Abstract: Various embodiments are provided herein for systems and methods for real-time, or near real-time, multi-dimensional ranking of experts. In at least one embodiment, receiving one or more evaluation datasets; for each given evaluation dataset, associating the evaluation dataset with: (i) at least one evaluation data category, (ii) at least one taxonomy category, and (iii) at least one expert of the plurality of experts; subsequently, receiving one or more search filter criteria for ranking at least a subset of the plurality of experts; generating a ranking of the at least subset of the plurality of experts, wherein the ranking is based on the search filter criteria, and is further generated based on the associations determined for each evaluation dataset; and displaying, on a user interface, at least a portion of the plurality of rankings as the multi-dimensional ranking of the plurality of experts.

Abstract: The present disclosure relates to a method of loading data. The method includes checking a topic corresponding to a search word among a plurality of topics in response to acquiring a search word for a topic of a distributed messaging system from a user, checking a data format including one or more fields of a message loaded into a topic, and then loading data generated based on the checked data format and the read message into a data lake.

Abstract: A computer-implemented method for k-anonymizing a dataset to provide privacy guarantees for all columns in the dataset can include obtaining, by a computing system including one or more computing devices, a dataset comprising data indicative of a plurality of entities and at least one data item respective to at least one of the plurality of entities. The computer-implemented method can include clustering, by the computing system, the plurality of entities into at least one entity cluster. The computer-implemented method can include determining, by the computing system, a majority condition for the at least one entity cluster, the majority condition indicating that the at least one data item is respective to at least a majority of the plurality of entities. The computer-implemented method can include assigning, by the computing system, the at least one data item to the plurality of entities in an anonymized dataset based at least in part on the majority condition.

Abstract: There may be provided a method for managing column extents of a tabular database, the method may include (a) generating a multi-snapshot row score to each row of a group of rows of the tabular database multiple rows; wherein the multi-snapshot score is indicative of fullness of the row in the multiple snapshots; wherein the group of rows comprises column extents associated with column extents metadata; and (b) improving a column extent metadata parameter by reordering the rows of the group of rows according to the multi-snapshot row scores to provide a re-ordered group of rows that comprises re-ordered group extents associated with re-ordered column extents associated with re-ordered column extents metadata.

Abstract: A method for faceted search comprises generating a search user interface comprising a facet hierarchy interface configured to display a representation of a facet hierarchy in conjunction with results displayed in a search results display, updating the search results display to display faceted search results and updating the facet hierarchy interface to display, in conjunction with the faceted search results displayed in the updated search results display, a control to allow a user to select a search facet topic where the search facet topic is a facet topic from the facet hierarchy that is not represented in the faceted search results.

Abstract: In one embodiment, a method includes receiving an input vector corresponding to a query at a neural network model comprising a plurality of layers, wherein the plurality of layers comprise a last layer associated with a mapping matrix, generating a binary matrix based on the mapping matrix, an identity matrix, and one or more Gaussian vectors, generating an integer vector based on the binary matrix and a binary vector associated with the input vector, identifying a plurality of indices corresponding to a plurality of top values of the integer vector for the integer vector, generating an output vector based on the input vector and a plurality of rows of the mapping matrix, wherein the plurality of rows is associated with the plurality of identified indices, respectively, and determining the query is associated with one or more classes based on the output vector.

Abstract: Exemplary embodiments pertain to new techniques for classifying or labeling organized data. A major impediment to implementing high-quality machine learning is the lack of readily accessible labeled data. In some cases, data can be classified using a classifier, but these solutions can be inaccurate and slow. Exemplary embodiments address the problem of obtaining accurate labeled data in a timely manner by applying a classifier configured to operate on character-level embeddings. Among other advantages, this can help the classifier to recognize information contained within a data unit, such as a cell of a table. The classifier may operate within the organizational structure of the data, such as by operating across a particular row or column of a table. Because data within a particular row or column is often temporally organized (e.g., transactions that are logged in chronological order), row- or column-based approaches can yield more accurate results.

Abstract: Systems and methods are disclosed for comparing selections of text to show differences between the two selections. The text may be selected from the same source or from two different sources. In one implementation, a system receives a first selection of text for comparison and places the selection in a first buffer. The system receives a second selection of text for comparison and places the second selection in a second buffer. The system compares the first buffer and the second buffer to determine differences and displays the differences. In some embodiments, the system may allow a user to choose two buffers from among a plurality of buffers for comparison.

Abstract: The subject technology selects a particular zone among multiple zones based on a target skew to meet a global balancing of cluster instances. The subject technology deploys a particular type of cluster instance to the particular zone. The subject technology, for each zone from the multiple zones, determines a respective number of cluster instances. The subject technology identifies a second particular type of cluster instance to remove based on a total number of the second particular type of cluster instance in the multiple zones and a second total number of the particular type of cluster instance in the multiple zones. The subject technology removes the second particular type of cluster instance from a second particular zone to meet the global balancing of cluster instances in the multiple zones.

Abstract: A method, system, and computer program product for correlation detection between artificial intelligence (AI) transactions. The method stores a set of transaction records associated with an AI decision engine. Each transaction record has a set of record characteristics. The method assigns the set of transaction records to a set of batches on the set of record characteristics. A set of batch characteristics are determined for a batch of the set of batches. The method determines one or more correlations among the set of batch characteristics. The one or more correlations are compared with one or more threshold batches. The method determines, from the one or more correlations and the comparing, an impact of one or more recommendations of the AI decision engine. The one or more recommendations are defined by the set of transaction records.

Abstract: Various embodiments relate generally to data science and data analysis, computer software and systems, to provide a platform to facilitate updating compatible distributed data files, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate correlation of event data via analysis of electronic messages, including executable instructions and content, etc., via a cross-stream data processor application configured to, for example, update or modify one or more compatible distributed data files automatically.

Abstract: Described are computer-based methods and apparatuses, including computer program products, comprising the steps of, or structure for, storing a plurality of expert profiles in a database, each of the plurality of expert profiles comprising information associated with a person having knowledge in a particular category, subject or topic; receiving search criteria over a network from a query source; selecting at least one of the plurality of expert profiles comprising information that satisfy the search criteria; and transmitting expert profile data for each of the selected expert profiles to the remote search engine, the expert profile data comprising data that defines a displayable representation of a corresponding expert profile, the expert profile data further comprising data that facilitates a client-initiated, real-time communication session over the network with a person associated with the corresponding expert profile.

Abstract: By the abovementioned technical solutions, the present disclosure provides optimizing a vector autoregressive model conforming to structural constraints of sparsity and acyclicity. A regularization term is introduced to the model to impose the sparsity structural constraint such that most off-diagonal coefficients of an autoregressive coefficient matrix are forced to zero values. One or more penalty terms are introduced to the model to impose the acyclicity structural constraint such that coefficients of the main diagonal are not causally self-related. The resulting model is then reformulated for computation as an augmented Lagrangian function, and further computed for different parameters in alternating iterations to make the computations tractable and within magnitude and precision limits of digital computers.

Abstract: The present disclosure relates to a digital asset synchronization system that provides improved local and remote synchronization of digital assets. In particular, the digital asset synchronization system manages digital assets by separating each digital asset into multiple components stored as a set of distributed individual files. Employing individual components for a digital asset rather than single monolithic file enables the digital asset synchronization system to provide safe concurrent access to the digital asset from multiple applications on the same device and across different devices. In addition, using components for a digital asset provides the digital asset synchronization system with the ability to efficiently store and synchronize multiple versions of the digital asset, both locally and remotely.

Abstract: A system includes first logic to obtain a fixed constituent data object and a variable constituent data object for each data object of a set of objects, second logic to analyze the fixed constituent data objects to allocate each fixed constituent data object to one of a plurality of fixed groups, third logic to analyze the variable constituent data objects to allocate each variable constituent data object to one of a plurality of variable groups, fourth logic to determine a net magnitude for each fixed group and for each variable group, fifth logic to determine a historical component for each variable constituent data object, sixth logic to determine a net historical magnitude for each variable group based on the historical components, and seventh logic to define at least one representative data object to represent the fixed and variable constituent data objects in each pair of fixed and variable groups having a matching common set of properties, the at least one representative data object maintaining the net

Abstract: A computer-implemented system or process is programmed or configured to use a configuration file to specify one or more tasks to apply to raw ingested data. A task may be a sequence of instructions programmed or configured to format raw ingested data into a dataset in a CSV format. Examples of tasks may include: a parser to parse Cobol data into a CSV, a parser to parse XML into a CSV, a parser to parse text using fixed-width fields to a CSV, a parser to parse files in a zip archive into a CSV, a regular expression search/replace function, or formatting logic to remove lines or blank lines from raw ingested data. In one embodiment, the configuration file may specify a schema definition for a task to use for generating a dataset. In one embodiment, the configuration file may also include one or more access control list (ACL) definitions for the generated dataset. In one embodiment, the building of datasets using the configuration file is automated, for example, on a nightly basis.

Abstract: Techniques for implementing user interfaces, systems, and processes for multidimensional clustering and analysis are described herein. In one aspect, an application or cloud service receives a request to cluster a set of records where the request identifies a first set of one or more dimensions to use for clustering and a second set of one or more dimensions to analyze for correlation patterns. Responsive to receiving the request to cluster the set of records, the system generates clusters based at least in part on variances in the first set of one or more dimensions, wherein each cluster includes at least one record from the set of records. The system may generate, for each respective cluster, an analytic result that identifies how strongly the second set of one or more dimensions correlate to the respective cluster. The system may present the clusters and analytic results for further processing.

Abstract: An information handling system for managing detection of objects includes a storage and a processor. The storage is for storing an encoder; a critical class classifier; a general classifier; and a decoder. The processor obtains data that may include one or more of the objects; encodes the data using the encoder to obtain encoded data; obtains a critical class classification for the encoded data using the critical class classifier; obtains a general classification for the encoded data using the general classifier; conditions the encoded data to obtain conditioned encoded data; decodes the conditioned encoded data using the decoder to obtain reconstructed data; makes a determination that the reconstructed data and the critical class classification indicate that the data is an unknown classification; classifies the data as being an unknown classification based on the determination; and performs an action set based on the unknown classification of the data.

Abstract: A computer-implemented method for storing documents in a centralized database system based on geographical data localization includes receiving, by a database manager, a request to update a document according to a data localization criterion, the document stored in a local storage device of the centralized database system. The method further includes determining, by the database manager, a geographical location for a local copy of the document based on an attribute of the document matching the localization criterion. The method further includes creating, by the database manager, a remote copy of the document on a storage device that is physically located in the geographical location. The method further includes modifying, by the database manager, the local copy of the document to include a location information of the remote copy.

Abstract: Pairwise entity matching systems and methods are disclosed herein. A deep learning model may be used to match entities from separate data tables. Entities may be preprocessed to fuse textual and numeric data early in the neural network architecture. Numeric data may be represented as a vector of a geometrically progressing function. By fusing textual and numeric data, including dates, early in the neural network architecture the neural network may better learn the relationships between the numeric and textual data. Once preprocessed, the paired entities may be scored and matched using a neural network.