Systematic Generation of Non-equivalent Expressions for Relational Algebra

2018 ◽  
pp. 105-120 ◽  
Author(s):  
Kaiyuan Wang ◽  
Allison Sullivan ◽  
Manos Koukoutos ◽  
Darko Marinov ◽  
Sarfraz Khurshid
Keyword(s):  
Relational Algebra ◽  
Equivalent Expressions

Extending the Relational Algebra for Relations with Maybe Tuples and Existential and Universal Null Values

1984 ◽  
Vol 7 (1) ◽  
pp. 129-150
Author(s):  
Joachim Biskup
Keyword(s):  
Relational Algebra ◽  
Null Value ◽  
Null Values ◽  
A Minor

We study operations on generalized database relations which possibly contain maybe tuples and two types of null values. The existential null value has the meaning “value at present unknown” whereas the universal null value has the meaning “value arbitrary”. For extending a usual relational operation to generalized relations we develop three requirements: adequacy, restrictedness, and feasibility. As demonstrated for the natural join as an example, we can essetially meet these requirements although we are faced with a minor tradeoff between restrictedness and feasibility.

Incomplete information in a relational database

1980 ◽  
Vol 3 (3) ◽  
pp. 363-377
Author(s):  
John Grant
Keyword(s):  
Incomplete Information ◽  
Relational Database ◽  
Functional Dependency ◽  
Relational Algebra ◽  
Database Model ◽  
Character String

In this paper we investigate the inclusion of incomplete information in the relational database model. This is done by allowing nonatomic entries, i.e. sets, as elements in the database. A nonatomic entry is interpreted as a set of possible elements, one of which is the correct one. We deal primarily with numerical entries where an allowed set is an interval, and character string entries. We discuss the various operations of the relational algebra as well as the notion of functional dependency for the database model.

SQL query optimization through nested relational algebra

2007 ◽  
Vol 32 (3) ◽  
pp. 18 ◽  
Author(s):  
Bin Cao ◽  
Antonio Badia
Keyword(s):  
Query Optimization ◽  
Relational Algebra ◽  
Sql Query

Relational Algebra

2014 ◽  
pp. 129-148
Author(s):  
Elvis C. Foster ◽  
Shripad V. Godbole
Keyword(s):  
Relational Algebra

scholarly journals Desiderata for computable representations of electronic health records-driven phenotype algorithms

2015 ◽  
Vol 22 (6) ◽  
pp. 1220-1230 ◽  
Author(s):  
Huan Mo ◽  
William K Thompson ◽  
Luke V Rasmussen ◽  
Jennifer A Pacheco ◽  
Guoqian Jiang ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Language Processing ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Relational Algebra ◽  
Common Data Model ◽  
Health Records ◽  
Electronic Health ◽  
Value Sets ◽  
Text Searching

Abstract Background Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

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