162results about "Object oriented databases" patented technology

The invention relates to the field of financial science and technology, and particularly discloses a cross-platform financial data integration and real-time analysis system and method. The system is characterized in that a distributed heterogeneous data acquisition module accesses multi-source data in parallel through a pluggable protocol adapter; the streaming data cleaning engine realizes field-level normalization based on a dynamic template and semantic mapping; the hybrid storage architecture forms a three-level system by a memory database, a time sequence database and distributed file storage; the stream processing core engine adopts an elastic sliding window to execute CEP complex event processing; and the dynamic analysis decision module integrates online machine learning to generate a real-time transaction signal. The method comprises the steps of multi-source concurrent acquisition and microsecond-level timestamp injection, context sensing data standardization, dual-channel data storage, event time window aggregation, dynamic risk calculation and block chain audit evidence storage. The defects of low data fusion efficiency, high real-time analysis delay and the like in the prior art are overcome, and cross-platform data processing timeliness and decision accuracy are remarkably improved.

The invention discloses a spacecraft space environment safety guarantee system and method based on three-step calculation, and the system comprises two subsystems: a space environment data service subsystem and a three-step calculation and analysis subsystem, and the three-step calculation and analysis subsystem calls data and an algorithm model from the space environment data service subsystem. The space environment safety guarantee analysis based on the three-step calculation is executed; the space environment data service subsystem comprises a metadatabase and a comprehensive database; the metadatabase comprises space environment space-based monitoring data, space environment foundation monitoring data and data from a specified space environment mechanism; the comprehensive database comprises a space environment numerical calculation model library and a spacecraft business application rule algorithm model library; the three-step calculation and analysis subsystem comprises a sun-earth space environment analysis and calculation module, a spacecraft orbit ring layer environment analysis and calculation module and a spacecraft task influence risk assessment and calculation module, and three-step calculation is executed through the three modules.

A method includes determining a set of features associated with a set of vertices of a directed graph, obtaining a set of feature values associated with the set of vertices, where each respective vertex of set of vertices is associated with a respective subset of feature values. The method includes determining updatable features based on the set of features, selecting a first subset of features based on the set of updatable features. Selecting the first subset of features includes determining candidate subsets of features, determining feature subset scores associated with the candidate subsets of features based on a category label, and selecting the first subset of features based on the feature subset scores. The method includes performing a first operation to determine extracted feature values by determining feature extraction input values.

The embodiment of the invention provides a data processing method based on a three-dimensional hierarchical data model, a platform and a medium. The method comprises the following steps: firstly, acquiring multi-source hydrogeological data; classifying the data to obtain a first type of data and a second type of data; then, according to the three-dimensional hydrogeological hierarchical model, extracting a hierarchical association relationship of the two types of data; the first class data and the second class data are used for representing the incidence relation of the first class data and the second class data at different levels of geological observation angles; storing the first type of data in a relational database, and storing the second type of data in a non-relational database; and finally, according to the hierarchical association relationship, establishing a hydrogeological knowledge graph and storing the hydrogeological knowledge graph in an interactive coordination system. According to the method, after the multi-source hydrogeological data are classified, the incidence relations at different levels of geological observation angles are extracted and stored in the relational database and the non-relational database respectively, mapping between the databases is established by adopting the knowledge graph, the multi-source hydrogeological data are effectively integrated, and meanwhile query is facilitated.

Various examples of improving an in-memory graph query engine using a persisted storage component are provided. The method includes updating data stored in an in-memory graph query engine and, based on updating the data, converting the data to a plain text form that may be more efficiently stored in the persistent storage component. The method further includes updates to additional in-memory graph query engines from the persistent storage component such that in-memory data stored in the graph query engines is synchronized.

The invention provides an SQL (Structured Query Language) statement generation system, a multi-source knowledge base cluster module comprises a plurality of vector libraries, each vector library is used for storing a data vector of a data type corresponding to the vector library, a query understanding module can analyze and process a natural language query problem input by a user after receiving the natural language query problem, and the query understanding module can obtain a query result. According to the technical scheme, the vector engine module can perform collaborative retrieval on at least one vector library according to the processed query problem to obtain the target data information, multiple data types are respectively stored in different vector libraries, so that only the vector libraries related to the processed query problem need to be retrieved, and the retrieval efficiency is improved. According to the method, the retrieval accuracy is improved, multiple vector libraries correspond to multiple data vectors of different data types, and the accuracy of the generated SQL statements can be improved.

The invention provides a data synchronization optimization method and system based on metadata version control, and the method comprises the steps: responding to the change of metadata of a barrel in a source site, generating a metadata synchronization log, and recording the version number of the barrel in the metadata synchronization log; pulling the metadata synchronization log from a site, and analyzing the metadata synchronization log to obtain a version number of the bucket; if the metadata of the bucket does not exist in the slave site or the version number of the bucket in the slave site is smaller than the version number recorded in the metadata synchronization log, stopping the current data synchronization operation, and adding the data synchronization task of the bucket into a data synchronization retry queue; and in response to the fact that the metadata version number of the bucket is consistent at the master site and the slave site, allowing the slave site to continue to execute the data synchronization operation of the bucket. Through combination of various technologies, the version of the metadata is automatically identified, so that the object is synchronized to the correct target bucket.

A computer-implemented method implemented in a blockchain network is described. Validation nodes receive data regarding a newly mined block comprising a plurality of transactions and send a delete request to a distributed memory pool to delete the plurality of transactions from the distributed memory pool. Nodes storing the distributed memory pool store a plurality of transactions, the plurality of transactions forming at least part of a distributed memory pool of transactions waiting to be mined into a block of a blockchain. The computer-implemented method further comprises receiving a delete request from a validation node of the blockchain network, the delete request identifying one or more transactions which have been included in a newly mined block, the delete request indicating that the one or more transactions should be deleted from the distributed memory pool.

The described technology pertains to a distributed computing environment, specifically implementing a streaming protocol on a scalable object storage service. The technical problem addressed is the high computing resource demand of distributed streaming platforms. The solution involves separating content from location metadata, allowing centralized, scalable storage of metadata. Brokers store content in object storage services based on specific metrics, such as fast read / write times or greater storage capacity. A background platform remaps metadata sequences based on logical timestamps, optimizing data retrieval. This system enhances the efficiency of computing resources by reducing the load on brokers and improving data access speeds. A use can be in cloud computing systems for efficient content streaming and storage management.

The invention provides a memory-based man-machine interaction method, electronic equipment, a medium and a program product, and relates to the technical field of artificial intelligence. Based on an interaction scene corresponding to the multi-modal interaction input information, performing long and short term memory hierarchical storage processing, memory mixed retrieval processing and / or memory active forgetting processing on the multi-modal interaction input information to obtain processed target memory data; and generating a multi-modal interaction output result based on the target memory data. By adopting the method and the device, memory-based dynamic response can be realized in human-computer interaction based on a scenarized layered humanoid memory architecture.

The invention provides a database index implementation method based on object storage, which is used for solving the problem of efficient retrieval when object storage is used as a database storage engine. According to the method, a special index storage structure is constructed in object storage and comprises an index metadata area and an index data area, and index data is organized through a segmentation management mechanism. Each segment in the index data area comprises a Bloom filter file, an index entry file and a segment abstract file, and efficient organization and access of index data are achieved. According to the method, a batch writing and parallel retrieval strategy is adopted, and a segment merging and garbage collection mechanism is matched, so that the index performance and the storage efficiency are ensured. The method has the beneficial effects that the query performance when the object storage is used as the database is remarkably improved, the storage overhead is reduced, meanwhile, the data consistency is ensured, and a key technical support is provided for realizing a database system based on the object storage.

Techniques related to searches of highly structured data are described. A body of data may be represented by an object-centric data model. For a search of the body of data, an indication of a particular search template to use may be received. The particular search template may specify one or more hierarchical object types that are within a scope of the search. The one or more hierarchical object types may be defined in the object-centric data model. The particular search template may specify at least one search field. A user interface may be generated based on the particular search template. The user interface may include the at least one search field.

A system for managing versioning of ontology data represented in multiple forms respectively in multiple databases and related methods are disclosed. The system is programmed to determine which changes to the ontology are to be incorporated into a new version of specific ontology data. In response to a write request to write a latest version of specific ontology data, the system is programmed to start representing the latest version in the multiple databases according to a first predetermined strategy. In response to a read request to read a latest version, the system is programmed to return a particular version from at least one of the multiple databases according to a second predetermined strategy.

The invention particularly relates to a method for realizing object storage fuzzy retrieval based on a large model. According to the method for realizing the fuzzy retrieval of the object storage based on the large model, each object in the object storage is summarized through the large model to form an object summarizing result; the object summary result is converted into a feature vector through an embedded vector technology, and the object name, the object summary result and the feature vector are cached; the query statement is converted into a feature vector, neighbor indexing is carried out in a vector database, if the difference between a neighbor indexing result and the feature vector is smaller than a self-defined threshold value, it is considered that the search is hit, and a corresponding object name and an object summary result are returned. According to the method for realizing fuzzy retrieval of object storage based on the large model, the storage and maintenance cost is reduced, the application scene of object storage is expanded, the usability of the system and the user experience are improved, the universality is high, and the method can be suitable for all object storage systems.

The invention discloses a database object manager and a management method, the manager realizes operation on a database object through interface design, and the manager comprises a database object creating module for creating and modifying the database object, the database object comprises one or more of a library, a mode, a table, an index, a partition, a constraint, a view, a sequence value, a storage process, a function, a packet, a user and a role; the database object management module is used for checking and modifying a database object, operating data, editing SQL (Structured Query Language), checking a table structure, and importing and exporting the table structure and table data; and the database object maintenance module is used for realizing table statistical analysis, index reconstruction, view compilation and modification, trigger compilation and modification, storage process and function compilation of database objects. Through the interface operation, the user can better use the database.

A device, system and method for distributing and locally persisting provider objects is provided. A first device provides, in a provider object stream, first indications of first provider objects, generated and locally persisted in association with a first region, to a second device associated with a second region. The first device receives, in the stream, from the second device, second indications of second provider objects generated locally persisted in association with the second region, the second indications including respective identifiers identifying the second region. The first device: discards given second provider objects, indicated by the second indications, which do not meet a local persistence condition; and locally persists other second provider objects, as indicated by the second indications, which meet the local persistence condition, the other second provider objects locally persisted in association with the first region.

A solution for transforming and managing data in a data lake environment is disclosed. A definition of a transformation stream is received via a graphical user interface. The transform stream specifies an operation to transform source data into target data stored in the first format object store. And generating a virtual process corresponding to the transformation stream and storing the virtual process in a memory cloud database. A virtual process is then executed such that metadata is retrieved from the first format object store and a transform job is triggered on the compute cluster. The transform job reads the source data from the first format object store, applies one or more transforms specified in the definition of the transform stream, and writes the result to the target data in the first format object store.

In an example embodiment, a data warehousing environment, or a similar architecture, is extended to permit data storage in either an in-memory database or a Lakehouse architecture, which leverages one or more hyperscalers for the underlying storage. More specifically, a single artifact is defined in Datasphere that stores data in either the in-memory database or the Lakehouse architecture, and does so in a storage-agnostic manner.

A solution for transforming and managing data in a data lake environment is disclosed. A definition of a transformation flow is received via a graphical user interface. The transformation flow specifies operations to transform source data into target data stored in a first format object storage. A virtual procedure corresponding to the transformation flow is generated and stored in an in-memory cloud database. Then the virtual procedure is executed, causing retrieval of metadata from the first format object storage and triggering a transformation job on a computing cluster. The transformation job reads the source data from the first format object storage, applies one or more transformations specified in the definition of the transformation flow, and writes results to target data in the first format object storage.

A system, method, and computer-readable medium for performing a data center monitoring and management operation. The data center monitoring and management operation includes: generating a tag for an object within a data center asset, the tag for the object associating a contextual meaning with the object; registering the tag when the tag is generated; storing the tag within a tag storage repository when the tag is registered; and, managing an aspect of the tag via a data tag management operation.

The invention relates to the technical field of artificial intelligence, discloses a business risk prediction method and device for setting and adjusting a risk threshold value based on a profit and loss balance point, equipment, a medium and a product, and aims to solve the problems that the balance of cost and profit is not considered, dynamic adjustment capability is lacked, the risk threshold value cannot be dynamically adjusted, and the risk threshold value cannot be dynamically adjusted in the prior art. And the accuracy and flexibility of credit risk control are poor. The method comprises the following steps: constructing an initial prediction model, and collecting and training the initial prediction model based on historical business data to obtain a business risk prediction model; calculating a profit and loss balance point of the business based on the historical business data, and setting an adjustment risk threshold based on the profit and loss balance point; and obtaining a to-be-predicted target service, calling the service risk prediction model to perform service information prediction on the target service, obtaining a service information prediction value, comparing the service information prediction value with the set and adjusted risk threshold, and determining a final service risk result. According to the method, the accuracy and flexibility of the business risk prediction method are improved.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and / or the like that are configured to enable effective and efficient browser event synchronization management in a federated system. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and / or the like that are configured to manage browser event synchronization using techniques that utilize object-oriented event management database stored at client computing device and asynchronous browser event processing.

The invention discloses a plant growth key element multi-modal database creation method, a growth state prediction method and a monitoring system, and relates to the technical field of agricultural information. The method comprises the following steps: collecting multi-modal data in a plant growth process, and preprocessing the multi-modal data; a deep learning model is adopted to extract preprocessed data features, and then time alignment is carried out on all data; determining a database table structure according to a multi-modal data acquisition requirement; the database table structure comprises a feature storage field, an index field and a hyperlink identifier, and the hyperlink identifier is used for establishing a hyperlink relationship between each piece of data and modal data except a modal to which each piece of data belongs; and according to the database table structure, establishing a storage container for each modal data, storing the data, and obtaining the multi-modal database of the plant growth key elements. The multi-dimensional features of the plant growth key elements can be effectively extracted and analyzed, multi-modal data are fused, and efficient storage and management of large-scale plant growth data are achieved.

A computer-implemented method comprises applying at least one processing action to at least one source data object, and responsive to applying the at least one processing action, generating one or more derived objects from the at least one source data object. For each derived object, the method comprises storing the derived object in a first data store, determining association information for the derived object and the at least one source data object from which the derived object is generated, and writing the association information in a second data store to associate the derived object with the at least one source data object, such that an origin of the derived object can be determined. Responsive to modifying the at least one source object, the method further comprises creating at least one modified object, and for each derived object of the at least one source object: writing new association information between the derived object and the at least one modified object, and removing previous association information between the derived object and the at least one source data object, or generating a new derived object from the at least one modified object and writing new association information to associate the new derived object with the at least one modified object.

This technology relates to intelligently generate a search result. A method, computing device, and non-transitory computer readable medium include identifying a plurality of factors relating to a dominant feature of a stored object, and retrieving data associated with the stored object from a storage. For each of the plurality of factors, quantifying a significance of influence the factor on the dominant feature of the stored object, and determining whether the factor is a target factor influencing the dominant feature of the stored object. A generative artificial intelligence processing model is trained with the target factors and associated significances. In response to receiving a search input regarding the stored object, executing the trained generative artificial intelligence processing model to generate and then display a search result.

Disclosed herein are systems and methods for generating notional data. The method includes: receiving seed data of one or more object types in a base dataframe; defining one or more functional relationships associated with the one or more object types, at least one functional relationship of the one or more functional relationships specifying a change to seed data of one object type of the one or more object types; generating data of the one or more object types based at least in part on the seed data in the base dataframe and the one or more functional relationships; and generating the notional data based at least in part on the generated data of the one or more object types.

Methods, systems, devices, and tangible non-transitory computer readable media facilitating assignment and / or dynamic application of a permission rule to a group of entities. In an example embodiment, the disclosed technology can: define a group of entities having a common attribute; assign a permission rule to the group of entities based at least in part on the common attribute; project the permission rule onto one or more entities in the group of entities based at least in part on assignment of the permission rule to the group of entities; obtain data indicative of a change in group membership status of an entity in the group of entities; and / or update a projection of the permission rule onto the entity to modify an association of the entity with the group of entities and / or the permission rule based at least in part on receipt of the data.

The present invention discloses a heterogeneous data fusion indexing method and system based on knowledge graph, comprising the following steps: analyzing entity types and relationship types in heterogeneous graphs, synchronizing and defining entity type data respectively, and defining heterogeneous entity mapping relationships; using graph adaptation to obtain entity and relationship data from two types of heterogeneous graphs respectively, and caching the data such as message middleware; constructing a view domain knowledge graph and a business domain knowledge graph; fusing the constructed view domain knowledge graph and business domain knowledge graph to construct a fusion index subgraph; performing mapping relationship analysis and mining based on the constructed fusion index subgraph to achieve disambiguation and fusion of entities in the original graph; providing a standard and unified relationship query interface for heterogeneous graphs and fusion index subgraphs; this solution constructs a data fusion index subgraph through entity mapping of the two types of knowledge graphs to achieve fusion of heterogeneous data.

The invention discloses a data query method and device, electronic equipment, a storage medium and a program product. According to the specific implementation scheme, the method comprises the steps of obtaining object description information, determining attribute description information corresponding to a to-be-queried attribute according to the object description information, determining an attribute offset array according to storage structure information of an object column, determining query position information of the to-be-queried attribute according to the attribute description information and the attribute offset array, and determining the to-be-queried attribute according to the query position information. And in combination with the storage structure information of the object column, querying data corresponding to the to-be-queried attribute from the object column. The problem that when the data in the object column is queried, the object column data needs to be deserialized firstly and then the specific attribute data is positioned is solved, the burden of a central processing unit and a memory is reduced, the function of querying the data by directly obtaining the storage position of the data corresponding to the attribute to be queried through the attribute offset array is achieved, and the data query efficiency is improved. The data query steps are simplified, and the data query performance is improved.

A system facilitates a process for automatically generating artificial intelligence (AI) models. The system receives a first natural language input from a user that includes a set of phrases and an instruction to analyze data associated with the set of phrases using an AI model. The system accesses a metadata graph to determine a node corresponding to the set of phrases, where nodes in the metadata graph indicate internal data objects stored in data silos. The system processes the internal data objects indicated by the determined node to generate a first set of application data. The AI model is applied to the first set of application data to generate one or more outputs. Additional user inputs can be received to modify the first set of application data and apply the AI model to the modified data, until a desired data pipeline for the model has been constructed.