23 results about "Very large database" patented technology

Techniques are described for efficient transfer of large data type data (large data) among database servers for executing a distributed query. In an implementation, a database server receives object reference data, including an indication of whether the object reference data is a quasi-locator. The receipt of the quasi-locator indicates that the large data is to be streamed from the large database repository without any further request from the receiving database server. Accordingly, the receiving database server receives and stores the requested large data without generating and sending any further requests to the large data repository database server.

The invention discloses a database query optimization method and system based on a general relation view. According to the method and the system, a query instruction input by a user in a similar natural language is received, and a database table associated with target attributes and conditions is automatically analyzed; a weighted graph model is constructed based on table relation topology, an improved path optimization algorithm is adopted to dynamically generate a table connection sequence, and a path selection mechanism preferentially minimizes the calculation complexity of connection operation; for a cross-multi-table query scene, generating an optimal connection path through a global model; finally, an optimization result is converted into a standard database query statement to be executed. Compared with the prior art, the method and the system have the advantages that the user operation threshold is obviously reduced, non-technical personnel can directly realize complex query through attribute names, the multi-table connection query efficiency is effectively improved, and the performance is close to that of native query in a large database environment; and a mainstream database system can be compatible through a middleware framework, so that the method has wide applicability.

Latency, response times, and efficiency improvements for data querying are provided herein, particularly in the context of querying large database systems and data tables from disparate data sources. There are provided systems and methods for automatic query and data retrieval optimization through procedural generation of data tables from query patterns. A service provider may utilize different computing services for query processing and data retrieval for different applications and services used by internal and / or external users. Instead of querying large database systems and numerous data tables, pre-aggregated data tables may instead be used and searched by procedurally generating such tables based on precomputation rules and query patterns. Once patterns have been identified in queries, corresponding data may be aggregated from data sources in a pre-aggregated data table. Query optimization rules may then be used to have these data tables queried in place of their original sources.

Techniques are provided for optimizing storage of database records in segments using sub-segments. A base segment is a container used for storing records that belong to a database object. A database management system receives a request to load, into the database object, a first set of records that are in a first state. In response to receiving the request, the system generates a new sub-segment, which is a container that is separate from the base segment. The system stores the first set of records, in their first state, within the sub-segment. The system then monitors one or more characteristics of the database system. In response to the one or more characteristics satisfying criteria, the system performs a migration of one or more records of the first set of records from the sub-segment to the base segment. During the migration, the system converts the one or more records from the first state to a second state and stores the one or more records, in their second state, in the base segment.

Latency, response times, and efficiency improvements for data querying are provided herein, particularly in the context of querying large database systems and data tables from disparate data sources. There are provided systems and methods for automatic query and data retrieval optimization through procedural generation of data tables from query patterns. A service provider may utilize different computing services for query processing and data retrieval for different applications and services used by internal and / or external users. Instead of querying large database systems and numerous data tables, pre-aggregated data tables may instead be used and searched by procedurally generating such tables based on precomputation rules and query patterns. Once patterns have been identified in queries, corresponding data may be aggregated from data sources in a pre-aggregated data table. Query optimization rules may then be used to have these data tables queried in place of their original sources.

Methods and apparatus for sparse column-aware encodings for numeric data types, including integer data and floating-point data (float, double, etc.). The encoding schemes are tailored to take advantage of column addressable memories such as stochastic associative memories (SAM) to enable Stochastic Associative Search (SAS), which is a highly efficient and fast way of searching through a very large database of records (order of Billions) and finding similar records to a given query record (search key). Techniques are also disclosed for performing range searches for both integer and floating-point data types. The integer or float data is converted to Hexadecimal form and encoded using an m-of-n constant weight encoding. Only the columns with set bits in search keys need to be read, which significantly reduces the number of reads required for searches.

A database system comprised of a decoupled compute layer and storage layer is implemented to store, build, and maintain a canonical dataset, a temporary buffer, and projection datasets. The canonical dataset is a set of batch updated data. The data is appended in chunks to the canonical dataset such that the canonical dataset becomes a historical dataset over time. The buffer is a write ahead log that contains the most recent chunks of data and provides atomicity and durability for the database system. The projection datasets are indexes of the canonical dataset and / or the buffer that may have single or multiple column sort-orders and / or particular data formats. The writes to the canonical dataset, projection datasets, and buffer may be asynchronous and therefore the database system is advantageously less resource constrained.

The application belongs to the field of building indoor navigation, and provides a building indoor positioning method and system based on BIM cross-domain image retrieval, which comprises BIM model rendering video acquisition, BIM model rendering image set extraction, rendering image position data set acquisition, BIM model rendering image set and rendering image position data set fusion, indoor image set acquisition, feature extraction, k-d tree construction, to-be-retrieved feature acquisition and target position data matching. The application realizes the migration of image style through a CycleGAN style migration model, solves the problem of low recognition accuracy caused by large style difference in the prior art, and improves the recognition accuracy and reliability of the model; through BIM model rendering video acquisition, the construction of a large database is realized, the positioning accuracy and efficiency of the model are improved, and the demand for computing power is reduced.

The method can involve displaying a multitude of parallel graphs, each extending linearly from a first end to a second end along a time axis spanning incremental time values. Each graph represents a sequence of severity levels for a given parameter, encoded as different colors or shades across these incremental time values. The incremental time values ​​are the same for each of the multiple graphs, so severity levels oriented perpendicular to the time axis correspond to time points. The resulting dashboard can help transform complex analyses into key performance indicators and / or identify areas of interest in large databases.

A computer program product, system, and computer implemented method for automatic, repetitive, and gradual shrink of very large database object structures. The approach disclosed herein generally comprises an iterative best efforts approach to automatically shrink a large database object structure(s). The process may comprise identifying a large object (LOB) structure shrink event and LOB structure or LOB structure segment to be shrunk. And executing a shrink operation on the LOB structure or LOB structure segment without blocking access to other extents in the LOB structure or LOB segment structure. This process allows an extent to be shrunk and deallocated. As a result, the process can operate to continually clean a LOB structure or LOB structure segment to decrease fragmentation within the extents of the LOB structure or LOB segment structure.

The invention discloses a semantic extraction method and device, equipment and a medium, relates to the technical field of artificial intelligence, and is used for shortening the semantic extraction duration of a dialogue type business analysis system on a large database. The method comprises the following steps: receiving a first request; in response to the first request, obtaining structure information of the to-be-analyzed table from a database; inputting the structural feature vector corresponding to the structural information into a first prediction model, and predicting to obtain first prediction response time; if the first prediction response duration is greater than the response time threshold, segmenting the to-be-analyzed table according to a segmentation strategy to obtain at least two pieces of segmented data meeting a preset condition; the preset condition includes that the second prediction response duration of each piece of segmented data is smaller than or equal to a response duration threshold value; and calling the large language model to extract semantic information from the at least two pieces of segmented data, and determining a result for responding to the first request according to the extracted semantic information, so that the semantic extraction time of the dialogue type business analysis system on the large database can be shortened.

Methods and apparatus for sparse column-aware encodings for numeric data types, including integer data and floating-point data (float, double, etc.). The encoding schemes are tailored to take advantage of column addressable memories such as stochastic associative memories (SAM) to enable Stochastic Associative Search (SAS), which is a highly efficient and fast way of searching through a very large database of records (order of Billions) and finding similar records to a given query record (search key). Techniques are also disclosed for performing range searches for both integer and floating-point data types. The integer or float data is converted to Hexadecimal form and encoded using an m-of-n constant weight encoding. Only the columns with set bits in search keys need to be read, which significantly reduces the number of reads required for searches.

An object of a system according to an embodiment is to propose an optimal cocktail based on a customer's desire.SOLUTION: A system includes a reception part, a generation part, an output part, a feedback collection part, and a fine tuning part. The reception unit inputs a desire of a customer. The generation unit analyzes the information input by the reception unit and generates a recipe of an appropriate cocktail from a large-scale database. The output unit outputs the recipe of the cocktail generated by the generation unit in a moving image format. The output unit outputs the appearance of the cocktail generated by the generation unit in an image format. The output unit outputs the name of the cocktail generated by the generation unit in a text format. The feedback collection unit collects feedback on the recipe of the cocktail output by the output unit. The fine-tuning unit fine-tunes the model of the generative AI based on the feedback collected by the feedback collection unit.SELECTED DRAWING: Figure 1

The method can include displaying a plurality of graphs disposed parallel to one another, each graph extending linearly from a first end to a second end along a temporal axis spanning incremental values of time, and representing a sequence of severity levels of a corresponding one of the parameters encoded as different colors or tones over the incremental values of time, the incremental values of time being common to each one of the plurality of graphs such that severity levels aligned transversally to the temporal axes correspond to simultaneous moments in time. The dashboard so made available can help convert complex analyses into key performance indicators, and / or to identify regions of interest in large databases.

This disclosure relates to a method and system for data scrubbing in very-large-databases (VLDB) within regulated industries. The method involves segmenting a copy of a production database into manageable chunks based on row identifiers, allowing for parallel processing without database contention. Each chunk is processed by concurrently executing instances of a data scrubbing component, which replaces sensitive data with anonymous data of the same type using specified scrubbing algorithms. The system ensures that scrubbed data maintains the same structure and statistical properties as the original data while preventing the restoration of sensitive information. The process is meticulously documented to meet regulatory standards and facilitate audits, making it a robust solution for data scrubbing in environments with stringent compliance requirements.

A method and system utilize unexpected combinations of cognitive exercises to provide routines performed simultaneously with aerobic exercise to address cognitive impairments. Cognitive exercises comprise specific steps in order to interact with a specific cognitive domain. All stimuli may be aural. Specific combinations of cognitive exercises are created to engage selected brain circuits and are correlated to specific cognitive impairments. The method may remediate selected cognitive impairments and strengthen cognitive abilities. The user responds to instructions for each exercise and performs each exercise in a specific order for a specific duration of time and through a specific number of sets. A processor performs a program comprising the routines and evaluates performance of a user. A program may be customized for an individual user. An individual user's data is processed to evaluate progress. Large libraries of data for multiple users may be processed to provide statistical databases.

The present invention relates to the technical field of databases, and relates to a large database algorithm based on two-factor encryption and decryption, comprising: performing content encryption and file structure encryption processing on a personal information portion in a medical database file, so as to implement secure storage of a database file written into a disk; and when the database file is read, using parallel processing and buffer processing techniques, so as to implement storage of personal information and public information in different buffer areas, and performing corresponding decryption processing on the personal information to obtain original user record data. The present invention is compatible with traditional SQL statement query and segmented full-text search processing, and rapid data conversion from a memory to a disk and from the disk to the memory is achieved. The present invention solves the problem of data security management and control of a disk and memory buffer areas, improves the security of a database, and reduces the risk of data leakage.

This disclosure relates to a method and system for data scrubbing in very-large-databases (VLDB) within regulated industries. The method involves segmenting a copy of a production database into manageable chunks based on row identifiers, allowing for parallel processing without database contention. Each chunk is processed by concurrently executing instances of a data scrubbing component, which replaces sensitive data with anonymous data of the same type using specified scrubbing algorithms. The system ensures that scrubbed data maintains the same structure and statistical properties as the original data while preventing the restoration of sensitive information. The process is meticulously documented to meet regulatory standards and facilitate audits, making it a robust solution for data scrubbing in environments with stringent compliance requirements.

This disclosure relates to a method and system for data scrubbing in very-large-databases (VLDB) within regulated industries. The method involves segmenting a copy of a production database into manageable chunks based on row identifiers, allowing for parallel processing without database contention. Each chunk is processed by concurrently executing instances of a data scrubbing component, which replaces sensitive data with anonymous data of the same type using specified scrubbing algorithms. The system ensures that scrubbed data maintains the same structure and statistical properties as the original data while preventing the restoration of sensitive information. The process is meticulously documented to meet regulatory standards and facilitate audits, making it a robust solution for data scrubbing in environments with stringent compliance requirements.

Embodiments of the present application disclose a database data row number counting method and device, equipment and a storage medium, comprising: when counting a row number of target data in a target database of an electronic device, acquiring an index range corresponding to the target data; wherein the target data is data meeting a counting condition in all to-be-counted data contained in the target database; generating a plurality of counting tasks according to the index range; wherein each counting task is used for counting the row number of the target data in a partial index range; distributing the plurality of counting tasks to at least one thread created in advance for processing to obtain a plurality of counting results; and adding the plurality of counting results to obtain a target counting result of the row number of the target data. Through implementation of the method, the counting speed of the row number of data meeting the counting condition can be improved for a large database with a data volume of ten million or more when the index cannot be compressed.

The application provides a database natural language query method and device, an electronic device and a storage medium, wherein the method comprises the following steps: obtaining a natural language question sentence to be queried, determining a question vector corresponding to the question sentence, then calculating the similarity between the question vector and table vectors corresponding to each data table in the database, then determining a target data table according to the similarity, and finally determining a query result corresponding to the question sentence from the target data table by using a natural language escape model. The data tables in the database are screened, the target data table screened out is input into the natural language escape model, and the query result corresponding to the question sentence is obtained; the calculation efficiency of the database natural language query process can be improved, and the database natural language query interface can support a large database with hundreds or even thousands of data tables.