Example implementation disclosed herein include techniques for methods, systems, and devices for a foster twin data structure. The data structure includes data pages, keys, and pointers. The pointers define hierarchical parent-child relationships among the data pages based on the keys. Transactions that result in inserts into the data pages are handled using foster twin data pages that split key ranges and the corresponding data records into two foster twin data pages. New pointers are added to the parent data page of the split data pages to indicate the new parent child relationship between the parent data page and the foster twin data pages. The old data page can be retired by adding a moved-bit and removing the old pointers from the parent data page to guide concurrent transaction and prevent future transactions.

A computer system is coupled to one or more servers which run one or more applications. The computer system comprises: a memory storing key data, value data associated with each of the key data, and application mask data, the application mask data indicating, for each of the value data, which application is allowed to access said each value data based on the key data associated with the value data; and a processor configured to: receive a get operation which includes a first key data and a first application identifier, the first application identifier identifying a first application which issues the get operation; determine whether the first application is allowed to access a first value data associated with the first key data based on the application mask data; and return the first value data if the application mask data indicates the first application is allowed to access the first value data.

Described are methods, systems and computer readable media for data source refreshing.

A key-value storage system is described herein for interacting with key-value entries in a content store using a resource-efficient index. The index provides a data structure that includes a plurality of hash buckets. Each hash bucket includes a linked list of hash bucket units. The key-value storage system stores hash entries in each linked list of hash bucket units in a distributed manner between an in-memory index store and a secondary index store, based on time of their creation. The key-value storage system is further configured to store hash entries in a particular collection of linked hash bucket units in a chronological order to reflect time of their creation. The index further includes various tunable parameters that affect the performance of the key-value storage system.

Techniques for searching an inverted index associating byte sequences of a fixed length and files that contain those byte sequences are described herein. Byte sequences comprising a search query are determined and searched in the inverted index. In some examples, training data for training machine learning model(s) may be created using pre-featured data from the inverted index. In various examples, training data may be used to retrain a ML model until the ML model meets a criterion. In some examples, the trained ML model may be used to perform searches on the inverted index and classify files.

The present disclosure relates to methods and systems for querying data in a data repository. According to a first aspect, this disclosure describes a method of querying a database, comprising: receiving, at a computing device, a plurality of keywords; determining, by the computer device, a plurality of datasets relating to the keywords; identifying, by the computer device, metadata for the plurality of datasets indicating a relationship between the datasets by examining an ontology associated with the datasets; providing, by the computer device, one or more suggested database queries in natural language form, the one or more suggested database queries constructed based on the plurality of keywords and the metadata; receiving, by the computing device, a selection of the one or more suggested database queries; and constructing, by the computer device, an object view for the plurality of datasets based on the selected query and the metadata.

The present technology relates to identifying an artificial intelligence model based on a received first key value to write a received first block of data associated with a file. The received first key value is applied to the identified artificial intelligence model which is trained to output one of a plurality of actual index values where the identified artificial intelligence model and the plurality of data blocks are stored as a neural tree. The one of the actual index values is compared to a range within the actual index values to determine when the one of the actual index value points to a first data block of the plurality of data. The received first block of data associated with the file is written into the determined first data block.

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 more aggregate tiles; and output the one or more aggregate tiles.

In some embodiments, the present disclosure provides for an exemplary computer-implemented system that may include a longitudinal data engine, including: a processor and specialized index generation software to generate: an index data structure for a respective event type associated with each respective subject or object; where each respective index data structure is a respective event type-specific data schema, defining how to store events of a particular event type to form longitudinal data of each respective subject or object; an ontology data structure that is configured to describe one or more properties of a respective event of a respective subject or object; and longitudinal data extraction software to extract a respective longitudinal data for a plurality of index data structures and a plurality of ontology data structures associated with a plurality of subjects or objects.

The present disclosure provides method and system to facilitate definition, tuning and visualization of a geo-fence at a computer system. The method comprises: receiving input parameters for a geo-fence, the input parameters including one or more parameters specifying a geographical region; sampling historical mobile signals based on one or more of the input parameters; dividing the geographical region into a plurality of areas; determining a weight for each respective area of the plurality of areas based at least on density of sampled mobile signals associated with geographical locations in the respective area; selecting a subset of the plurality of areas based on respective weights of the plurality of areas; and forming the geo-fence using the subset of the plurality of areas, the geo-fence including one or more contiguously closed regions each formed by a cluster of adjacent areas among the subset of the plurality of areas.