CN101566981A - Method for establishing dynamic virtual data base in analyzing and processing system - Google Patents
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Abstract
The invention relates to a method for establishing a dynamic virtual data base in an analyzing and processing system, which comprises the following steps: (1) defining metadata; (2) constructing a dimension indexing table; (3) constructing data marts; (4) instantiating the data marts; (5) constructing and instantiating a plurality of the data marts; and (6) dynamically modeling and instantiating the virtual data base. The method collects service required data from an enterprise database, defines fields with analysis significance into the metadata, carries out dynamic modeling, dynamic extraction and conversion processing on the corresponding metadata, constructs a plurality of the data marts conforming to an analysis subject and instantiates the virtual data base so as to realize the zero storage of the data in the virtual data base, overcome contradictions between system equipment resources and continuously increased information quantities and meet the requirements of high efficiency and data real-time for a management decision database of a decider.
Description
Technical field
The present invention relates to a kind of method of in analysis process system, setting up dynamic virtual data.
Background technology
Today of global economy fast development, no matter be transregional company, enterprise or government bodies, all can have every day lot of data to need to handle, these data often are dispersed in the affairs type database under each different application system, as data such as producing and selling, finance.The decision maker often needs to find from these lot of data own needed data to analyze, so as to carry out correctly, decision-making timely.But in the face of lot of data like this, rely on traditional data warehouse implementation can't accomplish that dynamic modeling, data in real time to data dynamically extracts and the function of the real-time online analysis of information.
As everyone knows, by utilization ERP (abbreviation of English full name Enterprise Recourse Planning, Chinese is an Enterprise Resources Plan) system, enterprise can manage all kinds of resources of this enterprise more efficiently.Need multilist operation but the database that utilizes ERP system carries out analyzing and processing, its long operational time, efficient are low, even cause Network Transmission busy.Therefore when using the ERP system database, need be used in combination data warehouse (English name is Data Warehouse) disposal system toward contact.Data warehouse is the data acquisition of a subject-oriented (English name is Subject Oriented), integrated (English name is Integrate), metastable (English name is Non-Volatile), reflection historical variations (English name is Time Variant), is used to support management decision.Data warehouse can be organized the data of enterprise by ad hoc fashion, thereby produces new commercial knowledge, and brings new visual angle for the running of enterprise.
After data warehouse used, enterprise's collected information from all distributed data bases was concentrated and is stored together, and pressed certain way reorganization affairs type data and decision support type data, the data of this two classes different performance.Though yet the affairs type database can be stored the data that produced in the enterprise operation process, but it is often unsatisfactory that it is analyzed with working ability, and the affairs type data always are among the dynamic change and unstable, are difficult to tackle decision support type and handle needs to the relatively stable processing of data.Even makeshift is up to the present also just separated by the fixed time period that configures in advance the decision support type data processing from the affairs type data processing, importing to the decision support type database by data extraction tool is in the data warehouse.By the fixation problem of enterprise aspect, divide " theme " tissue, store data.But such processing mode has been brought new problem thereupon.
Decision support type data demand in the data warehouse stores with static mode, and the data of being analyzed are according to set analysis subject extraction.Consequently, only can access some fixing analytical statements, fixing analytical model, yet data are difficult to synchronously and exist the lot of data redundancy.Particularly when analyzing that theme changes and historical data changes, originally the data failure of Chou Quing must redefine rule, again extracted data.This has not only greatly reduced the service efficiency of data warehouse, and makes maintenance cost raise.Also limited simultaneously the various visual angles variation of decision-making application to information, be difficult to the real-time online analysis of the information that realizes, restricted the efficient that decision-making is used, also need a large amount of equipment inputs, make system more huge, also efficient is but more low thereupon increasing for maintenance cost.
Summary of the invention
The technical problem to be solved in the present invention is, defective at the prior art existence, provide the method that realizes dynamic virtual data in a kind of analysis process system, so as to optimizing the real-time online analysis of above-mentioned data warehouse technology, the modeling of realization Data Dynamic, Real-time and Dynamic extraction and information.
In the technical solution of the present invention, the method for setting up dynamic virtual data in the described analysis process system is to be following steps:
(1), definition metadata: from enterprise database, collect the business demand data, collected data are divided into the technical element data and commercial metadata two classes define.The purpose of meta data definition is that this catalogue has been described the type of all data of depositing, the source and the access mode of these data in the data warehouse comprehensively for the visit dynamic virtual data provides a message catalog.The user can understand and visit data by it.The specific practice that defines above-mentioned data is as follows:
1., definition technical element data: the object in the affairs type database and data structure information, the description of data-switching, source data are defined as the technical element data to the mapping of destination data, possible computing method, access privilege, information issue historical record.And these data that are defined as the technical element data are offered the design and the managerial personnel of dynamic virtual data, be used for exploitation and daily management;
2., define commercial metadata: with the data of describing with the business event angle in the affairs type database, for example data recording such as client, product, supplier are defined as commercial metadata.
(2), make up the dimension concordance list:
1., above-mentioned various commercial metadata are configured to the dimension table model;
2., the various of above-mentioned various commercial metadata may array mode be configured to dimension concordance list model.Promptly the dimension index in the dimension table is carried out 2 n power and calculate,, be combined to high combination and define in regular turn by low with all result of calculation.
(3), make up Data Mart: the data in the Data Mart are data of concluding out at user's demand.
1., make up subject data: in above-mentioned enterprise database, user's analysis theme is carried out the abstract subject data that is treated as;
2., make up Data Mart: retrieve above-mentioned enterprise database, organize the technical element data of implication and commercial metadata to be configured to a data fairground with meeting subject data in this database.
(4), instantiation Data Mart: the instantiation Data Mart is needed visual information directly to be provided and to need not to create fixing static data warehouse for the decision maker.According to the online demand of user, from the technical element data, retrieve corresponding analytical approach, the commercial metadata of the subject data of the step of step (3) being set in 1. according to this method extracts with conversion process and is the Data Mart instantiation then.Thereby the Data Mart of this instantiation provides the foreground of data to show and realizes information visualization and do not take database space, does not more need to create fixing static data warehouse.
(5), make up the also a plurality of Data Marts of instantiation: according to the multiple needs of user, according to above-mentioned steps (3)～(4), simultaneously technical element data and commercial metadata are carried out association process, corresponding a plurality of Data Marts and the described a plurality of Data Marts of instantiation of making up are built into virtual dynamic data warehouse on the logical meaning by a plurality of Data Marts.
(6), dynamic modeling and instantiation virtual data warehouse: with above-mentioned analysis theme be dispersed in each affairs type database in different metadata carry out association process and require modeling according to Data Mart.When the data variation in analysis theme or the Data Mart, according to the situation of change of zone, customers, product class, the inferior factor of administration and supervision authorities, similarity searching, collaborative filtering and the cluster analysis principle in the application high dimensional data handling principle finished the instantiation in virtual data warehouse.
Principle of work of the present invention is: analyze the business demand data from enterprise database, the field that will have analysis significance is technical element data and commercial metadata according to its attribute definition, and commercial metadata is set up the dimension concordance list.In enterprise database according to analyzing the abstract subject data of theme, make up Data Mart and instantiation Data Mart, metadata corresponding is carried out dynamic modeling, dynamically extracted and conversion process, make up and meet a plurality of Data Marts and the instantiation virtual data warehouse of analyzing theme.
The invention has the beneficial effects as follows:
1, use this method can realize the dynamic data in virtual data warehouse is extracted:
1., the analysis theme that proposes according to the decision maker conducts interviews to metadata.Wherein relevant various metadata are made up processing, make it to form the tuple of various array modes, online in real time is finished the conversion of final demonstrating data then, fights to the finish and makes supporting movement to cut off enemy with nonsensical data automatic fitration; Data in the Data Mart are converted to unified data name and definition; Counting statistics and derivative data; Invest default value for the missing value data; Different data definition modes is unified;
2., data extract in real time by theme, when each affairs type database structure changes or historical data when changing, extraction model adapts to adjustment automatically; Have Dynamic Data Processing efficient height, data processing response time rapidly, low, easy to maintenance, the running environment of system equipment investment and characteristics such as maintenance cost is low, safety, stability height, dirigibility height.
2, use method of the present invention also can realize zero of data in the virtual data warehouse are deposited.Be only to show after the different virtual data fairground instantiation not account for any space, backstage by result data, need not newly-increased memory capacity, thereby avoided database constantly occupying system resource on the foreground.Solve the difficult problem that decision-making type data warehouse zero is deposited, solved the contradiction between system equipment resource and the ever-increasing quantity of information.
3, adopt the dynamic virtual data method, realized the dynamic modeling of virtual data warehouse analysis theme; Theme as guidance with performance analysis, realize that dynamic extraction conversion, intelligently filters arrangement, Dynamic Display analysis, the navigation formula location of decision information data excavated.The existing problem in aspect such as solved that the static data warehouse extracts at dynamic data modeling, dynamic data, the real-time online analysis of information, data zero are deposited.Really realized high-level efficiency and the real-time property requirement of decision maker to management decision type database.
Embodiment:
Embodiment 1: set up dynamic virtual data in a plurality of affairs type database collections:
(1), definition metadata: from a plurality of affairs type database collections, collect the business demand data, collected data are divided into the technical element data and commercial metadata two classes define.The specific practice that defines above-mentioned data is as follows:
1., definition technical element data: the object in a plurality of affairs type databases and data structure information, the description of data-switching, source data are defined as the technical element data to the mapping of destination data, possible computing method, access privilege, information issue historical record.And these data that are defined as the technical element data are offered the design and the managerial personnel of dynamic virtual data, be used for exploitation and daily management;
2., define commercial metadata:, be about to data recording such as client, product, supplier and be defined as commercial metadata with the data of describing with the business event angle in a plurality of affairs type databases.
(2), make up the dimension concordance list:
1., above-mentioned various commercial metadata are configured to the dimension table model;
2., the various of above-mentioned various commercial metadata may array mode be configured to dimension concordance list model.Promptly the dimension index in the dimension table is carried out 2 n power and calculate,, be combined to high combination and define in regular turn by low with all result of calculation.
(3), make up Data Mart: the data in the Data Mart are data of concluding out at user's demand.
1., make up subject data: in above-mentioned enterprise database, user's analysis theme is carried out the abstract subject data that is treated as;
2., make up Data Mart: retrieve above-mentioned enterprise database, organize the technical element data of implication and commercial metadata to be configured to a data fairground with meeting subject data in this database.
(4), instantiation Data Mart: the instantiation Data Mart is needed visual information directly to be provided and to need not to create fixing static data warehouse for the decision maker.According to the online demand of user, from the technical element data, retrieve corresponding analytical approach, the commercial metadata of the subject data of the step of step (3) being set in 1. according to this method extracts with conversion process and is the Data Mart instantiation then.Thereby the Data Mart of this instantiation provides the foreground of data to show and realizes information visualization and do not take database space, does not more need to create fixing static data warehouse.
(5), make up the also a plurality of Data Marts of instantiation: according to the multiple needs of user, according to above-mentioned steps (3)～(4), simultaneously technical element data and commercial metadata are carried out association process, corresponding a plurality of Data Marts and the described a plurality of Data Marts of instantiation of making up are built into virtual dynamic data warehouse on the logical meaning by a plurality of Data Marts.
(6), dynamic modeling and instantiation virtual data warehouse: with above-mentioned analysis theme be dispersed in each affairs type database in different metadata carry out association process and require modeling according to Data Mart.When the data variation in analysis theme or the Data Mart, according to the situation of change of zone, customers, product class, the inferior factor of administration and supervision authorities, similarity searching, collaborative filtering and the cluster analysis principle in the application high dimensional data handling principle finished the instantiation in virtual data warehouse.
Embodiment 2: set up dynamic virtual data in single toy data base:
(1), definition metadata: from single affairs type database collection, collect the business demand data, collected data are divided into the technical element data and commercial metadata two classes define.The specific practice that defines above-mentioned data is as follows:
1., definition technical element data: the object in the single affairs type database and data structure information, the description of data-switching, source data are defined as the technical element data to the mapping of destination data, possible computing method, access privilege, information issue historical record.And these data that are defined as the technical element data are offered the design and the managerial personnel of dynamic virtual data, be used for exploitation and daily management;
2., define commercial metadata:, be about to data recording such as client, product, supplier and be defined as commercial metadata with the data of describing with the business event angle in the single affairs type database.
Step (2)～step (6) is with embodiment 1.
Claims (1)
1, set up the method for dynamic virtual data in a kind of analysis process system, this method is following steps:
(1), definition metadata: collect the business demand data from enterprise database, collected data are divided into the technical element data and commercial metadata two classes define, the specific practice that defines above-mentioned data is as follows:
1., definition technical element data: the object in the affairs type database and data structure information, the description of data-switching, source data are defined as the technical element data to the mapping of destination data, possible computing method, access privilege, information issue historical record;
2., define commercial metadata: the data recording of describing with the business event angle in the affairs type database is defined as commercial metadata;
(2), make up the dimension concordance list:
1., above-mentioned various commercial metadata are configured to the dimension table model;
2., the various of above-mentioned various commercial metadata may array modes be configured to dimension concordance list model, promptly the dimension index in the dimension table is carried out 2 n power and calculate,, be combined to high combination and define in regular turn by low with all result of calculation;
(3), make up Data Mart: the data in the Data Mart are data of concluding out at user's demand;
1., make up subject data: in above-mentioned enterprise database, user's analysis theme is carried out the abstract subject data that is treated as;
2., make up Data Mart: retrieve above-mentioned enterprise database, organize the technical element data of implication and commercial metadata to be configured to a data fairground with meeting subject data in this database;
(4), the instantiation Data Mart: the instantiation Data Mart is needed visual information directly to be provided and to need not to create fixing static data warehouse for the decision maker, according to the online demand of user, from the technical element data, retrieve corresponding analytical approach, the commercial metadata of the subject data of the step of step (3) being set in 1. according to this method extracts with conversion process and is the Data Mart instantiation then, thereby the Data Mart of this instantiation provides the foreground of data to show and realizes information visualization and do not take database space, does not more need to create fixing static data warehouse;
(5), make up the also a plurality of Data Marts of instantiation: according to the multiple needs of user, according to above-mentioned steps (3)～(4), simultaneously technical element data and commercial metadata are carried out association process, corresponding a plurality of Data Marts and the described a plurality of Data Marts of instantiation of making up are built into virtual dynamic data warehouse on the logical meaning by a plurality of Data Marts;
(6), dynamic modeling and instantiation virtual data warehouse: with above-mentioned analysis theme be dispersed in each affairs type database in different metadata carry out association process and require modeling according to Data Mart, when the data variation in analysis theme or the Data Mart, according to the situation of change of zone, customers, product class, the inferior factor of administration and supervision authorities, similarity searching, collaborative filtering and the cluster analysis principle in the application high dimensional data handling principle finished the instantiation in virtual data warehouse.
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