Hi Bruno, Your research topic is very interesting and I believe openEHR architecture ease hierarchical temporal data management. I don't have openEHR data instances which satisfy your requirement. However, if you or anybody have real case scenario, I would be able to generate the instances for you.
Cheers, Chunlan ---------------------------------------------------------- Dr Chunlan Ma (Med) PhD (Health Informatics) Software Architect, Clinical Interoperability t: +61 (0)8 8223 3075 | m: 0405 139 586 f: +61 (0)8 8223 2570 | skype: chunlan_ma Ocean Informatics Pty Ltd Ground floor, 64 Hindmarsh Square Adelaide SA 5000 http://www.oceaninformatics.com -----Original Message----- From: [email protected] [mailto:openehr-technical-bounces at openehr.org] On Behalf Of Bruno Cadonna Sent: Thursday, June 19, 2008 3:20 AM To: For openEHR technical discussions Subject: Re: Request: openEHR temporal data instance needed ... Hi Ian, I am sorry for my explanation being too vague. Temporal data management is a field of data management focusing on the temporal aspect of data. Given temporal data, i.e. data with one or more time dimensions, operations like temporal joins and temporal aggregates are different compared to their counterparts for non-temporal data. To make things clearer, the following example describes one type of temporal aggregation called instant temporal aggregation: A patient was prescribed 2 medications. The first medication was prescribed for the interval 0 to 10, the second medication was prescribed for the interval 5 to 15. Assume you want to know how many medications were prescribed for this patient over time. First, you have to compute time interval for which the data does not change in time. This operation is called time slicing. In this example the constant intervals after time slicing are: [0, 4] [5, 10] [11, 15] The second step in temporal aggregation is to calculate the aggregate value -- in this case the number of medications -- for each constant interval, which are: [0, 4], 1 [5, 10], 2 [11, 15], 1 In this example there are only two intervals but there might be a lot more with much more overlapping sections becoming a challenge regarding computing. Instant temporal aggregation is just one type of temporal aggregation, there are some more. With relational DBMSs you do temporal aggregation with using complicated SQL queries, however, those are rather inefficient. In the last two decades researchers in temporal data management came up with some temporal data models, temporal operations and corresponding efficient algorithms, mainly for the relational data model. My research focuses on temporal data management on hierarchical data, like XML. Since I like the openEHR idea and I have worked in Health Informatics for the last years, I would like to use openEHR data instances. At the moment I am looking for a sound running example, which is clinically relevant and needs temporal data management. I was thinking about some temporal aggregates over a prescription list, a problem list or some other archetyped data with potentially overlapping time intervals. If somebody has an idea, s/he is really welcome. I hope things are clearer now. Cheers Bruno
