Abstract: Assigning chromatic numbers based on a pre-assigned scheme to a stream of sensor data from sensors of Internet connected devices to “color” the message which can then be processed by chromatic number to obtain a pluralized stream of data.

Abstract: Application programming interface (API) discovery includes receiving source code associated with a computer system, and analyzing the source code to generate domain specific language (DSL) represented within the source code. The DSL is mapped to terms of reference associated with an enterprise, and at least one candidate API is identified based upon the terms of reference. The at last one candidate API is mapped to a portion of the source code. One or more patterns are identified between terms in the portion of source code. A source code component of the source code representative of a separate functional component within the source code is identified based upon the one or more patterns. The source code component is mapped to an enabling API.

Abstract: A computer system message generated by an application programming interface (API) or addressed to the API can be received. A first data sensitivity score for at least a first of a plurality of data elements in a payload of the computer system message and at least second data sensitivity score for at least a second of the plurality of data elements in the payload of the computer system message can be determined. Based on the first data sensitivity score and at least the second data sensitivity score, a differential security can be applied to the computer system message. Applying the differential security can include masking the first of the plurality of data elements and not masking the second of the plurality of data elements. The computer system message can be electronically communicated the to a destination to which the message is addressed.

Abstract: A computer-implemented method includes: receiving, by a computing device, information regarding a range in which an Internet-of-Things (IoT) network is to be implemented; determining, by the computing device, respective detection scores for a plurality of IoT devices for each of a plurality of proposed congregation of IoT devices; determining, by the computing device, a minimum number of the plurality of IoT devices to cover the range by incorporating the detection scores into a covariance model; and outputting, by the computing device, information identifying the minimum number of the plurality of IoT devices for designing the IoT network.

Abstract: A method (and structure and computer product) of recognizing and tracking an object includes receiving image data from at least two camera sensors and analyzing the image data to determine attributes of an object detected as present in the image data. A first decision tree (DT) is developed to classify the object, based on the attributes determined from the image data. A second DT is developed to refine the first DT, wherein the second DT is used to refine the first DT by determining which attributes are most significant in the classification of the object.

Abstract: A system and method for identifying a value gap in a patent portfolio strategy of an organization. A processor of a computing system defines a first data cluster that includes a first plurality of focus areas that are ranked according to an internal ranking of the organization, and a second data cluster that includes a second plurality of focus areas having a universal market significance. A patent worthiness is ranked so that the second plurality of focus areas is ranked according to a universal ranking. A Kemeny distance is calculated between the internal ranking and the universal ranking for each focus area of the first plurality of focus areas. A loss function is applied using the Kemeny distance to calculate the value gap score which defines a business penalty for the misalignment between the patent portfolio strategy of the organization and a universal market focus.

Abstract: A system and method for managing the provisioning of electricity to an electric vehicle includes identifying an electric vehicle when the electric vehicle connects to a charging point; determining whether the electric vehicle is associated with a smart contract; validating the smart contract associated with the electric vehicle; creating a charging transaction for charging the electric vehicle; and applying the smart contract associated with the electric vehicle against the charging transaction. The electric vehicle electricity contract management system and method may use a consensus system such as a distributed ledger system or blockchain.

Abstract: A method, computer program product, and a system where a processor(s), obtains content from a meeting hosting system during a pre-defined interval. The processor(s) parses the textual content to identify potential keywords. The processor(s) iteratively cognitively analyzes the potential keywords to determine which potential keywords comprise seed keywords, where the seed keywords meet a maturity threshold for inclusion in a data structure, where the iterative cognitive analysis of each potential keyword of the potential keywords is repeated a pre-defined number of times, and where the iteratively cognitively analyzing includes generating and updating the data structure. The processor(s) outputs, based on completing the pre-defined number of times, the data structure comprising the seed keywords.

Abstract: A computer-implemented method includes: receiving, by a computing device, information regarding a range in which an Internet-of-Things (IoT) network is to be implemented; determining, by the computing device, respective detection scores for a plurality of IoT devices for each of a plurality of proposed congregation of IoT devices; determining, by the computing device, a minimum number of the plurality of IoT devices to cover the range by incorporating the detection scores into a covariance model; and outputting, by the computing device, information identifying the minimum number of the plurality of IoT devices for designing the IoT network.

Abstract: An example operation may include one or more of receiving a request to store a skill-based attribute of a user at a database, temporarily storing the skill-based attribute and an identification of one or more storage nodes associated with the skill-based attribute in a temporary data structure of the database, determining whether a predetermined amount of the one or more storage nodes have authenticated the user as having the skill-based attribute, and, in response successful authentication of the skill-based attribute by the predetermined amount of storage nodes, transferring the authenticated skill-based attribute from the temporary data structure to a primary data structure of the database.

Abstract: Assigning chromatic numbers based on a pre-assigned scheme to a stream of sensor data from sensors of Internet connected devices to “color” the message which can then be processed by chromatic number to obtain a pluralized stream of data.

Abstract: Assigning chromatic numbers based on a pre-assigned scheme to a stream of sensor data from sensors of Internet connected devices to “color” the message which can then be processed by chromatic number to obtain a pluralized stream of data.

Abstract: A method, system and computer program product for determining API pricing. Consumption parameters are identified using a supervised learning model. The API consumption parameters refer to any parameters that can be used to describe an API (functionality or otherwise) and can be used to compare other comparable APIs in similar domains provided by other providers. Furthermore, reference pricing is determined using machine learning using the identified API consumption parameters. Additionally, the API price is determined dynamically using the identified API consumption parameters and the determined reference pricing. An API pricing score is then derived for the API price using the supervised learning model. The API price is selected as the suggested price for the API in response to the API pricing score exceeding a threshold value. In this manner, an API price is established that reflects the true value of the API assessed by the API consumer.

Abstract: A method and associated systems use DVFS performance-scaling technology to satisfy quality-of-service performance requirements when recovering a job that had been scheduled to run on a failed virtual machine. A Buffer Time specifies a duration of time remaining, at the time of failure, for the job to complete in order to satisfy the quality-of-service requirements. Depending on relative durations of time required to repair the failed virtual machine, to perform the job on an unscaled active-mode virtual machine, and to transfer the job to another virtual machine, the system determines whether to repair the failed virtual machine or to transfer the job. If the latter, the system then determines whether to select a destination virtual machine provisioned on a DVFS-compliant platform and, if so, the system scales the DVFS-compliant platform's performance to a level sufficient to complete the job within the Buffer Time.

Abstract: A method, system and computer program product for determining API pricing. Consumption parameters are identified using a supervised learning model. The API consumption parameters refer to any parameters that can be used to describe an API (functionality or otherwise) and can be used to compare other comparable APIs in similar domains provided by other providers. Furthermore, reference pricing is determined using machine learning using the identified API consumption parameters. Additionally, the API price is determined dynamically using the identified API consumption parameters and the determined reference pricing. An API pricing score is then derived for the API price using the supervised learning model. The API price is selected as the suggested price for the API in response to the API pricing score exceeding a threshold value. In this manner, an API price is established that reflects the true value of the API assessed by the API consumer.

Abstract: A method and associated systems use DVFS performance-scaling technology to satisfy quality-of-service performance requirements when recovering a job that had been scheduled to run on a failed virtual machine. A Buffer Time specifies a duration of time remaining, at the time of failure, for the job to complete in order to satisfy the quality-of-service requirements. Depending on relative durations of time required to repair the failed virtual machine, to perform the job on an unsealed active-mode virtual machine, and to transfer the job to another virtual machine, the system determines whether to repair the failed virtual machine or to transfer the job. If the latter, the system then determines whether to select a destination virtual machine provisioned on a DVFS-compliant platform and, if so, the system scales the DVFS-compliant platform's performance to a level sufficient to complete the job within the Buffer Time.

Abstract: Assigning chromatic numbers based on a pre-assigned scheme to a stream of sensor data from sensors of Internet connected devices to “color” the message which can then be processed by chromatic number to obtain a pluralized stream of data.

Abstract: A method, system and computer program product are disclosed for creating a dynamic heterogeneous IoT network from existing IoT network devices to identify and implement a dynamic response to an event. In an embodiment, the method comprises identifying an occurrence of a specified event; and creating a heterogeneous IoT network to respond to the event, including selecting a master IoT device and identifying one or more participatory IoT devices for the created network from multiple IoT devices from one or more existing IoT networks. The master IoT device is in bidirectional communications with the participatory IoT devices. The master device identifies a response to the specified event, translates the response into commands, and sends the commands to the participatory IoT devices to implement the response. The master IoT device may pass control to another master device in another heterogeneous IoT network if the response is not accomplished.

Abstract: A method and associated systems use DVFS performance-scaling technology to satisfy quality-of-service performance requirements when recovering a job that had been scheduled to run on a failed virtual machine. A Buffer Time specifies a duration of time remaining, at the time of failure, for the job to complete in order to satisfy the quality-of-service requirements. Depending on relative durations of time required to repair the failed virtual machine, to perform the job on an unsealed active-mode virtual machine, and to transfer the job to another virtual machine, the system determines whether to repair the failed virtual machine or to transfer the job. If the latter, the system then determines whether to select a destination virtual machine provisioned on a DVFS-compliant platform and, if so, the system scales the DVFS-compliant platform's performance to a level sufficient to complete the job within the Buffer Time.