52results about "Multi-dimensional databases" patented technology

According to embodiments, described herein are systems and methods for providing extensibility in an analytics application environment, including enabling the use of custom semantic extensions to extend a semantic layer of a semantic data model (semantic model). According to embodiments, the system enables the use of a segmented query model - when customizing a semantic model, the system is able to dynamically incorporate changes from various increments at query time at runtime to dynamically surface appropriate data based on the extended semantic model.

A data platform for executing containers is provided. In some examples, the data platform receives an application from an application package of a provider account, the application including a setup script and a manifest of a service. The data platform activates access roles based on the manifest and creates the service and a compute pool using the setup script and a specification file accessed from the application package using an access role. The service is executed in the compute pool, accessing objects of the application package and of the data platform using the access roles.

A data mining method includes extracting a main character column of a data table of a data sheet; determining an evaluation measurement value of the data table according to a variance and an outlier ratio of the main character column of the data table; and determining a data mining value of the data table according to the evaluation measurement value of the data table.

The present invention relates to a computer-implemented method to generate a process mining repository for analyzing process data, wherein the process data is a multidimensional large-scale dataset which is extracted from at least one external computer system and transformed into a number of data models. The process mining repository represents a process workspace, in which the user may conduct process mining on a valid data model, i.e., explore process data, for instance, using a dynamic question and answer framework.

By utilizing user-defined functions in the structured query language SQL, a system for performing entity resolution, specifically ranked personally identifiable information (PII) matching, can function directly within the data warehouse's execution environment, thereby eliminating the need to move data to external systems. This system uses SQL and user-defined function (UDF) implementations to utilize an inverted index system that can operate on the distributed processing capabilities of the data warehouse.

A hybrid transactional / analytical processing (HTAP) database is provided, which includes an online transaction processing (OLTP) engine, an online analytical processing (OLAP) engine provided separately from the OLTP engine, and a shared storage holding data. The shared storage includes an OLTP storage holding data in row format for responding to queries of the OLTP engine, and an OLAP storage holding data in column format for responding to queries of the OLAP engine. The OLAP storage includes a delta store configured to record changes made to the OLTP storage in an update, and a base store configured to implement the update from the delta store on data in persistent storage in a flush. The HTAP database further includes a table metadata service configured to generate and perform operations on table metadata of the data that is updated in the flush.

The present disclosure relates to a mobile terminal having an artificial intelligence unit, and a control method therefor. A mobile terminal according to the present disclosure includes: GPS configured to receive location information of the mobile terminal, a wireless communication unit configured to perform wireless communication with an external device, and a controller configured to store communication information when a communication event with the external device is generated. The controller matches the location information received from the GPS with the communication information and stores the matched information when the communication event is generated, and outputs at least one information corresponding to a search query for retrieving communication information from pre-stored communication information by using location information matched to the pre-stored communication information and the search query, when the search query is input by a user.

This invention discloses a data storage method and system based on a scalable multidimensional learning index structure, belonging to the field of computer data storage. This system fully utilizes storage characteristics to organize data into memory and disk, significantly reducing disk I / O with minimal memory usage, accelerating data search speed, and supporting data expansion. This invention can be applied to multidimensional data indexing, typically represented by geospatial data, meeting the need for rapid multidimensional key searching in the high-dimensional key space of such data.

The application provides a metadata analysis processing method and device, and relates to the technical field of big data analysis processing.The method comprises the following steps: collecting metadata in a data source; acquiring a business process, declaring the granularity of a metadata model according to the business process, and constructing a dimension table and a fact table according to the granularity; taking the business process as a modeling driver, and establishing a metadata model according to the metadata, the dimension table and the fact table; when a data analysis request is acquired, performing data analysis by using the metadata model to obtain an analysis result.The metadata model is established by collecting metadata, the metadata of enterprises scattered in multiple application systems is centrally managed, a complete and comprehensive data structure relationship is presented for the enterprises, the enterprises are assisted in centrally and efficiently managing data assets, the metadata analysis processing of different business requirements can be met, data value can be mined from the analysis result to guide business or product innovation, and the production of enterprises is empowered.

This application provides a method, apparatus, server, storage medium, and product for querying business data, belonging to the field of Internet technology. The method first parses the query request to obtain multiple query statements. Based on these multiple query statements, multiple data structures are generated. Based on these data structures, multiple query execution tasks are generated. Through the first and second nodes of the multiple query execution tasks, multiple data sources are queried in parallel. The multiple business sub-data obtained from the multiple data sources are summarized to obtain the business data, which is then returned to the terminal. This method can query multiple data sources in parallel based on multiple query fields, thus shortening the query time and improving query efficiency.

This application discloses a method for constructing a data model, a method for data querying, an apparatus, and a storage medium, relating to the field of data processing technology. The method includes: obtaining field information from a dimensional model and at least two dimensions of a dimension table, where the field information indicates a mapping relationship between at least two dimensions; constructing a full dimensional relationship graph based on the field information, where the field information also indicates the association relationship between each of the at least two dimensions and a fact table; and attaching the fact table to at least one dimension in the full dimensional relationship graph based on the field information, generating a directed acyclic connected graph centered on the fact table as a data model, used to determine the path from the query metric to the fact table based on the query metric and its dimensions. This method can construct a data model based on existing data warehouse construction achievements, thereby determining the path of the query metric in the data warehouse through the data model.

The present disclosure relates to a data query method and apparatus based on online analytical processing, a medium, and a device. The method includes: obtaining a plurality of target join keys between query tables in a structured query statement in response to reception of the structured query statement; constructing a first factor graph corresponding to the structured query statement based on the plurality of target join keys, where a factor node in the first factor graph is used to represent a conditional probability distribution in the case of presence of the target join keys in a query table corresponding to the factor node; and estimating an upper limit value of a join cardinality of the structured query statement based on the first factor graph.

Embodiments of the present application provide a method, system and storage medium for achieving a virtual data lake. The method includes: providing a SQL database interface to a user, and receiving a SQL query request from the user via the SQL database interface; parsing the SQL query request and obtaining corresponding query operation information, query object information and query entity information, wherein the query entity information comprises at least one entity in a knowledge graph, and the query object information comprises property information contained in each of the at least one entity; grouping the query operation information, query object information and query entity information by entities to obtain a query parse tree corresponding to each entity; distributing the query parse tree corresponding to each entity to a universal connectivity module, so that the universal connectivity module is to perform corresponding query operation on corresponding data in corresponding data source according to an obtained mapping connection between raw data of data sources and entities, properties of the knowledge graph. The technical solutions of the present application can achieve a virtual data lake.

The application discloses a kind of data warehouse data volume prediction and monitoring method, including according to selected data characteristics and time granularity, the time series set of each data layer of data warehouse is obtained.The data warehouse data volume prediction and monitoring method by each data layer of data warehouse establishes different prediction network model, obtains the optimal time period and prediction step of each data layer, obtains the data volume prediction value of each data layer, and then obtains the total data volume prediction value of data warehouse, improve the accuracy of prediction, solve the problem that prediction is not accurate in prior art by artificial, workload is large;The data warehouse data volume prediction and monitoring method compares prediction result and current actual data when monitoring, when difference degree is not in fluctuation rate range, current data is set as problem data, triggers alarm mechanism, carries out abnormal information push, improves the accuracy of monitoring.

The application belongs to the technical field of computers, and particularly relates to a time series data processing method and an electronic device. The method comprises the following steps: acquiring a plurality of data sets with the same time series attribute, wherein the data set comprises a plurality of time series data with the time series attribute; sorting the time series data in the data set according to time series nodes to obtain a data sequence; determining a sequence length for accommodating the time series nodes according to the minimum time series node and the maximum time series node in the data sequence, and determining the number of binary bits for accommodating attribute values according to the maximum attribute value in the data sequence; initializing a sub-bitmap corresponding to the data set according to the minimum time series node, the sequence length and the number of binary bits, and writing the data sequence into the sub-bitmap; the sub-bitmap is an array for storing the data set in binary format; and merging the sub-bitmaps of the plurality of data sets to obtain a main bitmap corresponding to the time series attribute. The application can improve the storage space utilization rate of the time series data.

The embodiment of the specification discloses a data processing method, device and equipment, the method comprises the following steps: receiving a query parameter related to target data, then, the query result data can be obtained from each predetermined data source according to the query parameter, the query result data can be converted into target reliable data conforming to a predetermined data reliability protocol, finally, the target reliable data can be sent to a block chain node.

This system and method described herein provide for efficient monitoring of machine learning models in a data warehouse toolset integrated with a cloud platform. Computation of statistics and anomalies for training data and serving data for the machine learning models may be distributed across physical shards in the data warehouse. Computed results may be converted to a format compatible with external platforms. Model monitoring may be supported using table valued functions (TVFs) to compute descriptive statistics of data and to compute the feature distribution difference between two different pieces of data, a scalar function to validate statistics, and an internal task handler to parse monitoring statistics results and save it as a job to make it visualizable.

This system and method described herein provide for efficient monitoring of machine learning models in a data warehouse toolset integrated with a cloud platform. Computation of statistics and anomalies for training data and serving data for the machine learning models may be distributed across physical shards in the data warehouse. Computed results may be converted to a format compatible with external platforms. Model monitoring may be supported using table valued functions (TVFs) to compute descriptive statistics of data and to compute the feature distribution difference between two different pieces of data, a scalar function to validate statistics, and an internal task handler to parse monitoring statistics results and save it as a job to make it visualizable.

Systems and methods for visualizing security coverage based on MITRE ATT&CK framework include obtaining cybersecurity monitoring data for an organization where the cybersecurity monitoring data is from a plurality of sources including from cybersecurity monitoring of a cloud environment associated with the organization; providing an interactive User Interface (UI), wherein the UI overlays a catalog of known malicious tactics with the cybersecurity monitoring data; and responsive to one or more selections within the UI, providing information related to coverage of one or more threat techniques.

The application relates to the technical field of big data, and provides a detection method and device of a data warehouse, equipment and a storage medium, wherein the method comprises the following steps: in response to a processing request of a terminal device for first data, determining a first execution plan corresponding to the first data based on the processing request; acquiring second data stored in the data warehouse and a second execution plan corresponding to the second data; determining the similarity between the first execution plan and the second execution plan based on a preset execution plan processing rule; generating a detection report of the data warehouse based on the first execution plan and the second execution plan with the similarity greater than or equal to a first similarity threshold value, outputting the detection report, and the detection report is used for instructing relevant personnel to adjust the data level of the data warehouse or adjust the processing request. The application aims to improve the accuracy of judging data repeated processing and reduce the repeated processing of data.

This application provides a data consistency detection method, apparatus, electronic device, and storage medium. The method includes: acquiring data to be inspected, the data to be inspected including first data to be inspected from a first application service and second data to be inspected from a second application service, wherein the first application service and the second application service are any two of a plurality of application services used to execute the same service request; if the first data to be inspected and the second data to be inspected do not meet a preset consistency rule, performing a first processing step to ensure that the data generated by the first application service and the second application service executing the same service request are consistent, thereby verifying whether the data between application services meets the preset consistency rule. When it does not meet the rule, the relevant application service can be subjected to the first processing step to ensure that the data of the application services are consistent, thus ensuring data consistency between application services during cross-service calls.

A device may receive data request information that includes business requirement information, pipeline information, and dataset information, and may process the data request information to generate metadata. The device may store the metadata in a repository, and may validate the metadata in the repository based on validation rules and to generate validated metadata. The device may determine whether the validated metadata is approved or disapproved, and may selectively merge the validated metadata to the repository based on the validated metadata being approved or notify a requester based on the validated metadata being disapproved.

Methods, systems, and apparatuses for implementing virtual warehouse configuration change rollbacks based on query monitoring. A computing device may identify a history of queries executed by one or more virtual warehouses over time. The computing device may associate each of the queries with a fingerprint, then use those fingerprint(s) to identify one or more similar queries. The computing device may then, based on determining that one or more similar queries are associated with virtual warehouse processing times that satisfy a threshold, perform one or more configuration rollbacks. For example, the computing device may identify one or more previously-made configuration changes and revert those changes by changing operating parameters of the one or more virtual warehouses. For example, size changes of the virtual warehouses may be rolled back, and / or new virtual warehouses may be instantiated to replace previously-terminated virtual warehouses.