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Name:	AlgorithmImplementationExecution
Submitted by:	AgnieszkaLawrynowicz, DiegoEsteves, PancePanov, SasoDzeroski, TommasoSoru, JoaquinVanschoren
Also Known As:
Intent:	To model algorithm specifications, their implementations and executions, together with parameters of implementations, settings of the parameters for the execution, and inputs the execution consumes (e.g., data) and outputs the execution produces (e.g., models, reports).
Domains:	General, Software, Software Engineering, Workflow
Competency Questions:	Which algorithm is implemented by this implementation? What are the implementations of this algorithm? Which implementation is executed? What are the parameters of this implementation? What are the parameter settings of particular parameters in this execution? What is the input to this implementation execution? What is the output produced by the this implementation execution? What task this execution realizes? What is the duration of this execution? What is the input this task is defined on?
Solution description:	Beloit it is provided the formalization of the pattern in the Web Ontology Language (OWL) in Manchester syntax: Algorithm SubClassOf InformationEntity, Implementation SubClassOf InformationEntity, Implementation SubClassOf implements some Algorithm, Implementation SubClassOf hasParameter some Parameter, Execution SubClassOfProcess, Execution SubClassOf hasInput some Input, Execution SubClassOf hasInput some ParameterSetting, Execution SubClassOf hasOutput some Output, Execution SubClassOf realizes some Task, Execution SubClassOf hasDuration some TimeInterval, Parameter SubClassOf InformationEntity, ParameterSetting SubClassOf InformationEntity, ParameterSetting SubClassOf specifiedBy some Parameter, ParameterSetting SubClassOf hasValue some refs:Literal, Input SubClassOf InformationEntity, Output SubClassOf InformationEntity, Task SubClassOf InformationEntity, Task SubClassOf definedOn some Input
Reusable OWL Building Block:	1 (0)
Consequences:
Scenarios:	Consider a scenario in machine learning (ML) domain. The scenario deals with a machine learning task realization and it is based on an example derived from the OpenML portal (http://www.openml.org/). There is an ML Task `:task29` which is a supervised classification task defined on the dataset `:credit-a`. This task is realized by the Execution `:run100241` which executes the Implementation `:wekaLogistic` of the Algorithm `:logisticRegression`. The Implementation `:wekaLogistic` has five hyperparameters (Parameter): `:wekaLogisticC`, `:wekaLogisticDoNotCheckCapabilities`, `:wekaLogisticM`, `:wekaLogisticOutputDebugInfo`, `:wekaLogisticR`. The values of two of these hyperparameters are set. The hyper parameter `:wekaLogisticM` has value set to -1 (expressed via the ParameterSetting `:wekaLogisticMSetting29`), and the hyper parameter `:wekaLogisticR` that has its value set to `"1.0E-8"^^xsd:float` (expressed via the ParameterSetting `:wekaLogisticRSetting29`). The Execution `:run100241` has on Input the `:credit-a` dataset and the parameter settings and its Output is the ML model `:wekaLogisticModel100241`.
Known Uses:	ML Schema, DMOP, OntoDM, OBI, MEX, Function Ontology
Web References:
Other References:
Examples (OWL files):
Extracted From:
Reengineered From:	ML Schema https://www.w3.org/community/ml-schema/
Has Components:	Submissions:TimeInterval
Specialization Of:
Related CPs:

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Submissions:AlgorithmImplementationExecution Revision as of 09:12, 6 July 2016 by AgnieszkaLawrynowicz (Talk | contribs) (diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)