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AMIA_Spring_IDR_v1.2.2.doc
to the partial existence of facts. A separate fact table was designed to accommodate aggregate facts. The development of aggregate fact tables is a common practice in data warehousing, typically accomplished through a materialized view against the detailed fact table. Aggregation also provides a valuable data setAMIA_Spring_IDR_v1.2.3.doc
to the partial existence of facts. A separate fact table was designed to accommodate aggregate facts. The development of aggregate fact tables is a common practice in data warehousing, typically accomplished through a materialized view against the detailed fact table. Aggregation also provides a valuable data set to clinicalAMIA_Spring_IDR_v1.2.doc
. Aggregation requirements posed a unique challenge related to the partial existence of facts. A separate fact table was designed to accommodate aggregate facts. The development of aggregate fact tables is a common practice in data warehousing, typically accomplished through a materialized view against the detailed factAMIA_Spring_IDR_v1.1.doc
. Aggregation requirements posed a unique challenge related to the partial existence of facts. A separate fact table was designed to accommodate aggregate facts. The development of aggregate fact tables is a common practice in data warehousing, typically accomplished through a materialized view against the detailed factAMIA_Spring_IDR_v1.2.1.doc
posed a unique challenge related to the partial existence of facts. A separate fact table was designed to accommodate aggregate facts. The development of aggregate fact tables is a common practice in data warehousing, typically accomplished through a materialized view against the detailed fact table. Aggregation alsoUse Case 2_ Add new facts
In this case new facts are added to the OBSERVATION_FACT table regardless of whether or not the fact's encounter exists. This involves overwriting any matching fields. i.e. if the incoming fact matches a particular stored fact and its update date is greater than the update of the matching fact, then the new fact1. Introduction
types, all patient observations are stored in a single "fact" table. A separate ontology describes the different codes that are placed in this fact table … in July, 2020. This document not only describes the database tables and fields in the i2b2 CDM, but also provides a set of recommendations and best practicesOntoMapper Minutes 8 19 08.doc
Ontology Mapper Minutes 8/29/08 Present on call: Hillari, Prakash, Marco, Davera, Maggie and Mark We walked through the schema design for OntoMapper and the choices we made regarding the Mapped Data Fact Table design. We walked through the new UI screen designs. We are really trying to get every little screenC_FACTTABLECOLUMN
The _C_FACTTABLECOLUMN_ is the name of a key in the fact table (OBSERVATION_FACT) that links to the dimension code we are querying for. Typical entries will be CONCEPT_CD, PATIENT_NUM, ENCOUNTER_NUM, or PROVIDER_ID.Query Building from Ontology
] [c_operator] [c_dimcode] The intent of this query is to associate a link between the dimension tables and the fact table for a given term. As a result every metadata SELECT SQL statement should return a fact table key. A sample concept_dimension-based term is shown: select concept_cd from