International Business Machines Corporation

Jeffrey Mark Achtermann

Ashish Verma

Shantanu Ravindra Godbole

Indrajit Bhattacharya

Kevin W English

Sachindra Joshi

Ashwin Srinivasan

Alberto Giammaria

Maharaj Mukherjee

Nikhil Gupta

Liliana Orozco

Arabinda Bose

Victor Gee Yiu

Bryan R Moore

Robert Earl Guthrie

Rahul Gupta

Joanna W Ng

Arnaud Airy Mathieu

Harrison Kurtz

Patrick Leo Woods

Todd Gerlach

Michael Bender

Hari Haranath Madduri

Denise Bell

Timothy James Hahn

Ajay Gupta

John Conrad Sanchez

Richard Walton Ragan, Jr

Christopher Victor Lazzaro

Ronald Bruce Baker

Gautham Pamu

Leucir Marin, Jr

David J Levinson

Eric Norman Azares

Mark Allen Sistrunk

Steven Armand Jarvis

Brian George Vassberg

Terence J Quinn

Lawrence H Maddox, Jr

Troy Allen Cranford

Shinta Dewi Arifin Tjio

Provided are a computer program product, system, and method for geo-spatial analysis to determine boundaries of traffic regions and classifications of the boundaries for controlling drop-off/pick-up traffic. A determination is made of drop-off/pick-up points for vehicles in traffic regions in which vehicles dropped-off and/or picked-up passengers. Geo-spatial cluster analysis is applied to the determined drop-off/pick-up points to determine boundaries of the traffic regions having drop-off/pick-up points forming clusters of similar drop-off/pick-up point densities. A determination is made of a respective classification for each respective one of the boundaries. Each respective classification indicates a relative density of drop-off/pick-up points in its respective boundary relative to densities of drop-off/pick-up points in other one of the boundaries. Classification related information based on the determined classifications is communicated to use to control drop-off/pick-up traffic in the traffic regions.

Geo-spatial analysis to determine boundaries of traffic regions and classifications of the boundaries for controlling drop-off/pick-up traffic

Methods and apparatus, including computer program products, implementing and using techniques for logically grouping Internet of Things (IoT) devices. One or more logical zones are defined. Each logical zone includes one or more physical zones, one or more virtual zones, or a combination of physical and virtual zones. Each IoT device is associated with at least one logical zone. Communication between the IoT devices is restricted based on the zones with which the IoT devices are associated.

Logical zones for IoT devices

Methods for logically grouping Internet of Things (IoT) devices are described. One or more logical zones are defined. Each logical zone includes one or more physical zones, one or more virtual zones, or a combination of physical and virtual zones. Each IoT device is associated with at least one logical zone. Communication between the IoT devices is restricted based on the zones with which the IoT devices are associated.

Methods and apparatus, including computer program products, implementing and using techniques for chain of custody tracking for an object. Several sets of Internet of Things (IoT) sensors are organized in a network. Each set of sensors is configured to record one or more events relating to the object. Each event includes an event time, an event location, and an entity that is a custodian for the object at the time of the event. When the object changes custodians, proper custodianship is verified based on input from at least one set of IoT sensors.

Tracking products with chain of custody using IOT devices

Methods for chain of custody tracking for an object are described. Several sets of Internet of Things (IoT) sensors are organized in a network. Each set of sensors is configured to record one or more events relating to the object. Each event includes an event time, an event location, and an entity that is a custodian for the object at the time of the event. When the object changes custodians, proper custodianship is verified based on input from at least one set of IoT sensors.

Tracking products with chain of custody using IoT devices

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 and system. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by a target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of a source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.

Cross-domain clusterability evaluation for cross-guided data clustering based on alignment between data domains

A method and system for evaluating cross-domain clusterability upon a target domain and a source domain. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by the target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of the source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.

Managing a display of objects is provided. A processor unit displays a graphical control configured to receive a user input of a value to control the display of a set of objects relative to a group of properties for the set of objects in a table. A processor unit changes the display of the set of objects to a ratio in a plurality of ratios in the table for the set of objects relative to the group of properties for the set of objects in the table based on the value.

Table management

Determining synonyms of words in a set of documents. Particularly, when provided with a word or phrase as input, in exemplary embodiments there is afforded the return of a predetermined number of 'top' synonym words (or phrases) for an input word (or phrase) in a specific collection of text documents. Further, a user is able to provide ongoing and iterative positive or negative feedback on the returned synonym words, by manually accepting or rejecting such words as the process is underway.

Method and system for interactively finding synonyms using positive and negative feedback

A process for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

A computer system for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

A computer program product evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

A system and associated method for cross-guided data clustering by aligning target clusters in a target domain to source clusters in a source domain. The cross-guided clustering process takes the target domain and the source domain as inputs. A common word attribute shared by both the target domain and the source domain is a pivot vocabulary, and all other words in both domains are a non-pivot vocabulary. The non-pivot vocabulary is projected onto the pivot vocabulary to improve measurement of similarity between data items. Source centroids representing clusters in the source domain are created and projected to the pivot vocabulary. Target centroids representing clusters in the target domain are initially created by conventional clustering method and then repetitively aligned to converge with the source centroids by use of a cross-domain similarity graph that measures a respective similarity of each target centroid to each source centroid.

Cross-guided data clustering based on alignment between data domains

A method, system and program of executing an interpreted programming language application from a server at a designated virtual machine on a client computer are provided. A manifest file is extracted which indicates at least one resource for the interpreted programming language application as well as the repository file associated with the resource. The repository file is located in a web start cache and moved to a run directory accessible by the designated virtual machine, outside the web start cache. The interpreted programming language application is then executed from the run directory using the at least one resource.

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Austin, TX, United States of America

Jeffrey Mark Achtermann