Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification. One example operation may comprise one or more of identifying determining a shared file is being edited by one or more entities, identifying one or more changes to the shared file while the shared file is being edited, signing the one or more changes with one or more public keys, and adding the one or more changes to a blockchain.

Abstract: Methods, systems and computer-readable storage media for scanning a document to detect a source of the document. The document can be scanned to obtain content from the document. An automatic search of an electronic network can be then performed to determine if there is an exact match or a partial match between the content of the document and the results of search automatic search. If an exact match or a partial match is generated as a result of automatically searching the electronic network, a user can be presented with various selection options via a GUI for retrieving, viewing and printing search results of the automatic search.

Abstract: A method and system for relation discovery from operation data includes classifying categories of extracted entities from operation data into three or more classes identified in a knowledge base. A log affiliation of the extracted entities is determined, and relations of the extracted entities are identified according to a log affiliation. The identified relations information of the extracted entities is associated with operation objects of the operation data.

Abstract: Provided are a computer program product, system, and method for using and training a machine learning module to determine actions to be taken in response to file system events in a file system. A file system event is detected. An action to be performed corresponding to the file system event is selected from an action list. A determination is made as to whether an outcome in the computing system resulting from the performed action satisfies an outcome threshold. A machine learning module is trained to increase a likelihood of selecting the performed action corresponding to the file system event when the outcome satisfies the outcome threshold. The machine learning module is trained to decrease a likelihood of selecting the performed action corresponding to the file system event when the outcome does not satisfy the outcome threshold.

Abstract: Various embodiments provide methods, systems, apparatus, computer program products, and/or the like for managing, ingesting, monitoring, updating, and/or extracting/retrieving information/data associated with an electronic record (ER) stored in an ER data store and/or accessing information/data from the ER data store, wherein the ERs are generated, updated/modified, and/or accessed via a graph-based domain ontology.

Abstract: A system includes a database, a memory, and a processor. The database stores a set of transactions, each of which includes a set of fields. The processor receives a query from a user through a channel. The query is directed to a transaction stored in the database. The transaction includes a set of fields. The processor applies a machine learning policy adapted to determine a view, i.e., a subset of the set of fields that the user is likely to access using the channel. The processor further accesses the database and retrieves the view. The processor then sends the view to the channel.

Abstract: A system performs operations that include determining match text data based on a comparison between input text data and an index of keywords and identifying a set of match word tokens from the match text data. The operations further include determining one or more solution sets of input word tokens based on comparing the set of match word tokens and the input text data. Additionally, the operations include calculating a respective solution set score for each of the one or more solution sets of input word tokens based on the calculated similarity values. A match between the input text data and the match text data is determined based on the first solution set score satisfying a score threshold.

Abstract: A computer implemented method for detecting a set of relevant communities of accounts, the accounts being connected by at least one communication platform, includes collecting data from a stream of messages circulating on at least one communication platform; extracting interaction data and shared objects, the shared objects being shared among the accounts; processing each collected message in order to obtain content metadata; processing collected data in order to obtain an account profile of each account; from collected data building shared objects graphs and interaction graphs; building a resulting graph; applying a community detection method to the resulting graph to obtain a set of communities of accounts; computing quality indicators of the set of communities to obtain at least one score for each community the score enabling the selection of at least a relevant community.

Abstract: A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Abstract: Disclosed herein are systems and method storing data. In an exemplary aspect, a method comprises determining a threshold data size for objects in a target data system; generating a data blob that has an allocated size of at least the threshold data size, wherein the data blob comprises at least one data set; identifying a data set stored in a source data system for backup to the target data system; determining whether a size of the data set is less than the threshold data size; and responsive to determining that the size of the data set is less than the threshold data size, inserting the data set into the data blob; and writing the data blob to the target data system.

Abstract: Systems and methods for generating embeddings for nodes of a corpus graph are presented. More particularly, operations for generation of an aggregated embedding vector for a target node is efficiently divided among operations on a central processing unit and operations on a graphic processing unit. With regard to a target node within a corpus graph, processing by one or more central processing units (CPUs) is conducted to identify the target node's relevant neighborhood (of nodes) within the corpus graph. This information is prepared and passed to one or more graphic processing units (GPUs) that determines the aggregated embedding vector for the target node according to data of the relevant neighborhood of the target node.

Abstract: The technology disclosed relates to creating and frequently updating multiple online analytic processing (OLAP) analytic databases from an online transaction processing (OLTP) transaction updatable system that includes transaction commit, rollback, and field level security capabilities. It also relates to transparently decoupling extraction from rebuilding of frequently updated OLAP analytic databases from the OLTP transaction updatable system.

Abstract: According to various embodiments, with respect to a target set of files being managed (e.g., protected by data snapshots), each file in the target set of files is classified into one of two or more filesets (discontiguous filesets), where each of these filesets comprises one or more files that are related to each other by one or more factors, such as frequency of file change or purpose of existence (e.g., used by a software application). Once classified, files within the target set of files can be uniquely processed by a data management operation (e.g., incremental data snapshot process) according to their association to a discontiguous fileset.

Abstract: System and methods are provided for searching users that meet one or more search requirements. Configuration profiles are obtained of computing systems operated by sample users that have at least one determined characteristic. A machine learning model is generated that associates the determined characteristic of the sample users with the configuration profiles of the computing systems of the sample users. Identifying at least one target user that matches the at least one determined characteristic specified in a search query based on analysis of the configuration profile of the computing system of said target user by the machine learning model.

Abstract: Provided is a method for clustering a data stream. The method comprises: acquiring a plurality of resulting models of a plurality of preceding data partitions prior to a current data partition in a data stream, wherein data partitions in the data stream have a temporal relationship, and wherein each of the plurality of resulting models is generated according to a clustering result of a corresponding preceding data partition, and each of the plurality of resulting models comprises one or more representative parameters in different categories; determining a starting model of the current data partition according to the plurality of resulting models, wherein the starting model comprises one or more representative parameters in different categories determined based on representative parameters of the same category in the plurality of resulting models; and clustering data records in the current data partition by using the starting model.

Abstract: Methods, systems, and computer-readable media for schema-based spatial partitioning in a time-series database are disclosed. A time-series database divides elements of time-series data of a plurality of time series into partitions according to a clustering scheme. The time series are associated with respective schemas. The schemas comprise dimension names and measure names. The clustering scheme is determined based (at least in part) on the schemas and dimension values, and the schemas are determined based (at least in part) on the elements of time-series data. The time-series database stores the elements of time-series data from the plurality of partitions into one or more storage tiers. The time-series database performs a query of the time-series data in one or more of the storage tiers. The query is performed based (at least in part) on the clustering scheme.

Abstract: A system and method of recognizing data in a table area from unstructured data includes a computer network, one or more processors communicatively coupled with the computer network, a storage location, and a graph-theoretic engine that receives an input stream of unstructured data associated. A table area is recognized from unstructured data, through one or more computer processors, from an input stream of unstructured data received over a computer network. One or more table headers associated with the detected one or more table areas are recognized. Further, one or more column delimiters associated with each column of the detected one or more table areas are determined. One or more tabular data associated with the detected one or more table areas are extracted. The extracted tabular data is mapped to one or more target schema to store onto a relational database.

Abstract: A computer may receive a request to generate a snapshot view of a virtual infrastructure. The virtual infrastructure may comprise a plurality of virtual server management applications, each managing a respective set of virtual machines. The computer may implement a multi-threaded process to contemporaneously query one or more databases and retrieve status and other information of the virtual machines from different virtual server management applications. The computer may aggregate the retrieved information to determine the summary counters and statistic information for the virtual machines. The computer may generate a snapshot view file based on the retrieved information. The snapshot view file may be in hypertext markup language (HTML) format. The computer may transmit a selectable link to the snapshot view file to multiple user devices. A user may select the link and the respective user device may display the snapshot view in an application such as a web browser.

Abstract: Systems and methods for structuring unstructured data according to a data object structure that enables fast query look-ups across a variety of space and time dimensions. Furthermore, many embodiments optimize the storage of the data objects using a set of compression techniques that configure the data types used for the data objects based on properties of the stored data. Furthermore, many embodiments provide are able to service query look-up requests without having to deserialize data within the byte stream format as stored in memory by encoding information that provide memory locations for requested data, thereby allowing for the immediate retrieval of the data as it is stored in the persistent memory.

Abstract: In one general embodiment, a computer-implemented method includes identifying an input search term, determining an additional term associated with the input search term, where the additional term targets a predetermined entity, altering the input search term utilizing the determined additional term, and performing a search, utilizing the altered input search term.