An Overview of R.A.M. – Relative Allocation Methodology
Our team is proposing a more innovative and assistive method of search in large-scale databases. Conceivably dubbed as Relative Allocation Methodology (R.A.M.), the system will create faster, and thereby more efficient, searches through a tagging system coupled with linking related data.
Our R.A.M. system would at first function like any other search system, by utilizing an existing tagging system and keywords found throughout the data to search the database. However, over time, R.A.M. would analyze the history of the searches to see which pieces of data are related. For example, data searched and accessed in a relatively close time frame and the search terms that were entered would be tagged under a new label, in an overlying tagging system dynamically built by the system. Therefore, when related terms are entered, every bit of data associated with the term is loaded in the background, quickening the research process. Intuitively tracing past searches, R.A.M. makes sure more relevant data is delivered in addition to what is searched for, loaded conveniently during the user's interaction with the database.
Our R.A.M. system would at first function like any other search system, by utilizing an existing tagging system and keywords found throughout the data to search the database. However, over time, R.A.M. would analyze the history of the searches to see which pieces of data are related. For example, data searched and accessed in a relatively close time frame and the search terms that were entered would be tagged under a new label, in an overlying tagging system dynamically built by the system. Therefore, when related terms are entered, every bit of data associated with the term is loaded in the background, quickening the research process. Intuitively tracing past searches, R.A.M. makes sure more relevant data is delivered in addition to what is searched for, loaded conveniently during the user's interaction with the database.
R.A.M. isn't meant for every database. Organizations such as libraries, medical facilities, and police departments that have databases would see clear benefits to their workflows. A library search term for research linked to multiple sources that normally wouldn't appear for the singular term would be a superior advantage to a student. Detective work could be facilitated by having access to related incidents of criminals, with clear patterns built into the search process. However, simpler, less complex databases would see no real gain in efficiency, such as attendance records or other menial data.
To conclude, R.A.M. is a powerful concept meant for complex and massive databases, those which commonly house "big data". We believe that it can revolutionize the often disregarded but ultimately crucial aspect of databases: searching. [Task Three]
To conclude, R.A.M. is a powerful concept meant for complex and massive databases, those which commonly house "big data". We believe that it can revolutionize the often disregarded but ultimately crucial aspect of databases: searching. [Task Three]
Note: This page completes Task Three of Component Two and includes our Design.