title: Exploring performance issues for a clinical database organized using an entity-attribute-value representation.
abstract: BACKGROUND
  The entity-attribute-value representation with classes and relationships (EAV/CR) provides a flexible and simple database schema to store heterogeneous biomedical data. In certain circumstances, however, the EAV/CR model is known to retrieve data less efficiently than conventionally based database schemas.
  
  
  OBJECTIVE
  To perform a pilot study that systematically quantifies performance differences for database queries directed at real-world microbiology data modeled with EAV/CR and conventional representations, and to explore the relative merits of different EAV/CR query implementation strategies.
  
  
  METHODS
  Clinical microbiology data obtained over a ten-year period were stored using both database models. Query execution times were compared for four clinically oriented attribute-centered and entity-centered queries operating under varying conditions of database size and system memory. The performance characteristics of three different EAV/CR query strategies were also examined.
  
  
  RESULTS
  Performance was similar for entity-centered queries in the two database models. Performance in the EAV/CR model was approximately three to five times less efficient than its conventional counterpart for attribute-centered queries. The differences in query efficiency became slightly greater as database size increased, although they were reduced with the addition of system memory. The authors found that EAV/CR queries formulated using multiple, simple SQL statements executed in batch were more efficient than single, large SQL statements.
  
  
  CONCLUSIONS
  This paper describes a pilot project to explore issues in and compare query performance for EAV/CR and conventional database representations. Although attribute-centered queries were less efficient in the EAV/CR model, these inefficiencies may be addressable, at least in part, by the use of more powerful hardware or more memory, or both.
authors.affiliation: Yale University, New Haven, Connecticut, USA.
authors.forename: R S
authors.initials: RS
authors.lastname: Chen
authors.forename: P
authors.initials: P
authors.lastname: Nadkarni
authors.forename: L
authors.initials: L
authors.lastname: Marenco
authors.forename: F
authors.initials: F
authors.lastname: Levin
authors.forename: J
authors.initials: J
authors.lastname: Erdos
authors.forename: P L
authors.initials: PL
authors.lastname: Miller
mesh_terms: D003628:Database Management Systems; D019992:Databases as Topic / Q000458:organization & administration; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D008829:Microbiology; D010865:Pilot Projects