Embodiments as disclosed herein allow simple specification of searches of values within regions and efficient implementation of such searches. Specifically, embodiments as disclosed may provide a search operator that addresses the problem of complex query construction for finding objects having a particular value, including a minimum or a maximum value, in one of a set of regions, and the efficient implementation of the searches specified by such search operators.

Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for blockchain data storage. One of the methods includes receiving a transaction associated with a blockchain network; determining, after the transaction is performed, an updated account state of a blockchain account involved in the transaction; adding, to a history state object database and after a current block is appended to a blockchain associated with the blockchain network based on performing a consensus algorithm, the updated account state, a hash value of the updated account state, an account identifier (ID) of the blockchain account, and a block ID of the current block; and updating, based on the hash value of the account state, the account ID, and the block ID, a state tree stored in a history state database.

A computer-implemented method includes tracking, by a computer device, movements of a user viewing a dashboard containing visualizations. The method also includes generating, by the computer device, heatmaps having hotspots onto the dashboards in view of the tracked movements of the user. Additionally, the method includes generating, by the computer device, bounding boxes around the hotspots. Further, the method includes mapping, by the computer device, the bounding boxes to the visualizations. The method also includes creating, by the computing device, a tree diagram listing the hotspots which correspond to the bounding boxes. Additionally, the method includes generating automatically, by the computing device, a story or exploration from the tree diagram.

A computer implemented method may include: storing key-value pairs in an index in persistent storage, where indirect nodes of the index include pointers, where each pointer identifies an index portion and includes a generation identifier for the identified index portion, where the index comprises a plurality of snapshots associated with a plurality of generations; receiving a request to read data of a particular snapshot of the index, wherein the particular snapshot is associated with a particular generation of the plurality of generations; in response to the request, performing a traversal starting from a particular root node associated with the particular generation; and providing the requested data based on the traversal.

A storage node of a database replica group may distribute different portions of data in local storage and external storage, where local storage and external storage are organized using different types of index structures. Responsive to receiving an access request for a database, a storage node may determine that an item of the database to be accessed by the request does not reside within a first portion of the database stored locally at the storage node. Responsive to this determination, the storage node may obtain from an external storage service a second portion of the database, the second portion including a plurality of items including the item, and the second portion organized according to a structure different from the first portion. The storage node may then store the plurality of obtained items in the first portion and process the request using the first portion of the database.

Data are maintained in a distributed computing system that describe a graph. The graph represents relationships among items. The graph has a plurality of vertices that represent the items and a plurality of edges connecting the plurality of vertices. At least one vertex of the plurality of vertices includes a set of label values indicating the at least one vertex's strength of association with a label from a set of labels. The set of labels describe possible characteristics of an item represented by the at least one vertex. At least one edge of the plurality of edges includes a set of label weights for influencing label values that traverse the at least one edge. A label propagation algorithm is executed for a plurality of the vertices in the graph in parallel for a series of synchronized iterations to propagate labels through the graph.

Various embodiments are generally directed to techniques for determining artificial neural network topologies, such as by utilizing probabilistic graphical models, for instance. Some embodiments are particularly related to determining neural network topologies by bootstrapping a graph, such as a probabilistic graphical model, into a multi-graphical model, or graphical model tree. Various embodiments may include logic to determine a collection of sample sets from a dataset. In various such embodiments, each sample set may be drawn randomly for the dataset with replacement between drawings. In some embodiments, logic may partition a graph into multiple subgraph sets based on each of the sample sets. In several embodiments, the multiple subgraph sets may be scored, such as with Bayesian statistics, and selected amongst as part of determining a topology for a neural network.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimized graph traversal are disclosed. In one aspect, a method includes the actions of receiving a given phrase that is input through a user interface by a digital component provider. The actions further include determining an entity that is being referred to by the given phrase. The actions further include identifying properties of the entity. The actions further include selecting a subset of the properties that were identified for the entity. The actions further include identifying additional phrases. The actions further include updating the user interface to present at least some of the additional phrases with programmatic controls that assign one or more of the additional phrase as distribution criteria for digital components of the digital component provider in response to activation of the programmatic controls.

Systems and methods for training a machine learning (ML) ranking model to rank genealogy hints are described herein. One method includes retrieving a plurality of genealogy hints for a target person, where each of the plurality of genealogy hints corresponds to a genealogy item and has a hint type of a plurality of hint types. The method includes generating, for each of the plurality of genealogy hints, a feature vector having a plurality of feature values, the feature vector being included in a plurality of feature vectors. The method includes extending each of the plurality of feature vectors by at least one additional feature value based on the number of features of one or more other hint types of the plurality of hint types. The method includes training the ML ranking model using the extended plurality of feature vectors and user-provided labels.

Computing systems and technical methods that transform data structures and pierce opacity difficulties associated with complex machine learning modules are disclosed. Advances include a framework and techniques that include: i) global diagnostics; ii) locally interpretable models LIME-SUP-R and LIME-SUP-D; and iii) explainable neural networks. Advances also include integrating LIME-SUP-R and LIME-SUP-D approaches that create a transformed data structure and replicated modeling over local and global effects and that yield high interpretability along with high accuracy of the replicated complex machine learning modules that make up a machine learning application.