Abstract: Techniques for orchestrating a workflow for configuring a computer networking environment or other complex workflows are described. A Directed Acyclic Graph (DAG) that defines a plurality of tasks to be executed to complete the workflow and a plurality of orders between the tasks is received. Embodiments generate a State-Machine Neural Network (SNN) based on the received DAG, by generating a plurality of SNN neurons for the SNN, based on the plurality of tasks within the received DAG and generating a plurality of SNN connections for the SNN, connecting pairs of SNN neurons within the plurality of SNN neurons, based on the plurality of connections within the received DAG. The SNN is executed to orchestrate the workflow by sending and receiving signals to and from the SNN neurons.

Abstract: A data management system and associated data management method is disclosed herein. An exemplary method for managing data includes receiving data records timestamped with times spanned by a defined time interval; generating a data cube that includes data planes, wherein each data plane contains a set of data records timestamped with times spanned by the defined time interval; generating an index hypercube for the data cube, wherein dimensions of the index hypercube represent hash values of index keys defined for accessing the data cube; and generating an indexed data cube for storing in a database, wherein the indexed data cube includes the data cube and the index hypercube. The index hypercube includes index hypercube elements, where each index hypercube element represents a unique combination of hashed index key values that map to a data plane in the data cube.

Abstract: A data management system and associated data management method is disclosed herein. An exemplary method for managing data includes receiving data records timestamped with times spanned by a defined time interval; generating a data cube that includes data planes, wherein each data plane contains a set of data records timestamped with times spanned by the defined time interval; generating an index hypercube for the data cube, wherein dimensions of the index hypercube represent hash values of index keys defined for accessing the data cube; and generating an indexed data cube for storing in a database, wherein the indexed data cube includes the data cube and the index hypercube. The index hypercube includes index hypercube elements, where each index hypercube element represents a unique combination of hashed index key values that map to a data plane in the data cube.

Abstract: A data management system and associated data management method is disclosed herein. An exemplary method for managing data includes receiving data records timestamped with times spanned by a defined time interval; generating a data cube that includes data planes, wherein each data plane contains a set of data records timestamped with times spanned by the defined time interval; generating an index hypercube for the data cube, wherein dimensions of the index hypercube represent hash values of index keys defined for accessing the data cube; and generating an indexed data cube for storing in a database, wherein the indexed data cube includes the data cube and the index hypercube. The index hypercube includes index hypercube elements, where each index hypercube element represents a unique combination of hashed index key values that map to a data plane in the data cube.

Abstract: A data management system and associated data management method is disclosed herein. An exemplary method for managing data includes receiving data records timestamped with times spanned by a defined time interval; generating a data cube that includes data planes, wherein each data plane contains a set of data records timestamped with times spanned by the defined time interval; generating an index hypercube for the data cube, wherein dimensions of the index hypercube represent hash values of index keys defined for accessing the data cube; and generating an indexed data cube for storing in a database, wherein the indexed data cube includes the data cube and the index hypercube. The index hypercube includes index hypercube elements, where each index hypercube element represents a unique combination of hashed index key values that map to a data plane in the data cube.

Abstract: In one embodiment, a method comprises generating, by a computing device from first and second images of a projected pattern of laser dots detected by respective camera devices in a physical environment, a stereoscopic two-dimensional (2D) object pair based on determining 2D positions for each of the laser dots detected in the first and second images, creating a first mesh of geometric patterns from the 2D positions in the first image and a corresponding second mesh of the geometric patterns from the 2D positions in the second image, and creating the stereoscopic 2D object pair based on matching corresponding geometric patterns from the first and second meshes of the geometric patterns. A three-dimensional model (3D) of the physical environment is generated based on executing stereoscopic triangulation of the stereoscopic 2D object pair. The 3D model causes a controllable device to interact with the physical environment.

Abstract: A data management system and associated data management method is disclosed herein. An exemplary method for managing data includes receiving data records timestamped with times spanned by a defined time interval; generating a data cube that includes data planes, wherein each data plane contains a set of data records timestamped with times spanned by the defined time interval; generating an index hypercube for the data cube, wherein dimensions of the index hypercube represent hash values of index keys defined for accessing the data cube; and generating an indexed data cube for storing in a database, wherein the indexed data cube includes the data cube and the index hypercube. The index hypercube includes index hypercube elements, where each index hypercube element represents a unique combination of hashed index key values that map to a data plane in the data cube.

Abstract: In one embodiment, a method comprises generating, by a computing device from first and second images of a projected pattern of laser dots detected by respective camera devices in a physical environment, a stereoscopic two-dimensional (2D) object pair based on determining 2D positions for each of the laser dots detected in the first and second images, creating a first mesh of geometric patterns from the 2D positions in the first image and a corresponding second mesh of the geometric patterns from the 2D positions in the second image, and creating the stereoscopic 2D object pair based on matching corresponding geometric patterns from the first and second meshes of the geometric patterns. A three-dimensional model (3D) of the physical environment is generated based on executing stereoscopic triangulation of the stereoscopic 2D object pair. The 3D model causes a controllable device to interact with the physical environment.

Abstract: The present disclosure generally relates to a new mass transit system in which cooperative transit vehicles follow dynamic routes and flexible schedules and perform coordinated transfers to provide personalized transit trips which are faster, more convenient, and more efficient than traditional public transportation services with fixed routes and rigid schedules. Passengers use rider devices such as a smart phone to request transit trips and receive personalized transit plans including pickup, transfer, and drop off instructions in real time. A transit coordination center continuously tracks transit vehicles and passengers, process trip requests, offers personalized transit plans, calculates dynamic transit routes and transfer points, and sends routing and ride instructions to coordinate transit vehicles and passengers.

Abstract: A data management system and associated data management method is disclosed herein. An exemplary method for managing data includes receiving data records timestamped with times spanned by a defined time interval; generating a data cube that includes data planes, wherein each data plane contains a set of data records timestamped with times spanned by the defined time interval; generating an index hypercube for the data cube, wherein dimensions of the index hypercube represent hash values of index keys defined for accessing the data cube; and generating an indexed data cube for storing in a database, wherein the indexed data cube includes the data cube and the index hypercube. The index hypercube includes index hypercube elements, where each index hypercube element represents a unique combination of hashed index key values that map to a data plane in the data cube.

Abstract: One example method is provided for detecting end-to-end packet loss and retransmission occurring in a connection of a network environment. The method can include monitoring packets transmitted from a sender to a receiver and acknowledgement packets from the receiver to the sender using a probe located in a path between the sender and the receiver in the network environment; identifying, by the probe, a first packet as a possibly-retransmitted packet if the first packet has a fall back sequence number; classifying, by the probe, the first packet as a retransmitted packet using one or more conditions based, at least in part, on one or more of the following: characteristic(s) of the possibly-retransmitted packet, characteristic(s) of sequence numbers observed by the probe, and characteristic(s) of acknowledgements observed by the probe.

Abstract: One example method is provided for detecting end-to-end packet loss and retransmission occurring in a connection of a network environment. The method can include monitoring packets transmitted from a sender to a receiver and acknowledgement packets from the receiver to the sender using a probe located in a path between the sender and the receiver in the network environment; identifying, by the probe, a first packet as a possibly-retransmitted packet if the first packet has a fall back sequence number; classifying, by the probe, the first packet as a retransmitted packet using one or more conditions based, at least in part, on one or more of the following: characteristic(s) of the possibly-retransmitted packet, characteristic(s) of sequence numbers observed by the probe, and characteristic(s) of acknowledgements observed by the probe.

Abstract: Methods and mechanisms for regulating network packet traffic in an integrated services network, by selectively dropping packets of particular traffic, are described. Packets of network traffic arriving at an input port of a router or switch are selectively copied to one of two queues according to the type of transmission (voice, video, data) represented by the packets. A first queue receives traffic requiring guaranteed bandwidth and the second queue receives traffic requiring guaranteed minimum delay. Data in the packets indicates when the packet should be dropped relative to other packets in a packet stream. The process of marking packets with data used to indicate a drop order is referred to herein as marking packets. A network device that receives marked packets, for example, a router, may drop some of packets according to a drop policy based on the values used to mark the packets.