CN101075304A - Method for constructing decision supporting system of telecommunication industry based on database - Google Patents
Method for constructing decision supporting system of telecommunication industry based on database Download PDFInfo
- Publication number
- CN101075304A CN101075304A CNA2006100127243A CN200610012724A CN101075304A CN 101075304 A CN101075304 A CN 101075304A CN A2006100127243 A CNA2006100127243 A CN A2006100127243A CN 200610012724 A CN200610012724 A CN 200610012724A CN 101075304 A CN101075304 A CN 101075304A
- Authority
- CN
- China
- Prior art keywords
- data
- analysis
- service
- business
- warehouse
- 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
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
A method for structuring decision-supporting system based on databank in telecommunication industry includes setting up databank model in according with character of telecommunication service, carrying out unified analysis on data of source service unit, carrying out extraction and conversion-loading on data of source unit, carrying out collection on data in databank at different levels, presenting analysis result through various access modes and setting various flowing-controls.
Description
Technical field
The present invention relates to the building method of a kind of telecommunications industry based on the decision support system (DSS) of data warehouse.
Background technology
Current domestic analytic system is to be based upon on each different production links basically, be aided with some instruments commonly used, easy, as database, report tool, even Excel (data form instrument) etc., directly production data is analyzed, understood the operation ruuning situation of enterprise.Its unavoidable problem is that the data source in the enterprise is disperseed, and the analytic system of Jian Liing must isolate on this basis.And lack effective association and analysis-by-synthesis one by one between " information island " at this, can't form the unified view of business data.In the analytic angle and the degree of depth, and association analysis and forecast analysis aspect are relatively weaker.
Propelling along with IT application in enterprise, the numerous and confused business operation system of setting up separately of each large enterprises, with the telecommunications industry is example, and each runs business system that the commercial city has oneself, network management system, charge system, account system, customer service system etc., and each big system develops separately, lack correlativity each other, bring very big difficulty for the decision maker's of enterprise decision-making, the numeral of adding up in each system has nothing in common with each other, and is difficult to win the confidence, in this case, data warehouse technology is arisen at the historic moment.
Data warehouse technology is integrated the data in each big system of enterprise, forms enterprise's " uniform data view ", for business decision provides comprehensively, unifies, accurate data.Data warehouse technology began to occur in the nineties in 20th century.Enter China at the beginning of the 21 century and begin to use, application at home appears at finance, aviation and field of telecommunications at first.Along with the construction in several years begin to take shape.
Summary of the invention
Technical matters to be solved by this invention is to avoid above-mentioned deficiency of the prior art, and the building method of a kind of telecommunications industry based on the decision support system (DSS) of data warehouse proposed, this method is to utilize data warehouse will be dispersed in each " information island " in the enterprise, effectively, complete gathers together, set up unified business data view, and on this basis, the aggregation of data of each production link of enterprise is analyzed together, and utilize data mining, technology such as olap analysis, form various association analysiss and forecast analysis, for the decision-making of enterprise provides more reliable and comprehensively supports.
Technical scheme provided by the present invention is: a kind of telecommunications industry is based on the building method of the decision support system (DSS) of data warehouse, the information resources of utilizing business support system to produce, the information that provides in conjunction with relevant support system, adopt data warehouse technology, data mining technology, the described decision support system (DSS) of multidimensional analysis technical construction, constitution step is as follows:
Step 3, the data in the origin system are extracted conversion load: ETL is data pick-up, conversion and loading, is in the data warehouse implementation procedure, carries out the main process that data are loaded to data warehouse by data source systems;
Step 4, need carry out varigrained gathering by analyzing the data in the data warehouse: can create the Data Mart of corresponding subordinate on the basis of central data warehouse according to analyze demands, the data of subordinate Data Mart directly come from central data warehouse;
Step 5, represent analysis result by various access modes: data access layer provides several data processing exhibiting method, comprise predefine form, extemporaneous inquiry, multidimensional performance analysis and data mining, and provide a unified door to enter the mouth and the interface, realize the seamless link of predefine form, extemporaneous inquiry and multidimensional performance analysis, and integrated authentication, information issue and management environment is provided;
Eight subject areas described in the step 1, wherein the client comprises that service has the focal pointe of reality or potential demand to telecommunications industry for all, the client is because buy the product of telecommunications industry or enjoy its service and become the telecommunications industry user, client's theme comprises all essential information and extend informations about the client, also comprise open an account, information such as cancellation; Service is used and is referred to that telecommunications company to the record that the client ordered, used the process of products ﹠ services, wherein mainly comprises user, standards service service recorder, inventory etc.; Customer service is customer service, has described telecommunications company and partner for all information that the client provides service, comprises traditional customer service and service handling, service department and services channels, and relevant behavior record; Marketing refer to telecommunications company for branch out, the ownership, at activities such as certain market and the specific market propaganda that the customer group carried out, sales promotion, comprise a series of marketing strategy and corresponding tactics; Service is the sensu lato product of telecommunications company, comprises all products and service that telecommunications company sells to the client; Clearing are meant that telecommunications company is with clearance of the expense between the partner services side and scribing relation; Resource is that telecommunications company has, and for the client provides all carriers of service, comprises number resource, terminal resource, Internet resources, and corresponding product vendor etc.; The account theme mainly reflects the relation between client and the account, comprises expense generation, expense payment etc.
Data model described in the step 1 adopts Star Schema, and Star Schema is made of fact table and Wei Biao, and fact table is deposited the detail data that needs analysis, and the dimension table is deposited the attribute of respectively analyzing dimension; Physical model leaves in the relevant database with Star Schema or makes up Cube.
Source operation system described in the step 2 comprises business system, charge system, account system, network management system and customer service system.
Data pick-up described in the step 3, be characteristics at the telecommunications industry source data, require and the operation system of portfolio and the source data of different pieces of information amount for different pieces of information platform, different source data form, different performance, different data pick-up interfaces will be taked, formulate corresponding strategy, comprise extraction mode, extraction opportunity, decimation periods; Data-switching is meant the requirement according to the data warehouse model of the source data that extracts from operation system, carry out data conversion, cleaning, processing such as split, gather, assurance is from the consistance and the integrality of the data of different system, different-format, and the data warehouse of packing on request, according to actual conditions specified data switch technology and strategy; Data load be with from data source systems, extract, data load after the conversion is in data warehouse, cost according to business diagnosis demand and system loads, the loading cycle different to the The data of different business systems, the integrality that can keep simultaneously same time business datum again is according to the strategy that appends of the extraction strategy of data and business rule specified data;
Described extraction mode comprises increment extraction and extracts fully that wherein the data adapting that flowing water type increases and data volume is big adopts the mode of increment extraction, is typically inventory, bill class data the most; Change data updated and be fit to adopt the mode that extracts fully, be typically the resource distribution class data of reflection current state the most; For the data of both combinations, if can extract increment information, then carry out increment extraction, otherwise adopt the mode that extracts fully to carry out, be typically customer data change data or other customer service record data the most.
The described strategy that appends, comprise three types: directly append, all cover and renewal is appended, wherein, directly append when being meant each loading directly data supplementing in the purpose table, for typical flowing water data, the general the method that adopts, data such as inventory, account can adopt the mode of directly appending; All cover: comprised the current of data and all historical situations for extracted data itself, object table is adopted whole coverage modes.Typical case is the mode that the data of tariff rule definition can all cover; Renewal is appended: for the state variation that needs the continuous recording business, the situation of comparing with historical state data with current last state adopts the mode of appending of upgrading.Typical case is the loading of customer service record data.
Predefine form described in the step 5, be a kind of business analyst in the process of using system, according to demand with after relevant analysis result carries out predefine, the relatively-stationary form of format content; The content of extemporaneous inquiry can freely be defined by the operation analysis system user of service, the access method of permission user control data, and the ways of presentation that provides plurality of optional to select to Query Result; The multidimensional performance analysis is based on the analytical approach of multidimensional data model, be used to support complicated analysis and prediction, comprise that trend analysis, What-if are (different with reason and impact analysis, it is to observe when artificially specifying the change condition that what if analyzes, result's situation of change, so that prediction is in order to reach target, what the top condition combination is) analyze etc.; Data mining is according to the set business objective of enterprise and the problem of existence, a large amount of business datums is explored, disclose the rule of hiding wherein, and with its modelling, guidance also is applied in the actual enterprise operation, in operation analysis system, the different data digging method differences that practical problems adopted, data mining method generally is divided into forecasting type and description type, wherein, forecasting type (Predictive) method comprises classification (Classification)/decision Tree algorithms (Decision Tree), regretional analysis, time series analysis (Time Series); Description type (Descriptive) method comprises association analysis (Association Analysis), serial correlation analysis (Sequential Analysis), cluster analysis (Clustering).Data mining and olap analysis, predefine form and extemporaneous inquiry etc. have very big difference.Back three is the operational indicator of user to being concerned about normally, analyzes according to known angle; The former then is in traffic issues and with clearly defined objective, but the angle of investigating explores data when not knowing, discloses the regularity of hiding wherein, and then with its modelling.
Meaning of the present invention is as follows: the present invention has adopted technology such as leading at present data mining, data warehouse, olap analysis, creationary business data is merged, for the management and production of enterprise provide omnibearing decision support.Comparing with homogeneous system has stronger advantage.The present invention utilizes data warehouse will be dispersed in each " information island " in the enterprise, and effective, complete gathers together.Set up unified business data view, and on this basis, the aggregation of data of each production link of enterprise is analyzed together.And utilize technology such as data mining, olap analysis, form various association analysiss and forecast analysis, and finally for market decision-making management persons at different levels provide in time, accurately, the aid decision making foundation of science, for the decision-making of enterprise provides more reliable and comprehensive support.
Description of drawings
Fig. 1 is the Organization Chart of decision support system (DSS) of the present invention;
Fig. 2 is the simple flow chart of ETL implementation procedure;
Fig. 3 is the system server pie graph of decision support system (DSS) of the present invention.
Fig. 4 is extemporaneous inquiry system assumption diagram;
Fig. 5 is the structural drawing of the OLAP of system;
Fig. 6 is the data mining system assumption diagram
Fig. 7 is data mining process figure.
Embodiment
Below in conjunction with description of drawings the specific embodiment of the present invention.
Telecommunications industry is based on the building method of the decision support system (DSS) of data warehouse, the information resources that this method utilizes business support system to produce, the information that provides in conjunction with relevant support system, adopt network technology, data warehouse technology, data mining technology, the described decision support system (DSS) of multidimensional analysis technical construction, constitution step is as follows:
1, shown in (3) among Fig. 1, makes up the data warehouse model that meets the telecommunication service characteristics
The design of data warehouse model is the core that total system makes up, and its logic high level data model will reflect the business relations of telecommunications industry conscientiously, comprises all themes and main entity and relation each other.Data warehouse model comprises logic data model and Physical data model, the logic high level data model reflects the business relations of telecommunications industry conscientiously, comprise all themes and main entity and relation each other, data model adopts Star Schema, and Star Schema is made of fact table and Wei Biao; Fact table is deposited the detail data that needs analysis, and the dimension table is deposited the attribute of respectively analyzing dimension; Physical model is the storage mode of data, and physical model leaves in the relevant database with Star Schema or makes up Cube.
The central data warehouse memory model should design in conjunction with the operation analysis system application demand and in conjunction with existing service system.The granularity of data warehouse is an importance of design data storage.Granularity is meant the refinement of preservation data in the Data Warehouse unit or the rank of degree of integration.Degree of refinement is high more, and particle size fraction is just more little; On the contrary, degree of refinement is low more, and particle size fraction is just big more.
Convenient and can answer the ability of inquiry problem with reference to expense, efficient, visit, so create two kinds of particle size fractions on the detailed level of data warehouse.Most analysis, inquiry are to carry out at the slight comprehensive level data compressed, that access efficiency is high.Analyze lower detailed level if desired, can arrive the true archives layer of data.
The data storage Model Design has following characteristics: the demand (comprising typical four kinds of front-end access modes) that 1) can satisfy various analytical business flexibly; 2) response performance fast; 3) data centralization management; 4) has extended capability flexibly; 5) give overall consideration to and implement step by step.
System's construction can be accomplished overall consideration, overall planning, can consider the needs of the many-sided function of growth data warehouse system from now on, lays good basis for setting up perfect data warehouse from now on.
In this design, the inventor considers the concrete characteristics of telecommunications industry, and whole data warehouse model is designed to following eight subject areas: client, service use, customer service, marketing, service, clearing, resource, account.Wherein the client comprises that service has the focal pointe of reality or potential demand to telecommunications industry for all, the client is because buy the product of telecommunications industry or enjoy its service and become the telecommunications industry user, client's theme comprises all essential information and extend informations about the client, also comprise open an account, information such as cancellation; Service is used and is referred to that telecommunications company to the record that the client ordered, used the process of products ﹠ services, wherein mainly comprises user, standards service service recorder, inventory etc.; Customer service is customer service, has described telecommunications company and partner for all information that the client provides service, comprises traditional customer service and service handling, service department and services channels, and relevant behavior record; Marketing refer to telecommunications company for branch out, the ownership, at activities such as certain market and the specific market propaganda that the customer group carried out, sales promotion, comprise a series of marketing strategy and corresponding tactics; Service is the sensu lato product of telecommunications company, comprises all products and service that telecommunications company sells to the client; Clearing are meant that telecommunications company is with clearance of the expense between the partner services side and scribing relation; Resource is that telecommunications company has, and for the client provides all carriers of service, comprises number resource, terminal resource, Internet resources, and corresponding product vendor etc.; The account theme mainly reflects the relation between client and the account, comprises expense generation, expense payment etc.
2, shown in (1) among Fig. 1, the data of source operation system are carried out unified analysis.
After building data warehouse model, be exactly will be to operation system, just the data in the Data Warehouse source are carried out unified analysis, with regard to the present telecom operators of China, no matter be telecommunications, Netcom, move or UNICOM, its operation system substantially all can comprise, business system, charge system, account system, network management system, customer service system or the like.The source data analysis comprises following content: the scope of business of data and professional implication, data place platform comprise system platform and database platform, data structure, Data Update cycle, data refresh mode and data volume.
What this step will do is exactly the data of analyzing in these systems, and the needs according to the data warehouse Physical data model mate source data with destination data, i.e. source data mapping.
3, shown in (2) among Fig. 1, the data in the origin system are extracted conversion load.
The function that this step is finished is that data are loaded into the data warehouse through after the necessary processing from data source.
ETL is data pick-up, conversion and loading, is in the data warehouse implementation procedure, carries out the main process that data are loaded to data warehouse by data source systems.Entity in entity in the BOSS DSN and the data warehouse subject area is not mapping relations simply one to one, but the many-to-many relationship of more complicated, this mapping relations have constituted the main contents of operation analysis system ETL process.The flow process of ETL implementation procedure as shown in Figure 2.
1) data pick-up
A) data pick-up interface
Typical data pick-up interface comprises database interface and file interface, requires and the operation system of portfolio and the source data of different pieces of information amount for different pieces of information platform, different source data form, different performance, will take different data pick-up interfaces.When data pick-up, need emphasis to consider the efficient of data pick-up, and to existing business system performance and safe influence.The source data of telecommunications industry has following characteristics: data volume is big especially; The operation system working load is heavy, 7 * 24 work; Having relatively high expectations of operation system performance, real-time.
In view of above characteristics, extract interface for mobile data and adopt the private database driving interface generally speaking, adopt the api interface programming to realize the extraction of data in the time of necessary, reduce Effect on Performance simultaneously to improve data pick-up efficient to operation system.
B) data pick-up strategy
The needs of data warehouse analysis and decision support can be fully satisfied in the extraction of data, simultaneously can guarantee can not influence the performance of operation system again, so must fully take into account these factors when carrying out data pick-up, formulate corresponding strategy, comprise contents such as extraction mode, extraction opportunity, decimation periods.
Extraction mode: increment extraction, extract etc. fully.
Extraction opportunity: avoid the peak period of operation system as far as possible, such as when the night service systematic comparison is not busy.
Decimation periods: to the data of different types source, should take all factors into consideration business demand and systematic cost, formulate rational decimation periods.
To the extraction of moving source data, must fully satisfy the needs of operation analysis system, simultaneously must assurance can not influence the performance of data source systems, so must take into full account following factor when carrying out data pick-up, made corresponding strategy:
Satisfy the extraction of multiple different Data Source is handled.Data source comprises that the Hebei mobile phase answers operation system, enterprise's external data source, can provide the artificial input function of some data, as advertising campaign information, social investigation information etc.
Support the data pick-up of multiple different system platform and data type.Comprise the source data of various system Rs, various file modes etc.
Take into full account the performance requirement of data source systems.According to portfolio size and data volume size, reduce influence to the data origin system as far as possible.
When formulating the extraction strategy, need consider above every combined factors.Generally, the data adapting that flowing water type increases and data volume is big adopts the mode of increment extraction, is typically inventory, bill class data the most; Change data updated and be fit to adopt the mode that extracts fully, be typically the resource distribution class data of reflection current state the most; For the data of both combinations, if can extract increment information, then carry out increment extraction, otherwise adopt the mode that extracts fully to carry out, be typically customer data change data or other customer service record data the most.In addition, to consider the demand of practical business and extract the systematic cost that carries out that for decimation periods under possible situation, shorten decimation periods, it is as shown in the table that source data extracts Policy description as far as possible.
| The source data mode classification | Data manipulation | Classification | Data characteristics | Data content |
| Flowing water type increases (INSERT) | Data produce by incremental mode, do not relate to the renewal operation to data with existing | Inventory, bill, order etc. | ||
| Change and upgrade (UPDATE) | Data with existing is upgraded | Resource allocation information etc. | ||
| Both are in conjunction with (INSERT/ DELETE+UPDATE) | When producing new data, also data with existing is upgraded by incremental mode | Customer service record etc. | ||
| Data volume | Greatly | Inventory, bill etc. | ||
| Less relatively | Resource management, system management | |||
2) data-switching
Data-switching is meant the requirement according to the data warehouse model of the source data that extracts from operation system, carry out data conversion, cleaning, processing such as split, gather, assurance is from the consistance and the integrality of the data of different system, different-format, and the data warehouse of packing on request.
A) Zhuan Huan major function
Data-switching is mainly finished the data inconsistency problem that causes owing to following reason:
The source data system is with the otherness of data warehouse on model;
The source data system platform is inconsistent: the data source of data warehouse may comprise the data of database based on different platform;
Source data structure inconsistent: some data source is because historical reasons causes same table inconsistent in different period data structures;
The source data definition data that lead to errors lack of standardization;
Constraint to data is not strict, causes nonsignificant data;
There is duplicate record;
Because may there be a large amount of transcoding work in the difference of plateform system.
B) data conversion technique and strategy
According to actual conditions, data-switching work is meeting specific implementation in following link generally: carry out data processing in extraction process; The use asynchronous data loads, and handles in the mode of file; In the data load process, carry out data processing; Enter data warehouse and carry out data processing later on again.
When employing is carried out data-switching in the data pick-up process, the performance that must consider extract and to the operation system Effect on Performance; Adopt asynchronous data to load in the time of to handle with file mode, the memory space of disk and the harmony work of the whole flow process of ETL in the middle of must taking into full account, and the programming of a large amount of non-SQL statement; When employing is carried out data-switching in the data load process, must consider loading performance; Adopt when earlier Data Loading being handled behind data warehouse again, must consider the mass data processing ability of data warehouse engine.
3) data load
A) data load major function
Data load be exactly with from data source systems, extract, data load after the conversion is in data warehouse.Require the data load instrument must have loading performance efficiently.
B) data load technology and strategy
Main loading technique: the data load instrument that uses data warehouse engine manufacturer to provide carries out data load; The API that provides by data warehouse engine manufacturer is programmed into the line data loading; The data load strategy will be considered the content of loading cycle and data supplementing strategy two aspects.
According to the actual conditions of mobile service data, loading cycle has been taken all factors into consideration the cost of business diagnosis demand and system loads, the loading cycle different to the The data of different business systems, and the while can keep the integrality of same time business datum again.
The strategy that appends of data determines generally there are following three types: directly append, all cover, upgrade and append according to the extraction strategy and the business rule of data.
Directly append: when being meant each loading directly with data supplementing in the purpose table.For typical flowing water data, generally adopt the method, data such as inventory, account can adopt the mode of directly appending;
All cover: comprised the current of data and all historical situations for extracted data itself, object table is adopted whole coverage modes.Typical case is the mode that the data of tariff rule definition can all cover;
Renewal is appended: for the state variation that needs the continuous recording business, the situation of comparing with historical state data with current last state adopts the mode of appending of upgrading.Typical case is the loading of customer service record data.
Specifically take which kind of mode, it is all multifactor to take all factors into consideration efficient, business realizing etc.
4, shown in (4) among Fig. 1, need carry out varigrained gathering by analyzing the data in the data warehouse.
Central data warehouse is according to the information model of enterprise's integral body, organizes and store data with the business unit of minimum as far as possible.The dirigibility of data access can be guaranteed like this, minimum data redundancy can be guaranteed again.
In the implementation process of data warehouse,, may adopt the mode of Data Mart that data are further organized according to theme for the business diagnosis problem of some theme.So on the basis of central data warehouse, can create the Data Mart of corresponding subordinate according to analyze demands.The data of subordinate Data Mart directly come from central data warehouse.Adopt this mode, can keep the consistance of overall data.The key business department very frequent for some visit data warehouses sets up the subordinate Data Mart, can improve the response speed of inquiry preferably.
The principle of design of Data Mart generally will consider the use needs of business department, and the data technique of foundation also is a department level.
5, shown in (5) among Fig. 1, represent analysis result by various access modes.
The major function of the data access layer of operation analysis system is to make the mode of the personnel of manipulating by form and figure, and is easy, visit various data in the operation analysis system and carry out various analyses and prediction operation quickly.Data access layer provides several data processing exhibiting method, comprises predefine form, extemporaneous inquiry, multidimensional performance analysis and data mining.
The predefine form, be a kind of business analyst in the process of using system, according to demand with after relevant analysis result carries out predefine, the relatively-stationary form of format content.
The content of extemporaneous inquiry can freely be defined by the operation analysis system user of service, the access method of permission user control data, and the ways of presentation that provides plurality of optional to select to Query Result.Extemporaneous inquiry provides one based on the service logic of server and the mapping layer of database structure, make the operation analysis system user of service see through extemporaneous inquiry and the analysis of this mapping layer realization to data, the mapping layer administration module is finished searching and managing work, and extemporaneous inquiry architecture as shown in Figure 4.
The multidimensional performance analysis is based on the analytical approach of multidimensional data model, be used to support complicated analysis and prediction, comprise that trend analysis, What-if are (different with reason and impact analysis, it is to observe when artificially specifying the change condition that what if analyzes, result's situation of change, so that prediction is in order to reach target, what the top condition combination is) analyze etc.
OLAP (On-Line Analytical Processing-on-line analytical processing) is one of main target of building in this stage of operation analysis system.
On-line analytical processing is at specific analysis theme, design multiple possible observation form, the corresponding thematic structure (promptly carrying out the design of fact table and Wei Biao) of analyzing of design, make that the management decision personnel carry out fast, the visit of stable and interactivity on the multidimensional data model based, and carry out the analysis and the prediction work of various complexity.
The classification of OLAP: divide according to storage mode, OLAP can be divided into MOLAP and ROLAP:
MOLAP (on-line analytical processing of Multi-Dimension OLAP-multidimensional): the deposit data that olap analysis is required is in multi-dimensional database.Data of analyzing theme form one or more multi-dimension data cubes.
ROLAP (Relational OLAP-concerns on-line analytical processing): the deposit data that olap analysis is required is in relevant database.A Star Schema tissue of analyzing the data of theme with " fact table-Wei Biao ".
Operation analysis system can adopt MOLAP and ROLAP dual mode, decides according to concrete data volume scale, corresponding requirements, the data organization characteristics of using during enforcement.
The fundamental analysis mode of OLAP comprises following several: section: get point of fixity on certain dimension, analyze other dimensions; Drill through: in the hierarchical structure of certain dimension, enter next detailed level and do analysis; Rotation: the dimension of changing olap analysis.
The structure of system OLAP as shown in Figure 5, in the data warehouse of operation analysis system, data are organized with the form of " data warehouse theme ".According to the demand of Hebei mobile service, data can be divided into 8 big themes: client's theme, and the resource theme, the account theme, the marketing theme, theme is used in service, clearing theme, customer service theme, service theme.Each theme is made up of a plurality of entities, and for example, service theme has service entities, product entity, rate and preferential policy entity or the like.These entities connect each other according to business rule and business structure.
Olap analysis one dish comprises 3 steps: determine the target analyzed and dimension → tectonic analysis model → frontal chromatography and represent
The first step is determined target and the dimension analyzed
Business is carried out olap analysis, at first should determine the target analyzed, propose dimension and the index analyzed then.
With the income macroanalysis in the income analysis is example, and target is decided to be " analyzing different areas, time, mobile service kind are taken in total amount to business influence ".After determining the target of analyzing, the index of analysis and the dimension of analysis are definite substantially.In a last example, the index of analysis is taken in total amount exactly; The dimension of analyzing has three, is respectively the area, time, mobile service kind.The dimension of analyzing need be segmented according to the actual requirements.
Second step, the tectonic analysis model
The analytical model of OLAP can be divided into logical model and physical model.
Logical model mainly adopts Star Schema.Star Schema is made of fact table and Wei Biao.Fact table has been deposited the detail data that needs analysis, and the dimension table has been deposited the attribute of respectively analyzing dimension.
Physical model refers to the storage mode of OLAP, leaves in the relevant database or make up Cube (multi-dimension data cube data set) etc. in Star Schema.
The 3rd the step, frontal chromatography with represent
Use at a concrete olap analysis, after finishing the structure of analytical model, should correspondingly dispose front end and represent instrument.
For the web access mode, as required related content is added among the OLAP Server, in addition WebServer is done corresponding configuration; For the conventional client access mode, need do corresponding configuration to client software.
After finishing configuration effort, the user can carry out various analysis operations at corresponding theme.Front end tool should provide the exhibition method of multiple figure, form.
Data mining is according to the set business objective of enterprise and the problem of existence, and a large amount of business datums is explored, and disclose to hide rule wherein, and with its modelling, instructs and is applied in the actual enterprise operation.The data mining architecture as shown in Figure 6.Data mining is with the flow process of data warehouse and Data Mart and application combination thereof, can be at first based on traffic issues and the target determined, data warehouse (or carry out data thus organize the Data Mart that generates again) excavated and find out rule by Data Mining Tools by the data mining analysis personnel of specialty, generation model, again this model is applied to data in the related data warehouse (or Data Mart), and then the generation form etc., excavate all kinds of results that produced for data mining results application personnel visit and application data, to understand professional and client's situation.In operation analysis system, the different data digging method differences that practical problems adopted, data mining method generally is divided into forecasting type and description type, wherein, forecasting type (Predictive) method comprises classification (Classification)/decision Tree algorithms (Decision Tree), regretional analysis, time series analysis (Time Series); Description type (Descriptive) method comprises association analysis (Association Analysis), serial correlation analysis (Sequential Analysis), cluster analysis (Clustering).Data mining and olap analysis, predefine form and extemporaneous inquiry etc. have very big difference.Back three is the operational indicator of user to being concerned about normally, analyzes according to known angle; The former then is in traffic issues and with clearly defined objective, but the angle of investigating explores data when not knowing, discloses the regularity of hiding wherein, and then with its modelling.Data mining is a process that moves in circles, and is usually directed to the selection of data, the conversion of data, sets up model, and assessment, interpretation model use and consolidate step such as model.Data mining process as shown in Figure 7.
Data access layer should provide unified a door inlet and an interface, realize the seamless link of predefine form, extemporaneous inquiry and multidimensional performance analysis, and provide integrated authentication, information to issue and management environment, make the operation analysis system user of service need not to be concerned about that concrete technology realizes approach, can realize visit and analysis the operation analysis system data.Simultaneously the portal also can be according to different analysis and decision personnel's demand, to required visit with analyze that content is carried out conveniently, simple and direct customization, with the demand of satisfying personalized information service.
6, for guaranteeing that the normal operation of system sets the control of various procedures.
1) automatic job scheduling monitoring
The management work meeting of operation analysis system comprises a large amount of periodicity, the work that need finish automatically.Management platform should comprise the automatic job scheduling system, and system can automatically perform corresponding operation by the time cycle of appointment, with mitigation system keeper's work load.
The operation of scheduling comprises the various operations that relational database, multi-dimensional database and operating system are carried out, as data extraction/conversion/loading, data backup etc.When the incident of system monitoring is triggered, can dispatches the operation of appointment automatically and handle.Increase is to the monitoring of automatic scheduling events implementation status.
Operation analysis system in large scale needs a comprehensive safety management, consider the protection of all safe weak links in the network, will take the concentrated realization of security strategy simultaneously into account.
2) network security management
Operation analysis system satisfies following security function:
A) by measures such as fire walls the packet that enters internal network is scanned filtration, can be according to modes such as user, IP address, the access type rule limits that conducts interviews, can judge and stop common intrusion behavior.
B) provide address translation function, shielding network interior details prevents that outside hacker from utilizing the IP Detection Techniques to find internal networking structure and server true address, thereby realizes attacking targetedly.
C) can monitor network communication, in time find anyly to come from network internal or outside hacker attacks or suspicious visit behavior, and accomplish in time to report to the police and blocking-up.
D) accomplish between each subnet or the data in the long-distance user transmission carry out safeguard protection, utilize mode such as encryption to guarantee that data are not are not illegally intercepted and captured, and functions such as authenticating user identification, mandate be provided.
3) system backup recovery management
In a large data warehouse system, except needs powerful server and reliable disk storage system, need jumbo tape library equipment toward contact, so that termly system is backed up.
Mainly consider following aspect for the backup/restoration system: formulate complete backup and recovery policy; Fully understand the data capacity of business datum in the data warehouse; Fully understand the frequency and the flow process of Data Update; Select the backup and the recovery system of high-performance, high reliability.
A) backup policy
Before backing up, at first to select backup policy, this will determine when need to back up, and the mode of recovering when breaking down.Normally used backup mode has three kinds: backup fully, incremental backup and renewal backup.
Backup fully: just system is once comprehensively backed up at regular intervals, interim problems such as loss of data occur in backup like this, can use the Backup Data of last time to return to situation when last time backing up.This is the most basic backup mode, but all need to back up all data, and the workload of each backup is also very big at every turn, needs too many backup medium, what therefore this backup can not be carried out is too frequent, can only just carry out once complete backup every one period long period.
Incremental backup: at first once back up fully, once back up every a short period then, but only back up the content of during this, changing.After through long time, once back up the cyclic process of beginning front more again fully.Since have only back up fully the first time of each backup cycle, the backup of the file that other change, so workload is little, just can carry out more frequent backup.
Upgrade backup: this backup method is similar to incremental backup, at first once backs up fully in every month, carries out once the more backup of new data then every day.But difference is that incremental backup is the data of backup this day change, is the back change was backed up in backup fully from last time entire data files and upgrade backup.In case the generation loss of data, the state that can use previous backup fully to return to previous month re-uses the situation that previous renewal backup returns to the previous day.The shortcoming of doing like this is that to make the task of little back-up job bigger than the workload of incremental backup at every turn, but benefit is, incremental backup all has backup every day, therefore it is too many to preserve data backup quantity, and it is quite different to upgrade backup, and backup and one upgrade backup and just can recover fault state in the past fully only to need to preserve one.In addition when resuming work, the recovery that incremental backup will in proper order repeatedly back up only needs twice recovery and upgrade backup, thus it resume work simple relatively.
Should there be good backup policy and recovery plan in system.But system data and business datum on-line backup, in-line recovery, data recovered must keep its integrality and consistance.
B) data level backup
Data level backs up the backup of the business datum that main index moves according to Hebei in the warehouse system.The general data that moves comprises customer data, detailed single information, comprehensive bill information, Customer Service Information, marketing information, pays the fees/arrearage information, network management information etc.
The data level backup is the part that needs emphasis planning in data backup and the recovery system.The data volume that move in Hebei is very huge, and detailed single information of some months just may go up the TB level, in the process of selecting data backup policy, takes into full account the factor of data volume with this, and the speed of assurance data backup and frequency can be finished in the requirement of regulation smoothly.
C) system-level backup
System-level backup can avoid beyond thought system data to lose, especially when system data is very important.Often carry out data backup and can reduce the accidental loss that causes that destroys, normal operation can recover in the assurance system from mistake.
System backup mainly comprises DB Backup, Application Backup, the backup of ETL data load system program and other related datas.
As shown in Figure 3, the hardware of this decision support system (DSS) is composed as follows:
Data warehouse server: the data warehouse server hardware configuration is 5350 * 12 nodes, every node 4GB internal memory, 84 * 36GB hard disk (6841-2456), 63AMPs altogether.Operating system is NCR UNIX MP-RAS 3.02, and database is Teradata V2R5.1,6466 tape pools, and 20 * LTO is with machine, 500slots.
The data warehouse management work station: this server has the IntelCPU of 2 Pentium 4 1.26GHz, have the 2G internal memory, this server connects the internal disk subsystem, and the hard disk subsystem has 2 18GB disc drivers, and adding up to the disk raw capacity is 36GB.
This server operation Windows 2000 server operating systems and AWS Console Software management work station software are carried out the management function to central data warehouse (data warehouse server and disc array system).
The ETL server: ETL server one has two, and this two-server all is IBM xSeries 360, and every station server has 4 Intel Xeon 1.5G CPU, and 4G internal memory, the hard disk subsystem possess the 2*36G hard disk, and adding up to the hard disk raw capacity is 72G.
Because the needs that are connected with charge system, the spy is provided with two interface message processor (IMP)s.Adopt IBM xSeries 345, have 1CPU/512M RAM.
Operation Microsoft Windows 2000 Advanced Server operating systems and ETLAutomation data load management software on the ETL server.Be responsible for loading data from charging bill, accounting data, big customer's data, 1860 customer service data
Olap server: olap server one has two, and this two-server all is IBM xSeries 360, and every station server has 4 Intel Xeon 1.5G CPU, and 4G internal memory, the hard disk subsystem possess the 2*36G hard disk, and adding up to the hard disk raw capacity is 72G.
Two-server is all installed Microsoft Windows 2000 Advanced Server operating systems and Microsoft Analysis Server multidimensional analysis server software, and ETL Automation data load management software also has been installed.This two-server mainly is to upgrade work the every day of being responsible for the CUBE of Microsoft OLAP Server application, manages the multidimensional analysis cube file that the data from data warehouse generate.Move ETL Automation simultaneously,, safeguard the scheduling of Microsoft OLAP Server operation with the renewal operation that ETL Automation is responsible for automatic management and running Microsoft OLAP Server.
The WEB server: this server is IBM xSeries 360, and server has 4 Intel Xeon 1.5GCPU, and 4G internal memory, the hard disk subsystem possess the 2*36G hard disk, and adding up to every hard disk raw capacity is 72G.
Server is installed Microsoft Windows 2000 Server operating systems and Cognos multidimensional analysis server Upfront Web Server software and BEA Weblogic, and server mainly is to be responsible for the WEB that foreground that Microsoft OLAP uses represents with Hebei mobile service personnel to browse.
The DM data mining server: data mining server is IBM pSeries 630 Unix minicomputers, and server has 2-Way/Power4/1.0GHz, and 4G internal memory, the hard disk subsystem possess the 2*36G hard disk, and adding up to every hard disk raw capacity is 72G.
Server is installed AIX 5.1 operating systems and SAS Data Mining data mining software.Server mainly is to be responsible for data mining to use.
Application server for storage: memory contents comprises ETL storage, OLAP storage and data mining storage, and the EMC C400 disk array of 3T capacity is shared in this three parts storage, connects by the SAN mode.The RAID5 mode of storage employing is carried out the disk tolerance management, and then the ETL storage has capacity 800G in fact, and the OLAP storage has capacity 800G, data mining 690G in fact.
Security of system and management server: these two servers all are IBM xSeries 345, and server has 1 Intel Xeon 2.4G CPU, and 1G internal memory, the hard disk subsystem possess the 2*36G hard disk, and adding up to every hard disk raw capacity is 72G.
Server is installed Microsoft Windows 2000 Server operating systems and system security management software, and the comprehensive safety protection to operation analysis system is provided.Comprise comprehensively Host Protection such as virus and malicious code protection, access control, user's centralized management, intrusion detection, panoramic catalogue management.
As shown in Figure 3, the physical connection between the system hardware is as follows:
In the data warehouse of telecommunications industry, its data traffic is huge, the reliability requirement height, in this invention, adopted advanced local area network technology, as core, setting up one is high speed data transmission networks high speed, choke free, full exchange of core with the IP technology, and utilizes redundant physical connection that highly reliable network environment is provided with two gigabit ethernet switchs.
Between data warehouse and other origin systems, connect by the MDCN network.
Moving present data warehouse with Hebei is example, have 12 data warehouse nodes, 13 supporting application servers, each application server is connected with the double star structure that data warehouse passes through to switch, and wherein any link disconnection and any center switch fault can not influence the operate as normal of system.
These 12 data warehouse node service data warehouse servers are organized in the mode of MPP (massively parallel processing technology), and each node is connected to two CISCO4506 switches with kilomega optic fiber and gigabit 6 class twisted-pair feeders respectively; Two ETL load servers are IBM x360 and x445, load the data that origin system provides to data warehouse, data derive and upload server is IBM x360, and they all move ETL (data pick-up, conversion, loading) and serve, and are connected to switch by kilomega optic fiber; Two OLAP (online analysis process) server is IBM x445, and operation multidimensional analysis service is connected to switch by kilomega optic fiber; Two WEB servers are IBM x360, form cluster, provide the page and form to show, operation WEB service and displaying service are connected to switch by kilomega optic fiber; A PORTAL (door) server is IBM x360, and the storage of page related data is provided, and the relevant database service of operation door is connected to switch by kilomega optic fiber; A data mining main frame IBM p630, service data mining analysis software is connected to switch by kilomega optic fiber; Also have domain server and security administration server in addition, they all are connected to switch by 100,000,000 twisted-pair feeders.
The software of system is composed as follows:
Data warehouse server: operating system: NCR UNIX MP-RAS 3.02; Database Systems: NCR Teradata magnanimity parallel processing database system.
System management workstation: work station type: NCR 4470 data warehouse management work stations; Operating system: Windows 2000 Server; SMT Station Management software.
ETL server: operating system: Windows 2000 Advanced Server; Database Systems: NCRTeradata magnanimity parallel processing data base management system (DBMS); Instrument: NCR Teradata common program.
Olap server: operating system: Windows 2000 Advanced Server; Instrument: MicrosoftAnalysis server; Instrument: ETL Automation data load server version.
WEB server: operating system: Windows 2000 Advanced Server; The Web version of instrument: Cognos and BEA Weblogic (Web Server); Instrument: ETL Automation data load server version
DM data mining server: operating system: AIX 5.1; Instrument: SAS Data Mining data mining software.
Security of system and management server: operating system: Windows 2000 Advanced Server; Instrument: system security management software.
Other client software: NCR Teradata Manager database management tools; CA ERWin modeling tool, these instruments can be carried out from any Windows 95/98/NT/2000 workstation.
Claims (8)
1, a kind of telecommunications industry is based on the building method of the decision support system (DSS) of data warehouse, it is characterized in that, the information resources of utilizing business support system to produce, the information that provides in conjunction with relevant support system, adopt data warehouse technology, data mining technology, the described decision support system (DSS) of multidimensional analysis technical construction, constitution step is as follows:
Step 1, structure meet the data warehouse model of telecommunication service characteristics: according to the concrete characteristics of telecommunications industry, whole data warehouse model is designed to following eight subject areas: client, service use, customer service, marketing, service, clearing, resource, account, data warehouse model comprises logic data model and Physical data model, the logic high level data model reflects the business relations of telecommunications industry conscientiously, comprises all themes and main entity and relation each other; Physical model is the storage mode of data;
Step 2, the data of source operation system are carried out unified analysis: after building data warehouse model, to the source operation system, just the data in the Data Warehouse source are carried out unified analysis; The source data analysis comprises following content: the scope of business of data and professional implication, data place platform, comprise system platform and database platform, data structure, Data Update cycle, data refresh mode and data volume, analyze the data in these systems, needs according to the data warehouse Physical data model, source data is mated i.e. source data mapping with destination data;
Step 3, the data in the origin system are extracted conversion load: ETL is data pick-up, conversion and loading, is in the data warehouse implementation procedure, carries out the main process that data are loaded to data warehouse by data source systems;
Step 4, need carry out varigrained gathering by analyzing the data in the data warehouse: can create the Data Mart of corresponding subordinate on the basis of central data warehouse according to analyze demands, the data of subordinate Data Mart directly come from central data warehouse;
Step 5, represent analysis result by various access modes: data access layer provides several data processing exhibiting method, comprise predefine form, extemporaneous inquiry, multidimensional performance analysis and data mining, and provide a unified door to enter the mouth and the interface, realize the seamless link of predefine form, extemporaneous inquiry and multidimensional performance analysis, and integrated authentication, information issue and management environment is provided;
Step 6, for guaranteeing that the normal operation of system sets the control of various procedures: the automatic job scheduling monitoring, management platform comprises the automatic job scheduling system, this system can automatically perform corresponding operation by the time cycle of appointment, the operation of scheduling comprises the various operations that relational database, multi-dimensional database and operating system are carried out, when the incident of system monitoring is triggered, can dispatch the operation of appointment automatically and handle, increase monitoring automatic scheduling events implementation status; Network security management; The system backup recovery management according to the telecommunications industry data characteristic, is formulated backup policy and is recovered plan, constructing system backup/restoration system.
2, building method according to claim 1, it is characterized in that: eight subject areas described in the step 1, wherein the client comprises that service has the focal pointe of reality or potential demand to telecommunications industry for all, the client is because buy the product of telecommunications industry or enjoy its service and become the telecommunications industry user, client's theme comprises all essential information and extend informations about the client, also comprise open an account, cancellation information; Service is used and is referred to that telecommunications company to the record that the client ordered, used the process of products ﹠ services, wherein mainly comprises user, standards service service recorder, inventory; Customer service is customer service, has described telecommunications company and partner for all information that the client provides service, comprises traditional customer service and service handling, service department and services channels, and relevant behavior record; Marketing refer to telecommunications company for branch out, the ownership, at certain market and the specific market propaganda, advertising campaign that the customer group carried out, comprise a series of marketing strategy and corresponding tactics; Service is the sensu lato product of telecommunications company, comprises all products and service that telecommunications company sells to the client; Clearing are meant that telecommunications company is with clearance of the expense between the partner services side and scribing relation; Resource is that telecommunications company has, and for the client provides all carriers of service, comprises number resource, terminal resource, Internet resources, and corresponding product vendor; The account theme mainly reflects the relation between client and the account, comprises that expense takes place, expense is paid.
3, building method according to claim 1 is characterized in that: the data model described in the step 1 adopts Star Schema, and Star Schema is made of fact table and Wei Biao, and fact table is deposited the detail data that needs analysis, and the dimension table is deposited the attribute of respectively analyzing dimension; Physical model leaves in the relevant database with Star Schema or makes up Cube.
4, building method according to claim 1 is characterized in that: the source operation system described in the step 2 comprises business system, charge system, account system, network management system and customer service system.
5, building method according to claim 1, it is characterized in that: the data pick-up described in the step 3, be characteristics at the telecommunications industry source data, require and the operation system of portfolio and the source data of different pieces of information amount for different pieces of information platform, different source data form, different performance, different data pick-up interfaces will be taked, formulate corresponding strategy, comprise extraction mode, extraction opportunity, decimation periods; Data-switching is meant the requirement according to the data warehouse model of the source data that extracts from operation system, carry out conversion, cleaning, fractionation, the aggregation process of data, assurance is from the consistance and the integrality of the data of different system, different-format, and the data warehouse of packing on request, according to actual conditions specified data switch technology and strategy; Data load be with from data source systems, extract, data load after the conversion is in data warehouse, cost according to business diagnosis demand and system loads, the loading cycle different to the The data of different business systems, the integrality that can keep simultaneously same time business datum again is according to the strategy that appends of the extraction strategy of data and business rule specified data;
6, building method according to claim 4 is characterized in that: described extraction mode comprises increment extraction and extracts fully that wherein the data adapting that flowing water type increases and data volume is big adopts the mode of increment extraction; Change data updated and be fit to adopt the mode that extracts fully; For the data of both combinations, if can extract increment information, then carry out increment extraction, otherwise adopt the mode that extracts fully to carry out.
7, building method according to claim 4, it is characterized in that: the described strategy that appends, comprise three types: directly append, all cover and renewal is appended, wherein, directly append when being meant each loading directly data supplementing in the purpose table, for typical flowing water The data the method; All cover: comprised the current of data and all historical situations for extracted data itself, object table is adopted whole coverage modes; Renewal is appended: for the state variation that needs the continuous recording business, the situation of comparing with historical state data with current last state adopts the mode of appending of upgrading.
8, building method according to claim 1, it is characterized in that: the predefine form described in the step 5, be a kind of business analyst in the process of using system, according to demand with after relevant analysis result carries out predefine, the relatively-stationary form of format content; The content of extemporaneous inquiry can freely be defined by the operation analysis system user of service, the access method of permission user control data, and the ways of presentation that provides plurality of optional to select to Query Result; The multidimensional performance analysis is based on the analytical approach of multidimensional data model, is used to support complicated analysis and prediction, comprises that trend analysis, What-if analyze; Data mining is according to the set business objective of enterprise and the problem of existence, a large amount of business datums is explored, disclose the rule of hiding wherein, and, instruct and be applied in the actual enterprise operation, in operation analysis system its modelling, the different data digging method differences that practical problems adopted, data mining method generally is divided into forecasting type and description type, and wherein, the forecasting type method comprises classification/decision Tree algorithms, regretional analysis, time series analysis; Description type method comprises association analysis, serial correlation analysis, cluster analysis.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNA2006100127243A CN101075304A (en) | 2006-05-18 | 2006-05-18 | Method for constructing decision supporting system of telecommunication industry based on database |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNA2006100127243A CN101075304A (en) | 2006-05-18 | 2006-05-18 | Method for constructing decision supporting system of telecommunication industry based on database |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN101075304A true CN101075304A (en) | 2007-11-21 |
Family
ID=38976347
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNA2006100127243A Pending CN101075304A (en) | 2006-05-18 | 2006-05-18 | Method for constructing decision supporting system of telecommunication industry based on database |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101075304A (en) |
Cited By (85)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101374076B (en) * | 2008-10-06 | 2010-09-29 | 中国移动通信集团设计院有限公司 | Method for forecasting and planning server handling ability of telecom business support system |
| CN101576882B (en) * | 2008-05-08 | 2011-01-12 | 中国人民解放军第四五五医院 | Traceable data collecting and analyzing system |
| CN102043841A (en) * | 2010-12-10 | 2011-05-04 | 上海市城市建设设计研究院 | Multi-source information supplying method based on Web technology and integrated service system thereof |
| CN102053951A (en) * | 2009-10-28 | 2011-05-11 | 无锡华润上华半导体有限公司 | Processing method of special character |
| CN101226527B (en) * | 2007-12-28 | 2011-06-08 | 中国移动通信集团四川有限公司 | Data synchronization method for double-nucleus library |
| CN102117454A (en) * | 2009-12-30 | 2011-07-06 | 中国银联股份有限公司 | Bank card safe payment method and system |
| CN101499921B (en) * | 2008-01-29 | 2012-01-11 | 华为技术有限公司 | Service object cleaning method and apparatus |
| CN102339256A (en) * | 2011-09-15 | 2012-02-01 | 东北大学 | Complex event detection method on basis of IMF (instance matching frequency) internal and external memory replacement policy |
| CN101256650B (en) * | 2008-03-21 | 2012-02-15 | 中国科学院软件研究所 | Method and system for extracting enterprise data based on service entity |
| CN102411569A (en) * | 2010-09-20 | 2012-04-11 | 上海众融信息技术有限公司 | Database conversion and cleaning information processing method |
| CN102629267A (en) * | 2012-03-08 | 2012-08-08 | 北京神州数码思特奇信息技术股份有限公司 | Data processing method and data processing module |
| CN102750653A (en) * | 2011-04-20 | 2012-10-24 | 中国南方电网有限责任公司 | Method and system for performing multi-dimensional benchmarking analysis on electric power scheduling information |
| CN102081625B (en) * | 2009-11-30 | 2012-12-26 | 中国移动通信集团北京有限公司 | Data query method and query server |
| CN102867282A (en) * | 2012-09-13 | 2013-01-09 | 福建富士通信息软件有限公司 | Implementation method for mobile Internet-based customer service quality analysis system |
| CN101958987B (en) * | 2009-07-14 | 2013-06-26 | 中国电信股份有限公司 | Method and system for dynamically converting telecommunications service data |
| CN101770461B (en) * | 2008-12-31 | 2013-09-25 | 中国移动通信集团黑龙江有限公司 | Data processing method and data processing system |
| CN103473305A (en) * | 2013-09-10 | 2013-12-25 | 北京思特奇信息技术股份有限公司 | Method and system for performing decision-making process show in statistic analysis |
| CN103577590A (en) * | 2013-11-12 | 2014-02-12 | 北京润乾信息系统技术有限公司 | Data query method and system |
| CN103577605A (en) * | 2013-11-20 | 2014-02-12 | 贵州电网公司电力调度控制中心 | Data warehouse based on data fusion and data mining and application method of data warehouse |
| CN103605732A (en) * | 2013-11-19 | 2014-02-26 | 北京京东尚科信息技术有限公司 | Data warehouse, data warehouse system and data warehouse construction method based on Infobright |
| CN103617448A (en) * | 2013-12-05 | 2014-03-05 | 用友软件股份有限公司 | Method for immediately analyzing business data |
| CN103679383A (en) * | 2013-12-25 | 2014-03-26 | 武汉武船信息集成有限公司 | Method for establishing shipbuilding enterprise group management and control platform |
| CN103679309A (en) * | 2012-09-13 | 2014-03-26 | 余明阳 | Intelligent statistical analysis method for power data warehouse services |
| CN103746854A (en) * | 2014-01-27 | 2014-04-23 | 国家电网公司 | Communication mechanism for information management layer and function application layer of communication network system |
| CN103761309A (en) * | 2014-01-23 | 2014-04-30 | 中国移动（深圳）有限公司 | Operation data processing method and system |
| CN103984755A (en) * | 2014-05-28 | 2014-08-13 | 中国地质大学（北京） | Multidimensional model based oil and gas resource data key system implementation method and system |
| CN104008428A (en) * | 2014-05-19 | 2014-08-27 | 上海交通大学 | Product service demand forecasting and resource optimization configuration method |
| CN104298773A (en) * | 2014-10-30 | 2015-01-21 | 北京思特奇信息技术股份有限公司 | Automatic base switching system and method for ETL operations |
| CN104394039A (en) * | 2014-12-09 | 2015-03-04 | 南京华苏科技股份有限公司 | Evaluation system and evaluation method for network performance maturity based on Sigmoid exponential model |
| CN104573071A (en) * | 2015-01-26 | 2015-04-29 | 湖南大学 | Intelligent school situation analysis system and method based on megadata technology |
| CN104574127A (en) * | 2013-10-21 | 2015-04-29 | 北京中海纪元数字技术发展股份有限公司 | Innovative marketing platform |
| CN104657475A (en) * | 2015-02-16 | 2015-05-27 | 百度在线网络技术（北京）有限公司 | Method and system for data analysis |
| CN104731908A (en) * | 2015-03-24 | 2015-06-24 | 浪潮集团有限公司 | ETL-based data cleaning method |
| CN104809110A (en) * | 2014-01-23 | 2015-07-29 | 安徽海汇金融投资集团有限公司 | Large data system |
| WO2016008092A1 (en) * | 2014-07-15 | 2016-01-21 | Microsoft Technology Licensing, Llc | Managing data-driven services |
| CN105373895A (en) * | 2015-11-24 | 2016-03-02 | 苏州科技学院 | Green building power distribution management system |
| CN105631004A (en) * | 2015-12-28 | 2016-06-01 | 华夏银行股份有限公司 | Data processing method and system |
| CN105868222A (en) * | 2015-09-17 | 2016-08-17 | 乐视网信息技术（北京）股份有限公司 | Task scheduling method and device |
| CN105956084A (en) * | 2016-04-29 | 2016-09-21 | 浪潮(北京)电子信息产业有限公司 | Changed data extracting method and apparatus |
| CN105975613A (en) * | 2016-05-19 | 2016-09-28 | 国网重庆市电力公司客户服务中心 | Service channel recommendation method, device and system based on historical access data |
| CN106156040A (en) * | 2015-03-26 | 2016-11-23 | 阿里巴巴集团控股有限公司 | multi-dimensional data management method and device |
| 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 |
| CN106777345A (en) * | 2017-01-16 | 2017-05-31 | 山东浪潮商用系统有限公司 | A kind of data pick-up loading method based on mass data migration |
| CN106790504A (en) * | 2016-12-16 | 2017-05-31 | 北京北科亿力科技有限公司 | A kind of metallurgical big data service platform |
| CN106934023A (en) * | 2017-03-13 | 2017-07-07 | 山东浪潮云服务信息科技有限公司 | A kind of data managing method and device |
| CN107256264A (en) * | 2017-06-14 | 2017-10-17 | 上海找钢网信息科技股份有限公司 | Self-service access instrument and access method |
| CN107346110A (en) * | 2017-07-03 | 2017-11-14 | 上海德衡数据科技有限公司 | A kind of multi-modal decision system prototype of O＆M based on polycaryon processor |
| CN107491563A (en) * | 2017-09-28 | 2017-12-19 | 深圳市爱贝信息技术有限公司 | Towards the data processing method and system of settlement for account |
| CN107704608A (en) * | 2017-10-17 | 2018-02-16 | 北京览群智数据科技有限责任公司 | A kind of OLAP multidimensional analyses and data digging system |
| CN107818177A (en) * | 2017-11-23 | 2018-03-20 | 山东浪潮通软信息科技有限公司 | A kind of business intelligence model buildings method and build device |
| CN107895235A (en) * | 2017-11-27 | 2018-04-10 | 安徽经邦软件技术有限公司 | Financial modeling system based on decision tree |
| CN107908640A (en) * | 2017-09-27 | 2018-04-13 | 国网浙江省电力公司杭州供电公司 | A kind of Business Entity relation Intelligent exploration engine implementing method |
| CN108062320A (en) * | 2016-11-08 | 2018-05-22 | 长沙博为软件技术股份有限公司 | A kind of method for the data acquisition of multipad, conversion and loading |
| CN108132997A (en) * | 2017-12-21 | 2018-06-08 | 国网江苏省电力公司泰州供电公司 | A kind of electric network data management sums up structure and its resolution principle |
| CN108228747A (en) * | 2017-12-20 | 2018-06-29 | 江苏数加数据科技有限责任公司 | Data genetic connection visualized graphs system in data improvement |
| CN108462888A (en) * | 2018-03-14 | 2018-08-28 | 江苏有线数据网络有限责任公司 | The intelligent association analysis method and system of user's TV and internet behavior |
| CN109063078A (en) * | 2018-07-24 | 2018-12-21 | 武汉胖猫智能科技有限公司 | The data analysing method and system of steel trade financial business |
| CN109241179A (en) * | 2018-08-01 | 2019-01-18 | 协同数据技术（深圳）有限公司 | Data administering method, system and computer equipment based on data space |
| CN109376147A (en) * | 2018-11-26 | 2019-02-22 | 浙江中智达科技有限公司 | A kind of data processing method and system |
| CN109408448A (en) * | 2018-12-05 | 2019-03-01 | 江苏恒创软件有限公司 | One kind can carry out centralized processing integration data to data and show platform |
| CN109597850A (en) * | 2018-11-22 | 2019-04-09 | 四川省烟草公司成都市公司 | Tobacco integrated information data mart modeling stores platform and data processing method |
| CN109669934A (en) * | 2018-12-11 | 2019-04-23 | 江苏瑞中数据股份有限公司 | A kind of data warehouse and its construction method suiting electric power customer service |
| CN109726174A (en) * | 2018-12-28 | 2019-05-07 | 江苏满运软件科技有限公司 | Data archiving method, system, equipment and storage medium |
| CN109902117A (en) * | 2019-02-19 | 2019-06-18 | 新华三大数据技术有限公司 | Operation system analysis method and device |
| CN110019397A (en) * | 2017-12-06 | 2019-07-16 | 北京京东尚科信息技术有限公司 | For carrying out the method and device of data processing |
| CN110019477A (en) * | 2017-12-27 | 2019-07-16 | 航天信息股份有限公司 | A kind of method and system carrying out big data processing using HIVE backup table |
| CN110069499A (en) * | 2019-04-18 | 2019-07-30 | 中国联合网络通信集团有限公司 | Data managing method, device, system and storage medium |
| CN110232098A (en) * | 2019-04-22 | 2019-09-13 | 汇通达网络股份有限公司 | A kind of data warehouse administered based on data and genetic connection designs |
| CN110334088A (en) * | 2019-07-11 | 2019-10-15 | 江苏曲速教育科技有限公司 | Educational data management system |
| CN110362605A (en) * | 2019-06-04 | 2019-10-22 | 苏州神州数码捷通科技有限公司 | A kind of E book data verification method based on big data |
| CN110414802A (en) * | 2019-07-08 | 2019-11-05 | 南京华盾电力信息安全测评有限公司 | Conglomerate Analysis of Policy Making flight deck system |
| CN110609860A (en) * | 2018-05-29 | 2019-12-24 | 中国移动通信集团重庆有限公司 | Data ETL processing method, device, equipment and storage medium |
| CN110941659A (en) * | 2019-11-26 | 2020-03-31 | 上海景域文化传播股份有限公司 | Data layered splitting method for composite revenue |
| CN110955717A (en) * | 2019-11-19 | 2020-04-03 | 深圳市航通智能技术有限公司 | Visual dynamic display method and system based on big data |
| CN110955413A (en) * | 2019-11-28 | 2020-04-03 | 国网河北省电力有限公司沧州供电分公司 | Financial data processing system |
| CN111382155A (en) * | 2018-12-28 | 2020-07-07 | 上海连尚网络科技有限公司 | Data processing method of data warehouse, electronic equipment and medium |
| CN112182089A (en) * | 2020-10-15 | 2021-01-05 | 南方电网数字电网研究院有限公司 | Report generation method, device and equipment based on data warehouse model |
| CN112579648A (en) * | 2020-12-23 | 2021-03-30 | 赛尔网络有限公司 | Data processing system, method, device and medium for enterprise |
| CN112579869A (en) * | 2019-09-29 | 2021-03-30 | 北京星选科技有限公司 | Object extraction method and device, electronic equipment and storage medium |
| CN112966860A (en) * | 2021-02-23 | 2021-06-15 | 武汉育知联信息科技有限公司 | Data analysis and prediction system based on attached pre-chain |
| CN112988783A (en) * | 2021-03-12 | 2021-06-18 | 李涛 | Public opinion occurrence time sequence analysis method based on multidimensional data model |
| CN113485989A (en) * | 2021-07-02 | 2021-10-08 | 中国建设银行股份有限公司 | Comprehensive analysis method, system, medium and equipment for supervision data |
| CN113742320A (en) * | 2021-11-05 | 2021-12-03 | 亿景智联(北京)科技有限公司 | Management method and device of OLAP data warehouse |
| CN114358812A (en) * | 2021-11-26 | 2022-04-15 | 国网浙江省电力有限公司营销服务中心 | Multi-dimensional power marketing analysis method and system based on operation and maintenance big data |
| CN115794804A (en) * | 2023-02-07 | 2023-03-14 | 北京至臻云智能科技有限公司 | Engineering internal control data visualization processing system and method based on big data technology |
-
2006
- 2006-05-18 CN CNA2006100127243A patent/CN101075304A/en active Pending
Cited By (101)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101226527B (en) * | 2007-12-28 | 2011-06-08 | 中国移动通信集团四川有限公司 | Data synchronization method for double-nucleus library |
| CN101499921B (en) * | 2008-01-29 | 2012-01-11 | 华为技术有限公司 | Service object cleaning method and apparatus |
| CN101256650B (en) * | 2008-03-21 | 2012-02-15 | 中国科学院软件研究所 | Method and system for extracting enterprise data based on service entity |
| CN101576882B (en) * | 2008-05-08 | 2011-01-12 | 中国人民解放军第四五五医院 | Traceable data collecting and analyzing system |
| CN101374076B (en) * | 2008-10-06 | 2010-09-29 | 中国移动通信集团设计院有限公司 | Method for forecasting and planning server handling ability of telecom business support system |
| CN101770461B (en) * | 2008-12-31 | 2013-09-25 | 中国移动通信集团黑龙江有限公司 | Data processing method and data processing system |
| CN101958987B (en) * | 2009-07-14 | 2013-06-26 | 中国电信股份有限公司 | Method and system for dynamically converting telecommunications service data |
| CN102053951A (en) * | 2009-10-28 | 2011-05-11 | 无锡华润上华半导体有限公司 | Processing method of special character |
| CN102081625B (en) * | 2009-11-30 | 2012-12-26 | 中国移动通信集团北京有限公司 | Data query method and query server |
| CN102117454A (en) * | 2009-12-30 | 2011-07-06 | 中国银联股份有限公司 | Bank card safe payment method and system |
| CN102411569A (en) * | 2010-09-20 | 2012-04-11 | 上海众融信息技术有限公司 | Database conversion and cleaning information processing method |
| CN102043841A (en) * | 2010-12-10 | 2011-05-04 | 上海市城市建设设计研究院 | Multi-source information supplying method based on Web technology and integrated service system thereof |
| CN102750653A (en) * | 2011-04-20 | 2012-10-24 | 中国南方电网有限责任公司 | Method and system for performing multi-dimensional benchmarking analysis on electric power scheduling information |
| CN102339256A (en) * | 2011-09-15 | 2012-02-01 | 东北大学 | Complex event detection method on basis of IMF (instance matching frequency) internal and external memory replacement policy |
| CN102339256B (en) * | 2011-09-15 | 2013-10-16 | 东北大学 | Complex event detection method on basis of IMF (instance matching frequency) internal and external memory replacement policy |
| CN102629267A (en) * | 2012-03-08 | 2012-08-08 | 北京神州数码思特奇信息技术股份有限公司 | Data processing method and data processing module |
| CN102867282A (en) * | 2012-09-13 | 2013-01-09 | 福建富士通信息软件有限公司 | Implementation method for mobile Internet-based customer service quality analysis system |
| CN103679309A (en) * | 2012-09-13 | 2014-03-26 | 余明阳 | Intelligent statistical analysis method for power data warehouse services |
| CN103679309B (en) * | 2012-09-13 | 2017-11-03 | 国网湖北省电力公司 | A kind of electric power data warehouse business intelligence statistical analysis technique |
| CN103473305A (en) * | 2013-09-10 | 2013-12-25 | 北京思特奇信息技术股份有限公司 | Method and system for performing decision-making process show in statistic analysis |
| CN104574127A (en) * | 2013-10-21 | 2015-04-29 | 北京中海纪元数字技术发展股份有限公司 | Innovative marketing platform |
| CN104574127B (en) * | 2013-10-21 | 2018-07-06 | 北京中海纪元数字技术发展股份有限公司 | innovative marketing platform |
| CN103577590A (en) * | 2013-11-12 | 2014-02-12 | 北京润乾信息系统技术有限公司 | Data query method and system |
| CN103605732A (en) * | 2013-11-19 | 2014-02-26 | 北京京东尚科信息技术有限公司 | Data warehouse, data warehouse system and data warehouse construction method based on Infobright |
| CN103577605A (en) * | 2013-11-20 | 2014-02-12 | 贵州电网公司电力调度控制中心 | Data warehouse based on data fusion and data mining and application method of data warehouse |
| CN103617448A (en) * | 2013-12-05 | 2014-03-05 | 用友软件股份有限公司 | Method for immediately analyzing business data |
| CN103679383A (en) * | 2013-12-25 | 2014-03-26 | 武汉武船信息集成有限公司 | Method for establishing shipbuilding enterprise group management and control platform |
| CN103761309A (en) * | 2014-01-23 | 2014-04-30 | 中国移动（深圳）有限公司 | Operation data processing method and system |
| CN104809110A (en) * | 2014-01-23 | 2015-07-29 | 安徽海汇金融投资集团有限公司 | Large data system |
| CN103761309B (en) * | 2014-01-23 | 2017-04-26 | 中国移动（深圳）有限公司 | Operation data processing method and system |
| CN103746854A (en) * | 2014-01-27 | 2014-04-23 | 国家电网公司 | Communication mechanism for information management layer and function application layer of communication network system |
| CN104008428B (en) * | 2014-05-19 | 2017-07-11 | 上海交通大学 | Service of goods requirement forecasting and resource preferred disposition method |
| CN104008428A (en) * | 2014-05-19 | 2014-08-27 | 上海交通大学 | Product service demand forecasting and resource optimization configuration method |
| CN103984755A (en) * | 2014-05-28 | 2014-08-13 | 中国地质大学（北京） | Multidimensional model based oil and gas resource data key system implementation method and system |
| US10348595B2 (en) | 2014-07-15 | 2019-07-09 | Microsoft Technology Licensing, Llc | Managing data-driven services |
| WO2016008092A1 (en) * | 2014-07-15 | 2016-01-21 | Microsoft Technology Licensing, Llc | Managing data-driven services |
| CN104298773A (en) * | 2014-10-30 | 2015-01-21 | 北京思特奇信息技术股份有限公司 | Automatic base switching system and method for ETL operations |
| CN104298773B (en) * | 2014-10-30 | 2018-01-09 | 北京思特奇信息技术股份有限公司 | A kind of ETL operations automatically cut storehouse system and method |
| CN104394039A (en) * | 2014-12-09 | 2015-03-04 | 南京华苏科技股份有限公司 | Evaluation system and evaluation method for network performance maturity based on Sigmoid exponential model |
| CN104394039B (en) * | 2014-12-09 | 2017-12-19 | 南京华苏科技有限公司 | One kind is based on Sigmoid exponential models network performance maturity assessment system and its evaluation method |
| CN104573071A (en) * | 2015-01-26 | 2015-04-29 | 湖南大学 | Intelligent school situation analysis system and method based on megadata technology |
| CN104657475A (en) * | 2015-02-16 | 2015-05-27 | 百度在线网络技术（北京）有限公司 | Method and system for data analysis |
| CN104731908A (en) * | 2015-03-24 | 2015-06-24 | 浪潮集团有限公司 | ETL-based data cleaning method |
| CN106156040A (en) * | 2015-03-26 | 2016-11-23 | 阿里巴巴集团控股有限公司 | multi-dimensional data management method and device |
| CN105868222A (en) * | 2015-09-17 | 2016-08-17 | 乐视网信息技术（北京）股份有限公司 | Task scheduling method and device |
| CN105373895A (en) * | 2015-11-24 | 2016-03-02 | 苏州科技学院 | Green building power distribution management system |
| CN105631004A (en) * | 2015-12-28 | 2016-06-01 | 华夏银行股份有限公司 | Data processing method and system |
| CN105956084A (en) * | 2016-04-29 | 2016-09-21 | 浪潮(北京)电子信息产业有限公司 | Changed data extracting method and apparatus |
| CN105975613A (en) * | 2016-05-19 | 2016-09-28 | 国网重庆市电力公司客户服务中心 | Service channel recommendation method, device and system based on historical access data |
| 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 |
| CN108062320A (en) * | 2016-11-08 | 2018-05-22 | 长沙博为软件技术股份有限公司 | A kind of method for the data acquisition of multipad, conversion and loading |
| CN106790504A (en) * | 2016-12-16 | 2017-05-31 | 北京北科亿力科技有限公司 | A kind of metallurgical big data service platform |
| CN106777345A (en) * | 2017-01-16 | 2017-05-31 | 山东浪潮商用系统有限公司 | A kind of data pick-up loading method based on mass data migration |
| CN106777345B (en) * | 2017-01-16 | 2020-07-28 | 浪潮软件科技有限公司 | Data extraction loading method based on mass data migration |
| CN106934023A (en) * | 2017-03-13 | 2017-07-07 | 山东浪潮云服务信息科技有限公司 | A kind of data managing method and device |
| CN107256264A (en) * | 2017-06-14 | 2017-10-17 | 上海找钢网信息科技股份有限公司 | Self-service access instrument and access method |
| CN107346110A (en) * | 2017-07-03 | 2017-11-14 | 上海德衡数据科技有限公司 | A kind of multi-modal decision system prototype of O＆M based on polycaryon processor |
| CN107908640A (en) * | 2017-09-27 | 2018-04-13 | 国网浙江省电力公司杭州供电公司 | A kind of Business Entity relation Intelligent exploration engine implementing method |
| CN107491563A (en) * | 2017-09-28 | 2017-12-19 | 深圳市爱贝信息技术有限公司 | Towards the data processing method and system of settlement for account |
| CN107704608A (en) * | 2017-10-17 | 2018-02-16 | 北京览群智数据科技有限责任公司 | A kind of OLAP multidimensional analyses and data digging system |
| CN107818177A (en) * | 2017-11-23 | 2018-03-20 | 山东浪潮通软信息科技有限公司 | A kind of business intelligence model buildings method and build device |
| CN107818177B (en) * | 2017-11-23 | 2021-06-15 | 浪潮通用软件有限公司 | Business intelligent model building method and building device |
| CN107895235A (en) * | 2017-11-27 | 2018-04-10 | 安徽经邦软件技术有限公司 | Financial modeling system based on decision tree |
| CN110019397B (en) * | 2017-12-06 | 2021-06-29 | 北京京东尚科信息技术有限公司 | Method and device for data processing |
| CN110019397A (en) * | 2017-12-06 | 2019-07-16 | 北京京东尚科信息技术有限公司 | For carrying out the method and device of data processing |
| CN108228747A (en) * | 2017-12-20 | 2018-06-29 | 江苏数加数据科技有限责任公司 | Data genetic connection visualized graphs system in data improvement |
| CN108132997A (en) * | 2017-12-21 | 2018-06-08 | 国网江苏省电力公司泰州供电公司 | A kind of electric network data management sums up structure and its resolution principle |
| CN110019477A (en) * | 2017-12-27 | 2019-07-16 | 航天信息股份有限公司 | A kind of method and system carrying out big data processing using HIVE backup table |
| CN108462888A (en) * | 2018-03-14 | 2018-08-28 | 江苏有线数据网络有限责任公司 | The intelligent association analysis method and system of user's TV and internet behavior |
| CN108462888B (en) * | 2018-03-14 | 2021-01-19 | 江苏有线数据网络有限责任公司 | Intelligent correlation analysis method and system for user television and internet behavior |
| CN110609860A (en) * | 2018-05-29 | 2019-12-24 | 中国移动通信集团重庆有限公司 | Data ETL processing method, device, equipment and storage medium |
| CN109063078A (en) * | 2018-07-24 | 2018-12-21 | 武汉胖猫智能科技有限公司 | The data analysing method and system of steel trade financial business |
| CN109241179A (en) * | 2018-08-01 | 2019-01-18 | 协同数据技术（深圳）有限公司 | Data administering method, system and computer equipment based on data space |
| CN109597850A (en) * | 2018-11-22 | 2019-04-09 | 四川省烟草公司成都市公司 | Tobacco integrated information data mart modeling stores platform and data processing method |
| CN109376147A (en) * | 2018-11-26 | 2019-02-22 | 浙江中智达科技有限公司 | A kind of data processing method and system |
| CN109408448A (en) * | 2018-12-05 | 2019-03-01 | 江苏恒创软件有限公司 | One kind can carry out centralized processing integration data to data and show platform |
| CN109669934A (en) * | 2018-12-11 | 2019-04-23 | 江苏瑞中数据股份有限公司 | A kind of data warehouse and its construction method suiting electric power customer service |
| CN111382155A (en) * | 2018-12-28 | 2020-07-07 | 上海连尚网络科技有限公司 | Data processing method of data warehouse, electronic equipment and medium |
| CN111382155B (en) * | 2018-12-28 | 2024-04-09 | 上海尚往网络科技有限公司 | Data processing method of data warehouse, electronic equipment and medium |
| CN109726174A (en) * | 2018-12-28 | 2019-05-07 | 江苏满运软件科技有限公司 | Data archiving method, system, equipment and storage medium |
| CN109902117A (en) * | 2019-02-19 | 2019-06-18 | 新华三大数据技术有限公司 | Operation system analysis method and device |
| CN110069499B (en) * | 2019-04-18 | 2021-05-18 | 中国联合网络通信集团有限公司 | Data management method, device, system and storage medium |
| CN110069499A (en) * | 2019-04-18 | 2019-07-30 | 中国联合网络通信集团有限公司 | Data managing method, device, system and storage medium |
| CN110232098A (en) * | 2019-04-22 | 2019-09-13 | 汇通达网络股份有限公司 | A kind of data warehouse administered based on data and genetic connection designs |
| CN110362605A (en) * | 2019-06-04 | 2019-10-22 | 苏州神州数码捷通科技有限公司 | A kind of E book data verification method based on big data |
| CN110414802A (en) * | 2019-07-08 | 2019-11-05 | 南京华盾电力信息安全测评有限公司 | Conglomerate Analysis of Policy Making flight deck system |
| CN110334088A (en) * | 2019-07-11 | 2019-10-15 | 江苏曲速教育科技有限公司 | Educational data management system |
| CN112579869A (en) * | 2019-09-29 | 2021-03-30 | 北京星选科技有限公司 | Object extraction method and device, electronic equipment and storage medium |
| CN110955717A (en) * | 2019-11-19 | 2020-04-03 | 深圳市航通智能技术有限公司 | Visual dynamic display method and system based on big data |
| CN110941659A (en) * | 2019-11-26 | 2020-03-31 | 上海景域文化传播股份有限公司 | Data layered splitting method for composite revenue |
| CN110955413B (en) * | 2019-11-28 | 2023-04-18 | 国网河北省电力有限公司沧州供电分公司 | Financial data processing system |
| CN110955413A (en) * | 2019-11-28 | 2020-04-03 | 国网河北省电力有限公司沧州供电分公司 | Financial data processing system |
| CN112182089A (en) * | 2020-10-15 | 2021-01-05 | 南方电网数字电网研究院有限公司 | Report generation method, device and equipment based on data warehouse model |
| CN112579648A (en) * | 2020-12-23 | 2021-03-30 | 赛尔网络有限公司 | Data processing system, method, device and medium for enterprise |
| CN112966860A (en) * | 2021-02-23 | 2021-06-15 | 武汉育知联信息科技有限公司 | Data analysis and prediction system based on attached pre-chain |
| CN112988783A (en) * | 2021-03-12 | 2021-06-18 | 李涛 | Public opinion occurrence time sequence analysis method based on multidimensional data model |
| CN113485989A (en) * | 2021-07-02 | 2021-10-08 | 中国建设银行股份有限公司 | Comprehensive analysis method, system, medium and equipment for supervision data |
| CN113742320A (en) * | 2021-11-05 | 2021-12-03 | 亿景智联(北京)科技有限公司 | Management method and device of OLAP data warehouse |
| CN113742320B (en) * | 2021-11-05 | 2022-03-01 | 亿景智联(北京)科技有限公司 | Management method and device of OLAP data warehouse |
| CN114358812A (en) * | 2021-11-26 | 2022-04-15 | 国网浙江省电力有限公司营销服务中心 | Multi-dimensional power marketing analysis method and system based on operation and maintenance big data |
| CN115794804A (en) * | 2023-02-07 | 2023-03-14 | 北京至臻云智能科技有限公司 | Engineering internal control data visualization processing system and method based on big data technology |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101075304A (en) | Method for constructing decision supporting system of telecommunication industry based on database | |
| US11803873B1 (en) | Systems and methods for providing a direct marketing campaign planning environment | |
| US9218408B2 (en) | Method for automatically creating a data mart by aggregated data extracted from a business intelligence server | |
| Géczy | Big data characteristics | |
| US9195700B1 (en) | Systems and methods for storing time-series data | |
| CN103577605A (en) | Data warehouse based on data fusion and data mining and application method of data warehouse | |
| CN1959717A (en) | System and method for preprocessing mass remote sensing data collection driven by order form | |
| CN1918590A (en) | Using technical performance metrics for business and usage analysis and cost allocation | |
| CN1347529A (en) | Method for visualizing information in data warehousing environment | |
| CN1794645A (en) | Invading detection method and system based on procedure action | |
| US20180013643A1 (en) | Determining events by analyzing stored electronic communications | |
| Sreemathy et al. | Data integration and ETL: a theoretical perspective | |
| US8201145B2 (en) | System and method for workflow-driven data storage | |
| US20070239587A1 (en) | System and Method For Dynamically Utilizing and Managing Financial, Operational, and Compliance Data | |
| CN103823895A (en) | Cutter cutting database management system | |
| CN101714150A (en) | System and method for analyzing technical hotspots and blank spots in patent analysis | |
| Hu | Information lifecycle modeling framework for construction project lifecycle management | |
| TW200419388A (en) | System and method for automatically comparing and recoding difference between bills of material (BOM) | |
| CN115082039A (en) | Data center management method and device, electronic equipment and storage medium | |
| CN1588405A (en) | Data processing device and method for risk control system | |
| Motahari et al. | Process spaceship: discovering and exploring process views from event logs in data spaces | |
| CN109684398A (en) | The system and method for data-optimized management function is realized under big data environment based on multimode | |
| US11734245B1 (en) | Systems and methods for storing time-series data | |
| Kirkgöze et al. | The use of data warehouses as a basis for strategic decision in tourism | |
| Ng et al. | Concurrent engineering performance: An empirical study on the use of CAD for design and cross-functional information sharing in a semiconductor manufacturing firm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Open date: 20071121 |