International Business Machines Corporation

Linsong Chu

Mudhakar Srivatsa

Raghu Kiran Ganti

Joshua M Rosenkranz

Pranita Sharad Dewan

Dinesh C Verma

Mathews Thomas

Hongfei Li

Utpal Mangla

Ronald Bruce Baker

Carlos Henrique Andrade Costa

Shreeranjani Srirangamsridharan

Satishkumar Sadagopan

Otis Smart

Yeon-Sup Lim

Tuan Minh Hoang Trong

Karina Elayne Kervin

Suresh Kumar Jasrasaria

Venkatesh Ashok Rao Rao

Michael Khayyat

Naveen Mathew Nathan Sathiyanathan

Charles Lynn

Karthik Jayaraman

A system, computer program product, and method are provided for distributed data workflow semantics. A pipeline, such as a machine learning (ML) pipeline, is represented in a data flow graph (DFG). The represented pipeline is subject to annotations, with the annotations including pipeline nodes and object references. The pre-processed pipeline is subject to execution or processing with the annotated object references capturing object lineage. Output from the executed pipeline is constructed and a corresponding control signal is formatted to dynamically and selectively control an operatively coupled physical hardware device or software.

Annotation of a machine learning pipeline with operational semantics to support distributed lineage tracking

Aspects of the present disclosure relate to annotating or tagging customer data. In some embodiments, the annotating can include summarizing touchpoints into k-hot encoding feature vectors, mapping the feature vectors onto an embedding layer, predicting a hierarchical data sequence using the embedding layer and the feature vectors, extracting the feature vectors that are most influential in predicting the embedding layer, and outputting the touchpoints associated with the most influential feature vectors.

Annotating customer data

A computer implemented method performs data skipping in a hierarchically organized computing system. A group of processor units determines leaf node data sketches for data in leaf nodes in the hierarchically organized computing system. The leaf node data sketches summarize attributes of data in the leaf nodes. The group of processor units aggregates the leaf node data sketches at intermediate nodes in the hierarchically organized computing system to form aggregated data sketches at the intermediate nodes and retains data sketches received at the intermediate nodes from a group of child nodes to form retained data sketches. The retained data sketches are one of leaf node data sketches and the aggregated data sketches. The group of processor units searches the data using the retained data sketches and the data skipping within the hierarchically organized computing system in response to queries made to the intermediate nodes in the hierarchically organized computing system.

Hierarchical data skipping using data sketches

Aspects of the invention include receiving, by a processor, airspace data associated with a predefined airspace, obtaining roadway data associated with the predefined airspace, determining a set of air travel channels within the predefined airspace based on the roadway data, defining a set of travel lanes within the set of air travel channels, wherein each travel lane in the set of travel lanes comprises an altitude range, receiving unmanned aircraft (UA) data associated with a set of UAs within the predefined airspace, wherein the UA data comprises one or more flight paths for each UA in the set of UAs, and assigning each UA in the set of UAs to a travel lane in the set of travel lanes based on the one or more flight paths.

Traffic management for unmanned aircraft

Aspects of the present disclosure relate to identifying friction points in customer data. In some embodiments, identifying friction points can include receiving a set of input sequence data and predicted class labels for the set of input sequence data; selecting input sequences, from the set of input sequence data, that have class labels matching a ground truth class label; reducing the selected sequences to anchor points; and grouping the reduced selected sequences into critical data set signatures using discriminatory subsequence mining.

Identifying friction points in customer data

A method, system and computer program product for identifying a geographic market share. Mobility data is acquired from applications running on mobile devices of users located within a geographic area. Mobility data is then used to infer shopping habits within the geographic area. Geo-demographic profiles are then created. The geographic market share is then determined using the created geo-demographic profiles.

Identifying geographic market share

Aspects of the invention include generating a combined raster image from point cloud data and reference data describing an original location of a power line. Selecting a set of candidate pixels from the combined raster image describing an updated location of a power line, wherein the selection is based at least in part on a location of pixels in the combined raster image that describe the original location. Detecting pixels from the set of candidate pixels that describe an updated location of a power line. Modifying the combined raster image to reflect the updated location of the power line.

Power line georectification

A computer-implemented method for generating and deploying a reinforced learning model to train a chatbot. The method includes selecting a plurality of conversations, wherein each conversation includes an agent and a user. The method includes identifying, in each of the conversations, a set of turns and on or more topics. The method further includes associating one or more topics to each turn of the set of turns. The method includes, generating a conversation flow for each conversation, wherein the conversation flow identifies a sequence of the topics. The method includes applying an outcome score to each conversation. The method includes creating a reinforced learning (RL) model, wherein the RL model includes a Markov is based on the conversation flow of each conversation and the outcome score of each conversation. The method includes deploying the RL model, wherein the deploying includes sending the RL model to a chatbot.

Reinforcement learning for chatbots

An action recommendation system uses reinforcement learning that provides a next action recommendation to a traffic controller to give to a vehicle pilot such as an aircraft pilot. The action recommendation system uses data of past human actions to create a reinforcement learning model and then uses the reinforcement learning model with current ABS-B data to provide the next action recommendation to the traffic controller. The action recommendation system may use an anisotropic reward function and may also include an expanding state space module that uses a non-uniform granularity of the state space.

Traffic control with reinforcement learning

Aspects of the invention include receiving a point cloud data set describing a target area. Receiving a first parameter describing a power line segment at an original location within the target area. Using the processor to extract from the point cloud data set a power line segment at an updated location distinct from the original location, wherein analysis of each candidate data point of the point cloud data set is constrained by the first parameter, and wherein the first parameter causes the power line segment at the updated location to be parallel with the power line segment at the original location. Generating a vector describing the updated location of the power line segment with respect to geospatial coordinates of the target area.

Power line extraction using reference data

A computer system merges location-based data sets. Each of a plurality of data sets are transformed into a standardized schema, including at least two data sets including information indicating a geographic location. The schemas of the plurality of data sets are combined by data set type to produce a resulting data set for each data set type. The schemas of a first and second data sets are joined to produce a merged data set using a machine learning model to identify corresponding rows of the schemas. The schema of the merged data set is joined with the schemas of the resulting data sets for the data set types to produce a new data set. A resulting merged data set in the standardized schema is produced. Embodiments of the present invention further include a method and program product for merging location-based data sets in substantially the same manner described above.

Processing multiple data sets to generate a merged location-based data set

Methods and systems for detecting events. A satellite image is obtained and the satellite image is processed using a first convolutional neural network (CNN) to produce a satellite vector that identifies at least one fire. A mobile sensor is automatically directed to a mobile location based on the satellite vector.

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White Plains, NY, United States of America

Linsong Chu