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Decision Support System Based on Data Warehouse Yang Bao Lujing Zhang World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering Vol:4, No:11, 2010 Decision Support System Based on Data Warehouse Yang Bao LuJing Zhang Abstract—Typical Intelligent Decision Support System is Warehouse) compared with traditional DSS, it provided three 4-based, its design composes of Data Warehouse, Online Analytical kinds of decision tools:MDSS(Model DSS), DM, OLAP. The Processing, Data Mining and Decision Supporting based on models, figure 2 can be seen, DSSBDW including DW, Model which is called Decision Support System Based on Data Warehouse Analyzing, metadata, OLAP, user interface,etc. (DSSBDW). This way takes ETL,OLAP and DM as its implementing means, and integrates traditional model-driving DSS and data-driving DSS into a whole. For this kind of problem, this paper analyzes the II. THE KEY TECHNOLOGY OF DW IN DSS DSSBDW architecture and DW model, and discusses the following key issues: ETL designing and Realization; metadata managing technology using XML; SQL implementing, optimizing performance, data mapping in OLAP; lastly, it illustrates the designing principle and method of DW in DSSBDW. Distill,Clear DW up,Load、 Keywords—Decision Support System, Data Warehouse, Data Refresh Mining. I. DECISION SUPPORT SYSTEM AND DATA WAREHOUSE A. Decision Support System (DSS) Data Sources Data Mart Traditional DSS generally is consisted of three bases model, Fig. 1 Architecture of Data Warehouse and IDSS （ Intelligent Decision Support System ） is consisted of four bases structure[1], based on three bases increasing the knowledge base system and its reasoning Model Analyzing system, the IDSS is considered of an increase of expert External Data Preparation system in the general DSS. Knowledge base is intelligent Data DW Data Mining component in IDSS, used to simulate some smart activity in （ the human decision making process, that can make DSS Operational Relational supporting to decision makers been greatly enhanced. Data DB） Multidimensio OLAP B. Data Warehouse (DW) Data nal-Database Sources User B.1 Composition of the DW system I f DW system contains three levels of architecture, as shown in figure 1. The three levels respectively: data sources, data storage and management , OLAP（On-Line Metada DSSBDW Analytical Processing）and data mining tools. B.2 Data organization structure of DW The data organization style of DW contains three Fig. 2 Architecture of Decision Support System Based on Data Warehouse kinds:virtual database relations based on the storage and multidimensional databases. Data Warehouse, the data is A. Data Preparation divided into four categories: Early details, the details, light Data preparation, also known as data pretreating, is the basic degree integrated, highly integrated level. Data Warehouse, premise on building DW. It majorly includes extraction, International Science Index, Computer and Information Engineering Vol:4, No:11, 2010 waset.org/Publication/10424 there is an important data-metadata (metadata). The data transformation, loading, commonly known as ETL (Extract, storage environment, there are two main metadata: The first is Transform, Load). Data preparation is the data entry for the the operational environment to the data warehouse and whole DW, this link will complete the task of data acquisition, conversion of $data, including all source data of members, cleaning, certification, amalgamation and integration, loading, properties and the data warehouse in the transformation and the filing, data reproducing, reducing paradigm degree and two million data for the OLAP and Data Warehouse building summary. map.Figures B. Constructing metadata model C. DSS Architecture based on DW DSSBDW[2] (Decision Support System Based on Data International Scholarly and Scientific Research & Innovation 4(11) 2010 1659 scholar.waset.org/1307-6892/10424 World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering Vol:4, No:11, 2010 Metadata takes on extremely important role in the design, operation of DW, it describes the various objects of DW in all its aspects, which is the core of DW. For it,there is two major metadata standards to be used in the DW areas,: OIM (Open Information Model) Standards of MDC (Meta Data Star Standard DW with Multi-Star Coalition) and CWM (Common Warehouse Model, CWM) aggregation standards of OMG.Such as the figure3 is the composition of Fig. 5 Data Model of Data Warehouse CWM Metamodel: management Warehouse Process Warehouse Operate analysis Transform OLAP Data Mining Information Visualization Commercial Terms resource UML Relational Resource Recording Multidimension XML foundation Business Information Data Type Expression Keys Index Type Mapping Software Release UML 1.3 (Foundation,Behavior Element,Model Management ) Fig. 3 Package Structure of CWM Metamodel important link in the implementation of DW, There are five C. OLAP Operation kinds of relative Modeling Technology commonly like figure 5 The OLAP operating mainly aims to online data access and shown: analysis of specific problems, the object is to meet the need of E.2 The main implementing methods and steps of DW specific query and reporting of decision support or To highlight the development process with its uncertain multidimensional environment, focusing on the decision demand , DW design method is described as CLDS support of decision makers and senior managers, and so it is a methods,and SDLC on the contrary,the keystone is through powerful tool to analyze and make decision.It contains adopting the External Data Access to complete DW modeling, Slice,Dice,Rotate and Drill. data acquisition integrating, DW building , DSS application programming,system testing, requirement understanding, and D. Data Mining Operation then going trace back to the DW modeling. The implementing Typical Data mining methods in accordance with the steps of DW contain:collect and analyzing business different apply mode contain:Related analysis, Time order needs,establish the data models and the physical design of Dw mode,Cluster,Classification,Deviation testing and Forecasting ， select DW technique and platform,define data models[3]. It is an orderly and complete process. fundamental sources,develope DSS application, strengthen management, processes of data mining with ETL, OLAP of DW have many take the active participation with end users,decompound the coincide ways, shown in figure 4 below. Logical Selected Pretreatment Transformed Extracted Assimilative DB data ed data data inf knowledge Select Pretreatment Transform Mine Analyze and Assimilate Fig. 4 Main Steps of Data Mining International Science Index, Computer and Information Engineering Vol:4, No:11, 2010 waset.org/Publication/10424 complex needs , prepare data, provide OLAP and data mining tools,etc. E. Designing and Implementing of DW E.1 Three levels of model of DW III. THE DSS DESIGN WITH DW TECHNOLOGY Three levels of model of DW contain:Conception Model, Logical Model and Physical Model.In these,Logic model is the A. Architecture design of DSS International Scholarly and Scientific Research & Innovation 4(11) 2010 1660 scholar.waset.org/1307-6892/10424 World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering Vol:4, No:11, 2010 DSS design adopts the architecture shown as figure 6.Data TABLE I CLASSIFICATION OF ETL OPERATION FLOW Functional Freque Processing Steps C/S Categories ncy DSS Metadata Tools Process 1．set data information in data once Management Management Tl source Process once 2 set target data information B/S Management ． Process 3．data mapping on source data and once Management target data OLAP&D Process once 4 data replication mode definition MTools Management ． Old Business Process Systems 5 schedule ETL task times Management ． Data Acquisition 6．achieve source data times 7．transform between source data Old Assistant Business Data Transformation and target data based on mapping times Business ETL System Systems Tools regulation DW Data Loading 8．load data to target data times accordi Process ng to Old Other 9．revise execution definition Business Management the Systems needs C.2 ETL elementary COMPONENTS Fig. 6 Achitecture of Decision Support System The ETL integrated components include: Data Access Component, Data Conversion Component, Data Loading source is from the original operational system database and Component, Process Management Component, Systems stored into the Data Warehouse Center storage through ETL Management Component. tools. OLAP and DM tools provide multidimensional analyzing C.3 Data Conversion mapping relation and mining means in DSS, and show to the final decision In DSS, the source field should be related to the property makers by performance tools. field of target data object, and be defined the length,data type of B. The typical realizing scheme of System Architecture property field in the source field record, and so on. Targets DSS can be used "Three layers plus two layers" structure objects and properties are the table and field in DW or Data In model, namely that ETL/system management module use data objects and properties in transaction component. Rule Client/Server mode, OLAP module use Browser/Server mode. information describe the corresponding data conversion Its main design scheme in figure 7. regulation and transaction. Completed regulation description and the corresponding definition information shown as table II. TABLE II Old Business S DEFINITIONS AND DESCRIPTIONS
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