SPARQL-to-SQL on internet of things databases and streams
SPARQL-to-SQL on internet of things databases and streams
  To realise a semantic Web of Things, the challenge of achieving efficient Resource Description Format (RDF) storage and SPARQL query performance on Internet of Things (IoT) devices with limited resources has to be addressed. State-of-the-art SPARQL-to-SQL engines have been shown to outperform RDF stores on some benchmarks. In this paper, we describe an optimisation to the SPARQL-to-SQL approach, based on a study of time-series IoT data structures, that employs metadata abstraction and efficient translation by reusing existing SPARQL engines to produce Linked Data ‘just-in-time’. We evaluate our approach against RDF stores, state-of-the-art SPARQL-to-SQL engines and streaming SPARQL engines, in the context of IoT data and scenarios. We show that storage efficiency, with succinct row storage, and query performance can be improved from 2 times to 3 orders of magnitude.
  
  
  515-531
  
  
    
      Siow, Eugene
      
        01f33f70-e412-467c-aab2-5509d58d1b94
      
     
  
    
      Tiropanis, Thanassis
      
        d06654bd-5513-407b-9acd-6f9b9c5009d8
      
     
  
    
      Hall, Wendy
      
        11f7f8db-854c-4481-b1ae-721a51d8790c
      
     
  
  
    
  
    
  
    
  
    
  
    
  
    
  
    
  
    
  
   
  
  
    
    
  
    
    
  
    
      October 2016
    
    
  
  
    
      Siow, Eugene
      
        01f33f70-e412-467c-aab2-5509d58d1b94
      
     
  
    
      Tiropanis, Thanassis
      
        d06654bd-5513-407b-9acd-6f9b9c5009d8
      
     
  
    
      Hall, Wendy
      
        11f7f8db-854c-4481-b1ae-721a51d8790c
      
     
  
    
  
    
  
    
  
    
  
    
  
    
  
    
  
    
  
       
    
 
  
    
      
  
  
  
  
    Siow, Eugene, Tiropanis, Thanassis and Hall, Wendy
  
  
  
  
   
    (2016)
  
  
    
    SPARQL-to-SQL on internet of things databases and streams.
  
  
  
    
      Groth, Paul, Simperl, Elena, Gray, Alasdair, Sabou, Marta, Krötzsch, Markus, Lecue, Freddy, Flöck, Fabian and Gil, Yoalnda 
      (eds.)
    
  
  
   In The Semantic Web – ISWC 2016: 15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016, Proceedings, Part I. 
  vol. 9981, 
      Springer. 
          
          
        .
    
  
  
  
   (doi:10.1007/978-3-319-46523-4_31).
  
   
  
    
      Record type:
      Conference or Workshop Item
      (Paper)
      
      
    
   
    
    
      
        
          Abstract
          To realise a semantic Web of Things, the challenge of achieving efficient Resource Description Format (RDF) storage and SPARQL query performance on Internet of Things (IoT) devices with limited resources has to be addressed. State-of-the-art SPARQL-to-SQL engines have been shown to outperform RDF stores on some benchmarks. In this paper, we describe an optimisation to the SPARQL-to-SQL approach, based on a study of time-series IoT data structures, that employs metadata abstraction and efficient translation by reusing existing SPARQL engines to produce Linked Data ‘just-in-time’. We evaluate our approach against RDF stores, state-of-the-art SPARQL-to-SQL engines and streaming SPARQL engines, in the context of IoT data and scenarios. We show that storage efficiency, with succinct row storage, and query performance can be improved from 2 times to 3 orders of magnitude.
         
      
      
        
          
            
  
    Text
 sparql2sql.pdf
     - Accepted Manuscript
   
  
  
    
  
 
          
            
          
            
           
            
           
        
        
       
    
   
  
  
  More information
  
    
      Accepted/In Press date: 1 June 2016
 
    
      e-pub ahead of print date: 23 September 2016
 
    
      Published date: October 2016
 
    
  
  
    
  
    
  
    
     
        Venue - Dates:
        ISWC2016: The 15th International Semantic Web Conference, Kobe, Japan, 2016-10-17 - 2016-10-21
      
    
  
    
  
    
     
    
  
    
  
    
     
        Organisations:
        Web & Internet Science
      
    
  
    
  
  
  
    
  
    
  
  
        Identifiers
        Local EPrints ID: 397863
        URI: http://eprints.soton.ac.uk/id/eprint/397863
        
          
        
        
        
          ISSN: 0302-9743
        
        
          PURE UUID: e39d4e23-4460-4983-b11b-ce2c1b157e5e
        
  
    
        
          
            
              
            
          
        
    
        
          
            
              
            
          
        
    
        
          
            
              
            
          
        
    
        
          
            
          
        
    
        
          
            
          
        
    
        
          
            
          
        
    
        
          
            
          
        
    
        
          
            
          
        
    
        
          
            
          
        
    
        
          
            
          
        
    
        
          
            
          
        
    
  
  Catalogue record
  Date deposited: 07 Jul 2016 15:39
  Last modified: 03 May 2025 01:43
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      Contributors
      
          
          Author:
          
            
              
              
                Eugene Siow
              
              
                
              
            
            
          
         
      
          
          Author:
          
            
              
              
                Thanassis Tiropanis
              
              
                
              
            
            
          
         
      
        
      
          
          Editor:
          
            
              
              
                Paul Groth
              
              
            
            
          
        
      
          
          Editor:
          
            
              
              
                Elena Simperl
              
              
            
            
          
        
      
          
          Editor:
          
            
              
              
                Alasdair Gray
              
              
            
            
          
        
      
          
          Editor:
          
            
              
              
                Marta Sabou
              
              
            
            
          
        
      
          
          Editor:
          
            
              
              
                Markus Krötzsch
              
              
            
            
          
        
      
          
          Editor:
          
            
              
              
                Freddy Lecue
              
              
            
            
          
        
      
          
          Editor:
          
            
              
              
                Fabian Flöck
              
              
            
            
          
        
      
          
          Editor:
          
            
              
              
                Yoalnda Gil
              
              
            
            
          
        
      
      
      
    
  
   
  
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