A construction project typically involves many participants such as the owner, contractors and engineers. The coordination of project participants is thus dependent on the large amount of information exchanged among project participants in forms of documents, oral messages and meetings, etc. Among these exchanged information, documents, either in paper or electronic form, are the carrier of most important management information pertaining to important aspects of construction project management.
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.
Conceptually the idea of a data warehouse is extremely simple. As popularized by Inmon (1994) and Inmon and Hackathorn (2002) a data warehouse is a “subject-oriented, integrated, time-invariant, non-updatable collection of data used to support management decision-making processes and business intelligence.” A data warehouse is a repository into which are placed all data relevant to the management of an organization and from which emerge the information and knowledge needed to effectively manage the organization (Watson, 2001).
While this is clearly a simplistic and idealistic view it allows us to begin the investigation of the foundations, key challenges, and research directions for this discipline. Importantly it highlights the purpose of a data warehouse: support for all levels of management decision-making processes through the acquisition, integration, transformation, and interpretation of internal and external data (Negash, 2004).
Data warehousing (DWG), which implements a shared data warehouse (DW) and/or subject-oriented data mart (DM), has become a central process for decision support-oriented data management. From its beginning as a little-understood experimental concept only a few years ago, it has reached a stage where nobody questions its strategic value.
Statistical indicators and surveys show that the number of companies that already own or are currently building the decision support platform is exploding; large enterprises are involved in at least one or more related projects (Sen & Jacob, 1998). Databases tuned for operational and transactional use are, in general, not structured to satisfy information demand from managers. There is a growing utility gap between operational systems and decision support systems, making DWG increasingly essential to organizational decision-makers. Its vital role continues to expand as the market becomes more customer-centered and demands sophisticated business intelligence.
It is well known that information becomes valuable resources only when it is utilized. For example, Song et al. (2002) introduced the concept of reusing building documents from design and construction phases. Soibelman and Kim (2002) explored the way of knowledge discovery by generalizing rules from existing databases. Chau et al. (2002) conducted research on the application of data warehousing technique. Αhmad et al. (2004) used data warehousing in a decision support system for site selection for land development projects.
Data warehousing technique is an extension of the transactional database technique. Comparing to the transactional database technique, the data warehousing technique has the following unique features (Inmon, 1993).
(1) Subject oriented: In a transactional database, data are organized around the application subsystems; while they are organized around a subject in a data warehouse. A subject represents an important aspect that the analyzer concerns with when he conducts in-depth data analysis, such as construction quality in project management. A subject consists of facts (indexes to measure the subject) and dimensions (i.e., the corresponding influential factors).
(2) Integrated: Usually, transactional databases are related to certain application subsystems, respectively, and they are independent and even heterogeneous; while a data warehousing is formed by extracting, processing and integrating from different databases, and thus, the data in it are consistent and global about the analyzed subject.
(3) Relatively stable: The data in a transactional database is updated in real time with the transactions; while inasmuch as the data warehousing is established for supporting decision making, once a batch of data is introduced in the data warehouse, normally, it will remain unchanged until they are updated.
(4) Time variant: A transactional database normally stores only the data for a certain period, while a data warehousing contains historical data, i.e., the data from the beginning point of the data warehousing to now, and can be used for predicting the trend on the specified subject.
It is obvious that the key to using data warehousing technique is to establish the contents of the data warehousing from the data sources, and the prerequisite is to establish the structure of the data warehouse. So the application field for using the data warehousing technique should be analyzed at first, and then the subjects and corresponding influential factors (including dimensions and properties) should be identified. Once a data warehousing is established, the front-end tools for data warehouses can be used to carry out in-depth data analysis of the information stored in the data warehouse.
Data warehousing concept is particularly useful for developing Decision support systems. A data warehouse is typically a read-only dedicated database system created by integrating data from multiple databases and other information sources. A data warehouse is separate from the organization's transactional databases.
Data Warehouses and Online Analytic Processing (OLAP) technologies are in full swing in industry as major business intelligence technologies. Many business intelligence applications currently run at companies not only demand more capacity, but also new methods, models, techniques or architectures to satisfy these new needs.
Some of the hot topics in Data Warehouses and OLAP include: a methodology covering design steps during a DW lifecycle, active Data Warehouses, distributed Data Warehouses, advanced OLAP for business intelligence, web warehouses, new visualization methods, Data Warehouses for new applications (such as XML documents), stream data, spatial or GIS data, or biomedical data. Moreover, there are other aspects developed in other software areas such as security or quality, which still remain uncovered by current design methods or technologies for Data Warehouses.
The site selection method symbolizes a single business decision with numerous business functions carry out by a typical construction firm. Instead of developing a full scale data warehouse, a data mart is developed for the purpose of this research. Existing data models used to design traditional OLTP systems are not appropriate to design a data mart. The reason is that these data models do not support analytical processing and also not efficient to answer complex queries requiring aggregation of data. Special database schemas are available for modeling data in a data mart.
There are a large number of obvious advantages involved with using a data warehouse. As the name suggests, a data warehouse is a computerized warehouse in which information is stored. The organization that owns this information can analyze it in order to find historical patterns or connections that can allow them to make important business decisions. In this article I will go over some of the advantages and disadvantages that are connected to data warehouses.
One of the best advantages to using a data warehouse is that users will be able to access a large amount of information. This information can be used to solve a large number of problems, and it can also be used to increase the profits of a company. Not only are users able to have access to a large amount of information, but this data is also consistent. It is relevant and organized in an efficient manner. While it will assist a company in increasing its profits, the cost of computing will greatly be reduced. One powerful feature of data warehouses is that data from different locations can be combined in one location.
There are a number of reasons why this is important. When data is taken from multiple sources and placed in a centralized location, an organization can analyze it in a way that may allow them to come up with different solutions than they would if they looked at the data separately. Data mining is connected to data warehouses, and neural networks or computer algorithms are responsible. When data is analyzed from multiple sources, patterns and connections can be discovered which would not be found otherwise. Another advantage of data warehouses is that they can create a structure which will allow changes within the stored data to be transferred back to operational systems.
However there are a number of disadvantages that need to be mentioned as well. Before data can be stored within the warehouse, it must be cleaned, loaded, or extracted. This is a process that can take a long period of time. There may also be issues with compatibility. For example, a new transaction system may not work with systems that are already being used. Users who will be working with the data warehouse must be trained to use it. If they are not trained properly, they may choose not to work within the data warehouse. If the data warehouse can be accessed via the internet, this could lead to a large number of security problems.
Another problem with the data warehouse is that it is difficult to maintain. Any organization that is considering using a data warehouse must decide if the benefits outweigh the costs. Once you have paid for the data warehouse, you will still need to pay for the cost of maintenance over time. The costs involved with this must always be taken into consideration. When it comes to storing information, there are two techniques which are used. The first is called the dimensional technique. When the dimensional technique is used, information will be stored within the data warehouse as facts. These facts will take the form of either text or numerical information.
Historical project data can assist construction managers in answering questions about the business, the performance of interested operations, business trends, and what can be done to improve the business. Αlthough the data is there, it is always a challenge to find the needed data in time when a decision is made. Decisions can be slowed down due to inconsistency and/or inaccuracy of data. Data warehousing provides the technology for storing historical construction project data which can be extracted from existing operational databases/systems.
However, unlike many other application systems, an organization cannot simply buy a data warehouse off the shelf. Instead, a proper design is needed to ensure that the data structure of the warehouse can address the nature of the organization and meet its business needs so that the data to be captured in the warehouse will reflect what is available and what will be needed by the company.
As for Dean and Dyball company Data warehousing strategy, approach and technique can be used effectively to increase their business efficiency, because it is clear that if populated with data from a number of projects, the data warehouse could generate site selection patterns for different areas reflecting their unique characteristics. These patterns can help users to further refine the site selection criteria.
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