A relational database is a collection of data items that are
organized in a manner that relates the parameters between the data items. In Hugh Darwen’s book “An Introduction to
Relational Database Theory,” he uses tables to explain the theory behind a
relational database. In a way, a table
can be seen as a relational database, where the various parameters are
presented in a way that lends itself to relation. However, Darwen argues that, while the
visualization is similar, a table is not really a relational database in
itself. In order to be a proper
relational database, the columns of a table must be algebraically or
operationally related to one another. [1]
This aspect of relational databases are the primary benefit to using
them in the organization of big data, especially in the construction and
building industry. The ability to
operate on parameters within a database based on known relations allows the
user to construct a systematic collection of information that is pertinent in
the building.
To understand the effects that this type of information can have on our industry, the mathematical theory is useful to grasp what should and should not be related. Eliezer Lozinski explains this theory rather well in his journal “Construction of Relations in Relational Databases.” He explains that all databases are made up of relations that are made up of a collection of attributes. These attributes are linked through a set of dependencies to the relation. This framework for relating the attributes is called a scheme. The scheme of a relation can be represented graphically, as Lozinski does below. This rather simplified progression shows how the various schemes can be put together to relate the appropriate attributes. [2]
Figure 1: Relational
Scheme Tree [2]
The impacts to the construction industry are numerous. As we heard in the Whiting Turner guest
lecture, a lot of firms are pushing to tabulate the entire building, from door
hardware to reheat coils. Relational
Databases can take it a step further, by joining the parameters of installed
components, we can understand the connections between key parameters and
design, maintain, and modify building components better.
Comments on Others
Andrew Ng – Your explanation of Structured Query Language
was very helpful, particularly with the examples that you gave. This system would be very useful to a project
manager in this way to cut back a lot on the unnecessary work that stems from
handling big data.
Prahlad Singh – I particularly liked your post because of
all the benefits you provided to having databases in our industry. I think that both designers and contractors
are constantly overwhelmed with large amounts of data, and any process by which
we can eliminate raw number sorting/crunching can make the entire process more
efficient.
Ray Powell – Your post was really interesting to read since
it connects what we have been learning so far to our next topic. Both BIM and databases are essential for
design firms now and it was really interesting to see how the two can interact
to make the design process more efficient.
References
[1] Darwen, Hugh. “An
Introduction to Relational Database Theory.”
Darwen & Ventus Publishing ApS 2010.
Web. Available: https://dvikan.no/ntnu-studentserver/kompendier/an-introduction-to-relational-database-theory.pdf
[2] Lozinski, Eliezer.
“Construction of Relations in Relational Databases.” The Hebrew University, Jerusalem, Israel. Web.
Available: http://delivery.acm.org/10.1145/330000/320155/p208-lozinskii.pdf?ip=129.25.36.43&id=320155&acc=ACTIVE%20SERVICE&key=A792924B58C015C1%2EE0B7266DB2A396BF%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=900189139&CFTOKEN=74586775&__acm__=1487008321_4d9c6ffee5fc5fe79479552f6b170344
