Abstract: Machine learning systems and methods for embedding attributed sequence data. The attributed sequence data includes an attribute data part having a fixed number of attribute data elements and a sequence data part having a variable number of sequence data elements. An attribute network module includes a feedforward neural network configured to convert the attribute data part to an encoded attribute vector having a first number of attribute features. A sequence network module includes a recurrent neural network configured to convert the sequence data part to an encoded sequence vector having a second number of sequence features. In use, the machine learning system learns and outputs a fixed-length feature representation of input attributed sequence data which encodes dependencies between different attribute data elements, dependencies between different sequence data elements, and dependencies between attribute data elements and sequence data elements within the attributed sequence data.

Abstract: Machine learning systems and methods for embedding attributed sequence data. The attributed sequence data includes an attribute data part having a fixed number of attribute data elements and a sequence data part having a variable number of sequence data elements. An attribute network module includes a feedforward neural network configured to convert the attribute data part to an encoded attribute vector having a first number of attribute features. A sequence network module includes a recurrent neural network configured to convert the sequence data part to an encoded sequence vector having a second number of sequence features. In use, the machine learning system learns and outputs a fixed-length feature representation of input attributed sequence data which encodes dependencies between different attribute data elements, dependencies between different sequence data elements, and dependencies between attribute data elements and sequence data elements within the attributed sequence data.

Abstract: There is provided a computer-implemented method of determining an execution ordering. An exemplary method comprises generating a directed graph based on a hierarchy. The hierarchy includes a plurality of pattern queries. The method also includes determining a minimum spanning tree of the directed graph. The method further includes determining an execution order of the pattern queries based on the minimum spanning tree.

Abstract: There is provided a computer-implemented method of determining an execution ordering. An exemplary method comprises generating a directed graph based on a hierarchy. The hierarchy includes a plurality of pattern queries. The method also includes determining a minimum spanning tree of the directed graph. The method further includes determining an execution order of the pattern queries based on the minimum spanning tree.

Abstract: There is provided a computer-implemented method of determining an execution ordering. An exemplary method comprises generating a directed graph based on a hierarchy. The hierarchy includes a plurality of pattern queries. The method also includes determining a minimum spanning tree of the directed graph. The method further includes determining an execution order of the pattern queries based on the minimum spanning tree.

Abstract: A method analyzes event patterns in multi-dimensional data and based on this analysis of the event patterns computes a hierarchical event pattern query from another hierarchical event pattern query. The method executes the hierarchical event pattern query on the multi-dimensional data.

Abstract: Systems and method are disclosed for applying a query to an event stream by storing one or more event constraints; performing constraint aware complex event processing on the query and the event constraints; and optimizing the query at run time.

Abstract: An apparatus, system and method for processing a stream of events in accordance with a set of queries that apply complex event pattern matching, and for performance of actions in accordance with active rules associated with those queries. The execution of actions can take the form of system state changes that in turn can affect the outcome of other queries. Scheduling policies assure both correct and high-performance execution of concurrent event pattern queries and active rules. The stream of events can be associated with a variety of applications including monitoring of hygiene and infection control activities associated with health care.

Abstract: Systems and method are disclosed for applying a query to an event stream by storing one or more event constraints; performing constraint aware complex event processing on the query and the event constraints; and optimizing the query at run time.