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1 Three Tier Architecture for Data Warehouse
Source publication
Data warehouses have become an instant phenomenon in many large
organizations that deal with a massive amount of information. Drawing on
the experiences from the systems development field, we surmise that an
effective CASE tool will enhance the success of warehouse
implementations. Thus, we present a CASE tool designed to generate the
SQL queries n...
Contexts in source publication
Context 1
... the warehouse database is physi- cally segregated from the operational databases, and is a col- lection of summaries and details, it provides a faster forum to answer the ad hoc information requirements of decision mak- ers. Figure 1.1 shows a three tier warehouse architecture pro- posed by McFadden and Watson[1996]. ...
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Citations
... Data warehousing is gaining its popularity as organizations realize the benefits of having a central database for supporting efficient management functions. It has become an instant phenomenon in many large organizations [10]. More than half of the companies in the United States have committed to implement the technology [11]. ...
A construction organization generates a great amount of operational data that are distributed across various functional systems to support its daily operations. Although those data may be potentially useful for future projects, they are not widely collected and centrally stored in the organization. This research presents a Project-oriented Data Warehouse (PDW) for contractors. PDW is designed with dimensional data models consisting of 26 tables. Sixteen of the tables are dimension tables for storing general descriptive information, and the other ten are fact tables for detailing various facts that are captured in the lifecycle of construction projects. PDW can be directly populated with data from existing operational systems, such as P3 files, MS Access, P3/e databases, and Excel files. It maintains each data in the context of its associated project so that a user can retrieve a specific piece of information plus any background information of the related project. PDW has been populated with three sample project data. Through the user interface, a user can generate interested query reports as needed. The presented warehouse structure and data models are scalable. They may be adopted by medium or large contractors for developing company-level data facilities.
... Many tools developed (see e.g. [7,18], [2], [17], [4,5], [9]) to support the process of data warehouse design are passive and incapable of supporting the basic characteristics of data warehouse design [10]. The tools are unable to fulfill specific user requirements since they could only build the data warehouse model based on what have been stated explicitly in the design sources, thus could only produce the 'first cut' of the model. ...
... One such popular and increasing in-demand applications is the data warehouse system. As a result, effort in building automated data warehouse tools have received great attentions by researchers as illustrated in [1][2][3][4][5][6][7][8][9][10]. However, almost all of these tools lack elements of intelligence that could interactively guide users during a design process. ...
... o The participating object is the old entity o The relationship attribute is set to nil. o The first relationship constraint is set to (1,1). o The second relationship constraint is set to the first relationship constraint of the old entity. ...
... o The participating object is the old entity o The relationship attribute is set to nil. o The first relationship constraint is set to (1,1). o The second relationship constraint is set to the first relationship constraint of the old entity. ...
... Explicitly, some preliminary works on the development of CASE tool for data warehouse design could also be found on several research works. In their CASE tool, Miller and Nilakanta (1998) and Wu et al. (2001) focused on the generation of SQL query from a set of operational relational database in order to build a data warehouse. The user interface facilitates the creation of query in the form of command line by choosing a list of attributes and conditions. ...
Data warehouse has become more and more popular for an enterprise as a data repository system. Yet tools to appropriately design its conceptual model are rarely available, even though this model is known as a key for the successful of the overall design. In this paper we propose an approach and a tool to guide the decision makers in designing data warehouse conceptual model based on the Entity Relationship (ER) model of the existing operational database systems. Using this approach, the ER model is automatically transformed into the multidimensional model.
A data warehouse is an integrated set of data, derived basically from operational data to use in decision making strategy and business intelligence using (OLAP) techniques. Most of the creation of data multidimensional data warehouses is done manually, but it is a very complex and takes a long time and usually has a risk of fail. In addition, a set of complex mappings must be performed. Despite this, there is no noticeable efforts has been done in order to find a practical solution structured to resolve the issue. To overcome, the proposed method presents a new strategy to automate the multidimensional design of Data Warehouses; this algorithm uses an enterprise schema of an operational database as a starting point to design data warehouse schema. As a result, the user has to choose the candidate schema which meets the system requirements.
The research subject is the technology of generating the multivariable data representation with using separate formation of dimensions and measures. The purpose of the study is to provide a full level of the user’s work automation at the formation of new data cubes. In the course of investigation, the following problems are solved: the sequence of generating intermediate data representations is determined; the correctness of these representations is studied; efficient algorithms for generating the representations and checking their correctness are developed. The theoretical basis is the methods of the inter model mapping. Herewith, a classical relational model is used as a source data model, an extended model of the multidimensional data with unbalanced hierarchies in dimensions - as a target one. The work result is the multidimensional data construction technology. Consequently, the results obtained can be used by the analytical departments at various enterprises in processing large data volumes. The proposed technology of the multidimensional data formation is the traditional OLAP-technologies development.
The aim of this paper is to study Hypercube building from relational database using separate specification of dimensions and measures. We don't require any transformation of the initial Database schema, even to the hierarchical model; that allows us to apply this technology without modification of existing databases and their applications. Also we don't force the dimensions to functionally determine measures. In this case, providing data integrity becomes more complicated, but in exchange we get the ability to use non-key attributes as dimensions. Furthermore, we can store a list of homogeneous values in a single cell. We present a formal definition of source and target data representation. The hypercube building algorithm is based on a sequence of intermodel mappings. Hypercube integrity is achieved by using an intermediate representation, called Table of Joins (TJ). This approach is the development of traditional OLAP technology.
