This paper compares the relational database model with the associative database model. This paper briefly summarizes the relational and other familiar data models. The remainder of this paper will introduce and describe the associative database model. The associative model is less known because it is relatively new and does not have a large software supplier base. While it seems to offer a number of benefits and advantages over other database structures, it has yet to become a commercial success in the mainstream database market.
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HOMAN, Joseph V and KOVACS, Dr Paul J, 2009. A comparison of the relational database model and the associative database model. Issues in Information Systems. 2009. Vol. X, no. 1, p. 6. doi 
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Data are separate bits of information with no related context to give them meaning. For example, this string of data elements – #PM764, 60, SN 1239876a, 814-555-1212 – each represent something, but are meaningless with no context or connection to other data. […] Information grows by adding Perspective and Intent.
To take information to the level of Intelligence, it must be further contextualized. […] Intelligence is purposed information. […] Taking that concept one step further, you can convert that intelligence to Knowledge by adding Perspective, history, and broader global context as follows: […] Knowledge is beyond the ability of today‟s computational capabilities, but can be enabled by making available the appropriate data, information, and contextualized intelligence.
The introduction, history and explanation of moving from data to information to intelligence and knowledge are important to understand the importance of database modeling. So how does one organize, model, and store data to give you information, intelligence, and knowledge? To achieve this goal, a number of database models have been developed. Four that will be described in the following pages include: hierarchical, network, relational, and associative. The majority of this paper will focus on the last two.
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