. The computer further executes evaluation functions on the set of variables to determine compliance in relation to the log data based on the updated value. The evaluation functions represent compliance rules based on the set of variables including the cross-trace variable.

Abstract: This disclosure relates to analyzing log data of a system. This comprises evaluating a first graph model with multiple log events in the log data. The first graph model comprises a first set of nodes connected by a first set of edges representing a first behaviour. A processor determines a first correspondence value based on the first graph model and indicative of a correspondence between the multiple log events and the first behaviour. The processor repeats the steps of evaluating the first graph model for one or more further graph models representing one or more further behaviors and determining the first correspondence value to determine one or more further correspondence values. The processor finally determines a classification of the multiple log events as representing one of the behaviors based on the correspondence values. The use of multiple graph models allows a more granular classification than binary intrusion detection.

Abstract: The present disclosure relates to monitoring processes. A processor creates a numerical representation of a nominal operation of the process. The processor then encrypts the numerical representation using homomorphic encryption to determine an encrypted numerical representation that blocks access to the numerical representation but allows calculations on the numerical representations. The processor proceeds by creating smart contracts on a blockchain platform using the encrypted numerical representation as a first input to the calculations of the smart contract. Next, the processor attempts execution of the smart contract using the current operation of the process as a second input to the calculations of the smart contract. The execution of the smart contract generates an output result by performing the calculations on the encrypted numerical representations.

Abstract: The present disclosure relates to manufacturing a pharmaceutical compound according to a manufacturing process. The manufacturing process comprises process parameters and rules that use the process parameters as literals. A control system determines a partition of the rules into subsets of rules, each being independent from the other subsets of rules in relation to the literals used in the rules. The controller creates simplified representations of the manufacturing process, which comprise the literals from a corresponding subset of rules and are created by eliminating literals used by other subsets of rules. The controller checks compliance of the manufacturing process based on the simplified representations of the manufacturing process by comparing values of the literals of a current process execution against literals in the simplified representations and adjusts the manufacturing process where the comparing identifies a difference.

Abstract: This disclosure relates to analyzing log data of a system. This comprises evaluating a first graph model with multiple log events in the log data. The first graph model comprises a first set of nodes connected by a first set of edges representing a first behaviour. A processor determines a first correspondence value based on the first graph model and indicative of a correspondence between the multiple log events and the first behaviour. The processor repeats the steps of evaluating the first graph model for one or more further graph models representing one or more further behaviors and determining the first correspondence value to determine one or more further correspondence values. The processor finally determines a classification of the multiple log events as representing one of the behaviors based on the correspondence values. The use of multiple graph models allows a more granular classification than binary intrusion detection.