A knowledge-based method for temporal abstraction of clinical data
A knowledge-based method for temporal abstraction of clinical data
A framework for knowledge-based temporal abstraction
Artificial Intelligence
Extreme programming explained: embrace change
Extreme programming explained: embrace change
Journal of Intelligent Information Systems - Special issue on integrating artificial intelligene and database technologies
Java tools for eXtreme Programming: mastering open source tools including, Ant, JUnit, and Cactus
Java tools for eXtreme Programming: mastering open source tools including, Ant, JUnit, and Cactus
Active Rules in Database Systems
Active Rules in Database Systems
JavaSpaces Principles, Patterns, and Practice
JavaSpaces Principles, Patterns, and Practice
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
Active Database Systems: Triggers and Rules for Advanced Database Processing
Active Database Systems: Triggers and Rules for Advanced Database Processing
Dynamic temporal interpretation contexts for temporal abstraction
Annals of Mathematics and Artificial Intelligence
CAPSUL: A constraint-based specification of repeating patterns in time-oriented data
Annals of Mathematics and Artificial Intelligence
The Conceptual Basis for Mediation Services
IEEE Expert: Intelligent Systems and Their Applications
Artificial Intelligence in Medicine
Incremental application of knowledge to continuously arriving time-oriented data
Journal of Intelligent Information Systems
Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data
Artificial Intelligence in Medicine
Incremental application of knowledge to continuously arriving time-oriented data
Journal of Intelligent Information Systems
Intelligent visualization and exploration of time-oriented data of multiple patients
Artificial Intelligence in Medicine
Intelligent selection and retrieval of multiple time-oriented records
Journal of Intelligent Information Systems
Using preaggregation to speed up scaling operations on massive spatio-temporal data
ER'10 Proceedings of the 29th international conference on Conceptual modeling
BPM' 2012 Proceedings of the 2012 international conference on Process Support and Knowledge Representation in Health Care
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An effective solution to the tasks of continuous monitoring and aggregation querying of complex domain-meaningful concepts and patterns in environments featuring large continuously changing data sets is very important for many domains. Typical domains include: making financial decisions, integrating intelligence information from multiple sources, evaluating the effects of traffic controllers' actions, detection of security threats in communication networks, planning and monitoring in robotics, and management of chronic patients in medical domains. In this paper, we present a general domain-independent method for an effective solution of these two tasks. Our method involves incremental creation of meaningful, interval-based abstractions, from raw, time-stamped data continuously arriving from multiple sources, which is supported by the accumulation and continuous validation of the created abstractions. We implemented our method in the Momentum system, which is an active knowledge-based time-oriented database--a temporal extension of the active-database concept that we propose for incremental application of knowledge to continuously arriving time-oriented data. We evaluated the Momentum system in a medical domain within a database of 1,000 patients monitored after bone-marrow transplantation, and a knowledge base of complex abstractions regarding more than 100 raw-data types and about 400 concept types derivable from them. Initial evaluations are highly encouraging with regards to the feasibility of the whole approach.