Momentum--—an active time-oriented database for intelligent abstraction exploration and analysis of clinical data

Alex Spokoiny, Yuval Shahar

Research output: Contribution to journalArticlepeer-review


The temporal-reasoning task focuses on intelligent analysis of time-oriented data, while the temporal maintenance task focuses on effective storage, query, and retrieval of these data. Both are highly relevant for biomedical applications such as monitoring, therapy, quality assessment, and visualization and exploration of time-oriented data, which cannot be expected to resolve each time both tasks, or to understand the internal details of specialized modules that perform them. Thus, it is imperative to supply a system, known as a temporal mediator, which integrates these tasks. One potential problem in existing temporal-mediation approaches is lack of sufficient responsiveness when querying the database for complex abstract concepts that are derived from the raw data, especially regarding a large patient group. We propose a new integration approach: The active time-oriented database. This approach is a temporal extension of the active-database concept, a merger of temporal reasoning and temporal maintenance within a persistent database framework. The approach preserves the efficiency of databases in handling data storage and retrieval while enabling specification and performance of complex temporal reasoning using an incremental-computation approach. The new approach provides persistence and truth-maintenance of the resultant abstractions. We implemented the active time oriented database approach within the Momentum system. Initial experiments with the Momentum system are encouraging,
and an evaluation is underway to assess its validity.


Dive into the research topics of 'Momentum--—an active time-oriented database for intelligent abstraction exploration and analysis of clinical data'. Together they form a unique fingerprint.

Cite this