International Business Machines Corporation

Kyndryl, Inc.

Prerna Agarwal

Manjit Singh Sodhi

Sri Harsha Varada

Nitin Kumar Gupta

Rahul Jain

Sampath Dechu

Monika Gupta

Vatche Isahagian

Shashank Mujumdar

Vinod Muthusamy

Siyu Huo

Prabhat Maddikunta Reddy

Renuka Sindhgatta Rajan

Naveen Eravimangalath Purushothaman

Sravani Madem

Shridhar Gurunath Domanal

Provided is a computer-implemented method, system, and computer program product for process mining asynchronous support conversations using attributed directly follows graphing. A processor may collect a plurality of conversation threads from an asynchronous data stream. The processor may label each utterance of a plurality of utterances from the plurality of conversation threads with an event label. The processor may analyze the event label for each utterance of the plurality of utterances. The processor may generate, based on the analyzing of the event label for each utterance, an attributed directly follows graph (DFG).

Process mining asynchronous support conversation using attributed directly follows graphing

According to one embodiment, a method, computer system, and computer program product for cloud service brokerage. The embodiment may include receiving a data set and user defined contextual parameters relating to a machine learning (ML) problem of a user to be performed on the data set. The embodiment may include identifying a resource requirement of the ML problem and available resources. The embodiment may include enabling user configuration of the contextual parameters in an interactive augmented reality (AR) view. The embodiment may include identifying a set of clusters upon which to execute computing tasks of the ML problem. The set of clusters is identified out of the available resources. The embodiment may include implementing a ML evaluation process to determine an optimized load distribution model for execution of the computing tasks within the set of clusters. The embodiment may include implementing the optimized load distribution model.

Interactive augmented reality based optimization of machine learning model execution on hybrid cloud

A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include extracting a first multiple dimensional dataset and a second multiple dimensional dataset and encoding the first multiple dimensional dataset into a first encoded dataset and the second multiple dimensional dataset into a second encoded dataset. The operations may include combining the first encoded dataset and the second encoded dataset into a combined encoded dataset and conveying the combined encoded dataset to a user.

Optimized transmission and consumption of digital content

A method, computer system, and a computer program product for anomaly detection is provided. The present invention may include converting business process logs into a graphical data structure. The present invention may include generating an optimized graph encoding for anomaly detection using an unsupervised machine learning model. The present invention may include computing an anomaly score for each activity of the business process log using a process aware metric based on feature representation. The present invention may include labeling each of the one or more data points with a high anomaly score.

Graph encoders for business process anomaly detection

Mechanisms are provided for automatically generating a user interactable what-is-new feature for an updated application. The mechanisms obtain footprint data from electronic documents generating during a software development lifecycle to generate an update to an application, where the footprint data specifies development requirements and actions performed by at least application developers during the software development lifecycle. The mechanisms further obtain test case generation data from a recording of test case generation interactions with a graphical user interface and execute computer natural language processing on the footprint data and analysis of the recording of the test case generation interactions to automatically generate a what-is-new container for the update to the application. Moreover, the mechanisms deploy the what-is-new container to a client computing device that executes a container daemon to implement a what-is-new feature for the updated application installed on the client computing device.

Leveraging digital footprints in software development life cycle

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New Delhi, India

Prerna Agarwal