Abstract: The embodiments described herein relate to a method and system for social awareness which may be based on social networks for knowledge exchange. More specifically, the embodiments may refer to specific social networks with social elements in the user interface based on knowledge exchange, social theory of group memberships within an enterprise or organization context. In addition, the disclosed group memberships may be predicated upon many different types of relationships. Furthermore, the social network (through a program interface) may provide to users the required specific project resources (project team members), which may be need to develop a better project performance according to the experience and knowledge of the new members. The required project team members may match with the attributes and criteria established during the project planning.

Abstract: A system and method for delivering an event from an event publisher to an event subscriber via an event broker are disclosed. The event broker performs several functions such as receiving an event published by an event publisher of a first domain, assigning a priority based on a second domain to a plurality of subscribers of the second domain, processing the event based on a rule and the priority, and further delivering the event published by the event publisher of the first domain to the plurality of event subscribers of the second domain in accordance with the priority.

Abstract: Methods and systems are provided for enhanced data loss prevention. The enhanced data loss prevention may be applied to data that is structured and/or semi-structured data, with the data comprising a plurality of records and a plurality of categories, with each record comprising a plurality of fields, each of which being associated with a different one of the plurality of categories. The enhanced data loss prevention may comprise selecting a subset of records of the data, with the selected subset comprising at least two records having between then one or more records not included in the subset. The fields of the selected subset may be scanned for sensitive information, and a likelihood to contain the sensitive information may be computed for each category based on the scanning. A subset of categories may be selected based on the computed likelihoods, and the sensitive information in the selected subset may be searched.

Abstract: Account information for a plurality of accounts and transaction information for a plurality of transactions conducted among the plurality of accounts are analyzed. A transaction volume threshold is determined based on the analysis. A first subset of the accounts is identified. The first subset of account each corresponds to an amount of conducted transactions below the transaction volume threshold. A second subset of the accounts is identified. The second subset of the accounts each corresponds to an amount of conducted transactions above the transaction volume threshold. In response to the identification of the first subset and the second subset of the accounts, the account information and the transaction information are stored into an electronic database collectively for the first subset of the accounts, and the account information and the transaction information are stored into the electronic database separately for the second subset of the accounts.

Abstract: A conversational analytics system may provide for a conversational interface to any relational database. A controlled natural language may be constructed in an automated manner from a given database (e.g., from schema and values associated with a relational database). For instance, a user natural language expression may be converted to an expression in the constructed controlled natural language and the controlled natural language expression may be converted into a sequence of one or more queries in a query language (e.g., queries in structured query language (SQL)). Such an intermediate controlled natural language may provide queries without ambiguity (e.g., as each expression or phrase in the controlled natural language may be mapped to one sequence of SQL queries). Accordingly, any natural language user utterance that ultimately follows the controlled natural language may be automatically converted into a sequence of one or more SQL queries sent to the database.

Abstract: A system that suggests and refines priority, severity, historical resolution, resolution time and assignment group to resolve auto-generated incidents is provided. The system leverages an incident characterization list tracking essence key phrases associated with historical incidents generated by an application performance management (APM) system and a machine data (MD) analysis system. The system automatically obtains a first auto-generated incident, from the APM system or the MD analysis system, based on any specified search criteria being met, or detection of a deviation in the value of key metrics from a specified threshold value.

Abstract: Techniques described herein relate to a method for managing jobs. Such techniques may include: generating a job completion entry that specifies that execution of a first job was completed within an estimated period of time for the first job, generating a second job completion entry that specifies that execution of a second job was not completed within a second estimated period of time for the second job, updating the job resource mapping model based on the first job completion entry and the second job completion entry to obtain a new updated job resource mapping model.

Abstract: Systems, methods, and non-transitory computer readable media may provide a view of geospatial information. A user's selection of a location may be obtained. Characteristic information describing characteristics of the location may be obtained. Activities information describing activities of the location may be obtained. An interface (e.g., user interface, API) enabling presentation of a geospatial view of the activities of the location with respect to the characteristics of the location may be provided.

Abstract: An online system receives a content item including an image from a content-providing user and/or receives an interaction with the content item from a viewing user. The online system accesses a machine-learning model that is trained based on a set of images of items associated with an entity and attributes of each image. The online system applies the model to predict a probability that the content item includes an image of an item associated with the entity based on attributes of the image included in the content item. Based on the predicted probability, the online system updates a profile of the user (i.e., the content-providing user and/or the viewing user) to include an affinity for the item. Upon determining an opportunity to present content to the user, the online system selects content for presentation to the user based on the profile and sends the content for presentation to the user.

Abstract: The technology disclosed herein provides a summary of a predicted timeline for an event that has yet to occur. In a particular implementation, a method provides identifying a first event that has yet to occur. The method further provides identifying first data objects from a plurality of data objects obtained from a plurality of information sources. The first data objects include information pertinent to the first event. The method also provides extracting first time information relevant to the first event from the first data objects, determining a confidence level for each portion of the first time information, and generating a summary of the first time information based on the confidence level for each portion of the first time information.

Abstract: Displaying multi-source content based on interaction data, including: identifying user interaction data associated with computer-implemented applications, the user interaction data including interaction data of a particular user; identifying social interaction data associated with the computer-implemented applications, the social interaction data including interaction data of one or more social contacts of the particular user; identifying regional interaction data associated with the computer-implemented applications, the regional interaction data including interaction data of users of a particular region; aggregating content associated with the computer-implemented applications based on the user interaction data, the filtered social interaction data, and the filtered regional interaction data; scoring the aggregated content based on weight components; applying, based on the content scores, interface adjustments to interface elements of a graphical timeline, the graphical timeline depicting the interface elem

Abstract: An illustrative report server interoperates with one or more enhanced storage managers to evaluate whether backup operations and restore operations meet their recovery point objectives (RPO) and recovery time objectives (RTO), respectively. RTO is evaluated using a tiered approach based on past performance of restore and/or backup operations. The illustrative storage manager executes pre-defined queries that extract relevant information from an associated database that houses information about storage operations. The report server recommends alternative kinds of backup operations for data that fails to meet its RTO using traditional backups. The report server is configured to analyze and report RPO and RTO readiness for several levels of data entities, including multiple systems, single system, groups of clients, single clients, and subclients.

Abstract: A system for data reconciliation is provided. The data reconciliation system includes a data processing subsystem. The data processing subsystem includes a computation module, configured to generate hash values for a set of tables located in a source database by a hashing technique and also configured generate hash values for a set of tables located in a destination database by the hashing technique. The data processing subsystem also includes an analysis module, configured to analyse the hash values located in the source database and the hash values located in the destination database by a pre-determined rule. The data processing subsystem also includes a suggestion module, configured to suggest output based on the analysis result. A data memory subsystem is configured to store the generated hash values for the source database and the generated hash values for the destination database. Present invention provides safe migration or transferring of data.

Abstract: Systems and methods of generating a new media data element from a source media data element, including: receiving the source media data element including one or more frames; applying a machine learning algorithm to predict at least one first Region of Interest (ROI) in one or more of the at least one frames; and cropping, the one or more frames to generate a new media data element based on the predicted at least one first ROI.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilize machine learning models to generate identifier embeddings from digital content identifiers and then leverage these identifier embeddings to determine digital connections between digital content items. In particular, the disclosed systems can utilize an embedding machine-learning model that comprises a character-level embedding machine-learning model and a word-level embedding machine-learning model. For example, the disclosed systems can combine a character embedding from the character-level embedding machine-learning model and a token embedding from the word-level embedding machine-learning model. The disclosed systems can determine digital connections between the plurality of digital content items by processing these identifier embeddings for a plurality of digital content items utilizing a content management model.

Abstract: Generating a universal learned model that appropriately controls a group of operating devices having the same configuration. Steps comprise subjecting a predetermined machine learning model to learning based on predetermined initial data to generate an initial learned model and an integration step of incorporating the initial learned model that controls a predetermined operating device into a plurality of operating devices, and integrating a plurality of individual learned models obtained by additional learning based on respective operation data obtained by operating the respective operating devices, thereby providing a universal learned model.

Abstract: Systems and methods for modeling higher-level metrics from graph data derived from already-collected but not yet connected data are disclosed. A method includes extracting a first set of actor-related data, a second set of object-related data, and a third set of temporal data from the set of the already-collected but not yet connected data representative of a unit-level contribution to the target activity. The method further includes generating graph data for a graph using the set of the already-collected but not yet connected data, where each of the plurality of nodes of the graph corresponds to the actor or the object, and where an attribute associated with each of the plurality of edges of the graph corresponds to a measurement associated with the target activity. The method further includes modeling a relationship between graph attributes associated with the graph data and a higher-level metric associated with the target activity.

Abstract: The present disclosure comprise: acquiring source data for generating a profile of a user and time data related with generation of the source data; clustering the source data based on the time data related with the generation of the source data as a category; generating a profile of the user by using the cluster generated through the clustering; and generating region of interest data including information of a geographic region that may be determined to be of interest to the user based on the profile of the user, and wherein the ROI data may include location information of the user, and the profile of the user associated with the time data may be labeled. The intelligent device of the present disclosure may be associated with an artificial intelligence module, drone (unmanned aerial vehicle, UAV), robot, augmented reality (AR) devices, virtual reality (VR) devices, devices related to 5G services, and the like.

Abstract: Techniques and solutions are described for compressing data and facilitating access to compressed data. Compression can be applied to proper data subsets of a data set, such as to columns of a table. Using various methods, the proper data subsets can be evaluated to be included in a group of proper data subsets to be compressed using a first compression technique, where unselected proper data subsets are not compressed using the first compression technique. Data in the data set can be reordered based on a reordering sequence for the proper data subsets. Reordering data in the data set can improve compression when at least a portion of the proper data subsets are compressed. A data structure is provided that facilitates accessing specified data stored in a compressed format.

Abstract: Systems and methods are disclosed herein for a recommendations engine that generates content recommendations using a trained model that is personalized based on the information corresponding to content consumption. The disclosed techniques herein provide a trained model to provide content recommendations. The trained model may have been trained using a predefined set of training data agnostic of a particular user profile. A system receives information corresponding to content consumption. The system may associate the information corresponding to content consumption with a profile. The system generates a personalized model based on the information corresponding to content consumption and on the trained model. The personalized model may be associated with the user profile. The system generates the content recommendations using the personalized model. The system then causes to be provided the content recommendations.