Abstract: Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes.

Abstract: Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes.

Abstract: Various embodiments of the present invention disclose techniques for orchestrating a complex data processing scheme for an investigative process using a machine-learning based orchestration model that is trained to optimize the use of computing resources based at least in part on a feedback loop. An input data object associated with an investigative process can be selected for investigation; a predictive data analysis sub-routine for processing the input data object can be intelligently selected from a plurality of predictive data analysis sub-routines by the machine-learning based orchestration model; and a processing orchestration action can be initiated based at least in part on an investigative score output by the predictive data analysis sub-routine. The processing orchestration action can include closing the input data object, continuing to process the input data object with additional predictive data analysis sub-routines, or passing the input data object to a predictive entity for further processing.

Abstract: To assess risk for a target dataset including a plurality of records, a system receives, from a data source, information related to a plurality of datasets including the target dataset; automatically determines dependencies on the target dataset using logs indicative of read and write operations related to the target dataset; generates, using the determined dependencies, a dependency graph indicative of dependencies, within the plurality of datasets, on the target dataset; determines, using the generated dependency graph, a level of risk associated with the target dataset; and provides an indication of the determined level of risk.

Abstract: Implementations are provided for providing responsive audio recordings to user queries that are prerecorded by human beings, rather than generated automatically using speech synthesis processing. In various implementations, a query provided by a user at an input component of a computing device may be used to search a corpus of voice recordings. From the searching, a plurality of candidate responsive voice recordings may be identified and ranked based on measures of credibility associated with speakers that created the candidate responsive voice recordings. Based on the ranking, one or more of the plurality of candidate responsive voice recordings may be provided for presentation to the user at an output component of the same computing device or a different computing device.

Abstract: In one embodiment, a computer implemented method includes comprises storing objects in a first bucket and files in a second bucket of a first storage cluster of the distributed storage system, initiating an audit job on the first storage cluster, synchronously replicating audit configuration data and mirroring audit data (e.g., audit files, logs) from the first storage cluster to the second storage cluster, performing a switchover process from the first storage cluster to the second storage cluster, and initiating an audit job on the second storage cluster based on the audit configuration during the switchover process. The first storage cluster initially handles input/output operations for a software application before the switchover process.

Abstract: A method can include determining a common mapping scheme for message queues and shards for an index message to be committed to the shards. The common mapping scheme can include a respective shard hash range for each shard, and a mapping function between the message queues and the shards. The method further can include determining a message hash value for the index message. Additionally, the method can include determining a target shard based on the message hash value and the respective shard hash range for each shard. The method also can include determining an intermediate message queue based on the message hash value and the mapping function. Moreover, the method can include storing the index message to the intermediate message queue. The method further can include causing the index message stored in the intermediate message queue to be committed to the target shard. Other embodiments are disclosed.

Abstract: A system can collect system logs from a computer, and store the system logs in data storage of a cloud computing platform. The system can instruct the cloud computing platform to structure and analyze the system logs. The system can, based on the analyzing, determine a probability of a failure of a component of the computer. The system can, based on determining that a value of the probability falls within a specified range of probability values, trigger a defined action of the defined actions that comprises triggering a data backup of the computer, determine a root cause of the failure of the component, and trigger a self-healing action of the computer, wherein the self-healing action is based on the root cause of the failure of the component. The system can, after the self-healing action is performed, recover the backed up data to the computer.

Abstract: The present invention provides a system for orchestrating the storage instances in a multi-cluster environment, said system comprises at least a processor and a memory. The program instructions are stored in the memory which when executed is configured to receive data from one or more devices associated with a user. Upon receiving the data, one or more profile of the user device are accessed and based on the profile data, one or more clusters and one or more databases are identified in a multi-cluster environment. Further, the processor executes a database optimization algorithms to determine one or more parameters of the database and dynamically route the data to the one or more database in the identified cluster based on identified parameters of the one or more databases. Subsequently, the memory is updated to store the available storage in the one or more clusters and the one or more databases.

Abstract: Systems, methods, and computer-program products for entity resolution are disclosed. Entity resolution embodiments include receiving tree data from each of a pair of entities, extracting and/or aggregating feature vectors or metric functions therefrom, and generating similarity scores between the pair of entities. The similarity scores may be weighted using machine-learned weights. The weighted similarity scores are used to generate a combinatorial probability score accounting for combined likelihoods of field values between the pair of entities. A classification of the pair of entities is performed based on the combinatorial probability score, with a genealogical database modified based on the classification.

Abstract: Various methods and processes, apparatuses/systems, and media for separating or distributing database calls between active and passive databases are disclosed. A processor establishes a communication link between a passive database and an active database via a communication network; executes real-time syncing of data between the active database and the passive database by implementing a replication algorithm; receives a request from an application user to generate a report corresponding to the application; implements a batch processor, at an application level, to separate read-only operations from read and write operations associated the application; and separates or distributes database calls, at the application level, between the active database and the passive database in a manner such that the read-only operations are directed to the passive database and the read and write operations are directed to the active database.

Abstract: A method for executing query processing includes, in response to a query from a host, generating a task graph indicating a plurality of task sequences, each of the task sequences capable f performing query processing corresponding to the query. The sequences includes a first sequence that outputs data in a first compression state to a one of output targets, and a second sequence of tasks that outputs the data in a second compression state different from the first compression state to the one of the output targets. The method further includes determining a processing cost for each of the task sequences, selecting one of the task sequences in accordance with the determined processing cost, and performing the query processing corresponding to the query in accordance with the selected task sequence.

Abstract: A method including receiving at least one input, detecting at least one parameter associated with a context, generating, by a machine learning model, a first set of data classes enriched with the context, determining if each data class is associated with a data class repository to define a subset of data classes not associated with a data class repository, and generating, by a machine learning model, at least one data class repository for each data class. The method includes generating a display signal to display information associated with at least one data class repository, the display signal associated with a graphical user interface, altering, using a machine learning model, the display signal by altering at least one portion of the graphical user interface associated with the context, and sending the display signal to display the at least one portion of the graphical user interface on the user device.

Abstract: Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes.

Abstract: Described herein are systems and methods for correcting one or more user aliases. One or more alias identifiers are identified by type based at least on one or more user aliases stored in an alias store, the one or more user aliases mapping the one or more alias identifiers to one or more users. Each of the one or more alias identifiers are assigned to one or more rules of a same type as the one or more alias identifiers. The one or more rules are executed against one or more user records in a user metadata store to establish a results table identifying one or more aliases by type found in the user metadata store. An alias correction table is established that identifies one or more aliases of the results table that do not match one or more user aliases and to one of flag, remove, or correct.

Abstract: Systems and methods related to maintaining a random sample of documents that is representative of a pool of documents are provided. As documents are ingested into the pool of documents, a random number may be assigned to the documents. The documents may then be sorted into an ordered list. As the documents in the pool are provided to a review platform for manual review, the documents may be included in a review queue based at least in part on the ordered list. As additional documents are added to the pool of documents, the new documents are interleaved into the ordered list to maintain the random and representative nature of the random sample.

Abstract: Disclosed is a method for an intermediary mapping an de-identification comprising steps of retrieving datasets and meta data from a data source; selecting a target standard; mapping the retrieved datasets and the metadata to the target standard, wherein the datasets and the metadata are mapped to the target standard using one of, a schema mapping, a variable mapping, or a combination thereof; infer one or more of, variable classifications, variable connections, groupings, disclosure risk settings, and de-identification settings using the dataset mapping and metadata; perform a de-identification propagation using the mapped datasets, the mapped metadata, the inferred variable classifications, the inferred variable connections, the inferred groupings, the inferred disclosure risk settings, the inferred de-identification settings, or a combination thereof.

Abstract: Example technologies involve media preferences corresponding to multiple user accounts. In an example, a server of a streaming audio service receives, from a first controller interface on a first mobile device, data representing a request to play back a first audio track on a playback device, the first controller interface associated with a first user account of the streaming audio service. In response, the server streams data representing the first audio track to the playback device for playback. While streaming, the server receives, from a second controller interface on a second mobile device, data representing a preference for the first audio track that the playback device is playing back, the second controller interface associated with a second user account of the streaming audio service. The server stores, in a preference database corresponding to the second user account, data representing the preference for the first audio track.

Abstract: Systems as described herein may provide multiple data labeling interfaces with a common data infrastructure. An annotation system may retrieve data from a plurality of data sources and convert the data to a common schema. The annotation system may train a machine learning classifier to output a plurality of label suggestions, which may be sent to a plurality of data labeling tools. A plurality of labels may be received from the data labeling tools. The annotation system may accordingly export the plurality of labels and the converted data in the common schema to a label database.

Abstract: A software-defined network (SDN) system, device and method comprise one or more input ports, a programmable parser, a plurality of programmable lookup and decision engines (LDEs), programmable lookup memories, programmable counters, a programmable rewrite block and one or more output ports. The programmability of the parser, LDEs, lookup memories, counters and rewrite block enable a user to customize each microchip within the system to particular packet environments, data analysis needs, packet processing functions, and other functions as desired. Further, the same microchip is able to be reprogrammed for other purposes and/or optimizations dynamically.