Sixth Meeting

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* Presentation Rakeb
* Presentation Rakeb
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Title: Predicting SPARQL query execution time
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Title: Predicting SPARQL query execution time and suggesting SPARQL queries based on query history
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In this talk we first discuss the problem of predicting SPARQL query execution time. Accurately predicting query execution time enables effective workload management, query scheduling, and query optimization. We use machine learning techniques to predict SPARQL query execution time. We generate the training dataset from real queries collected from DBPedia 3.8 query logs. As features of a SPARQL query, we use the SPARQL query algebra operators and different basic graph pattern types that we generate by clustering the training SPARQL queries. We achieved high accuracy (coefficient of determination value of 0.84) for predicting query execution time. Second, we discuss the problem of suggesting similar SPARQL queries based on query history. Users often need assistance to effectively construct and refine Semantic Web queries. To assist users in constructing and refining SPARQL queries, we provide suggestions of similar queries based on query history. Users can use the suggestions to investigate the similar previous queries and their behaviors.
In this talk we first discuss the problem of predicting SPARQL query execution time. Accurately predicting query execution time enables effective workload management, query scheduling, and query optimization. We use machine learning techniques to predict SPARQL query execution time. We generate the training dataset from real queries collected from DBPedia 3.8 query logs. As features of a SPARQL query, we use the SPARQL query algebra operators and different basic graph pattern types that we generate by clustering the training SPARQL queries. We achieved high accuracy (coefficient of determination value of 0.84) for predicting query execution time. Second, we discuss the problem of suggesting similar SPARQL queries based on query history. Users often need assistance to effectively construct and refine Semantic Web queries. To assist users in constructing and refining SPARQL queries, we provide suggestions of similar queries based on query history. Users can use the suggestions to investigate the similar previous queries and their behaviors.

Revision as of 23:15, 5 November 2013

Contents

Meeting description

Name : non-Kick-off meeting (the project has already been kicked off)

Description :

Start Date : 2013-11-07T09:30:00

End Date : 2013-AA-08T15:30:00

Laboratory : LIRIS

Location : Lyon



General information

Slides and talks can be made either in French or in English

Attending:

  • Nantes: Pascal Molli, Luis Ibanez, Diego Torres (by visio)
  • Nancy: Alice Hermann, Jean Lieber, Yannick and My Thao by visio
  • Lyon: Amélie Cordier, Pierre-Antoine Champin,
  • Nice: Olivier Corby, Rakeb Hasan, Fabien on 8

Locations

  • Meeting Address : Bâtiment Nautibus. Salle C1 le jeudi, Salle C2 le vendredi.

Plan d'accès : http://fst-informatique.univ-lyon1.fr/departement/acces-et-plans/


Detailed Schedule

Thurday 7 :

  • 9:00-10:00 Task1 Coordination, GDD (Project coordination)
Id deadline task number title delivered
D43 1515 Task4 4.3 Algorithm to provide performance and errors indicators 1true
D23 1818 Task23 2.3 Specification of a continuous knowledge extraction system 1true
D44 1818 Task4 4.4 Test and evaluation of the alter ego assistant with regard to the scenarios 1true
D53 1818 Task5 5.3 Algorithms and architecture for a distributed semantic wiki and a distributed alter-ego assistant 1true
D45 2020 Task4 4.5 Algorithm to explain ontology-based processing 1true
D32 2424 Task3 3.2 prototype and tests of the interactive semantic inconsistency managing system 1true
D63 2424 Task6 6.3 Man-machine collaboration scenarios progress report 1true
D46 2626 Task4 4.6 Algorithm to suggest queries and changes to queries 1true
D54 2828 Task5 5.4 Algorithm to distribute query-solving 1true
D55 3434 Task5 5.5 Algorithm to explain and document the distributed solving, Process language and machine for man-machine coordination 1true
D25 3636 Task23 2.5 Continuous extraction, edition and annotation in a semantic wiki ; report advances on this task at the end of the project. 1true
D24 3636 Task24 2.4 Dynamic semantic annotation in a semantic wiki: definitions and specifications 1true
D33 3636 Task3 3.3 prototype of the extension integrating management of ”human-machine” and ”logical” inconsistencies 1true
D56 3636 Task5 5.6 Algorithm adapted to conflict detection
D57 3636 Task5 5.7 Test and evaluation according to the project scenarios 1true
D64 3636 Task6 6.4 Man-machine collaboration scenarios progress report 1true


  • Tour des délivrables
  • préparation de l'évaluation du 25 novembre
  • 10:00-10:30 Coffee Break
  • 10:30-11:30 Task3 Inferences and Interactions for dealing with Semantic Inconsistencies, Orpailleur (Design and experiment strategies for dealing with Semantic Inconsistencies)
    1. Discussion on status of deliverables (maximum : 15 minutes) :
Id deadline task number title delivered
D32 2424 Task3 3.2 prototype and tests of the interactive semantic inconsistency managing system 1true
D33 3636 Task3 3.3 prototype of the extension integrating management of ”human-machine” and ”logical” inconsistencies 1true


  • Summary of task 3, by Alice Hermann (30 minutes)
    • Belief merging
    • Belief revision
    • Integrity constraint belief merging
  • 12:30-14:30 Lunch
  • 14:30-16:00 Task6 Scenarios and Evaluations, GDD (project evaluation)
    1. Discussion on status of deliverables:
Id deadline task number title delivered
D63 2424 Task6 6.3 Man-machine collaboration scenarios progress report 1true
D64 3636 Task6 6.4 Man-machine collaboration scenarios progress report 1true


  • Présentation Diego Torres par Skype sur "BlueFinder"


  • 16:00-16:30 Coffee Break
  • 16:30-18:00 Task2 Continuous extraction, edition, and annotation, Orpailleur (extracting knowledge units from texts using data mining)
    1. Discussion on status of deliverables:
Id deadline task number title delivered
D23 1818 Task23 2.3 Specification of a continuous knowledge extraction system 1true
D25 3636 Task23 2.5 Continuous extraction, edition and annotation in a semantic wiki ; report advances on this task at the end of the project. 1true


  • Présentation Yannick Toussaint/Mi thao.


  • 19h30 Dinner " ?

Friday 8

  • 09:00-10:30 Task5 Social semantic space, Silex (Design and experiment of a social semantic space)
    1. Discussion on status of deliverables:
Id deadline task number title delivered
D53 1818 Task5 5.3 Algorithms and architecture for a distributed semantic wiki and a distributed alter-ego assistant 1true
D54 2828 Task5 5.4 Algorithm to distribute query-solving 1true
D55 3434 Task5 5.5 Algorithm to explain and document the distributed solving, Process language and machine for man-machine coordination 1true
D57 3636 Task5 5.7 Test and evaluation according to the project scenarios 1true
D56 3636 Task5 5.6 Algorithm adapted to conflict detection


  • Presentation: Luis Ibanez "Writable Linked Data"


  • 10:30-10:45 Coffee Break


  • 10:45-12:00 Task4 Traces and explanations: documenting inferences, query solving and interactions, Wimmics (Design and experiment tracing and explanation approaches)
  1. Discussion on status of deliverables:
Id deadline task number title delivered
D43 1515 Task4 4.3 Algorithm to provide performance and errors indicators 1true
D44 1818 Task4 4.4 Test and evaluation of the alter ego assistant with regard to the scenarios 1true
D45 2020 Task4 4.5 Algorithm to explain ontology-based processing 1true
D46 2626 Task4 4.6 Algorithm to suggest queries and changes to queries 1true


  • Presentation Rakeb

Title: Predicting SPARQL query execution time and suggesting SPARQL queries based on query history

In this talk we first discuss the problem of predicting SPARQL query execution time. Accurately predicting query execution time enables effective workload management, query scheduling, and query optimization. We use machine learning techniques to predict SPARQL query execution time. We generate the training dataset from real queries collected from DBPedia 3.8 query logs. As features of a SPARQL query, we use the SPARQL query algebra operators and different basic graph pattern types that we generate by clustering the training SPARQL queries. We achieved high accuracy (coefficient of determination value of 0.84) for predicting query execution time. Second, we discuss the problem of suggesting similar SPARQL queries based on query history. Users often need assistance to effectively construct and refine Semantic Web queries. To assist users in constructing and refining SPARQL queries, we provide suggestions of similar queries based on query history. Users can use the suggestions to investigate the similar previous queries and their behaviors.


  • 12:30-14:30 Lunch
  • 14:30-15:30 Discussion
  • 15:30 Coffee Break & End of the day
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