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新西兰代写paper |Project Data Warehousing In Construct

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Project Data Warehousing In Construction


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.

Data Warehousing Technique

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 Advantages & Disadvantages

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.


A. Sen and V.S. Jacob (1998), Industrial-strength data warehousing. Commun. ACM 41 9, pp. 28–31.

H.J. Watson, (2001) Developments in data warehousing, Communications of the AIS 8.

I. Ahmad, S. Azhar and P. Lukauskis, (2004) Development of a decision support system using data warehousing to assist builders/developers in site selection, Automation in Construction 13 (4), pp. 525–542.

K.W. Chau, Y. Cao and M. Anson, (2002) Application of data warehousing and decision support system in construction management, Automation in Construction 11 (5), pp. 607–616.

L. Soibelman and H. Kim, (2002) Data preparation process for construction knowledge generation through knowledge discovery in databases, Journal of Computing in Civil Engineering 16 (1), pp. 39–48.

S. Negash, (2004) Business intelligence, Communications of the AIS 13.

W.H. Inmon and R.D. Hackathorn, (1994) Using the Data Warehouse, Wiley, New York.

W.H. Inmon, (1993) Building the Data Warehouse, John Wiley & Sons, New York

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Y. Song, M.J. Clayton and R.E. Johnson, (2002) Anticipating reuse: documenting buildings for operations using web technique, Automation in Construction 11 (2), pp. 185–197.


一个施工组织产生了大量的业务数据分布在各个功能系统,以支持其日常营运。虽然这些数据可能是潜在有用的,为今后的项目,他们没有得到广泛的收集和组织中的集中存储。这项研究提出了以项目为导向的数据仓库( PDW )承办。 PDW设计与三维数据模型,包括26表。
表中有16个维度表用于存储一般描述性信息,和其他十详述各种建设项目的生命周期中被捕获的事实,是事实表。 PDW可以直接从现有的业务系统,如P3文件的MS Access , P3 / E数据库和Excel文件的数据填充。它保持在其相关联的项目的上下文中的每一个数据,以便用户可以检索一段特定的信息附加的相关项目的任何背景资料。 PDW已经填充与三个样本项目数据。通过用户界面,用户可以根据需要生成感兴趣的查询报告。所提出的仓库结构和数据模型的可扩展性。他们可能会通过开发公司级的数据设施的中型或大型承包商。
从概念上讲,数据仓库的想法是非常简单的。推广inMon客户端(1994)和(2002) inMon客户端和Hackathorn的一个数据仓库是一个面向主题的,集成的,时间不变,不可更新的集合,用于支持管理决策流程和商业智能的数据。 “数据仓库是一个储存库,放置到相关的所有数据到一个组织的管理,并从中涌现的信息和知识,需要有效地管理组织(沃森,2001年) 。
数据仓库( DWG ) ,实现一个共享的数据仓库( DW )和/或面向主题的数据集市( DM ) ,已成为中央决策支持面向数据管理的过程。从其作为不大懂实验的概念几年前开始,它已经达到了一个阶段,没问题的战略价值。
统计指标和调查表明,公司已经拥有或正在建设的决策支持平台的数量爆炸,大企业都参与了至少一个或多个相关项目(森雅各,1998) 。调整运营和交易使用的数据库,在一般情况下,不规整,以满足管理者的信息需求。有越来越多的业务系统和决策支持系统的效用差距,使DWG组织的决策者越来越重要。其至关重要的作用继续扩大,因为市场变得更加以客户为中心,要求复杂的商业智能。
这是众所周知的信息变成只有当它是利用宝贵的资源。例如,宋等。 (2002)介绍了重用设计和施工阶段的建筑文件的概念。 Soibelman和Kim (2002)泛化规则从现有的数据库中的知识发现探索的方式。洲等。 (2002年)进行了数据仓库技术的应用研究。 αhmad等。 (2004年) ,用于土地开发整理项目的选址决策支持系统的数据仓库。
数据仓库技术是事务型数据库技术的延伸。数据仓库技术的事务型数据库技术相比,具有以下独特功能( inMon客户端,1993) 。
(1)面向主题:在事务数据库中,数据被组织起来的应用子系统,而他们是在数据仓库的主题围绕。一个主题是一个重要的方面,分析问题与他进行深入的数据分析,如建设项目管理质量。一个主题由事实(测量被摄对象的索引)和尺寸(即,相应的影响因素) 。
(2 )集成型:一般,事务处理数据库有关的目标应用程序子系统分别,它们是独立的,甚至异构;通过提取,处理和整合来自不同数据库的,并因此而形成的,而数据仓库,它的数据是全球统一的分析对象。
( 3 )相对稳定:事务数据库中的数据与交易实时更新,而支持决策,一次引进一批数据仓库中的数据,通常情况下,它会因为数据仓库的建立保持不变,直到它们被更新。
( 4)时间变体:一种事务数据库通常存储只在一段时间内的数据,而数据仓库中包含历史数据,即目前的数据仓库中的数据的开始点,并且可用于预测的趋势指定的主题。
很明显,使用数据仓库技术的关键是建立从数据源中的数据仓库的内容,前提是必须建立在数据仓库的结构。因此,该应用程序使用数据仓库技术领域应首先分析,然后构图,并应确定相应的影响因素(包括尺寸和特性) 。一旦建立数据仓库,数据仓库前端工具,可以用来进行深入的数据分析在数据仓库中存储的信息。
数据仓库和OLAP的热点话题包括:覆盖在一个DW生命周期的,活跃的数据仓库,分布式数据仓库,先进的OLAP的商业智能,网络仓库,新的可视化方法,数据仓库为新的应用(如设计步骤方法作为XML文档) ,数据流,空间或地理信息系统的数据,或生物医学数据。此外,还有其他方面发展,仍发现目前的设计方法或数据仓库技术在其他软件领域,如安全或质量。
历史项目数据可以回答有关业务,协助施工经理的性能感兴趣的业务,业务发展趋势,什么可以做,以提高业务。 Αlthough数据是存在的,它始终是一个挑战,找到所需要的数据,当作出决定。决定可以减缓由于不一致性和/或不准确的数据。数据仓库提供的历史建设项目可从中提取现有业务数据库/系统数据存储技术。
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