  While meta-analytic research is performed, it becomes time-consuming to
filter through the sheer amount of sources made available by individual
databases and search engines and therefore degrades the specificity of source
analysis. This study sought to predict the feasibility of a research-oriented
searching algorithm across all topics and a search technique to combat flaws in
dealing with large datasets by automating three key components of
meta-analysis: a query-based search associated with the intended research
topic, selecting given sources and determining their relevance to the original
query, and extracting applicable information including excerpts and citations.
The algorithm was evaluated using 5 key historical topics, and results were
broken down into 4 categories: the total number of relevant sources retrieved,
the efficiency given a particular search, the total time it takes to finish a
complete cycle, and the quality of the extracted sources when compared to
results from current searching methods. Although results differed through
several searches, on average, the program collected a total of 126 sources per
search with an average efficiency of 19.55 sources per second which, when
compared and qualitatively evaluated for definitive results, indicates that an
algorithm developed across all subject areas will make progress in future
research methods.
