International Business Machines Corporation

Namit Kabra

Yannick Saillet

Vijay Ekambaram

Ritesh Kumar Gupta

Smitkumar Narotambhai Marvaniya

Mike W Grasselt

Krishna Kishore Bonagiri

Likhitha Maddirala

Sarbajit Kumar Rakshit

Manish Anand Bhide

Hemant Kumar Sivaswamy

Eric Allen Jacobson

Tarun Tater

Kunjavihari Madhav Kashalikar

Jo Arao Ramos

Scott Louis Brokaw

Anvita Vyas

Saranga Dhar Tripathy

Martin Oberhofer

Neerju Neerju

Srinivas Mudigonda

Syam Dulla

Ron Reuben

Syed Shahid Ali Afser

Prashant Sharma

Alekhya Telekicherla

According to a computer-implemented method, an available amount of each of multiple computing resources is determined by machine logic over a period of time at a computing device. The machine logic also determines an expected usage of each computing resource to execute each workflow in a queue. The machine logic also determines a time of execution of each workflow in the queue based on the available amount of each of the multiple computing resources over time and the expected usage of each computing resource to execute each workflow in the queue.

Resource availability-based workflow execution timing determination

Targeted acquisition of data for model training includes identifying attributes of classified samples of a collection of samples classified by a classification model, and generating at least one query based on the identified attributes, the at least one query tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

Targeted data acquisition for model training

A database of deployed configurations, as well as attempted configurations that failed is maintained and used as reference to compare against configurations of attempted software deployments. Upon detecting a failed deployment, disclosed embodiments search the database for working configurations that most closely resemble the failed configuration, and rank the configurations based on various criteria. Disclosed embodiments may then automatically select a highest ranked working configuration, and perform an automatic upgrade of the necessary components to create a working configuration.

Automatic application dependency management

A computer-implemented method, system and computer program product for optimizing a flow of data within transform, load (ETL) data processing pipelines. Database columns from a source database that are to be transformed as well as not transformed in the data processing stages of a processing segment of an ETL data processing pipeline are identified. Those database columns that are to be transformed are grouped into a processing schema. Transformations are then performed on the database columns of the processing schema. Those database columns that are not to be transformed are grouped into a non-processing schema. A large object data type (LOB) is then created to reference the non-processing schema. An identifier is created and inserted in the data processing stages to identify the LOB in replace of the database columns that are not to be transformed thereby avoiding the copying of the database columns that are not to be transformed.

Optimizing flow of data within ETL data processing pipeline

A system and method are disclosed that utilizes an artificial intelligence based virtual proxy node. The virtual proxy node includes an intent resolution model and communicates between a smart audio device and at least one secondary device, wherein the at least one secondary device is configured to be controlled by a smart audio device or smart hub. The virtual proxy node tracks interactions between the smart audio device and the at least one secondary device to derive historical and context data from the tracking interactions. The virtual proxy node uses the historical and context data to predict which secondary device will be successful in responding to the user input command and broadcasts the input command to the virtual proxy node associated with one of the at least one secondary device. The virtual proxy node includes an intent resolution model trained by historical and context data.

AI-based virtual proxy nodes for intent resolution in smart audio devices

Targeted acquisition of data for model training includes automatically generating metadata describing samples, of an initial dataset, in neighborhoods of an embedding space in which the samples are embedded. The samples described by the automatically generated metadata are classified by a classification model, and include both correctly classified samples in the neighborhoods and incorrectly classified samples in the neighborhoods. Additionally, attributes of one or more correctly classified samples of the collection of samples and one or more incorrectly classified samples of the collection of samples are identified, and queries are generated based on the identified attributes, the queries tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

A method includes determining a presence of one or more people around a user and determining, based on the presence of the one or more people, an exposure level. The method also includes receiving a message for the user, the message comprising a first portion and a second portion and removing, based on the exposure level, the first and second portions from the message to produce a first message. The method further includes removing, based on the exposure level, the first portion from the message to produce a second message and presenting, based on the exposure level, the first message to the user. The method also includes receiving, from the user, feedback about the first message and presenting the second message to the user in response to the feedback.

Tiered audio notifications based on surrounding context

A method, computer system, and a computer program product for data pipeline prioritization is provided. Embodiments may include receiving, by a cognitive rules engine, one or more data pipelines. Embodiments may then include analyzing, using a computational method of the cognitive rules engine, the one or more data pipelines. Embodiments may lastly include prioritizing the one or more data pipelines based on a result of the computational method of the cognitive rules engine.

Prioritization and prediction of jobs using cognitive rules engine

A method provides for classifying data fields of a dataset. A classifier configured for determining confidence values for a plurality of data classes for the data fields may be applied. Using the confidence values, data class candidates may be identified. Data fields may be determined for which a plurality of data class candidates is identifiable. Using previous user-selected data class assignments, a probability may be determined for the data class candidates that the respective data class candidate is a data class to which the respective data field is to be assigned. The data fields may be classified using the probabilities to select for the data fields a data class from the data class candidates. The dataset may be provided with metadata identifying for the data fields the data classes to which the respective data fields are assigned.

Data classification

Data-deduplicating includes comparing a first record of a data-store with a second record of the data-store but instead of using a static weight for a field, the present data-deduplicating dynamically assigns a first weight for the first score to generate a first weighted score, wherein the first weight is based on one or both of the first value or the second value; and assigns a second weight for the second score to generate a second weighted score. A composite score is calculated based on the first weighted score and the second weighted score; and it is determined whether or not the first record and the second record are duplicate records, based on the composite score.

Generating weights for finding duplicate records

An approach is provided for completing a pipeline graph. Using a deep learning based sequence model, an initial data pipeline having a sequence of nodes is generated. Mismatch(es) between data formats required by input and output in the sequence of nodes is identified. Virtual gap node(s) that correct the mismatch(es) are added to the initial data pipeline. For a given virtual gap node, tentative graph structures are determined using knowledge graphs and a crowd sourced validation system. Reuse forecast scores and performance scores for the tentative graph structures are calculated. Based on the reuse forecast scores and the performance scores, a final graph structure for implementing the given virtual gap node is determined.

Automatically completing a pipeline graph in an internet of things network

An approach is disclosed that receives a code segment from a source code file. The received code segment is inputted to a trained knowledge manager from which one or more comparable code segments are received. Each of the comparable code segments are identified as having similar functionality as the selected code segment and each of the comparable code segments has a set of metadata. The metadata is used to select one of the comparable code segments and this selected code segment is used to replace the original code segment in the source code file.

Identify, suggest, and auto-implement best practices for requirement specific software development

A method, the method comprising retrieving a sound intensity map for a venue, wherein the sound intensity map is divided up into a plurality of regions, wherein the sound intensity map predicts a sound quality for each region during a current event. Receiving data from a plurality of IOT enabled operated aerial vehicles, where each IOT enabled operated aerial vehicle of the plurality of IOT enabled operated aerial vehicles travels around different regions of the plurality of regions, wherein each IOT enabled operated aerial vehicle collects data during the event. Comparing the received data to the sound intensity map to determine the region where an audio component of a venue audio needs to be adjusted. Determining the adjustment required for the audio component and adjusting the audio equipment.

Internet of things enable operated aerial vehicle to operated sound intensity detector

A computer-implemented method applies insights from a variety of data sources to each of the data sources. The method includes identifying a set of data sources, wherein each of the data sources are associated with a domain. The method includes analyzing documentation for each of the data sources. The method further includes extracting a set of attributes for each data source, and determining a data schema associated with each data source. The method includes mapping each data schema to a common domain schema. The method also includes linking, based on the mapping and on the set of attributes for each data source, common features across each data source. The method includes generating, in response to the linking, a knowledge graph. The method further includes preparing a visual display for a set of domain insights; and forking the set of domain insights into a first data source.

Insight expansion in smart data retention systems

Techniques are described relating to automatic data standardization in a managed services domain of a cloud computing environment. An associated computer-implemented method includes receiving a dataset during a data onboarding procedure and classifying datapoints within the dataset. The method further includes applying a machine learning data standardization model to each classified datapoint within the dataset and deriving a proposed set of data standardization rules for the dataset based upon any standardization modification determined consequent to model application. Optionally, the method includes presenting the proposed set of data standardization rules for client review and, responsive to acceptance of the proposed set of data standardization rules, applying the proposed set of data standardization rules to the dataset. The method further includes, responsive to acceptance of the proposed set of data standardization rules, updating the machine learning data standardization model accordingly.

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Hyderabad Telangana, India

Namit Kabra