Data warehouse provides storage and management for mass data, but data schema evolves with time on. When data schema is changed, added or deleted, the data in data warehouse must comply with the changed data schema, ...Data warehouse provides storage and management for mass data, but data schema evolves with time on. When data schema is changed, added or deleted, the data in data warehouse must comply with the changed data schema, so data warehouse must be re organized or re constructed, but this process is exhausting and wasteful. In order to cope with these problems, this paper develops an approach to model data cube with XML, which emerges as a universal format for data exchange on the Web and which can make data warehouse flexible and scalable. This paper also extends OLAP algebra for XML based data cube, which is called X OLAP.展开更多
Data warehouse (DW), a new technology invented in 1990s, is more useful for integrating and analyzing massive data than traditional database. Its application in geology field can be divided into 3 phrases: 1992-1996,...Data warehouse (DW), a new technology invented in 1990s, is more useful for integrating and analyzing massive data than traditional database. Its application in geology field can be divided into 3 phrases: 1992-1996, commercial data warehouse (CDW) appeared; 1996-1999, geological data warehouse (GDW) appeared and the geologists or geographers realized the importance of DW and began the studies on it, but the practical DW still followed the framework of DB; 2000 to present, geological data warehouse grows, and the theory of geo-spatial data warehouse (GSDW) has been developed but the research in geological area is still deficient except that in geography. Although some developments of GDW have been made, its core still follows the CDW-organizing data by time and brings about 3 problems: difficult to integrate the geological data, for the data feature more space than time; hard to store the massive data in different levels due to the same reason; hardly support the spatial analysis if the data are organized by time as CDW does. So the GDW should be redesigned by organizing data by scale in order to store mass data in different levels and synthesize the data in different granularities, and choosing space control points to replace the former time control points so as to integrate different types of data by the method of storing one type data as one layer and then to superpose the layers. In addition, data cube, a wide used technology in CDW, will be no use in GDW, for the causality among the geological data is not so obvious as commercial data, as the data are the mixed result of many complex rules, and their analysis always needs the special geological methods and software; on the other hand, data cube for mass and complex geo-data will devour too much store space to be practical. On this point, the main purpose of GDW may be fit for data integration unlike CDW for data analysis.展开更多
Modeling plays an important role for the solution of the complex research problems. When the database became large and complex then it is necessary to create a unified model for getting the desired information in the ...Modeling plays an important role for the solution of the complex research problems. When the database became large and complex then it is necessary to create a unified model for getting the desired information in the minimum time and to implement the model in a better way. The present paper deals with the modeling for searching of the desired information from a large database by storing the data inside the three dimensional data cubes. A sample case study is considered as a real data related to the ground water and municipal water supply, which contains the data from the various localities of a city. For the demonstration purpose, a sample size is taken as nine but when it becomes very large for number of localities of different cities then it is necessary to store the data inside data cubes. A well known object-oriented Unified Modeling Language (UML) is used to create Unified class and state models. For verification purpose, sample queries are also performed and corresponding results are depicted.展开更多
Along with the rapid development of internet, CRM has become one of the most important facts leading the enterprises to be competent. At the same time, the analytical CRM based on Date Warehouse is the kernel of CRM s...Along with the rapid development of internet, CRM has become one of the most important facts leading the enterprises to be competent. At the same time, the analytical CRM based on Date Warehouse is the kernel of CRM system. This paper mainly explains the idea of CRM and the DW model of analytical CRM system.展开更多
文摘Data warehouse provides storage and management for mass data, but data schema evolves with time on. When data schema is changed, added or deleted, the data in data warehouse must comply with the changed data schema, so data warehouse must be re organized or re constructed, but this process is exhausting and wasteful. In order to cope with these problems, this paper develops an approach to model data cube with XML, which emerges as a universal format for data exchange on the Web and which can make data warehouse flexible and scalable. This paper also extends OLAP algebra for XML based data cube, which is called X OLAP.
文摘Data warehouse (DW), a new technology invented in 1990s, is more useful for integrating and analyzing massive data than traditional database. Its application in geology field can be divided into 3 phrases: 1992-1996, commercial data warehouse (CDW) appeared; 1996-1999, geological data warehouse (GDW) appeared and the geologists or geographers realized the importance of DW and began the studies on it, but the practical DW still followed the framework of DB; 2000 to present, geological data warehouse grows, and the theory of geo-spatial data warehouse (GSDW) has been developed but the research in geological area is still deficient except that in geography. Although some developments of GDW have been made, its core still follows the CDW-organizing data by time and brings about 3 problems: difficult to integrate the geological data, for the data feature more space than time; hard to store the massive data in different levels due to the same reason; hardly support the spatial analysis if the data are organized by time as CDW does. So the GDW should be redesigned by organizing data by scale in order to store mass data in different levels and synthesize the data in different granularities, and choosing space control points to replace the former time control points so as to integrate different types of data by the method of storing one type data as one layer and then to superpose the layers. In addition, data cube, a wide used technology in CDW, will be no use in GDW, for the causality among the geological data is not so obvious as commercial data, as the data are the mixed result of many complex rules, and their analysis always needs the special geological methods and software; on the other hand, data cube for mass and complex geo-data will devour too much store space to be practical. On this point, the main purpose of GDW may be fit for data integration unlike CDW for data analysis.
文摘Modeling plays an important role for the solution of the complex research problems. When the database became large and complex then it is necessary to create a unified model for getting the desired information in the minimum time and to implement the model in a better way. The present paper deals with the modeling for searching of the desired information from a large database by storing the data inside the three dimensional data cubes. A sample case study is considered as a real data related to the ground water and municipal water supply, which contains the data from the various localities of a city. For the demonstration purpose, a sample size is taken as nine but when it becomes very large for number of localities of different cities then it is necessary to store the data inside data cubes. A well known object-oriented Unified Modeling Language (UML) is used to create Unified class and state models. For verification purpose, sample queries are also performed and corresponding results are depicted.
文摘Along with the rapid development of internet, CRM has become one of the most important facts leading the enterprises to be competent. At the same time, the analytical CRM based on Date Warehouse is the kernel of CRM system. This paper mainly explains the idea of CRM and the DW model of analytical CRM system.
