Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Dibakar Sigdel, Vincent Kyi, Aiden Zhang, Shaun P. Setty, David A. Liem, Yu Shi, Xuan Wang, Jiaming Shen, Wei Wang, Jiawei Han, Peipei Ping

Research output: Contribution to journalArticlepeer-review


The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications. We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm. Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents. CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.

Original languageEnglish (US)
Article numbere59108
JournalJournal of Visualized Experiments
Issue number144
StatePublished - Feb 2019


  • Issue 144
  • Medicine
  • cloud computing
  • data science
  • medical informatics
  • phrase mining
  • text mining

ASJC Scopus subject areas

  • Neuroscience(all)
  • Chemical Engineering(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)


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