Abstract: Systems and methods for enhanced rule-based querying of unstructured text using graph analysis are provided. Some embodiments apply machine learning to the analysis of unstructured text to identify regulatory, privacy, and cybersecurity risks. In some embodiments, a query string is provided to identify desired content in the unstructured text. The query string is tokenized and the tokenized parts are converted into nodes of a query graph and relationships are defining between the nodes. After identifying matches between terms in target text with nodes of the query graph, a graph is generated using matched terms as nodes. Based on the defined relationships, a determination is made whether any group of nodes on the graph satisfy the query string. Any matched nodes are mapped to the target text, and this information is provided to an analyst or reviewer for further consideration or action.

Abstract: Personal information management (PIM) systems and methods in which a plurality of datastores, including a graph datastore and a non-graph datastore are maintained. The graph datastore stores nodes representing each item of personal information data (PID) and edges representing relationships between the PID items. A user interface is generated to accept user input selecting a first PID item. A first node in the graph datastore representing the first PID item is identified and the graph datastore is searched to identify one or more additional nodes in the graph datastore which are connected by edges to the first node. One or more additional PID items that are represented by the additional nodes are retrieved from the non-graph datastore and the first PID item and the additional PID items are presented in the user interface.

Abstract: A data dimensionality reduction method includes: a step of dimensionally reducing a group of data from a high-dimensional space to a low-dimensional space using a distance function that defines a distance between any two vectors in the high-dimensional space; a step of dividing the dimensionally-reduced low-dimensional space into multiple subspaces; an analysis step of performing a regression analysis using a regression model based on at least one belonging data for each divided subspace; and a step of updating p first parameters included in the distance function based on results of the regression analysis in the multiple subspaces.

Abstract: In an example embodiment, an embedding model is used to generate an embedding of a natural language searching goal specified by a user, the embedding representing user intent of the user. Playbooks in a database of playbooks are also run through the embedding model to generate an embedding for each playbook indicative of a meaning of each playbook. A semantic relationship score can then be computed for each combination of the natural language search goal and a playbook, using the embeddings. These semantic relationship scores can then be passed into a ranking machine learning model, along with measured success rates for the playbooks, to generate a ranking of the playbooks. Based on this ranking, a set of filters and action corresponding to at least one of the playbooks may then be recommended to the user.

Abstract: Methods, storage systems and computer program products implement embodiments of the present invention for data access that include identifying a set of tables in a database to be accessed by an application, and identifying first and second application programming interface (API) calls having different, respective access properties for accessing records in the tables via an API. Respective counts of the records in the tables are computed by a processor, and the set of tables are partitioned into first and second subsets responsively to the respective counts. The records in the first subset of the tables are accessed by having the application convey the first API call to the API, and the records in the second subset of the tables are accessed by having the application convey the second API call to the API.

Abstract: Systems and methods described herein relate to automatic index recommendations for improved database query performance. Candidate indexes are identified. The candidate indexes are associated with a database query that is classified as a slow query. A feature vector is generated for each candidate index to represent statement features and statistical features associated with the candidate index. The feature vectors are provided to one or more machine learning models to obtain an index recommendation value for each candidate index. An index recommendation is presented at a user device. The index recommendation identifies a first index of the candidate indexes based at least partially on the index recommendation value obtained for the first index. User input indicative of a user selection of the first index is received. A database schema is updated to include the first index in response to the user input.

Abstract: Techniques for triggering pipeline execution based on data change (transaction commit) are described. The pipelines can be used for data ingestion or other specified tasks. These tasks can be operational across account, organization, cloud region, and cloud provider boundaries. The tasks can be triggered by commit post-processing. Gates in the tasks can be set up to reference change data capture information. If the gate is satisfied, tasks can be executed to set up data pipelines.

Abstract: An asset transfer recording system includes a node and a data storage server. The node includes a node processor programmed to generate a data processing rule according to a request by a transferor of an asset. The node processor is further programmed to store the data processing rule on the blockchain; generate a data processing command according to the data processing rule; and send the data processing command to the data storage server. The data storage server includes a server processor programmed to process the plurality of data items according to the data processing command and generate and send a processing result to the node processor. The node processor is further programmed to store the processing result on the blockchain.

Abstract: A computing device receives a natural language input specifying search terms directed to a dataset. The device parses the input into tokens and executes queries against a search index to retrieve a plurality of labeled trend events. Each labeled trend event has a respective chart identifier. The device determines a respective composite score for each labeled trend event and individually assigns each labeled trend event to a respective group. For each group, the device sorts the respective labeled trend events within the respective group according to respective composite scores, determines a respective final score for each group, and ranks the groups according to one or more determined final scores. The device retrieves data corresponding to a first subset of line charts having the respective chart identifiers of the ranked groups, generates the first subset of line charts, and displays one or more line charts of the first subset with annotation.

Abstract: A content management system enables a central server to connect to remote nodes at client sites. Software modules on the remote node is responsible for making necessary calls to the central server in order to create an index of the relevant data (or metadata) and fetch the appropriate binary information and files for the related metadata. Remote nodes are populated with data from the content management system via crawl/synchronize methods, or alternatively a hard drive of the data is initially configured at headquarters whereby data is saved to prevent trafficking data over a potentially unreliable connection over an extended period of time. The hard drive is then installed at the remote site and synchronized with the software module running in high performance enterprise library (HPEL) mode. The HPEL enabled server is configured to crawl and synchronize all the data and pick up differences in data using differential crawls.

Abstract: A versioning group cache system uses cache keys which each include, amongst other things, version information and a group key. When a cache key is first updated to include these, the cache system determines the group key and adds version information thereto to produce the cache key, which is mapped to cached data. When the cache key is later updated, the version information is updated to produce an updated cache key which is mapped to the cached data. The cache key updating is in response to a change request received at the cache system. The change request includes a command processable at the cache system in connection with cached data and/or cache keys. For example, the change request may be a request for a batch deletion of cache keys. The versioning group cache system fulfills the change requests with efficient use of system resources.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for managing data replication. A request associated with storing content of a file is received at a storage service provided by in a multiple availability zone cloud platform. A lock request is sent to an in-memory data grid at a first instance of the storage service to lock the file for accessing. An input stream of the file is received at the persistence interface to be read iteratively in portions. A read portion of the file is iteratively stored in a first file system storage associated with instances of the storage service at a first availability zone. The portions of the file are provided iteratively to a replication executor at the first instance of the storage service to request replication of the content of the file into a second file storage of a second availability zone of the cloud platform.

Abstract: One example method includes defining object groups by classifying each object in a backup saveset based on respective object types of the objects such that all objects in an object group are the same object type, assigning a different respective storage media type to each of the object groups, storing each object group at a respective storage target, representing each object group with a respective Merkle tree that includes a base hash, and mapping each base hash to the storage target where the object group associated with the Merkle tree that includes the base hash is stored.

Abstract: In various implementations, an asset gateway service receives log messages comprising asset data captured by a gateway device associated with an asset. The log messages each include a first identifier corresponding to a first representation of the gateway device, which itself is associated with a first organization. The service persists data from the log to a first datastore accessible by the first organization. The service also determines whether a second representation of the gateway device (i.e., a clone) exists. If so, the service persists at least some of the data from the log message to a second datastore, which itself may be associated with a second organization, thereby allowing both organizations to access the data in a secure yet convenient manner.

Abstract: Aspects described herein may relate to methods, systems, and apparatuses for generating synthetic data based on time series predictions and plural machine learning models. Generating the synthetic data may include receiving one or more data records that include transaction data of a user, categorizing and/or segmenting the one or more data records, determining a first predicted time step based on the categorizing and/or segmenting, and performing an iterative process that determines further predicted time steps. The first predicted time step may be determined using one model of a plurality of machine learning models. The iterative process may determine the further predicted time steps using the plural machine learning models. Based on iterations of the iterative process, potential time series may be determined. The synthetic time series may be determined from the potential time series. The synthetic time series may be used by additional processes, such as additional machine-learning processes.

Abstract: Systems and methods of delegating media capturing functionality from one device to another are presented. A first device configured with an object recognition engine captures a media representation of an environment and identifies an object within that environment. Then based on matched object traits from a database, the engine selects a delegation rules set, and delegates certain media capturing functionality to a second device according to the selected delegation rules set.

Abstract: The present technology is a backup service that can provide a menu of collaboration options that can be selected within the backup service for implementation by a synchronization service. In such an embodiment, when a user selects a collaboration option from the menu of collaboration options in the backup service, the backup service can call the synchronization service to copy the backup object into the synchronization service and implement the selected collaboration option. This can provide a benefit of reducing the number of user interfaces that need to be displayed and interacted with in order to carry out the intent of the user to offer a copy of the backup object for collaboration. The present technology also provides improved user interfaces.

Abstract: A database system is operable to store a plurality of column values for a first column of a plurality of rows. A query having a query predicate indicating a negation of a condition for the first column based on a first value is executed. A first index element is applied to identify a first proper subset of the plurality of rows having values for the first column meeting the condition based on accessing index data. At least one second index element is applied to identify a second proper subset of the plurality of rows having values for the first column meeting at least one missing data-based condition based on accessing the index data. A set difference between the plurality of rows and a union of the first proper subset and the second proper subset is identified. A query resultant for the query is generated based on the set difference.

Abstract: Systems and methods for platform independent secure customer data migration from one data storage to another data storage. A synchronization server includes a translation layer and an authentication layer. The translation layer encodes data from one data storage access language and schema to another distinct data storage access language and schema. Before the full transition to the second data storage, the translation layer also encodes, updates and inserts data into the first data storage. Existing references to the first data storage as well as new references to the second data storage are supported using an authentication layer. After a request for secure customer data, a determination is made which data storage is accessed from an obtained authentication factor. When the second data storage is accessed, the translation layer is employed to encode the first data storage object references to the corresponding second data storage object.

Abstract: Systems and methods for platform independent secure customer data migration from one data storage to another data storage. A synchronization server includes a translation layer and an authentication layer. The translation layer encodes data from one data storage access language and schema to another distinct data storage access language and schema. Before the full transition to the second data storage, the translation layer also encodes, updates and inserts data into the first data storage. Existing references to the first data storage as well as new references to the second data storage are supported using an authentication layer. After a request for secure customer data, a determination is made which data storage is accessed from an obtained authentication factor. When the second data storage is accessed, the translation layer is employed to encode the first data storage object references to the corresponding second data storage object.