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  1. 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 set
    Health Ontology Mapper / … / HOM HomeDec 20, 2010
  2. AMIA_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 clinical
    Health Ontology Mapper / … / HOM HomeDec 20, 2010
  3. AMIA_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 fact
    Health Ontology Mapper / … / HOM HomeDec 20, 2010
  4. AMIA_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 fact
    Health Ontology Mapper / … / HOM HomeDec 20, 2010
  5. AMIA_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 also
    Health Ontology Mapper / … / HOM HomeDec 20, 2010
  6. Use 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 fact
    Server (Cells) DesignSep 04, 2018
  7. 1. 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 practices
    BundlesFeb 03, 2021
  8. OntoMapper 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 screen
    Health Ontology Mapper / … / HOM HomeDec 20, 2010
  9. C_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.
    Server (Cells) DesignSep 04, 2018
  10. 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
    i2b2 Developer's ForumOct 12, 2010