The HiPAC project: combining active databases and timing constraints
ACM SIGMOD Record - Special Issue on Real-Time Database Systems
Situation monitoring for active databases
VLDB '89 Proceedings of the 15th international conference on Very large data bases
Maintaining views incrementally
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Snoop: an expressive event specification language for active databases
Data & Knowledge Engineering
A framework for knowledge-based temporal abstraction
Artificial Intelligence
Sentinel: an object-oriented DBMS with event-based rules
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Deriving Trends in Historical and Real-Time Continuously Sampled Medical Data
Journal of Intelligent Information Systems - Special issue on integrating artificial intelligene and database technologies
Active Rules in Database Systems
Active Rules in Database Systems
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
Temporal Triggers in Active Databases
IEEE Transactions on Knowledge and Data Engineering
Ode as an Active Database: Constraints and Triggers
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
Journal of Intelligent Information Systems
A framework for distributed mediation of temporal-abstraction queries to clinical databases
Artificial Intelligence in Medicine
Journal of Intelligent Information Systems
Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data
Artificial Intelligence in Medicine
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
Discovery and diagnosis of behavioral transitions in patient event streams
ACM Transactions on Management Information Systems (TMIS)
BPM' 2012 Proceedings of the 2012 international conference on Process Support and Knowledge Representation in Health Care
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In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.