Indexing, Clustering, and Search Engine for Documents Utilizing Elasticsearch and Kibana

With the rapidly growing progress of science and technology, a large quantity of data and information is generated utilizing computers. Traditional relational databases, such as MySQL, are becoming increasingly unable to meet the user demands for rapid retrieval. However, Elasticsearch compensates for the delayed retrieval by offering users a fast search compatibility while ensuring high quality. The paper outlines the design and development of a system for indexing, clustering, and searching scientific documents. A Java Spring web server with Bootstrap, jQuery, and Foamtree was developed, visualizing the data with Kibana, allowing an orchestration of used technologies, enabling an efficient search and analysis function. The clustering of the documents is based on the metadata of Zotero and accessed via Carrot 2.