Abstract: An efficient state-machine-based pattern matching technique processes tokens in an input queue and identifies patterns in the sequence of tokens that match one or more predetermined input patterns without backtracking. Tokens can include data or no data and a time component. The tokens can be a stream of data generated by a sensor, which transforms a physical property into a digital quantity. The pattern matching technique processes the input queue in a single direction, and does not examine any previously examined token. In an implementation, specific patterns to be matched are specified using a state machine, where the state machine is specified in a state table and operates using a state stack.

Abstract: A method for enabling intelligence at the edge. Features include: triggering by sensor data in a software layer hosted on either a gateway device or an embedded system. Software layer is connected to a local-area network. A repository of services, applications, and data processing engines is made accessible by the software layer. Matching the sensor data with semantic descriptions of occurrence of specific conditions through an expression language made available by the software layer. Automatic discovery of pattern events by continuously executing expressions. Intelligently composing services and applications across the gateway device and embedded systems across the network managed by the software layer for chaining applications and analytics expressions. Optimizing the layout of the applications and analytics based on resource availability. Monitoring the health of the software layer. Storing of raw sensor data or results of expressions in a local time-series database or cloud storage.

Abstract: A dataflow programming language can be used to express reactive dataflow programs that can be used in pattern-driven real-time data analysis. One or more tools are provided for the dataflow programming language for checking syntactic and semantic correctness, checking logical correctness, debugging, translation of source code into a secure, portable format (e.g., packaged code), translation of source code (or packaged code) into platform-specific code, batch-mode interpretation, interactive interpretation, simulation and visualization of the dataflow environment, remote execution, monitoring, or any combination of these. These tools embody a method of developing, debugging, and deploying a dataflow graph device.

Abstract: A dataflow programming language can be used to express reactive dataflow programs that can be used in pattern-driven real-time data analysis. One or more tools are provided for the dataflow programming language for checking syntactic and semantic correctness, checking logical correctness, debugging, translation of source code into a secure, portable format (e.g., packaged code), translation of source code (or packaged code) into platform-specific code, batch-mode interpretation, interactive interpretation, simulation and visualization of the dataflow environment, remote execution, monitoring, or any combination of these. These tools embody a method of developing, debugging, and deploying a dataflow graph device.

Abstract: An efficient state-machine-based pattern matching technique processes tokens in an input queue and identifies patterns in the sequence of tokens that match one or more predetermined input patterns without backtracking. Tokens can include data or no data and a time component. The tokens can be a stream of data generated by a sensor, which transforms a physical property into a digital quantity. The pattern matching technique processes the input queue in a single direction, and does not examine any previously examined token. In an implementation, specific patterns to be matched are specified using a state machine, where the state machine is specified in a state table and operates using a state stack.

Abstract: A technique implements a dataflow graph, taking a number of streams of data inputs and transforms these inputs into a number of streams of outputs. The dataflow graph can perform pattern matching. The technique implements reactions via the composition of pattern matching across joined streams of input data. A completeness of matching an input sequence to a particular input pattern can be characterized as having at least three different degrees, such as cold (not yet matched), warm (e.g., minimally matched), and hot (e.g., maximally matched). The input pattern to be matched can have a variable length, including zero length or unlimited or arbitrarily large length. Data flows can be on a push basis or pull basis, or a combination, and may change depending on the state.

Abstract: A method for enabling intelligence at the edge. Features include: triggering by sensor data in a software layer hosted on either a gateway device or an embedded system. Software layer is connected to a local-area network. A repository of services, applications, and data processing engines is made accessible by the software layer. Matching the sensor data with semantic descriptions of occurrence of specific conditions through an expression language made available by the software layer. Automatic discovery of pattern events by continuously executing expressions. Intelligently composing services and applications across the gateway device and embedded systems across the network managed by the software layer for chaining applications and analytics expressions. Optimizing the layout of the applications and analytics based on resource availability. Monitoring the health of the software layer. Storing of raw sensor data or results of expressions in a local time-series database or cloud storage.

Abstract: A dataflow programming language can be used to express reactive dataflow programs that can be used in pattern-driven real-time data analysis. One or more tools are provided for the dataflow programming language for checking syntactic and semantic correctness, checking logical correctness, debugging, translation of source code into a secure, portable format (e.g., packaged code), translation of source code (or packaged code) into platform-specific code, batch-mode interpretation, interactive interpretation, simulation and visualization of the dataflow environment, remote execution, monitoring, or any combination of these. These tools embody a method of developing, debugging, and deploying a dataflow graph device.

Abstract: A method for enabling intelligence at the edge. Features include: triggering by sensor data in a software layer hosted on either a gateway device or an embedded system. Software layer is connected to a local-area network. A repository of services, applications, and data processing engines is made accessible by the software layer. Matching the sensor data with semantic descriptions of occurrence of specific conditions through an expression language made available by the software layer. Automatic discovery of pattern events by continuously executing expressions. Intelligently composing services and applications across the gateway device and embedded systems across the network managed by the software layer for chaining applications and analytics expressions. Optimizing the layout of the applications and analytics based on resource availability. Monitoring the health of the software layer. Storing of raw sensor data or results of expressions in a local time-series database or cloud storage.