International Business Machines Corporation

Zhennan Wang

John Conrad Sanchez

Puthukode G Ramachandran

Eric Rozner

Chin Ngai Sze

John C Hartley

William Stephen Carter

Sophia A Simpson

Maggie Phung

Nitin Gaur

Leigh Allen Williamson

Gautham Pamu

Jinfang Chen

Benjamin S Vera-Tudela

Technical solutions are described for configuring a gateway in an internet of things (IoT) system. An example method includes receiving, by a gateway resolver, a request to access a management console of the IoT system, the request including access information associated with the gateway of the IoT system, the request being sent by an administration device. The method further includes determining, by the gateway resolver, an IP address of the gateway based on the access information. The method further includes obtaining, by the gateway resolver, a management console URL based on the IP address. The method further includes redirecting, by the gateway resolver, the administration device to the management console URL in response to the request.

Accessing gateway management console

An approach is provided that receives, over a computer network, transaction data from a number of clients that are running an app. The approach generates association rules by inputting the transaction data to an association rule learning algorithm, such as an Apriori algorithm. Each association rule is based on a user transaction pattern and a desired result, and each association rule includes a generated confidence value that pertains to an expected performance of one of the steps included in the respective association rule. The app is then modified based on an analysis of the generated confidence values, with the app modification being directed towards improving one or more of the confidence values.

Using learned application flow to predict outcomes and identify trouble spots in network business transactions

A method and apparatus for deduplication of sensor data is described. In one embodiment, a method includes receiving a plurality of sensor packets P at a network gateway apparatus. Each packet Pi has a corresponding timestamp Ti. The method includes storing a subset of the plurality of received packets P' for a first period of time T1. The method also includes comparing each of the stored packets P&#x2032;i to other stored packets P&#x2032;j to determine an equivalence. In response to determining the equivalence of the stored packet P&#x2032;i with P&#x2032;j, the method includes forwarding only one of packet P&#x2032;i or P&#x2032;j to a destination.

Method and apparatus for deduplication of sensor data

An approach is provided that receives transaction data from a client that is running an app. The received transaction data identifies a latest step performed by the client. The received transaction data is compared to rules that have been generated from previous inputs of transaction data to an association rule learning algorithm. Each of the rules include steps and a confidence value corresponding to a last step of each of the respective rules. The approach identifies, based on the comparison, some rules where the latest step performed by the client is the penultimate step of the rules. One of the rules is selected based on the confidence value of the respective rule. The process then transmits a message to the client to perform the last step of the selected rule.

Using learned application flow to assist users in network business transaction based apps

Embodiments of the invention relate to selecting a path for navigation based on a node signature and a device signature. The node signature is directly related to node attribute data. The device signature is directly related to device data. A route is selected based on a match or near match of the node and device signatures.

Automated crowd sourcing of a navigation route

Provided are techniques for automatically protecting portable and wearable electronic devices from potential hazards by predicting when such hazards may occur. Techniques may include monitoring a plurality of sensors on the mobile computing device; receiving, on the mobile computing device, context data from a plurality of context-service applications; selecting a set of device-protection policies based upon an availability of the plurality of sensors and the plurality of context-service applications, wherein the set of device-protection policies are configured to determine a level of risk to the mobile computing device based on sensor data received from the plurality of sensors and the context data; applying, the sensor data and the context data to the set of device-protection policies to generate the level of risk; and triggering a self-protection action if the level of risk exceeds a pre-determined threshold level of risk.

Automatic self-protection for a portable electronic device

Technical solutions are described for configuring a gateway in an IoT system. An example method includes providing, by an identification module, a user-interface (UI) access information to an administrative device. The method further includes receiving, by a setup module, from the administrative device, and via a management console generated based on the UI access information, setup data. The method further includes adjusting, by the setup module, one or more settings of the gateway according to the setup data.

Technical solutions are described for accessing a management console of a gateway device in an internet of things (IoT) system. An example method includes receiving wirelessly, by an administrative device, access information for the management console of the gateway device. The method further includes receiving, by the administrative device, from a gateway resolver device, an address for the gateway device in response to sending the access information to the gateway resolver device. The method further includes accessing, by the administrative device, the management console of the gateway device using the address. The method further includes adjusting, by the administrative device, one or more settings of the gateway device using the IoT system.

Provided are techniques for automatically protecting portable and wearable electronic devices from potential hazards by predicting when such hazards may occur. Techniques may include monitoring a plurality of sensors on the mobile computing device; receiving, on the mobile computing device, context data from a plurality of context-service applications; selecting a set of device-protection policies based upon an availability of the plurality of sensors and the plurality of context-service applications, wherein the set of device-protection policies are configured to determine a level of risk to the mobile computing device based on sensor data received from the plurality of sensors and the context data; applying the sensor data and the context data to the set of device-protection policies to generate the level of risk; and triggering a self-protection action if the level of risk exceeds a pre-determined threshold level of risk.

A computer implemented method, computer program product, and data processing system for predicting a future status of a memory leak. A first set of data including memory consumption data is received at a software bundle. The software bundle is operating in an open services gateway initiative environment. Responsive to a determination that a memory leak exists, the first set of data is analyzed to predict a future status of the memory leak. The future status is stored, as stored data, in a storage device.

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Austin, TX, United States of America

Zhennan Wang