164 results about "Query log" patented technology

The invention discloses a search method and system based on computer natural language processing, and the method comprises the steps: extracting a synonym set and a context association relationship of keywords in a query text through a preset semantic knowledge graph, and generating a semantic vector representing a semantic dimension in combination with a deep learning model; extracting a historical behavior feature sequence from the query log based on the semantic vector, and analyzing a user search intention by adopting an attention mechanism model; performing similarity matching on the pre-constructed database by utilizing a semantic matching algorithm, and screening an information matching set meeting a threshold value; performing distributed processing on the matching set through a context-aware dynamic fragmentation algorithm, and constructing an index fragmentation cluster; semantic aggregation is realized by adopting a cross-fragment graph attention network, and an optimized search result set is generated through dynamic semantic projection. According to the method, through multi-modal fusion of the semantic knowledge graph and deep learning and in combination with a dynamic distributed processing architecture, the accuracy of search intention recognition and the efficiency of large-scale semantic matching are improved.

The invention relates to the technical field of data processing, in particular to a cross-unit data management method, which comprises the following steps of: performing format standardization processing on data in a data pool of each organ and unit, converting the data into a uniform standard format, extracting semantic features to construct a semantic association network, and recording a semantic relationship of data items and a dynamic authorization and authority mapping mechanism. Then, according to the inquirer identity information, the unit attributes, the business requirements and the data sensitivity level, dynamically generating inquiry authorization and establishing an authority mapping table; and when a query condition is received, analyzing by utilizing a natural language processing technology, and generating a query route and optimizing a statement in combination with the semantic association network and the permission mapping table. And finally, calling data from each data pool according to the route, performing fusion processing, displaying the data to a querier in real time, and recording a query log. The invention aims to solve the problems of data semantic isolation, authority management staticization, low query efficiency and insufficient cross-unit data conflict resolution capability in the existing cross-unit data management.

The invention discloses an intelligent data query system and method based on natural language processing, and relates to the technical field of natural language processing and database query. According to the method, natural language input and database mode information are received, a historical query log is combined to construct mode knowledge representation, a query skeleton is generated on the basis, a fine-tuned large language model is called to generate candidate SQL statements, error detection and ambiguity recognition are carried out on the candidate statements, and the query result is obtained. And if necessary, triggering interaction clarification and updating a query result according to user feedback. And meanwhile, the wrong clauses are locally repaired through a gating mechanism, and the clauses are returned and regenerated when multiple times of repair fails, so that the correctness of the query statement is ensured. And finally, after the SQL is executed in the database, a result is fed back to the user. Besides, the system records generation and repair tracks in the operation process, and continuously optimizes the model based on reward shaping, comparative learning and self-game training, so as to improve the generalization ability in different business scenes.

A method for managing data items includes: obtaining a query log; initiating performance of a first recommendation cycle (RC) by analyzing the query log to generate a recommendation list (RL) including at least a data item with a high-level cache reward; providing the RL to an administrator via a graphical user interface (GUI); providing an administrator-confirmed recommendation list (ACRL) to a cache engine, in which the ACRL is received from the administrator in response to the RL being provided to the administrator; receiving a refreshment policy of the data item included in the ACRL from the administrator; storing, based on the ACRL, the data item as a cached data item in a storage device.

A system for cognitive inferencing for large language models (LLMs), the system including: a integration layer communicatively connected to a static LLM, wherein the integration layer includes a processor configured to receive one or more user requests from a user, wherein the one or more user requests include a user interaction with the static LLM, generate one or more cognitive inference (CI) units from the one or more user requests, assign a confidence-falsifiability (CF) delta for each of the one or more CI units using a Socratic engine and append the one or more CI units and the CF delta to a cognitive inference (CI) log associated with the user, wherein the CI log serves as an inference engine for the static LLM, wherein the integration layer is configured to query the CI log upon a subsequent user interaction with the static LLM.

An online system performs an inference task in conjunction with the model serving system and / or interface system to generate relevant product images for query auto-completion and query suggestion to help users better navigate their search experience. The online system generates a collection of query suggestions using search query log mining. For each query suggestion in the collection of query suggestions, the online system retrieves one or more catalog images that depict the query suggestion from a product catalog. The online system constructs a prompt to a text-to-image model including the query suggestion, and a request to generate one or more query images based on the query suggestion. The online system receives the query images from the text-to-image model and ranks the catalog and query images to identify an image to display to the user in association with the query suggestion.

A computer-implemented method for optimizing a distributed database includes receiving, at an aggregator, one or more query logs comprising past queries received by the distributed database. The method includes generating, by the aggregator, a query prediction model based on the one or more query logs. The method includes predicting, by the aggregator, a future query using the query prediction model. The predicted future query is predicted to be received by an edge device. The method includes causing, by the aggregator, data for responding to the predicted future query to be transmitted to the edge device.

Systems and methods for generating virtual assistant proactive queries improving virtual assistant-user interaction. Initial user prompts, including trigger conditions, corresponding to one or more initial user sessions are received. The initial user prompts, and trigger conditions, are stored in a query log database. Pattern recognition is performed on the stored initial user prompts, and the corresponding trigger conditions, to determine a proactive prompt for a subsequent user session. A proactive response is generated from the proactive prompt. Prior to receiving a subsequent user prompt, the proactive response is provided during the subsequent user session upon detection of one or more trigger conditions corresponding to the proactive prompt.

The present disclosure provides an artificial intelligence-based alarm management based on structured time series data and unstructured contextual information where the conventional methods fails to do. Initially, the system receives a user query associated with an industry automation system and classifies into one of a) data request query b) an anomaly detection and diagnosis query c) a log request query and d) feedback for previous response using a query classifier. Simultaneously, the user query is converted into an associated timeseries query representation. Furthermore, a plurality of database queries is generated based on the timeseries query representation using query generator. Further, data pertaining to the user query is retrieved from an associated database based on database queries using Deep Learning models, thereby identifying a critical alarm, and to take appropriate actions if there is any anomaly. Finally, the retrieved data is displayed to the user in a user readable format.

The invention is applicable to the technical field of cloud document management, and provides a cloud document management method and platform based on a block chain technology, after a user uploads a target document to a specified distributed cloud storage node driven by a block chain, an initial operation priority index of the target document is determined, and a file volume of the target document is acquired; acquiring a historical query log of a user for the specified distributed cloud storage node, determining an access heat interval of the user according to the historical query log, and determining a target node state parameter of the specified distributed cloud storage node for the access heat interval; the finally generated priority index comprehensively considers document characteristics, user behaviors and node performance, so that document scheduling is more intelligent and precise, the resource allocation efficiency and the node service response capability are remarkably improved, and the overall performance and the user experience of the distributed cloud storage system are enhanced.

In one example, a method performed by a processing system including at least one processor includes automatically inferring metadata about a structured query language database, constructing, based on the metadata, a prompt for generating a new structured query language query to be executed against the structured query language database, and providing the prompt to a language model for use in generating the new structured query language query.

The invention relates to the technical field of network communication, in particular to a DNS (Domain Name Server) hierarchical cache resolution acceleration method and system based on a prefetching mechanism, which comprises the following steps: by constructing a hierarchical cache model comprising a client layer, a local DNS server layer and an upstream DNS server layer, caching a resolved domain name and a corresponding resolution result into each layer of cache; a DNS request of a terminal user is received, whether the request hits the cache or not is judged, and corresponding operation is adopted; historical query logs are collected, a next DNS request is predicted in combination with a prefetching mechanism of real-time network topology perception, and an analysis result of the domain name is cached to a local DNS server layer in advance; a cross-layer collaborative cache updating mechanism is adopted to ensure synchronous updating of cache data of different levels; and a symmetric encryption algorithm and a TLS protocol are introduced, so that the security of the data in the storage and transmission process is ensured. According to the method, the response of DNS analysis is accelerated through a prefetching mechanism and layered caching.

The invention relates to a method for actively collecting a micro-service error log, which comprises the following steps: collecting a micro-service log according to pre-configured task information, the pre-configured task information comprising a micro-service name and a log path, and the micro-service log comprising log content, a link tracking ID and log context information; filtering the micro-service log according to the filtering rule to obtain a key error log, wherein the key error log is used for representing generated important error information; filling the abnormal type, the log context and the link tracking ID analyzed based on the key error log into a structure template to generate a structured log file; and sending the structured log file to the target receiving end according to the configuration information. According to the method, a rear-end collection mode is adopted, the micro-service logs are automatically collected, developers do not need to manually log in a server or query log files, and time and energy are saved.

The invention discloses a visual database analysis method and system. The method comprises the steps of shunting statistics, slow query optimization, back-end server establishment, CRUD operation visual display and development of a front-end display interface. The invention belongs to the technical field of database analysis, and particularly relates to a visual database analysis method and system.According to the scheme, slow query optimization is adopted, a MySQL slow query log is configured on a back-end server, the position and the moving speed of beetles are calculated, the inertia weight is adjusted, and factors causing slow query are searched and analyzed by means of slow query log data; the query efficiency is improved; the method comprises the following steps of: counting operation requests of all CRUDs, recording operation types and times, updating and providing the operation requests to a front end in real time, decomposing and sorting operation data, performing singular value decomposition, performing principal component compression and calculating a target function to evaluate the operation condition of a rear-end server on a database, including overall efficiency and resource utilization; and adjusting the back-end server according to the analysis of the target function.

The data query optimization method, system, database and electronic device based on machine learning provided by the present invention include: obtaining an index automatic recommendation model; wherein the index automatic recommendation model is obtained based on feature training of each historical query in the historical query log; obtaining a query statement, and when the query mode of the query statement is a full table scan, predicting the optimal index corresponding to the query statement through the index automatic recommendation model; creating an optimal index, and optimizing the optimal index through a preset enhanced learning mechanism combined with the query performance after using the optimal index. The present invention dynamically recommends the optimal index for the query statement through the index automatic recommendation model, thereby ensuring the accuracy and pertinence of the index. The present invention can also automatically optimize the index through the enhanced learning mechanism, greatly reducing the complexity and labor cost of index management.

The embodiment of the invention provides a database log processing method and device, a storage medium and electronic device.The method comprises the steps that based on the total number of logs within a specified log range requested to be queried by a current log query request and the number of query threads in a plurality of query threads, the query threads are queried according to the total number of the logs within the specified log range; segmenting a log requested to be queried by the log query request into a plurality of log ranges, and respectively constructing corresponding query statements to obtain a plurality of query statements; executing the corresponding query statements in parallel through a plurality of query threads, and querying log data in a corresponding log range from a log database; receiving at least one batch of log data returned by the log database through the data receiving thread, and writing the log data into the buffer block queue in a circular writing mode; and executing processing operation on the log data written in the buffer block in a circular processing mode through a data processing thread, and summarizing and sorting the processed log data to obtain a log query result corresponding to the log query request.

The present application provides an index persistence method and device for a KV storage system, wherein the method includes: querying the first preset layer of a log structure merge tree, and finding from the tail metadata area that the key value key range of the index is a first preset data range; querying the index file corresponding to the first preset range, finding a portion of the second preset data range from the first-level index area by a binary search method, and reading back the data results of the preset data; performing data query in the second preset layer of the log structure merge tree and the third preset layer of the log structure merge tree, and checking the key value key to be queried, and reading back the data results of the preset data; merging the data results read back in the first, second, and third preset layers and returning them. The disclosed index persistence method can reduce index file read amplification without increasing the metadata management pressure of the file system.

The invention relates to the technical field of power system data processing, in particular to a power system data processing method and system based on dynamic period detection, and the method comprises the steps: collecting a historical query log of a power business system, and extracting time sequence data of query features through a sliding window; training a recurrent neural network model by using the time sequence data, and predicting a current optimal query period length; on the basis of the optimal query period length, predicting an index combination required by query through a classification model, and generating a candidate index set; optimizing an execution strategy of the candidate index set by taking the minimum index construction cost and the maximum query coverage rate as targets; and obtaining query data of the power business system in real time, and updating the candidate index set and the execution strategy when the change of the business period characteristic is detected. The objective of the invention is to match the periodic change of the power system data, improve the indexing efficiency and reduce the maintenance cost.

The invention relates to the field of data processing, and discloses a database SQL query optimization method, device and equipment and a storage medium, and the method is used for intelligently generating and optimizing an SQL query. The method comprises the following steps: acquiring a user request, and generating an SQL statement; analyzing the SQL statement to extract a related table, and obtaining structure information of the related table from a database; obtaining a historical query log, performing multi-modal feature fusion on the SQL statement, the structural information of the table and the historical query log based on a pre-trained Transform model, and generating a context-aware joint embedded vector; inputting the joint embedded vector into a pre-trained optimization strategy generation model to obtain optimization suggestions applicable to cross platforms; and collecting user feedback data, updating parameters of the optimization strategy generation model to obtain an updated optimization strategy generation model, and replacing the existing optimization strategy generation model with the updated optimization strategy generation model.

Disclosed herein are embodiments of systems, methods, and products comprises a server for database search and knowledge mining. The server may learn different table's semantics, relationships, and usage by parsing historical query logs and analyzing tables' metadata (e.g., table descriptions). The analytic server may generate a graph database based on the table relationships obtained from the parsing. The graph database may be a relationship graph where tables are the nodes and edges represent the relationships among tables. When the server receives a query, the server extract semantics of the query, and return a set of tables that are semantically similar to the query. The set of tables may be a list of tables whose semantic similarities with the query satisfies a threshold. The analytic server may further generate a graph including the list of tables to show the relationships of these tables.

The invention relates to the technical field of artificial intelligence, and particularly provides an NL2SQL generation method based on a large language model. The method comprises the steps that SQL mode positioning is conducted by combining three dimensions of semantic mapping, historical query logs and external mode links; constructing a sample fusing hierarchical screening and comparative learning; based on the candidate SQL and the multi-dimensional confidence fusion, the multiple candidate SQL are generated and subjected to cross validation, and the method improves the recognition accuracy, reduces the error rate of a grammar structure and improves the reliability of a query result.

The invention provides an index optimization method and device, electronic equipment and a storage medium, and relates to the technical field of database query. The method comprises the steps that a query log of a database is analyzed, query detailed information is obtained, the query log comprises at least one query statement, and the query detailed information comprises query conditions indicated by the query statement, sorting fields, table connection relations and grouping information; the detailed query information is analyzed and counted to obtain query statistical data, and the query statistical data comprises the field access frequency, the data distribution condition and the index use condition; generating at least one candidate query path based on the query statistical data, wherein each candidate query path comprises a query index and a use sequence of the query index; and performing query cost evaluation on the at least one candidate query path, and determining a recommended index scheme. The method can automatically recommend a proper index scheme and improve the index optimization efficiency.

The invention discloses a slow query analysis method and system for an openGauss database, and relates to the technical field of database management. Comprising the following steps: acquiring a historical slow query, a resource consumption condition corresponding to the historical slow query and a reason for generating the historical slow query in a query log of an openGauss database; analyzing the execution plan corresponding to the historical slow query, and obtaining the structure of a table, the data type of a column, the type of an index and the establishment time related to the historical slow query; constructing a training data set; writing prompt words according to reasons generated by historical slow query, inputting the training data set into the large model agent, and training the large model agent to obtain a trained large model agent; and acquiring the latest slow query and inputting the trained large model agent to obtain the reason of the latest slow query. According to the method, the reason of slow query can be quickly positioned, and the manual analysis time is greatly shortened.

The invention discloses a materialized view processing method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: counting a historical slow query log of a service system in a preset time period, and determining a query statement which does not hit a cache according to the historical slow query log; counting query information of each query statement in the preset time period to screen out query statements of which the query information meets a preset requirement to obtain a query statement set; and determining a public query statement of the query statement set, obtaining offline data corresponding to the public query statement from the data lake, and performing materialization processing on the offline data to obtain a materialized view. According to the embodiment, the materialized view can be automatically created, and the problem that the traditional materialized view needs to be created strongly depending on manual analysis is solved, so that access to a data lake is remarkably reduced, and the query performance and user experience of a service system are greatly improved.

The present application provides a method and system for user behavior analysis based on big data. By obtaining the query logs of target users on an Internet search service platform; extracting topic tags and long-tail query data; determining multiple behavior interest degrees based on the long-tail query data, and determining multiple query interest sub-tags based on the query interest index and all the behavior interest degrees; performing query pointing association between the query interest sub-tags and the topic tags to obtain multiple pointing association vectors, and determining behavior trend characteristics based on all the pointing association vectors; obtaining the query phrase input by the target user, performing query matching between the query phrase and the behavior trend characteristics to obtain query behavior characteristics; and uploading the query behavior characteristics to the user behavior analysis center. The above solution can accurately capture the query phrases of users based on the query behavior characteristics, and make associated classification of the search interest directions of the query phrases, so as to realize the accurate analysis of the user search behavior pattern.

The invention discloses a database optimization method and system based on artificial intelligence, and belongs to the technical field of database management, the implementation of the method comprises: data collection: collecting operation data of a database, including query logs, performance indexes and system resource use conditions; intelligent analysis: analyzing the collected data by using a machine learning algorithm, and identifying performance bottlenecks and optimization points; optimization strategy generation: automatically generating an optimization strategy according to an intelligent analysis result; optimization execution: applying the optimization strategy to a database system, and monitoring an optimization effect; and feedback learning: carrying out self-learning and adjustment according to an optimization execution result so as to improve the accuracy of a future optimization strategy. The method can improve database performance, realize automatic optimization, adapt to dynamic change, enhance user experience, lower technical threshold, improve resource utilization rate and realize sustainable development.

The invention provides a log analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: generating a monitoring address rule, a log comparison rule, a data extraction rule and a log output rule according to an analysis service demand and system attributes of each subsystem; monitoring the operation condition of each subsystem in real time, and determining a query log from each subsystem according to a monitoring address rule; comparing the query logs according to a log comparison rule; when the query log is matched with the log comparison rule, extracting target data from the query log according to a data extraction rule; and outputting the target data as a display log according to a log output rule. According to the method, the computer integrated manufacturing system serves as an object, the logs generated by the subsystems in the computer integrated manufacturing system are stored and processed in a centralized mode, formats are unified, analysis and management of the logs are facilitated, and the working efficiency is improved.

The invention discloses a self-adaptive table structure generation and index optimization method for JSON data driven by a real-time mode, and relates to the field of computer information management application. According to the self-adaptive table structure generation and index optimization method for the JSON data driven by the real-time mode, efficient and automatic persistence from the JSON data to a relational database is achieved through a four-layer technical architecture including dynamic analysis, intelligent mapping, self-healing expansion and index optimization. Comprising a dynamic mode analysis engine which analyzes a JSON nested structure in real time and generates a mode feature tree with weight; the self-adaptive storage engine is used for dynamically generating or expanding a table structure according to the mode feature tree and executing data type mapping; the index optimizer is used for automatically creating / cleaning indexes based on query log analysis; and the uniform interface layer is compatible with multi-database dialects and shields bottom layer differences. Dynamic table structure generation, intelligent type inference and storage optimization, self-healing Schema extension, query-driven index self-optimization and cross-database compatibility are realized.

The invention discloses a risk control decision debugging method, device and equipment and a storage medium, and relates to the technical field of risk control decisions, and the method comprises the following steps: storing structured data of a risk control decision log into a distributed search engine; querying the target log data in the distributed search engine to obtain a log data query result; constructing a decision execution graph according to the log data query result, and constructing a graphical user interface according to the decision execution graph; breakpoint debugging is carried out based on a debugging instruction sent by a user on the graphical user interface, and parameter changes before and after the debugging instruction is executed are displayed. According to the method, the structured data of the risk control decision log is stored in the distributed search engine, and the decision execution graph is constructed based on the queried log data, so that comprehensive change verification can be realized, the representativeness and coverage of test data are enhanced, interactive debugging is realized based on a graphical user interface, and the debugging accuracy and efficiency are improved.

The invention relates to a log query method and device, computer equipment, a storage medium and a computer program product. The method comprises the steps of obtaining a log query text; determining a plurality of matching feature vectors corresponding to the log query text based on the keyword feature vectors corresponding to the log query text and a vector database; determining a target feature vector corresponding to the log query text based on the similarity between the semantic feature vector corresponding to the log query text and the matching feature vector; generating a target query statement corresponding to the log query text based on the target feature vector; and inputting the log query statement into a log query system to obtain a target query log corresponding to the log query text. By adopting the method, the log query efficiency can be improved.