论文标题
基于自我评估的搜索方法，用于分析研究和数据检索
A Self-Assessing Compilation Based Search Approach for Analytical Research and Data Retrieval
论文作者
论文摘要
在进行荟萃分析研究的同时，通过单个数据库和搜索引擎提供的大量来源过滤变得耗时，因此降低了源分析的特殊性。这项研究试图预测所有主题中的面向研究的搜索算法的可行性，以及通过自动化荟萃分析的三个关键组成部分来应对大型数据集的搜索技术：一种与预期的研究主题相关的基于查询的搜索，选择给定的源头，选择给定的源头并确定其与原始的Quere和Cite and Cripts and Crick and Crick and Crick和Crick and Incect，并确定其相关性。使用5个关键历史主题评估了该算法，并将结果分为4个类别：检索到的相关来源总数，给定特定搜索的效率，完成完整周期所需的总时间以及与当前搜索方法相比，提取的来源的质量。尽管结果平均而言，但该计划的平均搜索量有所不同，但每次搜索总共收集了126个来源，平均效率为19.55个来源，当比较和定性评估确定结果时，该效率表明在所有学科领域开发的算法都将在未来的研究方法中取得进展。
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.
