Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
A framework for knowledge-based temporal abstraction
Artificial Intelligence
Temporal logic in information systems
Logics for databases and information systems
Reasoning about qualitative trends in databases
Information Systems
Materialized views: techniques, implementations, and applications
Materialized views: techniques, implementations, and applications
SIGMOD '85 Proceedings of the 1985 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
Journal of Intelligent Information Systems - Special issue on integrating artificial intelligene and database technologies
SQL standardization: the next steps
ACM SIGMOD Record
Optimization of sequence queries in database systems
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The TSQL2 Temporal Query Language
The TSQL2 Temporal Query Language
Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
IEEE Transactions on Knowledge and Data Engineering
Trends in Databases: Reasoning and Mining
IEEE Transactions on Knowledge and Data Engineering
Coalescing in Temporal Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Abstracting Steady Qualitative Descriptions over Time from Noisy, High-Frequency Data
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
An EER-Based Conceptual Model and Query Language for Time-Series Data
ER '98 Proceedings of the 17th International Conference on Conceptual Modeling
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Temporal Abstractions for Diabetic Patients Management
AIME '97 Proceedings of the 6th Conference on Artificial Intelligence in Medicine in Europe
Representing and Reasoning about Temporal Granularities
Journal of Logic and Computation
Guest editorial: Temporal representation and reasoning in medicine
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Temporal abstraction in intelligent clinical data analysis: A survey
Artificial Intelligence in Medicine
Data mining with Temporal Abstractions: learning rules from time series
Data Mining and Knowledge Discovery
The t4sql temporal query language
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data
Artificial Intelligence in Medicine
Open Source BI Platforms: A Functional and Architectural Comparison
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Formal and conceptual modeling of spatio-temporal granularities
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Temporal data mining for the quality assessment of hemodialysis services
Artificial Intelligence in Medicine
Encyclopedia of Database Systems
Encyclopedia of Database Systems
Temporal Information Systems in Medicine
Temporal Information Systems in Medicine
IEEE Transactions on Information Technology in Biomedicine
Flexible and efficient retrieval of haemodialysis time series
BPM' 2012 Proceedings of the 2012 international conference on Process Support and Knowledge Representation in Health Care
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This paper focuses on the identification of temporal trends involving different granularities in clinical databases, where data are temporal in nature: for example, while follow-up visit data are usually stored at the granularity of working days, queries on these data could require to consider trends either at the granularity of months (''find patients who had an increase of systolic blood pressure within a single month'') or at the granularity of weeks (''find patients who had steady states of diastolic blood pressure for more than 3 weeks''). Representing and reasoning properly on temporal clinical data at different granularities are important both to guarantee the efficacy and the quality of care processes and to detect emergency situations. Temporal sequences of data acquired during a care process provide a significant source of information not only to search for a particular value or an event at a specific time, but also to detect some clinically-relevant patterns for temporal data. We propose a general framework for the description and management of temporal trends by considering specific temporal features with respect to the chosen time granularity. Temporal aspects of data are considered within temporal relational databases, first formally by using a temporal extension of the relational calculus, and then by showing how to map these relational expressions to plain SQL queries. Throughout the paper we consider the clinical domain of hemodialysis, where several parameters are periodically sampled during every session.