Abstract: A system and method for data stream processing. Two or more instances are connected as a topology, wherein at least one of the instances is a spout and at least one of the instances is a bolt. The topology is submitted to a scheduler, wherein the service scheduler receives resource offers from a cluster manager representing computing resources available on one or more of cluster nodes and determines resources to accept and computations to run on the accepted computing resources. The topology is scheduled as one or more jobs, wherein each job includes two or more containers, including a first container and a second container, the first container including a topology master and the second container including a stream manager and one or more stream processing system (SPS) instances, wherein each SPS instance represents one of the instances in the topology.

Abstract: A system and method for data stream processing. Two or more instances are connected as a topology, wherein at least one of the instances is a spout and at least one of the instances is a bolt. The topology is submitted to a scheduler, wherein the service scheduler receives resource offers from a cluster manager representing computing resources available on one or more of cluster nodes and determines resources to accept and computations to run on the accepted computing resources. The topology is scheduled as one or more jobs, wherein each job includes two or more containers, including a first container and a second container, the first container including a topology master and the second container including a stream manager and one or more stream processing system (SPS) instances, wherein each SPS instance represents one of the instances in the topology.

Abstract: Brain-Machine Interface (BMI) systems or movement-assist systems may be utilized to aid users with paraplegia or tetraplegia in ambulation or other movement or in rehabilitation of motor function after brain injury or neurological disease, such as stroke, Parkinson's disease or cerebral palsy. The BMI may translate one or more neural signals into a movement type, a discrete movement or gesture or a series of movements, performed by an actuator. System and methods of decoding a locomotion-impaired and/or an upper-arm impaired subject's intent with the BMI may utilize non-invasive methods to provide the subject the ability to make the desired motion using an actuator or command a virtual avatar.

Abstract: Brain-Machine Interface (BMI) systems or movement-assist systems may be utilized to aid users with paraplegia or tetraplegia in ambulation or other movement or in rehabilitation of motor function after brain injury or neurological disease, such as stroke, Parkinson's disease or cerebral palsy. The BMI may translate one or more neural signals into a movement type, a discrete movement or gesture or a series of movements, performed by an actuator. System and methods of decoding a locomotion-impaired and/or an upper-arm impaired subject's intent with the BMI may utilize non-invasive methods to provide the subject the ability to make the desired motion using an actuator or command a virtual avatar.

Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media for approximating item counts. One of the methods includes maintaining a collection of counters for a class of items, processing each item in an item stream as a current item, including determining whether or not the collection includes an item counter for the current item, and if the collection includes an item counter for the current item, updating each count level in the item counter for the current item.

Abstract: Methods, systems and apparatus are described herein that include processing a data stream as a sequence of batch jobs during collection of data in the data stream. Processing of successive batch jobs in the sequence includes creating a particular batch job upon completion of processing of a preceding batch job in the sequence. The particular batch job has a batch size that depends upon an amount of data in the data stream that has been collected since creation of the preceding batch job in the sequence, such that the batch size of the particular batch job self-adjusts to data rate changes in the data stream. The particular batch job is then processed to produce resulting data, where processing efficiency and processing time for the particular batch increase with the batch size.