Abstract: Various embodiments are provided herein for decreasing a central processing unit (CPU) inference time, thereby shortening processing time of a deep learning model for which run-time complexity is proportional to an output sequence length. A compacted sequence is developed that is used to train the deep learning model. An output of the trained model becomes the compacted sequence on which a post-processing method operates to create an uncompacted version of a predicted sequence.

Abstract: A method and system for generating a clarity grading look-up table includes collecting actual inclusion parameter data for a plurality of gems, where the actual inclusion parameter data includes an actual clarity grade and an actual inclusion parameter data combination. A mathematical relationship between a clarity grade and a particular inclusion parameter combination is then extrapolated from the actual inclusion parameter data. A derived clarity grade is then assigned to a plurality of inclusion parameter combinations as a function of the mathematical relationship and a set of inputted inclusion parameters. Also, a method and system for providing a clarity grade includes receiving a plurality of inclusion characteristics associated with a gem and parameterizing each of the inclusion characteristics, so that a parameter value is assigned to each inclusion characteristic. The parameter values are then input to a mathematical formula so as to provide a parameterized clarity grade for the gem.

Abstract: A method for managing the storage of file based backups (FBBs) includes obtaining, by a backup agent operating on a production host environment, a FBB generation request for plurality of assets at a point-in-time, wherein the backup agent is associated with a plurality of backup policies, in response to the FBB generation request: generating a FBB of the plurality of assets, performing an attribute analysis on the FBB metadata file to determine a directory service associated with each of the plurality of assets, obtaining a first sub-asset and a second sub-asset based on the attribute analysis, assigning a backup policy of the plurality of backup policies to each of the two sub-assets, and performing a storage of the FBB based on the backup policies assigned to each of the sub-assets.

Abstract: In the present application, a system and method for storing and retrieving time-series data in a time-series database are disclosed. A data stream collected at a periodic collection time interval is stored. The stored data stream is indexed in an index, including by storing in the index a representative index entry corresponding to a plurality of data elements of the stored data stream collected during a period of time longer than the periodic collection time interval of the data stream.

Abstract: A system includes a controller, a database controlled by the controller, and a memory including an end-user executable code and a business software management code. The controller receives a user update to modify the end-user executable code, converts the user update to a machine language input, and, in response to the machine language input, modifies one or more of the end-user executable code, the database structure, and a documentation of the end-user executable code. In response to executing the program instructions associated with the modified end-user executable code on the end-user device, the controller generates a data collection GUI according to the modified end-user executable code and stores data in the database structure according to the modified end-user executable code.

Abstract: Structured data comprising primary data is transformed into object-based data comprising the primary data and at least one search function. The object-based data when loaded into memory provides the search function as executable instructions for an object instance. When an application calls the object instance with search terms as input, the search terms are located within the primary data (which is loaded into memory) by the search function, and the object instance returns corresponding field values from the primary data associated with the search terms to the application.

Abstract: Database access request processing distribution is performed by receiving requests from a plurality of worker servers, the requests including database access requests, and maintaining a queue of the requests. Queues are maintained by adding each request to a queue in response to receiving the request, assigning, for each request, a master server among a plurality of master servers to process the request, and removing each request from the queue in response to processing of the request. The assigning includes assigning the first master server among the plurality of master servers to process the database access requests until the database access requests in the queue exceed a first threshold, and then distributing assignments to process the database access requests among the plurality of master servers.

Abstract: The present application discloses a batch-type image distribution method based on an IPFS, which includes: obtaining a list of cluster nodes needed to download images; calculating a caching ratio of an image layer of images to be distributed of each node, comparing the caching ratio of each node with a threshold of a preset node, and obtaining a complete image from an image repository; adding the node with the complete image to a list of source nodes, and adding remaining nodes needed to download images to a list of demand nodes, and constructing an IPFS network, and exporting and sharing the image; calculating target demand nodes using an optimization algorithm, and inputting the target demand nodes into the IPFS network for image download, and importing the downloaded image into a container and starting the container.

Abstract: An information processing device receives a request signal, read out a user class or a storage region class from a memory, based on which user class and storage region class the request signal requests, and generates an object corresponding to the read-out class, wherein each user class is a class relating to users and each storage region class is a class relating to storage regions utilized by the users, based on the generated object and the request signal. The information processing device executes a method corresponding to the object, and thereby acquire data corresponding to the request signal. The information processing device outputs the acquired data.

Abstract: A data quality evaluation system can automatically detect one or more types of data anomalies or other data quality issues associated with a data processing system that may impact the quality of output generated by the data processing system. For example, the data quality evaluation system can detect data errors associated with data, detect when data is not received by the data processing system in compliance with defined schedules, detect when elements of the data processing system may be mishandling data, and/or detect when patterns of data does not correspond with historical patterns or validation data. By automatically detecting such data quality issues, technical issues or problems causing the data quality issues can be investigated and corrected.

Abstract: An approach is provided for linear feature detection of cartographic features. The approach, for example, involves receiving a plurality of linear feature detections that represent one or more linear features of a geographic environment and are detected using at least one sensor. The approach also involves clustering the plurality of linear feature detections into at least one cluster and determining that the at least one cluster forms a polygon. The approach further involves cutting the polygon at one or more cut locations to form a plurality of sub-clusters of the plurality of linear feature detections. The approach further involves extracting respective centerlines for the plurality of sub-clusters and connecting the respective centerlines at the one or more cut locations to generate a network graph of the one or more linear features.

Abstract: The present disclosure provides techniques and solutions for storing data in a way that facilitates the update and creation of data models, as well as conserving computing resources when accessing such data. A number of data objects can be included in a data model, such as a core data model that can be used in various jurisdictions. A supplemental data object is created that includes data relevant to data objects in the data model. For example, the supplemental data object can include localization attributes. The supplemental data object can be considered as denormalized. A request for data can retrieve data from a data object in the data model, as well as data in the supplemental data object. Different sets of attributes can be retrieved from the supplemental data object based on a type associated with a data request for a data model object.

Abstract: Supplemental content related to an electronic book may be obtained by creating a search query using a keyword in selected text of the electronic book. The search query may also include details about the electronic book obtained from a manifest file for the electronic book, including the location of the selected text.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing event log data. An example event log processing method includes receiving an event log comprising a plurality of event records describing events that have occurred on each of one or more computer systems over a period of time; converting the event log into a graph, comprising: normalizing the plurality of event records, including anonymizing a unique identifier value in each event record and replacing a variable value in each event record with a predetermined value; representing each normalized event record as one or more nodes in the graph; and generating a plurality of event clusters, wherein each event cluster includes an aggregated group of nodes and is generated based on common attributes of and hierarchical relationships between the normalized event records represented by the nodes in the aggregated group.

Abstract: A database management system configured to perform a computer-implemented method is provided. The method involving configuring a first set of nodes to determine an instantaneous state of the database management system, generate system configuration data, and provide the system configuration data to a second set of nodes of the database management system, wherein the configuration data is replicated amongst the first set of nodes according to a consensus-based replication protocol so as to maintain the system configuration data. The method also involves configuring the second set of nodes to receive the system configuration data and implement a state machine that is arranged to modify a configuration of the second set of nodes according to the system configuration data, wherein the second set of nodes are arranged to replicate user data according to a non-consensus-based replication protocol.

Abstract: A computer processing device for determining whether to allow or deny access to a database associated with the device is provided. The device is configured to determine the origin of data signed with a first key by comparing the key to one or more keys stored in a further database to identify the source of the data; search the database to determine one or more access rules associated with the source of the data, wherein the access rules define whether write access to the database is allowed or denied for the data; and allow or deny write access to the database based on the determined rule or rules.

Abstract: A method for selecting items one by one from a set of items elected from a large dataset of items includes determining whether or not a density of the set is sparse. If the density is sparse, the method includes repeatedly performing an extreme item select (EIS) method to select a next one of the elected items from the set and removing the next one from the set to create a next set. If the density is not sparse, the method includes performing a next index select (NIS) method to create a linked list of the elected items and to repeatedly select a next elected item from the set.

Abstract: An object management method includes: obtaining position information of a first object added in a scene; determining, from an object management tree corresponding to the scene, a first node according to the position information of the first object; dividing, in response to that a quantity of objects contained in the area corresponding to the first node is greater than an upper limit after the first object is added to the area corresponding to the first node, the area corresponding to the first node into n sub-areas according to position information of objects contained in the area corresponding to the first node; and configuring, in the object management tree, n nodes in one-to-one correspondence with the n sub-areas.

Abstract: Disclosed herein are systems and methods to efficiently execute predictions models to identify future values associated with various nodes. A server retrieves a set of nodes and generates a primary prediction model using data aggregated based on all nodes. The server then executes various clustering algorithms in order to segment the nodes into different clusters. The server then generates a secondary (corrective) prediction model to calculate a correction needed to improve the results achieved by executing the primary prediction model for each cluster. When a node with unknown/limited data and attributes is identified, the server identifies a cluster most similar the new node and further identifies a corresponding secondary prediction model. The server then executes the primary prediction model in conjunction with the identified secondary prediction model to populate a graphical user interface with an accurate predicted future attribute for the new node.

Abstract: In a computer-implemented method for storing data in a network of linked computing units (10, 20, 30, 40, 50, 60) using a distributed transaction database (GDB), a distributed transaction database (GDB) in the form of a distributed graph database formed using nodes is used, and data is stored in at least one node (N) of the graph database, wherein the node (N) is stored using a real sub-quantity of the computing units (10, 20, 30, 40, 50, 60) of the network. The computer program product can be loaded directly into a storage device of an electronic computing unit (10, 20, 30, 40, 50, 60) and has program means in order to early out the steps of the method when the program is ran in a computing unit. The network of linked computing units (10, 20, 30, 40, 50, 60) stores a distributed transaction database (GDB) in the form of a distributed graph database comprising nodes (N) in which data is stored according to such a method.