A Self-Assessing Compilation Based Search Approach for Analytical Research and Data Retrieval
Ananth Goyal

TL;DR
This paper introduces a self-assessing search algorithm designed to automate key components of meta-analysis, improving efficiency and relevance in large dataset research across various topics.
Contribution
The study proposes a novel, automated search approach that predicts feasibility and enhances source selection and relevance determination in meta-analytic research.
Findings
Average of 126 relevant sources retrieved per search
Efficiency of 19.55 sources per second
Potential for improved research methods across disciplines
Abstract
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…
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Taxonomy
TopicsWeb Data Mining and Analysis · Data Management and Algorithms · Advanced Database Systems and Queries
