Abstract: A query operator execution flow includes: identifying an OR operator of the query expression; generating a plurality of parallel sub-flows based on a plurality predicates of the OR operator; and generating a plurality of serial sub-flows of the query operator execution flow based on the OR operator of the query expression. A first consecutive one of the plurality of serial sub-flows includes an identifier appending operator, a second consecutive one of the plurality of serial sub-flows includes a tee operator, a third consecutive one of the plurality of serial sub-flows includes the plurality of parallel sub-flows from the tee operator, and a fourth consecutive one of the plurality of serial sub-flows includes a union distinct operator applied to the plurality of parallel sub-flows. Execution of the query is facilitated by applying the query operator execution flow to a plurality of rows indicated by the query.

Abstract: A database system is operable to generating conjunctive normal form (CNF) processing cost data, disjunctive normal form (DNF) processing cost data, and non-normalized form processing cost data based on a plurality of operators of a query expression indicating a query for execution. Query processing selection data is generated based on the CNF processing cost data, the DNF processing cost data, and the non-normalized form processing cost data. The query processing selection data indicates a selection to execute the query in accordance with exactly one of: a CNF query operator execution flow; a DNF query operator execution flow; or a non-normalized query operator execution flow. Execution of the query is facilitated based on the query processing selection data.

Abstract: A server system provided according to an aspect of the present disclosure provides a shared connection pool to parallel execution entities accessing database instances of a distributed database when processing requests. A shared connection pool implies that each pool connection of the connection pool may be used for serving different execution entities requiring access to different database instances. In an embodiment, the database instances correspond to shards together implementing a distributed database, and the parallel execution entities correspond to threads.

Abstract: Methods, systems, and devices for displaying a user interface for frequent pattern (FP) analysis are described. In some cases, data stored at a multi-tenant database server may be analyzed to understand various interactions and patterns between data attributes associated with multiple users, or determine one or more attributes associated with a characterization of an individual (e.g., a persona). The multi-tenant database server may effectively cluster and/or perform calculations on attributes of the data to understand user patterns and determine common personas. The results may then be displayed by a user interface at a user device (e.g., associated with the user).

Abstract: Computer implemented methods for executing a database query in a database using an electronic computing device are presented, the method including: causing the electronic computing device to provide at least one data structure including at least one object having at least two properties of different data types; identifying a property of the at least one data structure that includes the smallest unique values in terms of type of data stored in that particular property; executing the database query, including any limiting parameters, configured to retrieve only data from the property including the smallest unique values that arc retrievable for a record; retrieving a predefined number of results of the executed database query; and for each set of predefined number of retrieved results, executing a new database access thread that is configured to retrieve data that are present in the records identified with the smallest unique values.

Abstract: A database system is operable to determine a plurality of queries for execution. Query data for each query of the plurality of queries is determined, where the query data for the each query indicates a memory usage estimate, an internal runtime estimate, and an external runtime estimate. Query initiation data is generated by determining whether to initiate execution of one query of the plurality of queries at a first temporal period occurring after initiating execution of a subset of the plurality of queries and occurs before the execution of the subset of the plurality of queries is complete, based on the query data of the one query. Execution of the one query is initiated when the query initiation data indicates a determination to initiate execution of the one query in the first temporal period.

Abstract: A new ecosystem monitoring solution provides novel features including a dashboard service, a dashboard notifier, and a near real time query processor. The dashboard service can continuously aggregate, through the dashboard notifier and optionally dashboard agents, metadata from disparate ecosystem components of a complex computing platform or ecosystem. The metadata include metrics of crawling, data ingestion, and content enrichment activities and health information of the disparate ecosystem components. The metrics are processed with respect to a time window utilizing an expression tree dynamically constructed by the query processor. The query processor can navigate the expression tree to form collection models. Each collection has aggregation functions for aggregating a set of metrics specified in a view model.

Abstract: A method and system for managing an execution plan in a graph database includes: acquiring a graph query language, analyzing the graph query language by using a scanner to generate a predefined Token, and parsing the predefined Token into a data structure by using a parser to obtain an abstract syntax tree; next, processing the abstract syntax tree by using a planner to generate an execution plan, registering a mapping table to store variables in the execution plan and corresponding variable values, and optimizing the execution plan by using an optimizer, where the execution plan includes customized execution operators and customized topologies; and finally, traversing and scheduling the execution plan by using a scheduler, to output an obtained execution result. The present disclosure modularizes the execution operators, improves reusability, and reduces the development cost. The execution plan is scheduled by using an asynchronous notification mechanism, to reduce blocking.

Abstract: Systems, methods, and other embodiments associated with assessing and adaptively managing query loads are described. In one embodiment, a method includes determining that a query invoked by a client terminal against an application object has a duration that exceeds a completion threshold. In response to receiving a subsequent invocation of the query, statistics for the query are obtained. The obtained statistics are analyzed to determine whether the query causes the unacceptable negative impact on the application object. A data structure is modified to include a classification of the query into one of a plurality of different query classes based on a result of the analysis, and execution of the query in response to a future invocation is controlled based on the classification of the query in the modified data structure.

Abstract: Provided is a method of searching for an app name in a user terminal. The user terminal requests a download of an app from an app storage server. The user terminal transmits a language set in the user terminal and an app name of the app to an app name processing server. The app name processing server acquires a word in the set language corresponding to the app name through a machine learning technique, and stores the acquired word in an app search database (DB) connected in the user terminal.

Abstract: A system for association of data elements within a document is disclosed. An input data receiving subsystem receives an input data source of the document. A feature generation subsystem obtains one or more lists of personal data, generates one or more personal data features representing a relationship between one or more personal data elements. An affinity computation subsystem assesses each of the one or more personal data features, computes affinity score between the one or more personal data elements, generates one or more affinities. A personal data relationship identification subsystem assigns the one or more personal data elements to corresponding one or more identification stages, derives a set of identities corresponding to the one or more personal data elements. An identity filtration subsystem receives the one or more affinities and the set of identities, determines a validation of the set of identities, filters out the set of identities.

Abstract: A database system operates by facilitating execution of a query, where each of a plurality of sequential operator execution steps includes: determining whether each operator of a plurality of operators of a query operator execution flow is currently executable; generating a plurality of priority values by calculating a priority value for each operator based on whether each operator is determined to be currently executable, and based on a position value of each operator; identifying one operator of with a most favorable priority value; facilitating execution of the one operator on a queued set of data blocks to generate at least one output data block; identifying a next operator serially positioned consecutively after the one operator; and appending the at least one output data block to another queued set of data blocks of the next operator.

Abstract: Various embodiments can comprise a system, which can comprise one or more processing modules; and/or one or more non-transitory memory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: receiving a request from a requesting party to produce a feature vector for a record, the feature vector being used to aid in placing the record in one of a first database cluster H or a second database cluster L; retrieving a first access history for the record at a first time period, the first access history indicating a number of times the record was accessed within the first time period that is a predetermined time period away from a current day; repeating retrieving one or more second access histories for the record for one or more additional time periods, each second access history of the one or more second access histories indicating a number of times the record was accessed within the one or more additional time periods, wherein each of the one or

Abstract: A method for execution by a query processing module includes determining a query expression indicating a query for execution. An operator tree is generated based on a nested ordering of a plurality of operators indicated by the query expression. Conjunctive normal form (CNF) conversion cost data is generated based on the operator tree, and disjunctive normal form (DNF) conversion cost data is also generated based on the operator tree. Conversion selection data is generated based on the CNF conversion cost data and the DNF conversion cost data. The conversion selection data indicates a selection to perform either a CNF conversion or a DNF conversion. A normalized query expression is generated by performing either the CNF conversion or the DNF conversion upon the query expression based on the conversion selection data. Execution of the query is facilitated in accordance with the normalized query expression.

Abstract: An apparatus, computer-readable medium, and computer-implemented method for data subsetting, including receiving a request comprising a criterion indicating a criterion table in a plurality of tables of a database, as schema of the database corresponding to an entity graph, the entity graph comprising a plurality of entities corresponding to the plurality of tables and a plurality of directed edges connecting the plurality of entities, determining directed edges in the plurality of directed edges that must be traversed in both directions in order to traverse all entities in the entity graph starting from a criterion entity corresponding to the criterion table, generating, an ordered list of edges for the entity graph based on the directed edges that must be traversed in both directions and topological ordering, and generating a subset of data from the plurality of tables based on the ordered list of edges for the entity graph and the request.

Abstract: A method of identifying relationships between data collections is disclosed. Each data collection comprises a plurality of data records made up of data fields. The method comprises performing a relationship search process based on a first seed value and a second seed value. A first set of records from the data collections is identified based on the first seed value. A second set of records from the data collections is identified based on the second seed value. The process then searches for a common value across the first and second record sets, wherein the common value is a value which appears in a first field in a first record of the first record set and in a second field in a second record of the second record set, wherein the first record is from a first data collection and the second record is from a second data collection.

Abstract: A backup and restore coordinator configured to receive a plurality of backup and restore requests from at least two uncoordinated backup functionalities implemented in a virtual environment, the virtual environment including a hypervisor hosting a plurality of virtual machines and a backup server. The backup and restore coordinator configured to extract respective information from the plurality of backup and restore requests including target data, backup resource information, and a type of request. The backup and restore coordinator configured to order the plurality of backup and restore requests in a prioritized queue based on the information extracted from the plurality of backup and restore requests.

Abstract: Data investigations are performed by querying a plurality of data sources. A system receives an investigation input and queries a plurality of data sources in accordance with the received input. The system receives, in response to the querying, response data from the plurality of data sources, and generates and stores a data structure representing relationships between the first investigation input and the first response data. The data structure may be in the form of a knowledge graph. The system may generate and display a visualization of the data structure. The system may generate and store a record of investigation steps used to generate the data structure, such that the investigation steps may be applied in future instances, for example using different inputs, to generate new data structures.

Abstract: A system and computer implemented method for execution of aggregation expressions on a distributed non-relational database system is provided. The method comprises the acts of determining, by a computer system, an optimization for execution of an aggregation operation, wherein the aggregation operation includes a plurality of data operations on a distributed non-relational database; modifying, by the computer system, the plurality of data operations to optimize execution; splitting the aggregation operation into a distributed aggregation operation and a merged aggregation operation; instructing each of a plurality of shard servers to perform the distributed aggregation operation; aggregating, at a merging shard server, the results of the distributed aggregation operation from each of the plurality of shard servers; and performing the merged aggregation operation on the aggregated results of the distributed aggregation operation from each of the plurality of shard servers.

Abstract: Systems, methods, and computer-readable media for determining column ordering of a data storage table for search optimization are described herein. In some examples, a computing system is configured to receive input containing statistics of a plurality of queries. The computing system can then determine a new column order (i.e., layout) based at least in part on the statistics. In some example techniques described herein, the computing system can determine the new column order based at least in part on the hardware components storing the data storage table, storage system parameters, and/or user preference information. Example techniques described herein can apply the new column order to data subsequently added to the data storage table. Example techniques described herein can apply the new column order to existing data in the data storage table.