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MEX Interfaces: Automating Machine
Learning Metadata Generation
D. Esteves, Pablo N. Mendes, D. Moussallem, J.C. Duarte, A...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
What’s metadat...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
=== Run inform...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
How costly is ...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Reproducible R...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Reproducible R...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
ML/DM Environm...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Machine Learni...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Workflow Syste...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Integrated Dev...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Integrated Dev...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Research Quest...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Existing solut...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
MEX Interfaces...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
MEX Interfaces...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
- Generates me...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Vocabularies a...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Vocabularies a...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
- Reduces the ...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
- Transparent ...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
- Transparent ...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
java -cp /home...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
MEX Annotation...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Metadata file ...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Features: Logg...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
- Transparent ...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
MEX Interfaces...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Features: Data...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Features: Data...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 30
- What are ...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Features: Data...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Features: Data...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Features: Data...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Conclusions an...
Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016
Conclusions an...
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Diego Esteves, Pablo Mendes, Diego Moussallem, Julio Cesar Duarte, Amrapali Zaveri, Jens Lehmann, Ciro Baron Neto, Igor Costa and Maria Claudia Cavalcanti | MEX Framework: Automating Machine Learning Metadata Generation

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Diego Esteves, Pablo Mendes, Diego Moussallem, Julio Cesar Duarte, Amrapali Zaveri, Jens Lehmann, Ciro Baron Neto, Igor Costa and Maria Claudia Cavalcanti | MEX Framework: Automating Machine Learning Metadata Generation

  1. 1. MEX Interfaces: Automating Machine Learning Metadata Generation D. Esteves, Pablo N. Mendes, D. Moussallem, J.C. Duarte, Amrapali Zaveri, Jens Lehmann, Ciro Baron Neto, Igor Costa and Maria Claudia Cavalcanti University of Leipzig September 13, 2016 1
  2. 2. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 What’s metadata and why is it so important? “Metadata is data that provides information about other data” - Data Management - Meta Analysis (ML) - Social Engines - ... 2
  3. 3. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 === Run information === Scheme:weka.classifiers.trees.J48 -C 0.25 -M 2 Relation: iris Instances: 150 Attributes: 5 sepallength sepalwidth petallength petalwidth class Test mode:evaluate on training data ... TP Rate FP Rate Precision Recall F-Measure ROC Area Class 1 0 1 1 1 1 Iris- setosa 0.98 0.02 0.961 0.98 0.97 0.99 Iris- versicolor 0.96 0.01 0.98 0.96 0.97 0.99 Iris- virginica Weighted Avg. 0.98 0.01 0.98 0.98 0.98 0.993 ... How costly is the metadata generation process? 3
  4. 4. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 How costly is the metadata generation process? 4
  5. 5. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Reproducible Research - in theory 1.“Data/Metadata publicly available” 2.“The computer code and all the computational procedures should be available” 3.“Ideally the computer code will encompass all of the steps of computational analysis” Dr. Peng / Dr. Jeff Leek 5
  6. 6. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Reproducible Research - in practice - Experiments are hard to reproduce, when not impossible. 6
  7. 7. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 ML/DM Environments 7 LIBSVM OpenML IDEsFrameworks Workflow Systems Collaborative Env.
  8. 8. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Machine Learning Frameworks 8 Platform Advantage Drawbacks MLF Front-end No (High) Interoperability No/low updates delay No much code flexibility (Low) Workflow Management LIBSVM Frameworks
  9. 9. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Workflow Systems / Collaborative Environments 9 Platform Advantage Drawbacks WFS High Provenance No (High) Interoperability Interoperabili ty (*) Updates are tool dependent Workflow Management No much code flexibility OpenML Workflow Systems Collaborative Env.
  10. 10. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Integrated Development Environments / Libraries 10 Platform Advantage Drawbacks IDE/MLL High code- flexibility Low Provenance No learning curve Low Interoperability Data Management costs more IDEs
  11. 11. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Integrated Development Environments / Libraries 11 Platform Advantage Drawbacks IDE/MLL High code- flexibility Low Provenance No learning curve Low Interoperability Data Management costs more IDEs motivation
  12. 12. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Research Question 1.How to export machine learning variables - incomes/outcomes (IDEs)? - Architecture - Schema 2.What is the existing approach that minimizes the coding effort (IDEs)? 12
  13. 13. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Existing solutions 13 Solution Advantage Drawbacks stdout No Extra Coding Effort Required Lack of Provenance Lack of Interoperability Lack of Data Query Feature DBMS Data Query Feature Extra Coding Effort (Integration) Lack of Provenance Lack of Interoperability Self-schema Definition Straightforward Solution Extra Coding Effort Extra Analysis Effort (modeling) Lack of Provenance Lack of Interoperability IDEs
  14. 14. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 MEX Interfaces: from annotations to semantic metadata 14 Method Advantage Drawbacks MEX Interfaces - Provenance - Interoperabili ty - Data Query Feature - Automatic Metadata Generation - Extra Processing Time - Security Issues (due to reflection) IDEs
  15. 15. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 MEX Interfaces: from annotations to semantic metadata 1. Allow metadata generation regardless of the IDE, machine- learning library and context of the experiment 15
  16. 16. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 - Generates metadata based on one of the state-of-the- art vocabularies for machine learning :exp_cf_1_2025644708_exe_2_algo a mexalgo:Algorithm ; rdfs:label "Support Vector Machines" ; mexalgo:hasAlgorithmClass mexalgo:SupportVectorMachines ; mexalgo:hasHyperParameter :exp_cf_1_2025644708_exe_2_hyperpar_4, :exp_ cf_1_2025644708_exe_2_hyperpar_3, :exp_cf_1_2025644708_exe_2_hyperpar_2, :exp_cf_1_2025644708_exe_2_hyperpar_1 ; dct:identifier "svm". ... MEX Vocabulary 16
  17. 17. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Vocabularies and Ontologies for ML/DM 17 MEX Onto-DM DMOP Exposé W3C ML-Schema Community Group https://www.w3.org/community/ml- schema/ KDD MLS
  18. 18. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Vocabularies and Ontologies for ML/DM: MEX 18 Agnieszka Lawrynowicz et al. “The Algorithm-Implementation-Execution Ontology Design Pattern”, WOP2016. Algorithm ⊑ InformationEntity Implementation ⊑ InformationEntity Implementation ⊑ ∃implements.Algorithm Implementation ⊑ ∃hasParameter.Parameter Execution ⊑ Process Execution ⊑ ∃hasInput.ParameterSetting Execution ⊑ ∃realizes.Algorithm ... is a lightweight and flexible schema for machine learning, based on PROV- O PROV-O: The PROV Ontology http://mex.aksw.org https://www.w3.org/TR/prov-o/
  19. 19. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 - Reduces the overall coding effort - No wrappers/adaptors/connectors do DBMS - No schema development required - Transparent process MEX Interfaces: Features 19
  20. 20. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 - Transparent process (annotations and reflection) Features: MEX Annotations 20 @ExperimentInfo(identifier = "e1", createdBy = "Esteves", email = "esteves@informatik.uni- leipzig.de", title = "Weka Lib Example", tags = {"WEKA","J48", "DecisionTable", "MEX", "Iris"}) @Hardware(cpu = MEXEnum.EnumProcessors.INTEL_COREI7, memory = MEXEnum.EnumRAM.SIZE_8GB, hdType = "SSD") @SamplingMethod(klass = MEXEnum.EnumSamplingMethods.CROSS_VALIDATION, trainSize = 0.5, testSize = 0.5, folds = 10) @InterfaceVersion(version = MEXEnum.EnumAnnotationInterfaceStyles.M1) public class WekaExample001 { … } java -cp /home/mexframework org.aksw.mex.framework.MetaGeneration -uc IrisWekaExample.java -out mymex01.ttl
  21. 21. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 - Transparent process (annotations and reflection) Features: MEX Annotations 21 @Algorithm(algorithmClass = MEXEnum.EnumAlgorithmsClasses.J48, algorithmID = "1", algorithmName = "J48", algorithmURI = "http://weka.sourceforge.net/doc.dev/weka/ classifiers/trees/J48.html") public J48 wekaJ48; @Algorithm(algorithmClass = MEXEnum.EnumAlgorithmsClasses.PART, algorithmID = "2", algorithmName = "PART", algorithmURI = "http://weka.sourceforge.net/doc.dev/weka/ classifiers/rules/PART.html") public PART wekaPART; @DatasetName public String ds = "iris.arff"; Instances data; ... java -cp /home/mexframework org.aksw.mex.framework.MetaGeneration -uc IrisWekaExample.java -out mymex01.ttl
  22. 22. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 java -cp /home/mexframework org.aksw.mex.framework.MetaGeneration -uc IrisWekaExample.java -out mymex01.ttl - Transparent process (annotations and reflection) @Measure(idMeasure = MEXEnum.EnumMeasures.ERROR) public List<Double> errors; @Measure(idMeasure = MEXEnum.EnumMeasures.ACCURACY) public List<Double> accuracies; @Start public void myMainMethod(){ throws Exception {...} Features: MEX Annotations 22
  23. 23. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 MEX Annotations Log 23 Starting the process: MetaGeneration -uc interfaces.WekaExample001 -out mymex01.ttl [main] INFO org.aksw.mex.interfaces.MetaGeneration - ********************** MEX Interfaces ********************** [main] INFO org.aksw.mex.interfaces.MetaGeneration - http://mex.aksw.org [main] INFO org.aksw.mex.interfaces.MetaGeneration - Starting the meta annotation for class named: WekaExample001 [main] INFO org.aksw.mex.interfaces.MetaGeneration - @ExperimentInfo - OK [main] INFO org.aksw.mex.interfaces.MetaGeneration - @Hardware - OK [main] INFO org.aksw.mex.interfaces.MetaGeneration - @SamplingMethod - OK [main] INFO org.aksw.mex.interfaces.MetaGeneration - invoking the main method: start [main] INFO interfaces.WekaExample001 - Accuracy of J48: 94.00% - Error: 6.00% [main] INFO interfaces.WekaExample001 - Accuracy of PART: 90.67% - Error: 9.33% [main] INFO interfaces.WekaExample001 - Accuracy of DecisionTable: 92.67% - Error: 7.33% [main] INFO interfaces.WekaExample001 - Accuracy of DecisionStump: 36.67% - Error: 63.33% [main] INFO org.aksw.mex.interfaces.MetaGeneration - invoking the features method: getFeatures [main] INFO org.aksw.mex.interfaces.MetaGeneration - @DataSet - OK [main] INFO org.aksw.mex.interfaces.MetaGeneration - @Algorithm - OK [main] INFO org.aksw.mex.interfaces.MetaGeneration - :: starting to add executions... [main] INFO org.aksw.mex.interfaces.MetaGeneration - :: nr. executions = 4 [main] INFO org.aksw.mex.interfaces.MetaGeneration - :: idExecution = C1_MEX_EXEC_D1 [main] INFO org.aksw.mex.interfaces.MetaGeneration - error of Execution C1_MEX_EXEC_D1 : 6.0 [main] INFO org.aksw.mex.interfaces.MetaGeneration - accuracy of Execution C1_MEX_EXEC_D1 : 94.0 [main] INFO org.aksw.mex.interfaces.MetaGeneration - :: idExecution = C1_MEX_EXEC_D2 [main] INFO org.aksw.mex.interfaces.MetaGeneration - error of Execution C1_MEX_EXEC_D2 : 9.333333333333329 [main] INFO org.aksw.mex.interfaces.MetaGeneration - accuracy of Execution C1_MEX_EXEC_D2 : 90.66666666666667 [main] INFO org.aksw.mex.interfaces.MetaGeneration - :: idExecution = C1_MEX_EXEC_D3 [main] INFO org.aksw.mex.interfaces.MetaGeneration - error of Execution C1_MEX_EXEC_D3 : 7.333333333333329 [main] INFO org.aksw.mex.interfaces.MetaGeneration - accuracy of Execution C1_MEX_EXEC_D3 : 92.66666666666667 [main] INFO org.aksw.mex.interfaces.MetaGeneration - :: idExecution = C1_MEX_EXEC_D4 [main] INFO org.aksw.mex.interfaces.MetaGeneration - error of Execution C1_MEX_EXEC_D4 : 63.333333333333336 [main] INFO org.aksw.mex.interfaces.MetaGeneration - accuracy of Execution C1_MEX_EXEC_D4 : 36.666666666666664 [main] WARN org.aksw.mex.log4mex.MEXSerializer - No model defined [main] WARN org.aksw.mex.log4mex.MEXSerializer - No tool defined [main] WARN org.aksw.mex.log4mex.MEXSerializer - No tool parameter defined [main] INFO org.aksw.mex.interfaces.MetaGeneration - The MEX file has been successfully created: share it ;-) [main] INFO org.aksw.mex.interfaces.MetaGeneration - process execution time (s): 1
  24. 24. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Metadata file example :hardware a mexcore:HardwareConfiguration, prov:Entity ; mexcore:cpu "Intel Core i7" ; mexcore:hd "SSD" ; mexcore:memory "8 GB" . :execution_3 a mexcore:OverallExecution, mexcore:group "true" ; prov:id "3" ; prov:used this:phaseTEST ; prov:wasInformedBy this:configuration1 . ... :sampling a prov:Entity , mexcore:CrossValidation ; mexcore:folds "10" ; mexcore:sequential "true"; mexcore:testSize "0.5" ; mexcore:trainSize "0.5" . :feature4 a mexcore:Feature, prov:Entity ; rdfs:label "petalwidth" ; dct:identifier "4" . :measure3_1 a mexperf:StatisticalMeasure; mexperf:error "7.333333333333329" ; prov:wasInformedBy this:execution_3 . ... Features: Output file sample 24
  25. 25. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Features: Logging Library - Transparent process (logging) 25 MyMEX mex = new MyMEX(); try{ mex.setAuthorName("D Esteves"); mex.setAuthorEmail("esteves@informatik.uni-leipzig.de"); mex.setOrganization("Leipzig University"); mex.setExperimentId("E001"); mex.setExperimentTitle("my first experiment"); … }
  26. 26. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 - Transparent process (logging) try{ MEXSerializer.getInstance().saveToDisk("./metafiles/log4mex/ ex003", "http://mex.aksw.org/examples/", mex, MEXConstant.EnumRDFFormats.TTL); }catch (Exception e){ System.out.print(e.toString());} Features: Logging Library 26 ... mex.Configuration().addExecution(EnumExecutionsType.OVERALL, EnumPhases.TRAIN); mex.Configuration().Execution(ex1).setAlgorithm(alg1);
 mex.Configuration().Execution(ex1).addPerformance(EnumMeasures.ACCUR ACY, x); ...
  27. 27. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 MEX Interfaces output x Weka default output Features: Output file sample 27 === Evaluation on training set === === Summary === Correctly Classified Instances 147 98% Incorrectly Classified Instances 3 2% Kappa statistic 0.97 Mean absolute error 0.0233 Root mean squared error 0.108 Relative absolute error 5.2482% Root relative squared error 22.9089% Total Number of Instances 150 this:m11 a prov:Entity, mexperf:PerformanceMeasure; dct:identifier "WekaPerformances"; mexperf:accuracy "0.9768"^^xsd:float; mexperf:truePositive "147"^^xsd:integer; mexperf:falsePositive "3"^^xsd:integer; mexperf:kappaStatistics "0.97"^^xsd:float; mexperf:meanAbsoluteError "0.0233"^^xsd:float; mexperf:rootMeanSquaredError "0.108"^^xsd:float; mexperf:relativeAbsoluteError "0.052482"^^xsd:float; mexperf:rootRelativeSquaredError "0.0229089"^^xsd:float; prov:wasGeneratedBy this:ep1;. ...
  28. 28. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Features: Data Visualization (http://lodview.it/) 28
  29. 29. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Features: Data Management “for free” 29 - ML Model that takes 3 days to be executed - Iterates 300 times - Produces/Has 5000 outcomes/incomes - ….
  30. 30. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 30 - What are the top 4 configurations?
  31. 31. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Features: Data Management “for free” 31 PREFIX mexcore: <http://mex.aksw.org/mex-core#>
 PREFIX mexperf: <http://mex.aksw.org/mex-perf#>
 PREFIX mexalgo: <http://mex.aksw.org/mex-algo#>
 PREFIX prov: <http://www.w3.org/ns/prov#>
 PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
 SELECT DISTINCT ?ExecutionID ?Algorithm ?Performance ?fMeasure WHERE {
 ?execution prov:used ?alg; prov:id ?ExecutionID.
 ?Performance prov:wasGeneratedBy ?execution.
 ?Performance mexperf:f1Measure ?fMeasure.
 ?alg a mexalgo:Algorithm.
 ?alg rdfs:label ?Algorithm.
 } 
 ORDER BY DESC (?fMeasure)
 LIMIT 4
  32. 32. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Features: Data Management “for free” 32 ?ExecutionID ?Algorithm ?Performance ?fMeasure "C0_MEX_EXEC_D44" "BaggingJ48" mea_clas_C0_MEX_EXEC_D44_cf_1_-568657719 0.9968 "C0_MEX_EXEC_D24" "Logistic Model Trees" mea_clas_C0_MEX_EXEC_D24_cf_1_-568657719 0.9952 "C0_MEX_EXEC_D16" "Random Forest" mea_clas_C0_MEX_EXEC_D16_cf_1_-568657719 0.9920 "C0_MEX_EXEC_D64" "Multilayer Perceptron" mea_clas_C0_MEX_EXEC_D64_cf_1_-568657719 0.99
  33. 33. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Features: Data Management “for free” 33
  34. 34. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Conclusions and Future Work Machine Learning Metadata Generation - Generating high quality metadata is not a straightforward process - Dealing with different outputs is not time-efficient RQ1/RQ2 -> New methodology - To automatize the process metadata generation for IDEs - Data Management - Based on one of the state-of-the-art vocabularies Future Work - Integrate others ML ontologies (ML-Schema) - Analyse the coverage of the methodology with more machine learning scenarios - To create a more robust framework (e.g.: automatic pipelines based on configuration files) 34
  35. 35. Esteves et al. (University of Leipzig) MEX Interfaces: Automating ML Metadata Generation September 13, 2016 Conclusions and Future Work Thank you! Questions? mex.aksw.org 35

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