Interaction History Management Systems (IHMSs)
and their application on evidence-based healthcare

Project Description

Interactive systems are software systems that incorporate "human-in-the-loop" in achieving complex tasks.   They can be single systems with interactive components (e.g., program debuggers), but frequently are ad hoc; consisting of a set of distinct and dedicated software tools (e.g., a DBMS) that are stitched together by a domain specialist who interprets the results from one tool to formulate the inputs to the next.

We introduce Interaction History Management Systems (IHMSs): systems that capture and manage sequences of user interactions (interaction histories, or IHs) that determine the behaviors of an interactive system. The interactions managed by an IHMS may be system and domain-specific and can include SQL queries, search keywords, annotations of results and applied processing algorithms.   By managing the histories of such interactions, it becomes possible both to optimize and add additional functionality (e.g., versioning, time travel, use analysis) to the underlying interactive system. 

Healthcare Applications

In this project, we intend to determine design strategies for IHMSs and as part of this effort, will collaborate with the Center of Evidence-Based Medicine at Brown University (formerly at Tufts Medical Center) to design and build an IHMS that uses interaction histories to support the formulation of "systematic reviews":  human-accumulated bodies of evidence that provide empirical evidence for the relative effectiveness of some treatment strategy ("intervention") in treating a given patient's diagnosed disease or disorder.  Evidence-Based Practice has been recognized in the recently passed Healthcare Reform Bill as a cornerstone to the improvement of healthcare in US, but systematic review formulation is extremely time-consuming and not scalable to general practice.  Our system will use interaction histories from formulations of prior systematic reviews to help simplify this process and make it more scalable.



Ugur Cetintemel (Brown University)
Mitch Cherniack (Brandeis University)
Olga Papaemmanouil (Brandeis University)
Stan Zdonik (Brown University)


Kyriaki Dimitriadou (Graduate Student, Brandeis University)

Dimokritos Stamatakis  (Graduate Student, Brandeis University)
Varenya Prasad (MSc Student, Brandeis University, graduated, currently in industry)

Alexander Pagan (Undergraduate Student, Brandeis University, graduated, currently a Ph.D. student  MIT)


Yanlei Diao (UMass, Amherst)

Joseph Lau (Professor of Health Services, Policy & Practice, Brown University)

Thomas Trikalinos (Associate Professor of Health Services, Policy & Practice, Brown University, Director for Evidence-Based Medicine Center)


  1. K. Dimitriadou, O. Papaemmanouil, Y. Diao. Explore-by-Example: An Automatic Query Steering Framework for Interactive Data Exploration. In Proceedings of 33rd ACM Special Interest Group in Data Management (SIGMOD '14), June 2014 [pdf].

  2. K. Dimitriadou, O. Papaemmanouil, Y. Diao. Interactive Data Exploration based on User Relevance Feedback.  In Proceedings of 9th International Workshop on Self-Managing Databases Systems (SMDB '14), April 2014 [pdf].

  3. U.Cetintemel, M. Cherniack, J. DeBrabant, Y. Diao, K. Dimitriadou, A. Kalinin, O. Papaemmanouil, S. Zdonik. Query Steering for Interactive Data Exploration,
    In Proceedings of the 6th Biennial Conference in Innovative Data Systems Research ( CIDR 2013), January 2013. [pdf]

  4. K. Dimitriadou, O. Papaemmanouil, Y. Diao, Query Auto-Steering for Interactive Data Exploration (poster), New England Database Summit, January 2013 [pdf]

  5. Alexander Pagan, STARS: A System To Aid Reuse in Systematic Reviewing, Undergraduate Honor Thesis, Brandeis University [pdf]


This project is sponsored by NSF  IIS-1049974.