Abstract: A method for functionally rewriting iterative queries for a relational database management system (RDBMS) is provided. The method comprises receiving a first iterative query, the first iterative query having a first non-iterative part that defines a first main table and a first iterative part that generates values in rows of a first working table based on values in rows of the first main table, determining that the first iterative part modifies all of the rows of the first working table, and rewriting the first iterative part, including: adding a renaming operation to rename the first working table to a new first main table and to rename the first main table to a new first working table, adding a first Delete operation to delete each row of the new first working table, and adding a first loop operation to repeat the first iterative part until a first termination condition is met.

Abstract: A method is described which includes receiving or obtaining a time series of data (S1). The method also includes storing the time series of data to a storage device without interrupting the reception of the time series of data (S2). The method also includes, for each of a plurality of base time periods, at the end of a most recently elapsed base time period (S5) and without interrupting the reception or storage of the time series of data, calculating (S6) one or more measurements based on the time series of data corresponding to the most recently elapsed base time period and updating a binary tree structure indexing the one or more measurements and the time series of data. Updating the binary tree structure includes generating a new binary tree leaf (1, 2, 4, 5) corresponding to the most recently elapsed base time period (S7).

Abstract: A method includes storing technical models in a network-accessible model repository. Each technical model is labeled with descriptive metadata and comprises one or more model views labeled with functional metadata. A request is received from a stakeholder device, the request specifying descriptive attributes and functional attributes applied to an associated stakeholder. Technical model(s) are retrieved based on the descriptive metadata labelling the retrieved technical models being determined to satisfy the descriptive attributes included in the request. For each retrieved technical model, one or more model views are compiled based on the functional metadata labelling the compiled model view being determined to satisfy the functional attributes included in the request.

Abstract: Methods and apparatus are described for splitting a page (or node) in an index tree database index for a multi-master database. The methods include creating a first split page of a first page. The first page belongs to a first set of pages and comprises a hinge page. The hinge page also belongs to a second set of pages. Updating the first set of pages may cause the hinge page itself to split, creating a split of the hinge page. The second set of pages is updated in response to creating the split of the hinge page. Described are techniques whereby the updates to the first and second sets of pages are atomic, that is, they all succeed or they all fail, which may realize any of several benefits.

Abstract: A model file management method includes that a terminal device receives a storage address of a target model file package from a server and the terminal device obtains the target model file package based on the storage address of the target model file package, where the target model file package is based on a parameter of a model file package locally stored in the terminal device and a parameter of a model file package managed by the server. In an artificial intelligence (AI) field, an application may implement a specific function by using an AI model file. An application is decoupled from an AI model file such that the terminal device performs centralized management on a general model file.

Abstract: Aspects of the present disclosure relate to performing agnostic data integrity checks on source data, and based on the data integrity checks, generating a human-readable report that may be useable to identify specific errors or anomalies within the source data. Example embodiments involve systems and methods for performing the data integrity checks and generating the human-readable reports. For example, the method may include operations to ingest data from a source database through a data pipeline and into a local database, access the data from the data pipeline, determine a data type of the data, determine subtypes of data elements which make up the data, determine a count of each subtype, and generate a human-readable report, to be displayed at a client device.

Abstract: Generation of new snapshots of a storage object are prompted based on changes to the storage object. An absolute storage change condition is satisfied when the aggregate amount of changed data since the most recent snapshot satisfies a threshold that represents a predefined amount of storage capacity, e.g., N GB. A percentage of used storage change condition is satisfied when the aggregate size of the changes expressed as a percentage of the provisioned storage capacity of snapped storage object satisfies a predetermined threshold, e.g., X % of Y TB. A percentage of provisioned storage change condition is satisfied when the aggregate amount of the changes in terms of percentage of utilized storage capacity of storage object satisfies a predetermined threshold expressed as a percentage of the utilized capacity of the storage object, e.g., P % of Z TB.

Abstract: An information processing apparatus according to the present disclosure includes a reception unit that receives pre-training data that is data used for pre-training in machine learning, and a search condition for similar pre-training data that is data similar to the pre-training data, a search unit that searches for similar pre-training data in accordance with the search condition, and a generation unit that performs pre-training based on the retrieved similar pre-training data, and generates a trained model by using a result obtained through the pre-training.

Abstract: The present disclosure provides novel techniques to segment mobile entities based on signals from mobile devices associated with these mobile entities. In certain embodiments, a data file is built for each mobile entity to transform originally extremely high dimensional raw data derived from mobile devices communicating with a network into structured data characterizing associated mobile entities. The data files are structured for filtering and searching with respect to features associated with the signals from mobile devices, including location and non-location features. The data files are used to determine feature gains corresponding respectively to features related to a mobile segment. The features include first features of a first type and second features of a second type. The feature gains are used, among other things, to rank mobile entities for the mobile segment, with feature gains corresponding to the first features given a larger weight than feature gains corresponding to the second features.

Abstract: A communication system provides feedback data for at least one water consuming device. The communication system includes a data collection interface, a controller, and an output interface. The data collection interface is configured to receive user data from at least one collection device. The controller is configured to perform an analysis of the user data from the at least one collection device. The output interface is configured to provide feedback data based on the analysis of the user data to a water consuming device.

Abstract: A version control interface for data provides a layer of abstraction that permits multiple readers and writers to access data lakes concurrently. An overlay file system, based on a data structure such as a tree, is used on top of one or more underlying storage instances to implement the interface. Each tree node tree is identified and accessed by means of any universally unique identifiers. Copy-on-write with the tree data structure implements snapshots of the overlay file system. The snapshots support a long-lived master branch, with point-in-time snapshots of its history, and one or more short-lived private branches. As data objects are written to the data lake, the private branch corresponding to a writer is updated. The private branches are merged back into the master branch using any merging logic, and conflict resolution policies are implemented. Readers read from the updated master branch or from any of the private branches.

Abstract: A data storage device includes a non-volatile memory device including one or more memory dies and a controller. The controller is configured to receive a pseudocode file and a search key from one or more external devices and perform an index search based on the received pseudocode and search key. The controller may further determine a data file associated with the performed index search and output the determined data file to the one or more external device.

Abstract: Systems and methods for automatically associating content characteristics to a third-party content are provided. A uniform resource locator identifying a resource is received from a content provider. The content is rendered to produce an object tree. A first node of the object tree is determined, where the first node represents a content slot. A second node of the object tree proximate to the first node is identified. The second node has a content characteristic, which is extracted. The extracted content characteristic is associated with the first node and stored.

Abstract: A processor-based method of building and appending data structures in a multi-host environment includes creating and storing data into a first data structure having a first root node, a first right leaf node and a plurality of other leaf nodes and non-leaf nodes defining multiple hierarchical levels; allowing a search of the first data structure to proceed while creating a second data structure that includes a second root node, a second left leaf node and a plurality of leaf nodes and non-leaf nodes defining multiple hierarchical levels; receiving a command to append the second data structure to the first data structure during searching the first data structure; creating a global lock for all hosts in the multi-host environment to the first root node and the first right leaf node of the first data structure while allowing access to the plurality of other leaf nodes and non-leaf nodes of the first data structure; updating the first root node and the first right leaf node with information about the second data

Abstract: A method for integrating heterogeneous computing systems including identifying, by an endpoint system agent operating on a client device, a read/write operation associated with the client device based on monitoring an operation of a program executed on the client device, extracting, by the endpoint system agent from at least one of the client device or a client system in communication with the client device, data having a first syntax associated with the client system, receiving, by the endpoint system agent from an endpoint system, one or more rules describing a mapping between the first syntax and a second syntax associated with the endpoint system, executing, by the endpoint system agent, the one or more rules to modify the data to conform to the second syntax to produce mapped data, and transmitting, by the endpoint system agent, the mapped data to the endpoint system.

Abstract: One or more computer processors facilitate compatibility between one or more multivariate regression models and a multidimensional dataset, wherein the program instructions. The one or more computer processors extract a plurality of unidimensional chains from the multidimensional dataset. The one or more computer processors double index the plurality of extracted unidimensional chains. The one or more computer processors construct a plurality of partial fit regression trees from the double indexed unidimensional chains. The one or more computer processors, responsive to a stop criterion, calculate one or more predictions utilizing the plurality of constructed partial fit regression trees. The one or more computer processors repopulate the multidimensional dataset with the one or more calculated predictions.

Abstract: A selective database rollback method includes identifying a table, identifying a root key, storing a target rollback date, retrieving schema information including keys, ordering the table, iterating over the table, storing the current table state, obtaining a transaction journal, and reverse-applying the transaction journal to generate an output file. A method or system for identifying database key includes iterating over a table's columns and rows, generating similarity metrics by comparing column-wise and/or row-wise data, and comparing the generated similarity metrics to a threshold value. A server includes a processor and a memory storing instructions that, when executed by the one or more processors, cause the server to identify a table, identify a root key, store a target rollback date, retrieve schema information including keys, order the table, iterate over the table, store the current table state, obtain a transaction journal, and reverse-apply the transaction journal to generate an output file.

Abstract: Systems and methods for automatically associating content characteristics to a third-party content are provided. A uniform resource locator identifying a resource is received from a content provider. The content is rendered to produce an object tree. A first node of the object tree is determined, where the first node represents a content slot. A second node of the object tree proximate to the first node is identified. The second node has a content characteristic, which is extracted. The extracted content characteristic is associated with the first node and stored.

Abstract: An example operation includes one or more of receiving a block, extracting information from the block, correlating an identifier to the information as previously stored in a first storage area, replacing the information in the block with the identifier, and submitting the block with the identifier for storage in a second storage area. The identifier links to the information previously stored in the first storage area in order to allow for recovery of the information, and at least the second storage area is in a blockchain.

Abstract: An encrypted search uses hash vectorization (HV) models, which are secure, one-way hash indices that are produced by a compression process, such as a modeling phase of Lempel-Ziv (LZ) parsing. Each HV model includes a hash filter and a chain vector. The hash filter is a Boolean quotient filter similar to Bloom filters, and allows for the quick elimination of negative query assessments. The hash filter is followed by a chain vector, which provides spatial modeling of hashed elements throughout the compressed and encrypted data, thereby providing increased levels of accuracy, efficiency, and query expressiveness as compared with known techniques.