Abstract: Systems and methods are provided for adaptive data replication. A first data object is stored. One or more first network characteristics of a communication network are detected. A first replication mode is triggered (e.g., a single-master replication mode) based on the one or more first network characteristics of the communication network. The single-master replication may be configured to facilitate real-time synchronizations (e.g., for critical updates). One or more second network characteristics of the communication network are detected. A second replication mode (e.g., a multi-master replication mode) is triggered based on the one or more second network characteristics. The second replication may be configured to facilitate a delayed synchronization (e.g., for non-critical updates) of the first data object and the second data object using the second update file.

Abstract: A system and a method for recommending feature sets for a plurality of equipment to a user. The method includes creating a library of contextual and preprocessed feature sets in a hierarchical manner for recommending features sets to a user. The method also includes compiling a plurality of hierarchical feature sets with a last feature set in a hierarchy being generated using an output of a module and incrementally adding the generated feature sets using different modules to the feature sets generated by the module. The method includes validating the generated feature sets to remove errors and using the validated feature sets as labelled data for previous feature sets and using attributes to categorize the labelled data corresponding to the contextual and pre-processed feature sets.

Abstract: A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

Abstract: Methods, apparatuses, or computer program products provide for triggering component workflows within a multi-component system. An update to one or more component metadata records of a component metadata vector associated with a first component identifier may be received. The component metadata vector may include a plurality of records. Each record of the plurality of records may include a unique component metadata record identifier and a component metadata value. The component metadata vector associated with the first component identifier may be traversed after updating the one or more component metadata records. Based at least in part on detecting a component metadata condition associated with a component workflow trigger associated with the first component identifier, a first component workflow action of a first component workflow action series comprising a plurality of component workflow actions may be executed.

Abstract: An embodiment includes deriving usage data associated with records of a database by monitoring requests to perform read operations on the records of the database. The embodiment generates record correlation data representative of correlations between respective groups of records of the database by parsing the usage data associated with the records of the database. The embodiment stores a plurality of records received as respective write requests during a first time interval in an intermediate storage medium. The embodiment identifies a correlation in the record correlation data between a first record of the plurality of records and a second record of the plurality of records. The embodiment selects, responsive to identifying the correlation, a first location in the database for writing the first record and a second location in the database for writing the second record based on a proximity of the first location to the second location.

Abstract: Various systems and methods for creating and managing a simulation database system are described herein. A system for creating and managing a simulation database system includes a processor subsystem; and memory including instructions, which when executed by the processor subsystem, cause the processor subsystem to: access an operating scenario, the operating scenario including parameters defining an environment for a simulated autonomous vehicle; access a rule set, the rule set controlling a search mechanism; search for content, the search controlled by the rule set and operable to find content relevant to the operating scenario, the search resulting in search results; process the search results for training autonomous vehicle operation; and store the processed search results.

Abstract: Described herein are techniques for improving disaster recovery, in particular disaster recovery pertaining to data transfer requests. The data transfer request can be received by each of multiple deployments; however, only a primary deployment can process the request. The data transferred by the primary deployment may be replicated in the secondary deployments. In response to a failover event, one of the secondary deployments can be designated as the new primary development and continue the data transfer based on the data transfer request and the replication information received from the old primary deployment prior to the failover.

Abstract: A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

Abstract: Improved intelligent personal assistant (IPA) software agents are disclosed that are configured to interact with various people, service providers, files, and/or smart devices. More particularly, this disclosure relates to an improved Natural Language Processing (NLP) Intent Determination Service (IDS) that is able to determine the likely best action to take in response to generic user commands and queries. The improved NLP IDS disclosed is said to be ‘search-based’ because, rather than attempt to parse incoming user commands and queries up front, the incoming user commands and queries are searched against a pre-generated database of exemplary user commands (e.g., having associated action or parsing identifiers) to determine the most relevant search result(s). The associated system actions and known grammar/parsing rules of the most relevant search result(s) may then be used to process the incoming user command or query—without having to actually parse the incoming user command or query from scratch.

Abstract: Embodiments of the disclosure relate to systems and methods for leveraging unsupervised machine learning to produce interpretable routing rules. In various embodiments, a training dataset comprising a plurality of data records is created. The plurality of data records includes message data comprising a plurality of messages and action data comprising a plurality of actions that correspond to the plurality of messages. A first machine learning model is trained using the training dataset. The first machine learning model as trained provides cluster data that indicates, for each data record of the plurality of data records of the training dataset, membership in a cluster of a plurality of clusters. An enhanced training dataset is created that comprises the message data from the training dataset, the action data from the training dataset, and the cluster data.

Abstract: Generating a data protection and recovery data backup option by identifying static and runtime metadata for a computing application, determining application criticality of the computing application according to the static metadata using a first machine learning model, determining a data backup option for the computing application according to application criticality and the runtime metadata, using a second machine learning model.

Abstract: An attribute correlation system reduces network traffic and processing cycles associated with impromptu item selections by generating attribute preference models based on disparate attribute spectrums. The attribute correlation system deploys the attribute preference models to select individual items from various disparate “candidate item categories.” Generally described, the attribute preference models facilitate analyzing item sets across a wide variety of disparate “candidate” item categories to preemptively identify individual items for a user. In this way, the individual items may be identified and, ultimately, selected for the user even absent any indication that the user has searched for otherwise identified these items or even other items from within the disparate “candidate” item categories. The “candidate” item categories may be determined to be disparate from one another based on a relationship void existing such that predefined relationships are missing between these “candidate” item categories.

Abstract: A method, computing platform, and computer program product are provided. A computing platform receives, for a clinical trial, study design information including a set of parameters related to a success of a clinical trial and factors related to a relevance of the parameters. The computing platform provides multiple sets of varying parameter values. At least one trained machine learning model is applied to the study design information including the sets of parameter values to predict multiple success scores including a predicted overall success score. The computing platform outputs the provided set of values for the set of parameters that are associated with the at least one trained machine learning model predicting a best overall success score for the clinical trial.

Abstract: Disclosed herein are system, method, and apparatus for generating labels for k-means clusters. The method includes accessing a plurality of data records from a database repository, and storing the plurality of data records into at least one of primary or secondary memory associated with at least one computer processor performing the method, along with a cluster number for each data record. All data records having a same cluster number form a cluster, and each record has been categorized or designated a cluster number out of a total K number of clusters. The method includes for each of a plurality of classification features, performing cluster-based analysis for a first cluster with respect to a single feature to generate a single feature overlap score. The method includes sorting, grouping, and generating a naming label for the first cluster based on the predetermined number of features having the lowest overlap scores.

Abstract: Techniques disclosed herein relate to generating a hierarchical classification model that includes a plurality of classification models. The hierarchical classification model is configured to classify an input into a class in a plurality of classes and includes a tree structure. The tree structure includes leaf nodes and non-leaf nodes. Each non-leaf node has two child nodes associated with two respective sets of classes in the plurality of classes, where a difference between numbers of classes in the two sets of classes is zero or one. Each leaf node is associated with at least two but fewer than a first threshold number of classes. Each of the leaf nodes and non-leaf nodes is associated with a classification model in the plurality of classification models of the hierarchical classification model. The classification model associated with each respective node in the tree structure can be trained independently.

Abstract: A method, system, and computer-readable media for identifying false positive data within a set of blast exposure data. After debiasing and filtering the blast exposure data, an algorithm identifies predetermined features within the data that may be indicative of false positive data. The predetermined features are used to calculate a false positive score and if the false positive score exceeds a predetermined score threshold, the data is flagged with a false positive flag and may be removed from the set of blast exposure data.

Abstract: A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

Abstract: A system for storing and moving data based on the location of a user or a change in location of a user is described herein. Data associated with a user may be stored at one or more storage devices associated with a location of the user. Some or all of the data associated with the user may be moved to one or more other storage devices if, for example, the user moves to a different location or is expected to move to a different location, so that the user's data may be more efficiently accessed by the user.

Abstract: A database index management system uses one or more machine learning models to analyze a query log in relation to a database. A machine learning model may identify a query pattern and/or a change in the query pattern from the query log, identify a column associated with the query pattern, and identify an addition, removal, or modification of an index related to the identified column. The database index management system may perform one or more additions, removals or modifications of indices of the database based on query patterns identified in the query log. The database index management system continuously improves database performance in response to changing database usage patterns over time.

Abstract: The present method and system provides a neuromodulation therapy including receiving a plurality of input data relating to a patient, the input data including brain value measurements and body value measurements. The method and system includes analyzing the input data in reference to reference data generated based on machine learning operations associated with existing patient data and reference database data. Based thereon, the method and system includes electronically determining, a brain malady and a severity value for the patient and electronically generating a treatment protocol for the patient, the treatment protocol includes transcranial stimulation parameters. Therein, the method and system includes applying a transcranial stimulation using the transcranial stimulation parameters based on the treatment protocol.