CN112084248A - Intelligent data retrieval, lookup and model acquisition method based on graph database - Google Patents
Intelligent data retrieval, lookup and model acquisition method based on graph database Download PDFInfo
- Publication number
- CN112084248A CN112084248A CN202010954341.8A CN202010954341A CN112084248A CN 112084248 A CN112084248 A CN 112084248A CN 202010954341 A CN202010954341 A CN 202010954341A CN 112084248 A CN112084248 A CN 112084248A
- Authority
- CN
- China
- Prior art keywords
- data
- tables
- graph
- model
- retrieval
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses an intelligent data retrieval, lookup and model acquisition method based on a graph database, and particularly relates to the technical field of graph data, and the method comprises the following steps: s1: data modeling, namely establishing a metadata model in a graph database according to a normalized database design document, storing tables in a node form and storing association relations among the tables in a relation form; s2: data extraction, namely converting the data into a text format file by adopting a corresponding technology according to specific relational data, and finishing the data extraction by using a driver of a relational database in a certain programming language; s3: data loading, including node loading and relation loading; s4: front-end data applications. The invention can efficiently, timely and accurately complete data search, extraction or statistical analysis, is convenient for personnel to quickly respond to the operation requirement of company data, can autonomously acquire global data and acquire exclusive data models without knowing data models of data warehouses, and can flexibly and efficiently develop various data application works.
Description
Technical Field
The invention relates to the technical field of graph data, in particular to an intelligent data retrieval, consultation and model acquisition method based on a graph database.
Background
At present, the economic development of China steps into a new normal state, and the innovation becomes a new engine for driving the economic and social development. With the deep fusion of information technology and economic society, the support effect of data on innovation of various industries and various fields of society is increasingly prominent, under the large background of building digital China and digital transformation, along with the rapid increase of the data volume of various industries, the large data gradually shifts from the concept import stage to a new stage of deep practical application, and in enterprises, the arrival of a large data era leads more and more enterprises to see the value of data assets. Data is regarded as important assets of an enterprise and is a common recognition in the industry, the enterprise also rapidly explores application scenes and business modes, starts to build a big data platform, develops data governance and data standard definition work on the big data platform, and establishes a traditional data application system facing business personnel, such as business intelligence and the like. Although self-service query can be combined at will, a business data analyst and a data warehouse development engineer need to communicate repeatedly for multiple rounds, the development engineer fully understands the business meaning and scenes, and the business data analyst can effectively promote related work after being familiar with the data model design theory and the implementation process. From the global perspective, how to efficiently and conveniently acquire data has become an urgent need.
In the prior art, a BI report system is a main data acquisition way, but all data cannot be concentrated in one report, and a dedicated report cannot be developed in advance according to the requirements of all users, and when the data items in the existing report are not enough to support the current analysis, only a data warehouse development engineer can manually write SQL statements to support or wait for the report to be upgraded in a temporary data extraction mode. The data warehouse development engineer needs to communicate with a service data analyst repeatedly to understand service scenes and meanings, manually searches and understands field meanings according to documents such as database design and the like, explores the association relation among tables, has insufficient standardization degree of demand description due to different experience levels, is low in efficiency and accuracy, and hinders efficient development of data operation work due to the fact that the number of response requirements is limited due to the fact that SQL forms are manually compiled. In the process, the knowledge is accumulated and then is stored in a supplementary document form, and the sharing performance is insufficient.
Therefore, an intelligent data retrieval, consultation and model acquisition method based on a graph database is needed in the industry to solve the problems in the industry.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide a method for intelligent data retrieval, review, and model acquisition based on a graph database, so as to solve the problems mentioned in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: the intelligent data retrieval, consultation and model acquisition method based on the graph database comprises the following steps:
s1: data modeling, namely establishing a metadata model in a graph database according to a normalized database design document, storing tables in a node form and storing association relations among the tables in a relation form;
s2: data extraction, namely converting the data into a text format file by adopting a corresponding technology according to specific relational data, and finishing the data extraction by using a driver of a relational database in a certain programming language;
s3: data loading, including node loading and relation loading;
s4: the method comprises the steps that a front-end data application is adopted, the interface layout of the front-end application comprises three areas, namely a retrieval area, a map area and a record display area, the retrieval area adopts an intelligent engine to assist in retrieving relevant tables through input fields and metadata information of a map database, the matched relevant tables are sorted according to user characteristics, and convenience of a user for searching the retrieval tables is improved; the map display area can display related data, the upper toolbar displays tables contained in the map, operations such as data searching and screening in the tables and display data in the locked graph can be performed, and nodes in the graph can perform operations such as drilling, data aggregation and more display; the record display area is used for displaying the table name, the field name and the specific field corresponding to the clicked node, a check box is arranged in front of the field, and the field can be used as the key focus content.
On the basis of the technical scheme, the importing modes which can be used for node loading and relationship loading include a Cypher CREATE statement, a Cypher LOAD CSV, a Java API provided by an official, an open-source Batch Import tool and a neo4j-Import provided by the official.
On the basis of the technical scheme, the relation between tables is searched, firstly, the incidence relation between every two tables is searched according to the shortest path graph algorithm, secondly, the path is cut, and the cutting method is that all paths in the graph are traversed, if the tables in the path repeatedly appear and the current relation also appears, the traversal of the repeated relation and the tables is stopped, the path is removed, and the non-repeated paths of the tables and the relations are reserved.
On the basis of the technical scheme, through data exploration, the map display area can display a plurality of associated main and sub-table data, the sub-table data are summarized through an aggregation function, and the summarized association relationship between the sub-table and the parent table is established according to the existing association relationship between the sub-table and the parent table. Similarly, other service type sub-tables under the parent table can also be summarized and establish an association relation with the parent table, so that automatic association of various service summarized data under the parent table is realized.
On the basis of the technical scheme, data derivation is derived in a form of associated tables, and a plurality of groups of tables without association relations are divided into a plurality of tables.
On the basis of the technical scheme, data models are extracted, based on metadata of a graph structure, the data models are extracted according to the most efficient shortest path association relationship by default exhaustive pairwise combination (the data models can be specified to retain practical significance or discard the data models without practical significance), a user is converted into the optimal association relationship data model which is most fit with the user according to practical needs, the intelligent engine can assist the user to select the optimal association relationship at the moment, the association relationship conversion is triggered by clicking a certain node table in the graph, multiple paths passing through the node are displayed, the user can select to delete the paths or select a certain optimal path to replace the shortest path, after the model is formed, the derived model is transmitted to a BI report tool, and data analysis and display are further performed.
The invention has the technical effects and advantages that:
1. compared with the prior art, the invention builds a front-end application system, carries out data retrieval, lookup and model extraction in a flow manner, constructs an intelligent engine under a retrieval module, better identifies the user intention and narrows the data range as small as possible. The graph database is an online data management system, uses nodes, edges and attributes to express and store data, supports the adding, deleting, modifying and searching method of a graph data model, can be searched through query sentences, and can better see the changeable incidence relation among the data, and the graph database can assemble the nodes and the incidence relation into a structure which is mutually associated in a simple abstract way, so that the graph database is different from a structure which needs to additionally add external key attributes or uses the additional processing of map-reduce to search for the incidence, thereby avoiding the Cartesian product problem of multi-table query, greatly improving the performance of the incidence search, the key concept of the graph database is a graph, and can search through a graph query language, in addition to storing the data in a graph form, and storing metadata in a graph form, not only can check the data, but also can extract the data model The extraction or statistical analysis is convenient for business personnel to quickly respond to the company data operation requirement, the global data can be automatically acquired under the condition of not knowing a data warehouse data model, a proprietary data model is acquired, and various data applications and other works can be flexibly and efficiently developed.
Drawings
FIG. 1 is a block diagram of the present invention.
FIG. 2 is a flow chart of the present invention.
Fig. 3 is a schematic diagram of a data aggregation function.
Fig. 4 is a schematic diagram of a path conversion function in a model extraction process.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method for intelligent data retrieval, review and model acquisition based on a graph database as shown in FIGS. 1-2 comprises the following steps:
s1: data modeling, namely establishing a metadata model in a graph database according to a normalized database design document, storing tables in a node form and storing association relations among the tables in a relation form;
s2: data extraction, namely converting the data into a text format file by adopting a corresponding technology according to specific relational data, and finishing the data extraction by using a driver of a relational database in a certain programming language;
s3: data loading, including node loading and relation loading;
s4: the method comprises the steps that a front-end data application is adopted, the interface layout of the front-end application comprises three areas, namely a retrieval area, a map area and a record display area, the retrieval area adopts an intelligent engine to assist in retrieving relevant tables through input fields and metadata information of a map database, the matched relevant tables are sorted according to user characteristics, and convenience of a user for searching the retrieval tables is improved; the map display area can display related data, the upper toolbar displays tables contained in the map, operations such as data searching and screening in the tables and display data in the locked graph can be performed, and nodes in the graph can perform operations such as drilling, data aggregation and more display; the record display area is used for displaying the table name, the field name and the specific field corresponding to the clicked node, a check box is arranged in front of the field, and the field can be used as the key focus content.
Further, the node LOAD and relationship LOAD may use the Import methods of Cypher CREATE statement, Cypher LOAD CSV, Java API provided by the official, open source Batch Import tool and neo4j-Import provided by the official.
Further, the relation search among tables is to firstly search the incidence relation between every two tables according to the shortest path graph algorithm, secondly to cut the path, the cutting method is to traverse all paths in the graph, if the tables in the path appear repeatedly and the current relation also appears, then to stop traversing the repeated relation and the table, and to remove the path, to keep the table and the non-repeated relation path.
Further, after data exploration, the map display area can display a plurality of associated main and sub-table data, as shown in fig. 3, the sub-table data is summarized through an aggregation function, and the summarized association relationship between the sub-table and the parent table is established according to the existing association relationship between the sub-table and the parent table. Similarly, other service type sub-tables under the parent table can also be summarized and establish an association relation with the parent table, so that automatic association of various service summarized data under the parent table is realized.
Further, data derivation is derived in a form of associated tables, and multiple groups of tables without association relations are divided into a plurality of tables.
Further, data model extraction is carried out, based on metadata of a graph structure, a data model is extracted according to the most efficient shortest path association relationship by default exhaustive pairwise combination (the data model can be specified to retain practical significance or discard the data model without practical significance), as shown in fig. 4, a user is converted into an optimal association relationship data model which is most fit with the user according to practical needs, an intelligent engine can assist the user to select the optimal association relationship at the moment, the association relationship conversion is triggered by clicking a certain node table in the graph, multiple paths passing through the node are displayed, the user can select to delete the paths or select a certain optimal path to replace the shortest path, after the model is formed, the model is exported and transmitted to a BI report tool, and data analysis and display are further carried out.
The working principle of the invention is as follows: data modeling is to change some irregular business fields into structured and controllable spaces, a natural expression mode does not exist for the actual existence mode of a transaction, but purposefully selection, abstraction and simplification can be carried out, a graph database modeling method well meets the aim because a logic model and a physical model are more compact and are basically isomorphic with business logic, a conventional data warehouse model firstly carries out preliminary design to obtain an E _ R diagram, secondly carries out normalized mapping of the E _ R diagram into a table and a relation, thirdly carries out denormalization design of artificially manufacturing repeated data so as to obtain query performance and reflect data relation in data, and generally requires expert assistance to a relational database in order to obtain the best effect on the relational database, converts a standard model into a denormalization model so as to meet the characteristics of a bottom layer RDBMS and physical storage, finally, a plurality of wide tables are established, data processing is carried out on the wide tables again to restore the real needs of business personnel, the data model is designed in a standardized manner by depending on a database without carrying out denormalization design, a metadata model is established in the database according to a standardized database design document, the tables are stored in a node manner, and the incidence relation among the tables is stored in a relation manner; according to the specific relational data, a corresponding technology is adopted, the data are converted into a text format file, and a certain programming language driver of the relational database can be used for completing data extraction; the invention discloses a technical scheme for introducing data storage by using a neo4j, and comprises two steps of node loading and relationship loading in an implementation process, wherein the importing modes which can be used in each step comprise a Cypher CREATE statement, a Cypher LOAD CSV, a Java API provided by an official, an open-source Batch Import tool and a neo4j-Import provided by the official, and the scheme is as follows: 1. node storage, initial import: neo4j-import, bulk import: load csv, 2. associative storage, initial import: neo4j-import, bulk import: load csv, execute Cypher statement; the interface layout of the front-end application comprises three areas, namely a retrieval area, an atlas area and a record display area, wherein the retrieval area adopts an intelligent engine to assist in retrieving related tables by combining input fields with metadata information of a database, the matched related tables are sorted according to user characteristics, the convenience of searching the retrieval tables by a user is improved, the atlas display area can display related data, an upper toolbar displays the tables contained in the atlas, the operations of searching and screening the data in the tables and locking the display data in the diagram can be carried out, the nodes in the diagram can carry out drilling, data aggregation, more display and other operations, the record display area is used for displaying the table names, the field names and the specific fields corresponding to click nodes, check boxes are arranged in front of the fields, the fields can be used as key attention contents, the relations between the tables are searched, firstly, the association relations between every two tables are searched according to a shortest path diagram algorithm, secondly, cutting the path, wherein the method comprises traversing all paths in the graph, stopping traversing the repeated relationship and the table if the table in the path appears repeatedly and the current relationship also appears, removing the path, reserving the table and the path with non-repeated relationship, displaying a plurality of related main and sub table data in the graph display area through data exploration, realizing sub table data summarization through an aggregation function, establishing the summarized sub table and parent table association relationship according to the existing association relationship of the sub table and the parent table, and similarly, other service type sub tables under the parent table can also be summarized and established with the association relationship with the parent table, realizing automatic association of various service summarized data under the parent table, data export, namely fields in the table in the graph are exported in an associated table form, a plurality of groups of tables without association relationship are divided into a plurality of tables, and data model extraction, based on metadata of a graph structure, a data model is extracted according to the most efficient shortest path association relationship by default exhaustion pairwise combination (the data model can be specified to retain practical significance or discard the data model without practical significance), a user is converted into an optimal association relationship data model which is most fit with the user according to practical needs, an intelligent engine can assist the user to select the optimal association relationship at the moment, the association relationship conversion is triggered by clicking a certain node table in the graph, a plurality of paths passing through the node are displayed, the user can select to delete the paths or select a certain optimal path to replace the shortest path, after the model is formed, the model is exported and transmitted to a BI report tool, and data analysis and display are further performed.
The points to be finally explained are: first, in the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" should be understood broadly, and may be a mechanical connection or an electrical connection, or a communication between two elements, and may be a direct connection, and "upper," "lower," "left," and "right" are only used to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed;
secondly, the method comprises the following steps: in the drawings of the disclosed embodiments of the invention, only the structures related to the disclosed embodiments are referred to, other structures can refer to common designs, and the same embodiment and different embodiments of the invention can be combined with each other without conflict;
and finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (6)
1. The intelligent data retrieval, lookup and model acquisition method based on the graph database is characterized in that: the method comprises the following steps:
s1: data modeling, namely establishing a metadata model in a graph database according to a normalized database design document, storing tables in a node form and storing association relations among the tables in a relation form;
s2: data extraction, namely converting the data into a text format file by adopting a corresponding technology according to specific relational data, and finishing the data extraction by using a driver of a relational database in a certain programming language;
s3: data loading, including node loading and relation loading;
s4: the method comprises the steps that a front-end data application is adopted, the interface layout of the front-end application comprises three areas, namely a retrieval area, a map area and a record display area, the retrieval area adopts an intelligent engine to assist in retrieving relevant tables through input fields and metadata information of a map database, the matched relevant tables are sorted according to user characteristics, and convenience of a user for searching the retrieval tables is improved; the map display area can display related data, the upper toolbar displays tables contained in the map, operations such as data searching and screening in the tables and display data in the locked graph can be performed, and nodes in the graph can perform operations such as drilling, data aggregation and more display; the record display area is used for displaying the table name, the field name and the specific field corresponding to the clicked node, a check box is arranged in front of the field, and the field can be used as the key focus content.
2. The intelligent graph-based data retrieval, review, and model acquisition method as claimed in claim 1, wherein: the node loading and the relationship loading can use the importing modes of Cypher CREATE statement, Cypher LOAD CSV, Java API provided by the official, open source Batch Import tool and neo4j-Import provided by the official.
3. The intelligent graph-based data retrieval, review, and model acquisition method as claimed in claim 1, wherein: and searching the relationship between tables, namely searching the association relationship between every two tables according to a shortest path graph algorithm, and cutting the paths by traversing all paths in the graph, if the tables in the paths repeatedly appear and the current relationship also appears, stopping traversing the repeated relationship and the tables, removing the paths, and keeping the tables and the paths with non-repeated relationship.
4. The intelligent graph-based data retrieval, review, and model acquisition method as claimed in claim 1, wherein: through data exploration, the map display area can display a plurality of associated main and sub-table data, sub-table data summarization is achieved through an aggregation function, and the summarized association relationship between the sub-table and the parent table is established according to the existing association relationship between the sub-table and the parent table. Similarly, other service type sub-tables under the parent table can also be summarized and establish an association relation with the parent table, so that automatic association of various service summarized data under the parent table is realized.
5. The intelligent graph-based data retrieval, review, and model acquisition method as claimed in claim 1, wherein: the data derivation is derived in a form of associated tables, and a plurality of groups of tables without association relations are divided into a plurality of tables.
6. The intelligent graph-based data retrieval, review, and model acquisition method as claimed in claim 1, wherein: extracting a data model, extracting the data model according to the most efficient shortest path association relation by default exhaustive pairwise combination (the data model can be specified to retain practical significance or discard the data model without practical significance) based on metadata of a map structure, converting the data model into an optimal association relation data model which is most attached to the intelligent engine according to practical needs by the user, assisting the user to select the optimal association relation at the moment by the intelligent engine, triggering the association relation conversion by clicking a certain node table in the map, displaying a plurality of paths passing through the node, selecting a deleted path or selecting a certain optimal path to replace the shortest path by the user, exporting the model to be transmitted to a BI report tool after the model is formed, and further performing data analysis and display.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010954341.8A CN112084248A (en) | 2020-09-11 | 2020-09-11 | Intelligent data retrieval, lookup and model acquisition method based on graph database |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010954341.8A CN112084248A (en) | 2020-09-11 | 2020-09-11 | Intelligent data retrieval, lookup and model acquisition method based on graph database |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112084248A true CN112084248A (en) | 2020-12-15 |
Family
ID=73737479
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010954341.8A Pending CN112084248A (en) | 2020-09-11 | 2020-09-11 | Intelligent data retrieval, lookup and model acquisition method based on graph database |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112084248A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112860254A (en) * | 2021-02-07 | 2021-05-28 | 武汉蓝星科技股份有限公司 | Method for presenting list control by using data model |
| CN113656663A (en) * | 2021-09-01 | 2021-11-16 | 广州游星弈科技有限公司 | Data linkage analysis system and method applied to digital enterprise |
| CN113672615A (en) * | 2021-07-22 | 2021-11-19 | 杭州未名信科科技有限公司 | Data analysis method and system for automatically generating SQL (structured query language) based on tree-type table relation |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106294588A (en) * | 2016-07-28 | 2017-01-04 | 广东中标数据科技股份有限公司 | The method and device of fast search content to be inquired about |
| CN107122443A (en) * | 2017-04-24 | 2017-09-01 | 中国科学院软件研究所 | A kind of distributed full-text search system and method based on Spark SQL |
| CN107402927A (en) * | 2016-05-19 | 2017-11-28 | 上海斯睿德信息技术有限公司 | A kind of enterprise's incidence relation topology method for building up and querying method based on graph model |
| CN107515887A (en) * | 2017-06-29 | 2017-12-26 | 中国科学院计算机网络信息中心 | A kind of interactive query method suitable for a variety of big data management systems |
| CN111221785A (en) * | 2018-11-27 | 2020-06-02 | 中云开源数据技术（上海）有限公司 | Semantic data lake construction method of multi-source heterogeneous data |
-
2020
- 2020-09-11 CN CN202010954341.8A patent/CN112084248A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107402927A (en) * | 2016-05-19 | 2017-11-28 | 上海斯睿德信息技术有限公司 | A kind of enterprise's incidence relation topology method for building up and querying method based on graph model |
| CN106294588A (en) * | 2016-07-28 | 2017-01-04 | 广东中标数据科技股份有限公司 | The method and device of fast search content to be inquired about |
| CN107122443A (en) * | 2017-04-24 | 2017-09-01 | 中国科学院软件研究所 | A kind of distributed full-text search system and method based on Spark SQL |
| CN107515887A (en) * | 2017-06-29 | 2017-12-26 | 中国科学院计算机网络信息中心 | A kind of interactive query method suitable for a variety of big data management systems |
| CN111221785A (en) * | 2018-11-27 | 2020-06-02 | 中云开源数据技术（上海）有限公司 | Semantic data lake construction method of multi-source heterogeneous data |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112860254A (en) * | 2021-02-07 | 2021-05-28 | 武汉蓝星科技股份有限公司 | Method for presenting list control by using data model |
| CN113672615A (en) * | 2021-07-22 | 2021-11-19 | 杭州未名信科科技有限公司 | Data analysis method and system for automatically generating SQL (structured query language) based on tree-type table relation |
| CN113672615B (en) * | 2021-07-22 | 2023-06-20 | 杭州未名信科科技有限公司 | Data analysis method and system for automatically generating SQL based on relationships among tree tables |
| CN113656663A (en) * | 2021-09-01 | 2021-11-16 | 广州游星弈科技有限公司 | Data linkage analysis system and method applied to digital enterprise |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107491561B (en) | Ontology-based urban traffic heterogeneous data integration system and method | |
| US8326857B2 (en) | Systems and methods for providing value hierarchies, ragged hierarchies and skip-level hierarchies in a business intelligence server | |
| US8108399B2 (en) | Filtering of multi attribute data via on-demand indexing | |
| CN103455540B (en) | The system and method for generating memory model from data warehouse model | |
| CN112084248A (en) | Intelligent data retrieval, lookup and model acquisition method based on graph database | |
| US7530012B2 (en) | Incorporation of spreadsheet formulas of multi-dimensional cube data into a multi-dimensional cube | |
| US7606817B2 (en) | Primenet data management system | |
| Morzy et al. | On querying versions of multiversion data warehouse | |
| US10268645B2 (en) | In-database provisioning of data | |
| US20020010700A1 (en) | System and method for sharing data between relational and hierarchical databases | |
| CN108446368A (en) | A kind of construction method and equipment of Packaging Industry big data knowledge mapping | |
| CN104809190B (en) | A kind of database access method of tree structure data | |
| US7844633B2 (en) | System and method for storage, management and automatic indexing of structured documents | |
| US20080222129A1 (en) | Inheritance of attribute values in relational database queries | |
| US20110153582A1 (en) | Handling of classification data by a search engine | |
| CN102819600B (en) | Keyword search methodology towards relational database of power production management system | |
| US20040181518A1 (en) | System and method for an OLAP engine having dynamic disaggregation | |
| CN112000851B (en) | Key value model, document model and graph model data unified storage method | |
| EP1510935A1 (en) | Mapping a data from a data warehouse to a data mart | |
| US20080313153A1 (en) | Apparatus and method for abstracting data processing logic in a report | |
| US20160364426A1 (en) | Maintenance of tags assigned to artifacts | |
| US9684704B2 (en) | Data extraction and sparseness discovery from multidimensional databases | |
| US20100121837A1 (en) | Apparatus and Method for Utilizing Context to Resolve Ambiguous Queries | |
| CN111709239A (en) | Geoscience data discovery method based on expert logic structure tree | |
| Silva et al. | Logical big data integration and near real-time data analytics |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |