Source integration in data warehousing
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Source integration is one of the core problems in data warehousing. Two critical factors for the design and maintenance of applications requiring source integration, and in particular data warehouse applications, are conceptual modeling of the domain, and reasoning support over the conceptual representation. We present a novel approach to conceptual modeling for source integration, which allows for suitably modeling the global concepts of the application, the individual information sources, and the constraints among different sources. Our methodological framework relies on the reasoning services associated with the modeling formalism to support an incremental source integration phase within the data warehouse design process.Keywords:
Information integration
Dimensional modeling
Formalism (music)
Conceptual model
Conceptual design
With the growing complexity of spatial data, present data database and data analyzing tool can't give enough help to decision-making, so it is necessary to establish many spatial data warehouses with much history data and on different scale. During the process of spatial data warehouses building, data integration is an important step and especially spatial data integration is a rough job. Based on present database, spatial data warehouse and spatial data integration, the article discusses thoroughly about the process of building the model of the spatial data warehouse and brings out systematically two aspects on data integration orientated to spatial data warehouse: one about data integration of different data resources, the other about data integration of different scale.
Dimensional modeling
Spatial Data Infrastructure
Online analytical processing
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Data warehouses using a multidimensional view of data have become very popular in both business and science in recent years. Data warehouses for scientific purposes such as medicine and bio-chemistry pose several great challenges to existing data warehouse technology. Data warehouses usually use pre-aggregated data to ensure fast query response. However, pre-aggregation cannot be used in practice if the dimension structures or the relationships between facts and dimensions are irregular. A technique for overcoming this limitation and some experimental results are presented. Queries over scientific data warehouses often need to reference data that is external to the data warehouse, e.g., data that is too complex to be handled by current data warehouse technology, data that is "owned" by other organizations, or data that is updated frequently. This paper presents a federation architecture that allows the integration of multidimensional warehouse data with complex external data.
Dimensional modeling
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Multidimensional data
Business Intelligence
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Various business organization or government bodies are enhancing their decision making capabilities using data warehouse. For government bodies, data warehouse provides a means by enabling policy making to be formulated much easier based on available data such as survey-based services data. In this paper we present a survey-based service data with the design and implementation of a Data Warehouse framework for data mining and business intelligence reporting. In the design of the data warehouse, we developed a multidimensional Data Model for the creation of multiple data marts and design of an ETL process for populating the data marts from the data source. The development of multiple data marts will enable easier report generation by identifying common dimension amongst the data marts. The cross-join capabilities of the data marts through common dimensions, demonstrate the ability to easily drill across the data marts for cross data analysis and reporting. In addition, we also have incorporate data quality checking on the data source as well as data detection rules to filter out unmatched data schema and data range from being stored in the data warehouse for analysis.
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This paper lists basic knowledge requirement to become a data warehouse artist which should have basic data warehouse knowledge in order to understand a data warehouse schema picture as a similarity when a picture or painting artist see an artwork. This paper does not discuss about data warehouse personal such as data warehouse development advisor, data warehouse consultant, data warehouse architect, data warehouse developer or any other jobs related to data warehouse. This paper only discuss how a people can be a data warehouse artist which can enjoy to see many database model design pictures, particularly for data warehouse schema pictures and enjoy to spend much time in front of those pictures. Moreover, A good datawarehouser or data warehouse artist should be able to represent their data warehouse pictures not only in usual and bored pictures but treat their data warehouse pictures as an artwork in order to increase audience's engagement. Furthermore, having knowledge and ability to build and develop data warehouse is value added for data warehouse artist. Thus, a data warehouse artist can recognize and differ each of database model picture as a database design model or data warehouse model and see them as science art.
Dimensional modeling
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Dimensional modeling
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Schema evolution
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Meta-model merging is the process of incorporating data models into an integrated, consistent model against which accurate queries may be processed. Within the data warehousing domain, the integration of data marts is often time-consuming. In this paper, we introduce an approach for the integration of relational star schemas, which are instances of multidimensional data models. These instance schemas represented as data marts are integrated into a single consolidated data warehouse. Our methodology which is based on model management operations focuses on a formulated merge algorithm and adopts first-order Global-and-Local-As- View (GLAV) mapping models, to deliver a polynomial time, near-optimal solution of a single integrated data warehouse. Keywords-Schema Merging; Data Integration; Model Management; Multidimensional Merge Algorithm; Data Warehousing
Online analytical processing
Star schema
Merge (version control)
Dimensional modeling
Multidimensional data
Schema (genetic algorithms)
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This definitive guide succinctly explains how to build a data warehouse by using actual case studies of existing data warehouses developed for specific types of business applications such as retail, manufacturing, banking, insurance, subcriptions and airline reservations. Describes a powerful new model of data warehouse design, the dimensional data warehouse, that provides readers with the ability to quickly analyze complex information in order to make sound decisions. The accompanying CD-ROM includes a toolkit for building dimensional data warehouses and examples of all the databases discussed in the text.
Dimensional modeling
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The remarkable characteristic of data warehouse is data integration. In this paper, we propose a frame for data integration in a data warehouse, and discuss the functions, problems, and workflows of the data integration. We introduce a data integration tool developed for data warehouse solution of railway freight information synthetical exploitation system according to this frame.
Information integration
Dimensional modeling
System Integration
Data transformation
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