An Entity-Relationship Model Database Schema for Implementing Knowledge Management in Construction

This paper proposes a database to store the knowledge of current construction professionals, to be used by future constructors. The authors use a diagrammatic approach to demonstrate the “Entity-Relationship” model for the database. The logical design of the model is examined along with the practicality of implementing it. Scalability of the model for future applications and for purposes of integration with current software packages is also discussed. Issues such as the hierarchy of contributors to the database and security concerns are addressed. The design of the front end to the database with possible dissemination devices such as the use of a web-based front-end, wearable computers, Palm PCs, digital mobile phones etc is discussed. The resource required for developing the database and implementing the database are discussed in this article.

Keywords: Knowledge Management, Database, System Boundary, Entity-Relationship Model

Introduction 

Several generations of human beings have been in the business of construction. Every generation has learned the various crafts involved in construction from the previous generation and tried to improve them further for the next generation. At the least, knowledge has been managed since humans first learned the skill to make fire as they then wanted to transfer this skill to the next generation. Although some attempts have been made to use different methods of passing this information from generation to generation to this point, it seems that the chosen and most easily manageable method has been by word of mouth. Consequently every generation has forgotten some of the lessons learned by their predecessors and had to re-learn those by trial and error. 

Some pioneers to this Knowledge Management (KM) system were libraries, schools and apprenticeships. Database managers were added to the list later and now people with the titles ‘Chief Information Officer’, ‘Chief Knowledge Officer’ and ‘Knowledge Engineer’ exist in several organizations. Interestingly, most companies will find that they are already managing knowledge (Sveiby, 2001). For instance consider the bidding process of construction companies. The first step is to create an estimate for the cost of a project. Historical cost data for projects is maintained by all companies and they tend to change their prices for future projects based on observations from past projects. The maintenance of the historical data is a deliberate effort by companies to manage knowledge, even though it may not be formally called as ‘Knowledge Management’. The main difference between the ways knowledge is being managed now and what the authors propose is the process of documenting and disseminating knowledge. The proposed process is a much more conscious, deliberate and structured approach to managing knowledge.  

Less than a decade ago we spent hours filling out forms in triplicate. Today few construction professionals are without mobile devices such as palm pilots, cell phones or beepers. We have to consider at what point have we reached technology overload? Instead of the latest gadgets, maybe we should determine what technology we really need and use it effectively and to its maximum benefit (Rogus, 2003). For all the good technology has done for us, it has created at least one problem. The problem is that of the ability to have too much knowledge available to us. At some point the ability to access information becomes too cumbersome because so much information has been stored and we get lost in the process of retrieval. Therefore, something is needed to enable companies to gather, filter, organize, store and retrieve this knowledge in a timely manner. 

The idea of documenting ‘Best-Practices’ and ‘Lessons-Learned’ in a database is a relatively new concept in the construction industry (Yu and Skibniowski, 1999). However, it seems to be the latest trend, although, there are very few Knowledge Management solutions designed for the construction industry (Rakow, 2003). The safety manuals used by construction companies are the closest thing to this concept. Part of the challenge for smaller companies is putting the technology in place to help manage their knowledge. As networks and servers become more capable, the ability to share information becomes easier. Document imaging will most likely play a major role in the management of knowledge. Certainly, as prices come down for these technologies, more companies will implement these tools (Rogus, 2003). 

We know that technology is changing the construction industry; however, the construction industry is and needs to change technology (Rogus, 2003). Part of what this paper and project will attempt to do is to adapt technology to help the construction industry manage knowledge, specifically things such as lessons learned. However, many more items will fit into the category of knowledge that must be managed for efficiency and effectiveness. Knowledge management in some ways is not different than project management. It organizes and follows some specific bit of knowledge and stores this information for easy retrieval (Rogus, 2002). In addition to improving performance based on lessons learned, KM can reduce manpower required for training. Once a “how-to” example has been created and stored, one can access this knowledge anytime and anywhere as long as it is stored in some structured manner that is easily retrievable. 

From an organizational point of view, the only guaranteed and lasting asset garnered from working on a project is the sum of experiences acquired throughout the process. As interest rates, materials and labor fluctuate, long term company success is hinged on the ability to anticipate risk and avoid unfavorable outcomes. Thus, the ability to accumulate and retain knowledge is paramount to insure a company’s longevity (Tserng & Lin, 2002). The capacity to be trained and to pass along the training is a significant attribute for a company and should be as carefully managed as the financial, physical or any other company resources. The rapid development of various information technologies has allowed companies to begin to adequately leverage expertise-based assets (Avery, et. al. 2003). KM for construction is the system that will tie all disconnected bits of knowledge together. It will help compile information and put it in a manageable form (Rakow, 2003). 

Several inhibitors exist for implementing a company wide formal KM system in construction. The knowledge in the construction industry is a fragmented by nature (Brensen & Marshall 2001). All companies do not follow a standard procedure in accomplishing the same task. It is a challenge for companies to get their employees to contribute to the system. Some form of reward system is conceivable to motivate employees to contribute to the system. The success of the system is entirely dependent on the ‘buy-in’ from upper level management. Several procedures must be put in place to validate the ‘knowledge’ in the database as incorrect and obsolete information is the last thing an organization would want in their KM system. It is a huge task for organizations to undertake this effort since this has to be done parallel to all the other mission critical activities they have to perform. 

A “Knowledge Management System” to document these ‘lessons learned’ is proposed by the authors. The paper provides some simple examples of how this concept may be used in construction in the present business environment. The paper suggests a method for the knowledge possessed by constructors to be stored in a manner that it may be retrieved at the precise time that it is needed. An entity-relationship database schema is proposed. The models’ viability in implementing it company-wide is discussed. The role of this database in creating best practices manuals for construction activities is discussed along with the potential reasons that might undermine the success of this model. 

Knowledge Management Entity-Relationship Database Model 

The current paradigm of a “Relational Database Model” is a relatively new form of database and has only been in existence for around thirty years (Connolly and Begg). The relational database model allows for a high degree of data independence from application programs. Any changes to the database will not affect the user interface to the data. The relational model also enables the data to be normalized or in other words reduces the amount of redundant data stored in the database. Entity-Relationship Model (ER Model) is one of the techniques used by database engineers to model a relational database. The ER model is based on the concept that the real world consists of objects that are referred to as entities and that a certain relationship exists between these entities. ER Modeling is gaining popularity in the computer programming world and in the industry (Gregersen and Jensen). An example of an entity could be a ‘Bank Account’. Every bank account has an account number and a balance associated with it. The account number and balance are referred to as the ‘Attributes’ of the ‘Bank Account’ entity. The bank account has a customer associated with it as well. The customer is a separate entity and the relationship between the customer and bank account is defined by the database engineer using one of the following structural constraints: “One-to-One” or “One-to-Many” or “Many-to-One” or “Many-to-Many”. In the ER model, relationships between entities are defined by ‘structural constraints’. Each one of these structural constraints describes the relationship between the number of customers and the number of bank accounts. For instance a “One-to-Many” constraint between the ‘Customer’ and the ‘Bank Account’ entities would indicate that for every customer there may be multiple bank accounts in the database. 

ER modeling involves several steps in designing a database. In this model the authors use the following steps: 

1.      System Definition and Boundary: The system definition and boundary identifies all the activities associated with the problem and identifies the scope of the current database. This is usually represented diagrammatically and is show in Figure 1 for the proposed Knowledge Management database. The various activities in a construction firm shown below and the relationship between these activities are also presented. The dashed line indicates the ‘System Boundary’ or the scope of the database. A construction firm performs ‘Operations’ for ‘Clients’. The ‘Operations’ of the firm are supported by ‘Marketing’, ‘Accounting’ and ‘Human Resource Management’ (HRM). The scope of this database in shown by the objects inside the dashed line. The ‘Media’ used in creating the ‘Knowledge’ along with the ‘Usage Statistics’ are within the scope of the database. The ‘Users’ of the database along with the ‘Security’ associated with each user will be managed by the database. The database will only address the issues that are within the scope of the ‘System Boundary’. 

2.      Diagrammatic Representation: In this section we will identify the various entities that will be in the database and also discuss their attributes. An example of diagrammatic representation of the same will also be presented. The following entities are present in the database:
 

ER modeling technique requires that a diagrammatic layout of all the entities be drawn before creating a physical database model. Each entity is drawn in a rectangle and its attributes are drawn in ellipses that are connected to the respective entities. The relationship between the entities is shown in a diamond. Figure 2 represents the entities ‘Users’ and ‘Knowledge’. The ellipses around the ‘Users’ and the ‘Knowledge’ entities are their respective attributes. The diamond shaped link between the two entities explains the relationship between the two entities. 

3.      Enhanced ER Model with Structural Constraints: Figure 3 shows the actual layout of the physical model along with the structural constraints. This model was developed using the database layout tool from the Microsoft Visio software. All the entities are shown along with their relationships. Each entity is shown in a rectangle and the attributes of each entity are in the column on the right. The primary key of each entity is underlined and is in the top row by itself. The primary key of an entity is a unique attribute for every single instance of the entity. For example every ‘UserName’ in the ‘UserData’ entity will be unique. The bold attributes in each entity are the required fields for every instance of the entity. All entities are related as shown in the figure below. Each relationship between the primary key of one entity and a related attribute also known as ‘Foreign Key’ in another entity are indicated by letters FKn in the model. For instance every ‘Level Number’ in the ‘Level’ entity has a one-to-many relationship with the ‘Level’ attribute in the ‘UserData’ entity.
 

Front End Tool for Knowledge Management Database 

The authors recommend a web-based front end for the Knowledge Management database. Several construction management programs are now being developed with a web-interface such as on-line project management software. This will enable integration of all these applications across the various platforms with lesser effort. The aspect of integration with other programs is critical for the success of the KM application since the data or the media needed to populate the KM database is in these other programs. Users must be able to seamlessly import the data from other programs so that they do not spend time re-entering the information into the KM database. Users should be able to use voice recordings to provide the description for each instance of the knowledge entity. This will enable the creators of knowledge to populate the database faster and it is also an incentive to use the database since each user will listen to the problem and the solution through another person’s voice rather than impersonal software. The front end tool must be created so that information can be disseminated via mobile devices such as Palm PC or Tablet PC. The database would be used more often if its content can be delivered on the job-site rather than only on a computer screen. 

Conclusions and Recommendations 

The concept of Knowledge Management will enable organizations to comprehensively gather, organize, store, share, and analyze data and experiences. The central theme of the proposed KM system is to effectively train construction professionals using real world examples. The KM model presented in this paper aims at providing knowledge to novices in a field through the experiences of veteran construction professionals. This model will also allow for each professional to share his/her expertise with their colleagues who are at the same level in a company. 

Any KM system will not be successful without the support of upper level management. The policy makers in a construction organization have to realize that this concept might indeed save them money by providing better training for their employees. An incentive program for contributing and using the KM system in a company could be an effective way to motivate employees. The authors recommend a company wide adoption of this system to maintain consistency in doing a particular process. If this system is practiced over a period of time, an organization can draw from the database and create a ‘Best Practices Manual’ for each process done by their employees. This would immensely reduce the costs of training new personnel since this model uses a ‘Just-In-Time’ learning concept wherein an employee can try to find the solution to a problem at the time it is needed. 

Integration, security and scalability issues must be given careful consideration in developing a system to enforce KM. As the amount ‘knowledge’ increases in the database the path the users take in browsing this ‘Knowledge database’ must be tracked to provide the designers with ‘learning patterns’. This information would help the system designers in developing better applications of the system in future. The validity of the data must be closely examined at periodic intervals to ensure that the ‘knowledge’ stored in the database is still valid. 

The authors are in the process of creating a physical database for the schema presented in the paper. The authors will also create a web based front end for the database to demonstrate the actual use of the schema. It is anticipated that the physical database and the front end will be completed by time of the ASC National Conference and it will be presented at the conference. The product will also be used to demonstrate to construction companies and the authors will seek industry partners in order to test its viability in an industry environment. 

References 

Avery B, Hicks K, Pfeffer J and Wallace, S (2003). Information Management.  Unpublished manuscript.  Auburn University, Department of Building Science, Auburn, Alabama. 

Brensen M and Marshall N (2001) Understanding the diffusion and application of new management ideas in construction. Engineering, Construction and Architectural Management 8 (5/6) 335-345 

Connolly T and Begg C Database Systems: A practical Approach to Design Implementation and Management Addison Wesley Publications 2002 

Gregersen H and Jensen C.S Temporal Entity-Relationship Models – A Survey Addison Wesley Publications 2002 

Rakow, B (2003). Managing Knowledge, Constructech [WWW document] URL http://64.29.216.69/printresources/v4n6/v4n6005.asp 

Rogus, D (2003). Collecting YOUR Thoughts.  Constructech [WWW document] URL http://64.29.216.69/printresources/v4n9/v4n9032.asp 

Sveiby, Karl.  (2003). What is Knowledge Management?  Community Intelligence Labs.   [WWW document] URL http://www.co-i-1.com/coil/knowledge-garden/kd/whatiskm.shtml 

Tserng P and Lin P (2002) An accelerated subcontracting and procuring model for construction projects. Automation in Construction 11 (1) 105-125 

Yu W and Skibniowski M (1999) Quantitative constructability analysis with a neuro-fuzzy knowledge-based multi-criterion decision support system. Automation in Construction 8 (5) 553-565