Abstract:
Techniques are presented herein to monitor a plurality of big data sources in order to dynamically identify keywords. The big data sources are analyzed to classify the keywords as related to either a technical problem or to a solution to the technical problem. In addition, data associated with the keywords is weighted based on one or more attributes of the data and stored in a database in a problem-solution format.
Abstract:
Techniques are presented herein to monitor a plurality of big data sources in order to dynamically identify keywords. The big data sources are analyzed to classify the keywords as related to either a technical problem or to a solution to the technical problem. In addition, data associated with the keywords is weighted based on one or more attributes of the data and stored in a database in a problem-solution format.
Abstract:
Various implementations disclosed herein enable blockchain programming in NB-IoT devices. In various implementations, a method of blockchain authentication is performed by a computing device including one or more processors, and a non-transitory memory. In various implementations, the method includes maintaining a blockchain for a machine-to-machine network, wherein the machine-to-machine network is a narrowband internet of things network. In some implementations, the method includes receiving a request for a first set of data from the blockchain by a second device. In some implementations, the method includes determining based on the request, the first set of data from the blockchain by traversing a series of blocks from the blockchain. In some implementations, the method includes packaging the first set of data from the blockchain according to a protocol into a packaged data unit and transmitting the packaged data unit to the second device.
Abstract:
In one embodiment, a processor receives data indicative of a plurality of conversations involving a primary user. The processor identifies a subset of the plurality of conversations that are regarding a particular topic. The processor adds a conversation to the subset based on a match between one or more keywords in the conversation matching a list of keywords associated with the particular topic. The processor uses a machine learning-based model to identify one or more context characteristics of the conversations in the identified subset. The processor updates the subset of conversations by adding at least one of the conversations to the subset based on the at least one conversation having at least one context characteristic identified by the machine learning-based model. The processor provides data indicative of the updated subset of conversations to a user interface for review by the primary user.
Abstract:
Presented herein are techniques for automatically creating communities of network-connected devices, i.e., Internet of Thing (IoT) devices. One or more of a plurality of network-connected devices are identified based on one or more policies that define one or more communities of network-connected devices. A community of network-connected devices includes network-connected devices that share common functional, physical or relational attributes. Information is stored that indicates the one or more communities of which each of the one or more of the plurality of network-connected devices is a member based on the policies that define the one or more communities and functional, physical or relational attributes of the one or more of the plurality of network-connected devices.
Abstract:
Methods are provided for a collaborative, decentralized insight engineering based on exchanging telemetry vectors with peer network devices. Each network device independently detects a deviation in its functioning using machine learning of generated feature vectors. Specifically, the methods involve obtaining, from at least one peer network device, at least a first feature vector that represents at least one insight generated from telemetry data of a respective peer network device. The intermediate network device and the at least one peer network device are configured to forward packets of a traffic flow. The methods further involve determining whether a deviation related to one or more of the network devices, exists based at least on the first feature vector and performing the at least one predefined action based on determining that the deviation exists.
Abstract:
In one embodiment, a processor receives data indicative of a plurality of conversations involving a primary user. The processor identifies a subset of the plurality of conversations that are regarding a particular topic. The processor adds a conversation to the subset based on a match between one or more keywords in the conversation matching a list of keywords associated with the particular topic. The processor uses a machine learning-based model to identify one or more context characteristics of the conversations in the identified subset. The processor updates the subset of conversations by adding at least one of the conversations to the subset based on the at least one conversation having at least one context characteristic identified by the machine learning-based model. The processor provides data indicative of the updated subset of conversations to a user interface for review by the primary user.
Abstract:
In one implementation, a “probe controller orchestrator” provides access to cross-domain probing via the probe controller orchestrator for a plurality of probe controllers across a plurality of different network domains with a respective different probing protocol and associated probing capability. The probe controller orchestrator, in particular, obtains domain-specific probe test results from each of the plurality of probe controllers, and correlates the domain-specific probe test results into cross-domain data formatted in a common data format understandable by each of the plurality of probe controllers. As such, the probe controller orchestrator may then respond to requests received from the plurality of probe controllers with the cross-domain data in order to cause respective domain-specific processing.
Abstract:
Presented herein are techniques for automatically creating communities of network-connected devices, i.e., Internet of Thing (IoT) devices. One or more of a plurality of network-connected devices are identified based on one or more policies that define one or more communities of network-connected devices. A community of network-connected devices includes network-connected devices that share common functional, physical or relational attributes. Information is stored that indicates the one or more communities of which each of the one or more of the plurality of network-connected devices is a member based on the policies that define the one or more communities and functional, physical or relational attributes of the one or more of the plurality of network-connected devices.