Abstract: A system may include processor(s) and a memory in communication with the processor(s) and storing instructions, that when executed by the processor(s), are configured to cause the system to perform a method for cross-region data processing. The system may receive data in a first processing region and may format the data into a plurality of data portions. The system may generate a copy of each data portion and may transmit each data portion copy to a cross-region processor. Responsive to determining the first processing region is functioning, the system may process data portion(s). The system may transmit, to a centralized location, the processed data portion(s). Responsive to determining the first processing region is not functioning, the system may transmit, from the cross-region processor, each data portion copy to a second processing region.

Abstract: A system for analyzing graph databases using intelligent reasoning systems including scalable collection of, and transformation of, graph data into facts suitable for use with programming logic languages doing deductive reasoning. A graph analyzer ingests disparate graph data from across the Internet and transforms the graph data into a fact table. In order to reduce latency and processing congestion, a stream processing engine and sharding strategy are employed to ensure scalability through parallelized processing of programming logic queries. Transformed graph data, now relational data, is utilized with programming logic languages that allow for hypothetical queries whereby an inference engine can deduce new information to satisfy such a query. Furthermore, the self-contained nature of inputs, outputs, and transformations of the system means strict data provenance can be observed and adhered to.

Abstract: In general, various aspects provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for performing data discovery on a target computing system. In various aspects, a third party computing connects, via a public data network, to an edge node of the target computing system and instructs the target computing system to execute jobs to discover target data stored in data repositories in a private data network in the target computing system. In some aspects, the third party computing system may schedule the jobs on the target computing system based on computing resource availability on the target computing system.

Abstract: Computer system compares data across two data sources. Database connectivity drivers access the first and second data sources. RAM receives and stores data from each of the first and second data sources accessed by the drivers. The data from the first data source are stored in a first table in the RAM and data from the second data source are stored in a second table in the RAM. A processor compares data in the first and second tables. The processor is programmed to compare the data by identifying a set of primary keys across the first and second tables. The set of primary keys are a set of one or more columns in the both first and second tables that uniquely identify rows across both the first and second tables.

Abstract: Described herein is a computer implemented method for inserting a new item to a tree-structured dataset. The method comprises: calculating a new item data summary; generating a new leaf node for storing the new item and the new item data summary; adding the new leaf node to the dataset; and recalculating data summaries for all internal dataset nodes in an update path starting at a parent of the new leaf node and ending at a root node of the dataset. The data summary for a given internal node in the update path is calculated based on data summaries for each of the given internal node's children nodes.

Abstract: A distributed database system executes transactions on a distributed database. A received transaction includes statements describing modifications of records stored in the distributed database. The distributed database system executes the transaction at a query server by obtaining copies of records corresponding to the statements of the transaction and performing the modifications specified by the statements of the transaction on the record copies. The distributed database system stores the modified record copies at the query server during execution of the transaction. After the transaction has successfully been executed at the query server, the distributed database system attempts to perform a commit process to update the records stored in the distributed database based on the modified record copies.

Abstract: A method for matching supplied organizational data with trade directory information includes: receiving a data file including a plurality of organizational entries, each entry including an entity name and a geographic location; normalizing the entity name in each of the organizational entries; identifying a plurality of matching entries for each organizational entries, each matching entry including a matching name and location, and where each matching entry is identified based on a first correspondence between the matching name and the entity name and a second correspondence between the matching location and the geographic location; determining a confidence level for each of the organizational entries based on the correspondence between the first correspondence and the second correspondence for at least one of the identified matching entries; and transmitting the plurality of matching entries and determined confidence level for each of the plurality of organizational entries.

Abstract: Various embodiments relate generally to data science and data analysis, computer software and systems, and wired and wireless network communications to provide an interface between repositories of disparate datasets and computing machine-based entities that seek access to the datasets, and, more specifically, to a computing and data storage platform that facilitates consolidation of one or more datasets, whereby a collaborative data layer and associated logic facilitate, for example, efficient access to, and implementation of, collaborative datasets. In some examples, a method may include receiving a dataset having a data format into a dataset ingestion controller configured to form a collaborative dataset, interpreting data of the dataset against data classifications at an inference engine to derive at least an inferred attribute, associating the data with annotative data identifying the inferred attribute, and converting the dataset at a format converter to form an atomized dataset.

Abstract: A method may include responding to a transaction by sending, to a first data partition participating in the transaction, a first request to set a first transaction control block at the first data partition to a preparing state. In response to the transaction affecting multiple data partitions, a second request to set a second transaction control block at a second data partition to the preparing state may be sent to the second data partition. A third request to add the first data partition and the second data partition as participants of the transaction may be sent to the transaction coordinator. The transaction coordinator may determine, based on a first response of the first data partition and a second response of the second data partition, an outcome of the transaction. The transaction may be rolled back if the first response and/or the second response indicate an inability to commit the transaction.

Abstract: Described is a system for intelligent re-tiering of backup data stored on a cloud-based object storage. More specifically, the system may re-tier objects such that the system retains the ability to efficiently perform a full restore of backup data even when incremental backups are performed to a cloud-based object storage. To provide such a capability, the system may maintain a specialized metadata database that stores information indicating the backup time for each backup, and a list of objects required to perform a full restore to each of the backup times. Accordingly, when using a threshold time (e.g. expiry) to select object candidates for re-tiering, the system may leverage the metadata database to ensure that objects that may still need to be referenced are not unnecessarily moved to a lower storage tier.

Abstract: A method, electronic device, and computer-readable medium for storage management is disclosed. The method for storage management includes acquiring a data change rate of a data source, the data change rate indicating an occurrence rate of data to be backed up in the data source. The method also includes acquiring a recovery capability of a backup system to recover backed-up data and determining, based on the data change rate and the recovery capability, a backup strategy for backing up the data to be backed up.

Abstract: The disclosed systems and methods may receive a data record from either a legacy data source or a modern data source and determine whether the record satisfies a first set of validation rules. When the record fails to satisfy the first set of rules, reject the record for storage in a data store. When the record satisfies the first set of rules, determine whether the record satisfies a second set of validation rules. When the record satisfies the second set of rules, store the record in the data store with an indicator that the record satisfies the all rules. When the record fails to satisfy the second set of rules, if the source was a modern data source reject the record, and if the source was a legacy data source store the record in the data store with an indicator that it fails to satisfy the second set of rules.

Abstract: Techniques and solutions are provided for integrating master data from multiple applications. Master data from multiple applications can be integrated for use in processing data associated with internet of things (IOT) devices, such as by joining master data with timeseries data (including aggregated values). Integrating master data from multiple applications can include converting master data from a schema used by an application into an analytics schema. New or updated master data can be indicated in a message sent by an application. In processing the message, additional master data, or data used to determine how master data should be processed, can be retrieved.

Abstract: Embodiments of the present disclosure provide a method, device and computer program product for backing up data. The method comprises obtaining a data attribute of specific data to be backed up from a client to a server, a resource utilization rate at the client, and a network condition between the client and the server. The method further comprises setting, based on the data attribute, the resource utilization rate and the network condition, a plurality of parameters for performing stream backup, wherein the plurality of parameters at least comprises a concurrent number of stream transmission and a concurrent number of data parsing. The method further comprises parsing, according to the set plurality of parameters, the specific data and backing up the specific data from the client to the server.

Abstract: A computer extracts from contact records that each include a contact identifier, a group identifier for each group with which the contact has had an interaction, and interaction information that indicates a number of interactions and a timing of a most recent interaction. The contact data records are processed to generate a contact profile record for each contact including group metric values and a corresponding value for each group metric value based on an interaction history of groups the contact has interacted with. An interaction analytics databases stores a set of contact profile records and group profile records for groups that include metric values associated with the group and an interaction history. They are processed with at least thousands of the contact profile records to determine group-contact compatibility factors. A compatibility parameter is generated and communicated for each of at least thousands of contacts based on the group-contact compatibility parameters.

Abstract: Techniques are described for automatic creation of optimal auditable entities (AEs) using a machine learning (ML)-based clustering model. The clustering model, when executed on one or more computing devices within an audit system of a company, is configured to automatically cluster the company's business processes into AEs based on similarity analyses of business process attributes. More specifically, in some examples, the clustering model ingests business processes and their corresponding attributes from a database, automatically clusters together business processes to achieve maximum intra-cluster similarity scores, and outputs the final clusters as model AEs. The resulting model AEs may be used as functional units for internal audits of the company's business processes. The resulting model AEs may improve audit efficiency due to the model AEs including only highly similar business processes.

Abstract: A data aggregation system is described, wherein the data aggregation system may include: a plurality of sensors distributed throughout an environment; a tile database comprising a memory for storing a hierarchy of tiled layers, wherein each layer in the hierarchy of tiled layers comprises a plurality of tiles; a tiling server, the tiling server configured to: receive sensor data from one or more sensors in the plurality of sensors; assign the sensor data to a base tile in a first layer in the hierarchy of tiled layers based on one or more properties of the one or more sensors; retrieve one or more aggregate tiles from the tile database based on an identity of the base tile in the first layer, the one or more aggregate tiles each taken from one or more further layers in the hierarchy of tiled layers; determine aggregate sensor data for each of the retrieved one or more aggregate tiles based on the sensor data stored on the base layer tile; assign the determined aggregate sensor data to the corresponding one or

Abstract: A method (300) and apparatus (110, 150) collect and upload implicit analytic data. The method can include storing (320) dependency rules corresponding explicit events to implicit events. The method can include collecting (330) and storing (340) implicit event data corresponding to implicit events. The method can include receiving (350) an explicit event at the device. The method can include evaluating (360) dependency rules corresponding to the explicit event. The method can include identifying (370) a relevant subset of implicit event data corresponding to the explicit event based on evaluating the dependency rules. The method can include uploading (390) the relevant subset of the implicit event data and explicit event data corresponding to the explicit event.

Abstract: Systems and methods are disclosed for processing and executing queries in a data intake and query system. The data intake and query system receives raw machine data at an indexing system, and stores at least a portion of the raw machine data in buckets. Based on a determination that the size of multiple buckets satisfies a threshold size, the data intake and query system converts the buckets to non-editable buckets and stores the data in a remote shared storage system.

Abstract: Systems and methods for de-serializing one or more data elements of a serialized structured data record are presented. In response to a request to de-serialize one or more data elements of a serialized structured data record of a first type, the location of the serialized structured data record is determined within a data file containing a plurality of serialized structured data records. Locations of the data of the one or more data elements are determined and the data of the one or more data elements is retrieved. The data is de-serialized and stored in corresponding data elements in an instantiated structured data record of the first type. The one or more data elements that are de-serialized are fewer than all of the data elements of the serialized structured data record.