International Business Machines Corporation

Hari Haranath Madduri

Ira Richard Forman

Maharaj Mukherjee

Virgil Anthony Albaugh

Rahul Gupta

Randy Allan Rendahl

Brian James Snitzer

Joanna W Ng

Messaoud Benantar

Anamitra Bhattacharyya

Ann Marie Fred

Thomas J Sarasin

Philip Yen-tang Chang

Rhonda L Childress

Susan F Wenzinger

David B Kumhyr

Timothy James Hahn

Li Li

Ryan D McNair

Scott Harrison Danforth

Leucir Marin, Jr

Michael P Robertson

Mark Christopher Waldroff

Dionysius D Bell

Grisselle Marie Keller

Michael Bender

Cesar Augusto Rodriguez Bravo

Puthukode G Ramachandran

Jeffrey Mark Achtermann

Marc Dickenson

Michael Starkey

Alfredo V Mendoza

Pu Yang

Kyusun Chang

Naeem Altaf

Jagadish Dasari

Marcin Labenski

Sumit Taank

Harrison Kurtz

Teodor Laurentiu Tecsa

Sudhee Nagabhushan Subrahmanya

John Alan Mims

Jogeswar Challapalli

Ranjit K Nayak

Justin Kai-Yuen Hong

Michael Edward Ortman

Neda Samani

Siu Hei Ng

Disclosed aspects relate to Internet of Things (IoT) data maturity management. A set of IoT data may be ingested by a data maturity engine. A set of maturity factors which indicates a fitness for utilization of the set of IoT data may be determined. The determining may occur with respect to the set of IoT data by the data maturity engine. A data management operation may be identified by the data maturity engine based on the set of maturity factors for the set of IoT data. The data management operation may be identified to manage the set of IoT data. The data management operation may be carried-out to manage the set of IoT data.

Data maturity management

A method for remote product invocation includes configuring an invocation framework that includes an integration module and an endpoint/handler module. Once configured, the integration module is configured to: receive a source object; format data from said source object for a desired operation; and utilize said endpoint/handler module to make a connection to an external service that executes said desired operation using said data from said source object. A system for remote invocation of external services includes a calling entity which generates a source object containing data for execution of a remote operation; and an integration module configured to receive the source object, interpret the source object, and pass the data to an endpoint/handler which opens a connection with an external service and executes the remote operation.

Remote product invocation framework

Embodiments for using a mixed internet of things (IoT) device network to improve user satisfaction by a processor. A collaboration of data associated with a user captured from a plurality of types of IoT devices in the IoT network may be gathered. The collaboration of data may be used to determine a level of satisfaction of the user according to a knowledge domain describing a plurality of satisfaction factors and levels in the service based industry such that the collaboration of data is associated with one or more of the plurality of satisfaction factors and levels.

User satisfaction in a service based industry using internet of things (IoT) devices in an IoT network

Provided are techniques for authenticating a device. Accepted communication patterns representing accepted modes of communication between devices in an internet of things network are stored. In response to receiving a new communication from a requesting device of the devices, it is determined whether the new communication matches at least one of the accepted communication patterns. In response to determining that the new communication matches, there is a response to the new communication. In response to determining that the new communication does not match, flagging the new communication as an anomaly and determining how to process the new communication based on the flagging.

Authenticating a device based on communication patterns in a group of devices

Aspects provide a roadway management device having a processor configured to collect roadway attribute data associated with accidents within accident data and assign positive correlation values to roadway attributes that match the accident data. Aspects cluster subsets of the accidents that share common values of subset of the roadway attributes assigned positive correlation values; select a corrective action to obviate a one of the clustered roadway attributes; determine whether implementation of the selected corrective action has reduced occurrences of accidents matching common accident data of the clustered accidents on a relevant roadway; and increase a selection weighting value of the selected corrective action in response to determining that implementation has reduced accident occurrences. The selection weighting value is used to bias selection for implementation to obviate a roadway attributes with respect to other clustered accidents.

Accident prevention device

Methods and apparatus, including computer program products, implementing and using techniques for processing of data in an intermediary server. Data is received from an Internet of Things (IoT) enabled device. The data includes a readable header and an encrypted payload. An opaque algorithm is selected to process the payload, based on information contained in the header of the data. The selected opaque algorithm processes the encrypted payload of the data, wherein the opaque algorithm is invisible to the intermediary server. The processing includes: decrypting the encrypted payload, applying the opaque algorithm to the decrypted payload, and encrypting the results from processing the decrypted payload by the opaque algorithm. A new readable header is added to the encrypted results. The data, including the new readable header and the encrypted results, is forwarded to a different server, based on the information in the new readable header.

Processing of IoT data by intermediaries

Provided are techniques for authenticating an unknown device. For a first device in an internet of things network that includes a second device and forms a list of trusted devices, a distance between the first device and the second device is determined. A level of trust for the second device is identified based on the distance, wherein the level of trust specifies a level of data sharing. Reputation ranking is performed for the second device based on the level of trust. The reputation ranking is used to assign a trust score to the second device. Based on determining that the trust score exceeds a trust threshold, access is provided to the second device.

Authenticating an unknown device based on relationships with other devices in a group of devices

Embodiments generally relate to detection of anomalies in driving. In some embodiments, a method includes receiving first vehicle data during operation of a vehicle, wherein the first vehicle data is associated with a plurality of vehicle parameters. The method further includes determining a first signature based on the first vehicle data, wherein the first signature indicates first driving behavior. The method further includes receiving second vehicle data during operation of the vehicle, wherein the second vehicle data is associated with the plurality of vehicle parameters. The method further includes determining a second signature based on the second vehicle data, wherein the second signature indicates second driving behavior. The method further includes performing one or more remediating actions based on the second signature.

Cognitive modeling for anomalies detection in driving

In a method for device unavailability management within a network, a network device selects an advocate device as an agent of the network device. The network device defines a set of actions to be executed when the network device ever becomes unavailable. The set of actions is stored in a datastore and associated with the network device. Upon determining that the network device has become unavailable, the advocate device is notified and obtains the set of actions associated with the network device. The advocate device then executes the set of actions by the advocate device.

Devices demise actions and notification

In a method for providing an automatic learned response in a network, a collection system observes user responses to the incoming system indicators and to parameter types and associated parameter values used in the user responses. The collection system creates alert event entries to includes the incoming system indicators, confidence thresholds, the user responses, and the parameter types and associated parameter values used in the user responses. When the collection system receives new system indicators, the collection system determines whether the new system indicators match the system indicators in one or more alert event entries. When the new system indicators match the system indicators in one or more alert event entries and the confidence level exceeds the confidence threshold, the collection system automatically creates a new response based on the user response, the parameter types, and the associated parameter values in the matching alert event entries.

Learned response for alerts

A method, a computing system and a computer program product are provided. A computing device identifies a credential of a first entity and determines one or more second entities, each electronically verifying the credential of the first entity. The computing device determines one or more third entities, each electronically verifying one or more from a group of second entities and other ones of the third entities, wherein the second and third entities form a set of chains of verification from the first entity. The computing device analyzes the set of chains of verification to detect one or more loops within the set of chains among the second and third entities. A verification of the credential is generated by the computing device based on detection of a trusted entity within one of the set of chains and not detecting a loop within the one of the set of chains.

Automated verification of chains of credentials

A method for automatically prioritizing startup of resource groups during a migration event. The method may include monitoring resource usage of a first and a second set of applications associated, respectively, with a first and a second resource group executing on a first computing node. The method may additionally include generating respective first and second resource usage models for the first and second resource groups based on resource usage. The method may then include extrapolating, based on the first and second resource usage models, respective first and second resource group usage scores for the first and second resource groups at a second time in response to a migration event, the second time occurring subsequent to the first time. The method may further include determining, based on the extrapolating, a priority order for serially starting the first and second set of applications on a second computing node at the second time.

Automatic serial order starting of resource groups on failover systems based on resource group usage prediction

In general, the embodiments of the present invention provide an approach for rebalancing/reallocating cloud resource capacities between resource pools that provide variable customer assurances and delivery penalties when assurances are not met. The variables that are considered hereunder include, overall 'reservations', total current capacity, remaining capacity against unused reservations and penalties that apply for failing to satisfy 'reservation' commitments. The approach uses a rate of capacity consumption to calculate the risk of consuming the available capacity in each resource pool (e.g., resource pools allocated to satisfy different levels of service with different SLA failure penalties). Based on the relative available capacity in each pool (as determined by the pool rate of consumption), resources are reallocated to maximize revenue (e.g., reduce financial penalty) across a resource pool set.

Reallocating resource capacity among resource pools in a cloud computing environment

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Austin, TX, United States of America

Hari Haranath Madduri