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Mobile Development Meets
Semantic Technology

David S. Read
CTO, Blue Slate Solutions

Semantic Technology and Business Conference
June, 2012



            0
                                   © Blue Slate Solutions 2012
Introductions

 • David Read, CISSP, GSEC, SCJP
    –   Blue Slate CTO and Chief Solution Architect
    –   25+ years tech leadership experience
    –   Strategy and technology focus
    –   Passionate about semantic technology
        and data mining
 • Attendees
    – Business?
    – Technology?
    – Production Use of Semantic
      Technologies?
    – Mobile Part of Business Vision?
        Source: http://crystalwashington.com/wp-content/uploads/2011/07/boringconference.jpg
                                           1                                 © Blue Slate Solutions 2012
What is mobile?




                  2   © Blue Slate Solutions 2012
Agenda

• Premise and Goal
• Mobile constraints
   – not focused on the UI
• Semantic technology components
• Leveraging semantic flexibility




                         3          © Blue Slate Solutions 2012
Premise




          4   © Blue Slate Solutions 2012
Multi-tier Architectures – Flexible at Design Time

               WS      .Net          iOS        Android       JSP              • View
Presentation
                                                                               • Layout
Tier                      Native/Proprietary Domain                            • Security

               App1    App2          App3              App4         App5
                                                                           •    Aggregation
                                                                           •    Business Rules
                                                                           •    SDO
                                WF             RE                          •    Work Flow
Business                                                                   •    Transactions
Tier                                                                       •    Security


                              Object Domain
                                                                           •   Data abstraction
                 O-R            O-XML                 O-Unstructured       •   DAO
                        Native/Proprietary Domain                          •   Transactions
                                                                           •   Security
                                  ESB                                      •   e.g. Hibernate,
                                                                               XSL, PL/SQL
Integration
                        WF         RE         Data
Tier
Data                                                                       • Persistence
                                                                           • Transactions
Tier                                                                       • Categorization
                 DB                                    Unstructured        •5Indexing
                                                                           • Security

                                        5                                      © Blue Slate Solutions 2012
Goal



             Use
   semantic technology
     to mitigate mobile
    platform constraints
         at runtime
             6       © Blue Slate Solutions 2012
MOBILE CONSTRAINTS


           7         © Blue Slate Solutions 2012
Application Memory

• Limits on runtime application and heap
   – Android devices
      • 16, 24 or 32 MB per application
      • Manifest: android:largeHeap=TRUE (ouch!)
   – iOS devices
      • Kill the application between 16 and 40 MB
   – BlackBerry devices
      • (COD files) 8 MB application and 8 MB for resources
   – Windows CE/Mobile
      • Older (v 5 and 6) limited to 32M, v7 peaks at ~90 MB




                                 8                             © Blue Slate Solutions 2012
CPU




      Source: http://www.passmark.com/forum/showthread.php?t=3381

            9                                   © Blue Slate Solutions 2012
Bandwidth


            10baseT




                           Sources: http://www.diffen.com/difference/3G_vs_4G
                           and http://www.hostile.org/coredump/bandwidth.html

                      10                                   © Blue Slate Solutions 2012
Connectivity

 • Verizon, AT&T and Sprint Coverage
   • Cellular coverage has gaps
   • 4G is far from the norm




                       Sources: http://www.verizonwireless.com/b2c/CoverageLocatorController,
                                                  http://www.wireless.att.com/coverageviewer/#,
               http://coverage.sprintpcs.com/IMPACT.jsp?covType=sprint&id9=vanity:coverage

                                    11                                      © Blue Slate Solutions 2012
Battery Life

 • Talk and standby time are common
   (but not meaningful) measures
 • Surfing the web, playing games, checking email, all
   require significant power: radio, CPU and screen




                Source: http://www2012.wwwconference.org/proceedings/proceedings/p41.pdf

                                 12                                    © Blue Slate Solutions 2012
SEMANTIC TECHNOLOGY
COMPONENTS

          13          © Blue Slate Solutions 2012
Data


•   Facts from remote and local sources
•   May be structured or unstructured
•   More value as it is federated
•   Standard semantic representation - RDF


       Subject     Predicate      Object




                       14              © Blue Slate Solutions 2012
Ontology


• Classification and rules (inferencing)
• Transform at any tier
   – Extrapolate redundant information
• Standard representation - RDFS and OWL




                                 Source: http://data.gov.uk/resources/payments

                            15                                  © Blue Slate Solutions 2012
Reasoner

• Software which applies the ontology to data
• Can assert new data
• Well understood technology
   – Available on a broad range of platforms, including mobile


                         Reasoner




                                    16               © Blue Slate Solutions 2012
Query Processing

 • CRUD operations on data
 • Standard semantic query language - SPARQL
               Relation1      Constant
                                                   Local or Remote

     ObjectA   Relation2      ObjectB       Relation3      ObjectD


               Relation2      ObjectC       Relation4      ObjectE




                           SPARQL
                           Processor


               ?theRelation       ?theObject
               Relation3          ObjectD
               Relation4          ObjectE

                                17                              © Blue Slate Solutions 2012
LEVERAGING SEMANTIC
TECHNOLOGY, MOBILE STYLE

           18        © Blue Slate Solutions 2012
Levers to Minimize Power Consumption




                  Remote Data Access

                   Local Computation




                            19         © Blue Slate Solutions 2012
Use Efficient Representations

 • Bandwidth is variable
 • Radio communication costs battery significantly
 • Little entropy per bit in most non-binary formats
    – XML-based information often contains less data than
      markup (far less than 1 bit entropy per byte)


                   Format         Size (KB) %
             RDF/XML                    693 100
             Turtle                     258 37
             Zipped RDF/XML              33   5
             Zipped Turtle               27   4


                             20                     © Blue Slate Solutions 2012
Reasoning Locally to Minimize Power Consumption

• CPU on phone is often underutilized
• Application behavior may have user-specific
  configurations controlling data relationships
• Reasoning result at server is equivalent to client
   – Same syntax




           Network                 Process
            Access                 Execution
                        ?
                          21                   © Blue Slate Solutions 2012
Reasoning Locally – An example

• Application to report on fuel consumption statistics
• Small ontology sets up relationships between
  vehicles, fuel purchases and gas stations
• Data for all fuel purchases
• Classify cars and gas stations, infer additional
  information (e.g. distance, MPG)




                          22                   © Blue Slate Solutions 2012
Simple Ontology and Data




                           23   © Blue Slate Solutions 2012
Initial Triple Count and Combined Inferred Assertions




                            24                    © Blue Slate Solutions 2012
What Just Happened?

                                                           Remote
                  Mobile Device
                                                           (Cloud)

    Local                                  Augmented       Dbpedia
    Data
              1     Reasoner      2         Local           Data
                                            Data
              1                                                6
                                             4
    Local
   Ontology            Local
                                                           DBpedia
                      Query           7                5   Endpoint
                      Engine
              3
    Local               8
    Query

                    Federated
                     Results


                                      25                    © Blue Slate Solutions 2012
How Does That Work on a Mobile Device?

• Semantic Reasoner and Query Libraries
   – Several available (commercial and open source)
   – Using Java (Android)-based Open Source libraries for
     this demonstration
• Principles are the same for any semantic library
   – Load an ontology and instances into working storage
      • Might exist locally or be loaded via network
   – Run the reasoner to create a model
   – Query the resulting model to obtain result sets
• Inferred data can be persisted to create a new local
  data set

                                  26                   © Blue Slate Solutions 2012
A Mobile Reasoner and Query Processor

• Jena
   – Java-based semantic library
        • Started by HP Labs as open source project in 2000
        • Apache project (incubator) since 2010
   –   Interfaces to reasoner, triple store
   –   Built-in reasoner (RDFS, OWL DL)
   –   http://incubator.apache.org/jena/
   –   ARQ
        • Jena’s SPARQL processor
• Android ports
   – Androjena and ARQoid
   – http://code.google.com/p/androjena/

                                  27                          © Blue Slate Solutions 2012
USING REASONING FOR
TUNING

           28         © Blue Slate Solutions 2012
Reasoner Has No Expectations of an Ontology


• The process and libraries we discussed do not
  require an ontology to be provided in order to
  create a model
• If only data is provided then the reasoner creates a
  model containing the data
• Allows us to tune behavior from the server side
  without separate code bases
   – Dynamically balance network bandwidth usage versus
     CPU
   – Server load, bandwidth limitations, mobile device
     limitations, …


                           29                   © Blue Slate Solutions 2012
Simple Example of Real-time Tuning

In our fuel consumption application…

    Partial Data
   and Ontology        Component       Full Data

                         Network

                       Local CPU

                       Server CPU



                           30               © Blue Slate Solutions 2012
Fuel Data and Ontology Overview

• Using raw fuel purchases as our representative
  data set
   – SPARQL endpoint:
     http://semantic.monead.com/vehicleinfo/mileage
   – Fully inferred data set: http://monead.com/semantic/data/
        HybridMileageOntologyAll.Inferenced.xml
• Sizing
           Information     RDF/XML     Turtle      %
                           Size (KB) Size (KB)
        Ontology                    6         2      1
        Minimal Data              200        68     27
        Fully Inferenced          692       257    100

                             31                      © Blue Slate Solutions 2012
Fuel Data and Ontology Details
 veh:fuelPurchase0381 a veh:FuelPurchase;
       veh:vehicle pveh:car2; veh:date "2011-08-14";
       veh:gallons 6.908; veh:usDollarsPerGallon 3.779;
       veh:totalUsDollarsCharged 26.11;
       veh:reportedMpg 45.4; veh:odometerMiles 106883.;
       veh:purchaseStation veh:Hess1.

 veh:Stoughton-MA a veh:Place;
       owl:sameAs
 <http://dbpedia.org/resource/Stoughton,_Massachusetts>;
       veh:placeName "Stoughton, MA".

 veh:Sunoco19 a veh:GasStation;
       veh:stationName "Sunoco";
       veh:brandInfo
 <http://dbpedia.org/resource/Sunoco>;
       veh:location veh:Glenville.



                                32                        © Blue Slate Solutions 2012
Use Case R-1: Slow Network


       Mobile
                               1.5MBPS                      Server
       Device




  Component     Full Data Set (Sec) Partial Data Set (Sec)   Difference (Sec)
Data                             171                     44                127
Ontology                           0                       2                -2
Radio Total                      171                     46                125
Rendering                          0                     15                -15
Total                            171                     61                110




                                    33                           © Blue Slate Solutions 2012
Use Case R-2: (Faster Network)


       Mobile
                               15 MBPS                       Server
       Device




  Component     Full Data Set (Sec) Partial Data Set (Sec)   Difference (Sec)
Data                              17                       4               -13
Ontology                           0                       1                  1
Radio Total                       17                       5               -12
Rendering                          0                     15                 15
Total                             17                     20                   3




                                    34                            © Blue Slate Solutions 2012
Create a Reasoner Instance

OntModel model;

Reasoner reasoner =
     ReasonerRegistry.getOWLReasoner();

Model infModel =
     ModelFactory.createInfModel(reasoner,
     ModelFactory.createDefaultModel());

model = ModelFactory.createOntologyModel(
     OntModelSpec.OWL_DL_MEM,infModel);

model.setStrictMode(false);


                             35           © Blue Slate Solutions 2012
Process the Ontology (Run the Reasoner)

inputStream =
     new StringReader(“Your Ontology”);

model.read(
     inputStream,
     null,
     “Turtle”);




                           36             © Blue Slate Solutions 2012
Client Behavior is Unchanged for Either Use Case

    Server                             Client
    Full                Request
    Data                                        Reasoner
    Set                  Data
                 1           2                   3

       ?                                Data

                                   4
   Partial
    Data                  Query                   Results
                                           5
   Set and                Engine
   Ontology



                           37                        © Blue Slate Solutions 2012
USING QUERYING FOR
TUNING

           38        © Blue Slate Solutions 2012
SPARQL Results Not Concerned with Data Source


• A SPARQL query can access a local model, remote
  data and/or remote SPARQL endpoint
• The results are processed in the same manner
   – Another tuning opportunity
• Dynamically shift between server and client
   – Network/Radio and CPU (federation)
   – CPU (sorting, filtering)




                            39                  © Blue Slate Solutions 2012
Query Execution Behavior – 2 Approaches

• Server can send queries to client device
• Client decides which query to use based on
  metadata from server or its own measurements




                          40              © Blue Slate Solutions 2012
Query Execution Behavior – Server Decides

   Server                         Client

                        Request
      ?         1                           Data
                         Query


   Queries                  2          3




                         Query              Results
    Data        3                     4
                         Engine


                           41                 © Blue Slate Solutions 2012
Query Execution Behavior – Client Decides

    Server                         Client

                            ?        2       Queries



                            1                 Data
                                     3


                          Query              Results
    Data        3                        4
                          Engine


                           42                  © Blue Slate Solutions 2012
Use Case Q-1 (Local Model)

• Query executes against local model
• If query requires a lot of CPU for (sorting, filtering)
  could be better off re-architecting to server

                              Client


                      Data             Queries




                     Query             Results
                     Engine




                              43                  © Blue Slate Solutions 2012
Setup a SPARQL Query Against Local Model

QueryExecution qe;
String query = “Your SPARQL Query”;

qe = QueryExecutionFactory.create(
     query, model);




                         44                © Blue Slate Solutions 2012
Use Case Q-2 (Single SPARQL Endpoint)

• Query executes against one SPARQL endpoint
• Have client device execute this directly
• One consideration: significant latency

              Server                             Client

     Query             SPARQL          Query              Results
     Engine            Endpoint        Engine




       Data                            Queries




                                  45                      © Blue Slate Solutions 2012
Use Case Q-3 (Federated SPARQL Endpoints)

• Query executes against several SPARQL
  endpoints
• Requires communications with all the endpoints
  and integration of the results
                                           Client

                                 Query              Results
                                 Engine




                                 Queries




                         46                         © Blue Slate Solutions 2012
Use Case Q-3 (Federated SPARQL Endpoints) - Alt

• If significantly complex, makes sense to proxy




                                             Client

                                  Query               Results
                                  Engine




                                   Queries




                          47                          © Blue Slate Solutions 2012
Setup a SPARQL Query Against Remote Endpoint

QueryExecution qe;
String query = “Your SPARQL Query”;
String queryUri = “Some SPARQL Endpoint URI”;
String queryDefaultGraphUri =
     “An Optional Graph Uri”;

if (queryDefaultGraphUri.length() > 0) {
  qe = QueryExecutionFactory.
     sparqlService(queryUri, query,
     queryDefaultGraphUri);
} else {
  qe = QueryExecutionFactory.
     sparqlService(queryUri, query);
}

                         48                    © Blue Slate Solutions 2012
Use Case Q-4 (Raw Data Sources)

• Query executes against semantic data sources
• Server aggregates results since the entire graph
  typically needs to be brought across the network

     Server                                      Client


       Data
                                       Queries



              Server

     Query             SPARQL          Query              Results
     Engine            Endpoint        Engine



                                  49                      © Blue Slate Solutions 2012
Setup a SPARQL Query Against Remote Graph

/* e.g. query contains FROM or SERVICE */

QueryExecution qe;
String query = “Your SPARQL Query”;

qe = QueryExecutionFactory.create(query);




                         50                 © Blue Slate Solutions 2012
Query Processing Behavior is Unchanged
for Any Use Case

• The query execution syntax differs for local versus
  remote data sources
• The result set, however, is processed in the same
  manner for any select query
• Results in a small amount of code to execute the
  correct query form (the three preceding code
  snippets) but all downstream code is consistent




                          51                 © Blue Slate Solutions 2012
Retrieve the SPARQL Results

ResultSet resultSet = qe.execSelect();
List<String> colNames=results.getResultVars();
for (String colName : colNames) {
while (results.hasNext()) {
  QuerySolution solution = results.next();
  for (String var : columnNames) {
    if (solution.get(var) != null) {
      if (solution.get(var).isLiteral()) {
        solution.getLiteral(var).toString();
      } else {
        solution.getResource(var).getURI();
            }
        }
    }
}
qe.close();
                          52            © Blue Slate Solutions 2012
Caching

• Caching is a good option with mobile devices
   – Cache the data received, assertions reasoned and
     results obtained
• Typically have access to “private” storage space,
  often located on removable storage (SD card)
• Doesn’t interfere with basic phone data (local,
  phone) storage
• Not as limited as native storage
   – For BlackBerry this is the only reasonable way to break
     out of the 8MB application data limitation
• Mature set of caching libraries do most of the
  interesting work for you
                             53                      © Blue Slate Solutions 2012
Summary

Semantic technology, by virtue of inferencing,
platform independence, consistent syntax and
standard protocols, enables dynamic intra-tier
tuning without significant coding and
configuration.




                      54                © Blue Slate Solutions 2012
Thank You

• I appreciate your taking the time
  to attend this session
• Contact and Business
   – David.Read@blueslate.net
   – www.blueslate.net
• Reference Information
   – Semantic technology thoughts and work
      • http://monead.com/semantic
   – Sparql Droid
      • https://play.google.com/store/apps/details?id=com.monead.sema
        ntic.android.sparql&hl=en



                                55                        © Blue Slate Solutions 2012

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Mobile Development Meets Semantic Technology

  • 1. Mobile Development Meets Semantic Technology David S. Read CTO, Blue Slate Solutions Semantic Technology and Business Conference June, 2012 0 © Blue Slate Solutions 2012
  • 2. Introductions • David Read, CISSP, GSEC, SCJP – Blue Slate CTO and Chief Solution Architect – 25+ years tech leadership experience – Strategy and technology focus – Passionate about semantic technology and data mining • Attendees – Business? – Technology? – Production Use of Semantic Technologies? – Mobile Part of Business Vision? Source: http://crystalwashington.com/wp-content/uploads/2011/07/boringconference.jpg 1 © Blue Slate Solutions 2012
  • 3. What is mobile? 2 © Blue Slate Solutions 2012
  • 4. Agenda • Premise and Goal • Mobile constraints – not focused on the UI • Semantic technology components • Leveraging semantic flexibility 3 © Blue Slate Solutions 2012
  • 5. Premise 4 © Blue Slate Solutions 2012
  • 6. Multi-tier Architectures – Flexible at Design Time WS .Net iOS Android JSP • View Presentation • Layout Tier Native/Proprietary Domain • Security App1 App2 App3 App4 App5 • Aggregation • Business Rules • SDO WF RE • Work Flow Business • Transactions Tier • Security Object Domain • Data abstraction O-R O-XML O-Unstructured • DAO Native/Proprietary Domain • Transactions • Security ESB • e.g. Hibernate, XSL, PL/SQL Integration WF RE Data Tier Data • Persistence • Transactions Tier • Categorization DB Unstructured •5Indexing • Security 5 © Blue Slate Solutions 2012
  • 7. Goal Use semantic technology to mitigate mobile platform constraints at runtime 6 © Blue Slate Solutions 2012
  • 8. MOBILE CONSTRAINTS 7 © Blue Slate Solutions 2012
  • 9. Application Memory • Limits on runtime application and heap – Android devices • 16, 24 or 32 MB per application • Manifest: android:largeHeap=TRUE (ouch!) – iOS devices • Kill the application between 16 and 40 MB – BlackBerry devices • (COD files) 8 MB application and 8 MB for resources – Windows CE/Mobile • Older (v 5 and 6) limited to 32M, v7 peaks at ~90 MB 8 © Blue Slate Solutions 2012
  • 10. CPU Source: http://www.passmark.com/forum/showthread.php?t=3381 9 © Blue Slate Solutions 2012
  • 11. Bandwidth 10baseT Sources: http://www.diffen.com/difference/3G_vs_4G and http://www.hostile.org/coredump/bandwidth.html 10 © Blue Slate Solutions 2012
  • 12. Connectivity • Verizon, AT&T and Sprint Coverage • Cellular coverage has gaps • 4G is far from the norm Sources: http://www.verizonwireless.com/b2c/CoverageLocatorController, http://www.wireless.att.com/coverageviewer/#, http://coverage.sprintpcs.com/IMPACT.jsp?covType=sprint&id9=vanity:coverage 11 © Blue Slate Solutions 2012
  • 13. Battery Life • Talk and standby time are common (but not meaningful) measures • Surfing the web, playing games, checking email, all require significant power: radio, CPU and screen Source: http://www2012.wwwconference.org/proceedings/proceedings/p41.pdf 12 © Blue Slate Solutions 2012
  • 14. SEMANTIC TECHNOLOGY COMPONENTS 13 © Blue Slate Solutions 2012
  • 15. Data • Facts from remote and local sources • May be structured or unstructured • More value as it is federated • Standard semantic representation - RDF Subject Predicate Object 14 © Blue Slate Solutions 2012
  • 16. Ontology • Classification and rules (inferencing) • Transform at any tier – Extrapolate redundant information • Standard representation - RDFS and OWL Source: http://data.gov.uk/resources/payments 15 © Blue Slate Solutions 2012
  • 17. Reasoner • Software which applies the ontology to data • Can assert new data • Well understood technology – Available on a broad range of platforms, including mobile Reasoner 16 © Blue Slate Solutions 2012
  • 18. Query Processing • CRUD operations on data • Standard semantic query language - SPARQL Relation1 Constant Local or Remote ObjectA Relation2 ObjectB Relation3 ObjectD Relation2 ObjectC Relation4 ObjectE SPARQL Processor ?theRelation ?theObject Relation3 ObjectD Relation4 ObjectE 17 © Blue Slate Solutions 2012
  • 19. LEVERAGING SEMANTIC TECHNOLOGY, MOBILE STYLE 18 © Blue Slate Solutions 2012
  • 20. Levers to Minimize Power Consumption Remote Data Access Local Computation 19 © Blue Slate Solutions 2012
  • 21. Use Efficient Representations • Bandwidth is variable • Radio communication costs battery significantly • Little entropy per bit in most non-binary formats – XML-based information often contains less data than markup (far less than 1 bit entropy per byte) Format Size (KB) % RDF/XML 693 100 Turtle 258 37 Zipped RDF/XML 33 5 Zipped Turtle 27 4 20 © Blue Slate Solutions 2012
  • 22. Reasoning Locally to Minimize Power Consumption • CPU on phone is often underutilized • Application behavior may have user-specific configurations controlling data relationships • Reasoning result at server is equivalent to client – Same syntax Network Process Access Execution ? 21 © Blue Slate Solutions 2012
  • 23. Reasoning Locally – An example • Application to report on fuel consumption statistics • Small ontology sets up relationships between vehicles, fuel purchases and gas stations • Data for all fuel purchases • Classify cars and gas stations, infer additional information (e.g. distance, MPG) 22 © Blue Slate Solutions 2012
  • 24. Simple Ontology and Data 23 © Blue Slate Solutions 2012
  • 25. Initial Triple Count and Combined Inferred Assertions 24 © Blue Slate Solutions 2012
  • 26. What Just Happened? Remote Mobile Device (Cloud) Local Augmented Dbpedia Data 1 Reasoner 2 Local Data Data 1 6 4 Local Ontology Local DBpedia Query 7 5 Endpoint Engine 3 Local 8 Query Federated Results 25 © Blue Slate Solutions 2012
  • 27. How Does That Work on a Mobile Device? • Semantic Reasoner and Query Libraries – Several available (commercial and open source) – Using Java (Android)-based Open Source libraries for this demonstration • Principles are the same for any semantic library – Load an ontology and instances into working storage • Might exist locally or be loaded via network – Run the reasoner to create a model – Query the resulting model to obtain result sets • Inferred data can be persisted to create a new local data set 26 © Blue Slate Solutions 2012
  • 28. A Mobile Reasoner and Query Processor • Jena – Java-based semantic library • Started by HP Labs as open source project in 2000 • Apache project (incubator) since 2010 – Interfaces to reasoner, triple store – Built-in reasoner (RDFS, OWL DL) – http://incubator.apache.org/jena/ – ARQ • Jena’s SPARQL processor • Android ports – Androjena and ARQoid – http://code.google.com/p/androjena/ 27 © Blue Slate Solutions 2012
  • 29. USING REASONING FOR TUNING 28 © Blue Slate Solutions 2012
  • 30. Reasoner Has No Expectations of an Ontology • The process and libraries we discussed do not require an ontology to be provided in order to create a model • If only data is provided then the reasoner creates a model containing the data • Allows us to tune behavior from the server side without separate code bases – Dynamically balance network bandwidth usage versus CPU – Server load, bandwidth limitations, mobile device limitations, … 29 © Blue Slate Solutions 2012
  • 31. Simple Example of Real-time Tuning In our fuel consumption application… Partial Data and Ontology Component Full Data Network Local CPU Server CPU 30 © Blue Slate Solutions 2012
  • 32. Fuel Data and Ontology Overview • Using raw fuel purchases as our representative data set – SPARQL endpoint: http://semantic.monead.com/vehicleinfo/mileage – Fully inferred data set: http://monead.com/semantic/data/ HybridMileageOntologyAll.Inferenced.xml • Sizing Information RDF/XML Turtle % Size (KB) Size (KB) Ontology 6 2 1 Minimal Data 200 68 27 Fully Inferenced 692 257 100 31 © Blue Slate Solutions 2012
  • 33. Fuel Data and Ontology Details veh:fuelPurchase0381 a veh:FuelPurchase; veh:vehicle pveh:car2; veh:date "2011-08-14"; veh:gallons 6.908; veh:usDollarsPerGallon 3.779; veh:totalUsDollarsCharged 26.11; veh:reportedMpg 45.4; veh:odometerMiles 106883.; veh:purchaseStation veh:Hess1. veh:Stoughton-MA a veh:Place; owl:sameAs <http://dbpedia.org/resource/Stoughton,_Massachusetts>; veh:placeName "Stoughton, MA". veh:Sunoco19 a veh:GasStation; veh:stationName "Sunoco"; veh:brandInfo <http://dbpedia.org/resource/Sunoco>; veh:location veh:Glenville. 32 © Blue Slate Solutions 2012
  • 34. Use Case R-1: Slow Network Mobile 1.5MBPS Server Device Component Full Data Set (Sec) Partial Data Set (Sec) Difference (Sec) Data 171 44 127 Ontology 0 2 -2 Radio Total 171 46 125 Rendering 0 15 -15 Total 171 61 110 33 © Blue Slate Solutions 2012
  • 35. Use Case R-2: (Faster Network) Mobile 15 MBPS Server Device Component Full Data Set (Sec) Partial Data Set (Sec) Difference (Sec) Data 17 4 -13 Ontology 0 1 1 Radio Total 17 5 -12 Rendering 0 15 15 Total 17 20 3 34 © Blue Slate Solutions 2012
  • 36. Create a Reasoner Instance OntModel model; Reasoner reasoner = ReasonerRegistry.getOWLReasoner(); Model infModel = ModelFactory.createInfModel(reasoner, ModelFactory.createDefaultModel()); model = ModelFactory.createOntologyModel( OntModelSpec.OWL_DL_MEM,infModel); model.setStrictMode(false); 35 © Blue Slate Solutions 2012
  • 37. Process the Ontology (Run the Reasoner) inputStream = new StringReader(“Your Ontology”); model.read( inputStream, null, “Turtle”); 36 © Blue Slate Solutions 2012
  • 38. Client Behavior is Unchanged for Either Use Case Server Client Full Request Data Reasoner Set Data 1 2 3 ? Data 4 Partial Data Query Results 5 Set and Engine Ontology 37 © Blue Slate Solutions 2012
  • 39. USING QUERYING FOR TUNING 38 © Blue Slate Solutions 2012
  • 40. SPARQL Results Not Concerned with Data Source • A SPARQL query can access a local model, remote data and/or remote SPARQL endpoint • The results are processed in the same manner – Another tuning opportunity • Dynamically shift between server and client – Network/Radio and CPU (federation) – CPU (sorting, filtering) 39 © Blue Slate Solutions 2012
  • 41. Query Execution Behavior – 2 Approaches • Server can send queries to client device • Client decides which query to use based on metadata from server or its own measurements 40 © Blue Slate Solutions 2012
  • 42. Query Execution Behavior – Server Decides Server Client Request ? 1 Data Query Queries 2 3 Query Results Data 3 4 Engine 41 © Blue Slate Solutions 2012
  • 43. Query Execution Behavior – Client Decides Server Client ? 2 Queries 1 Data 3 Query Results Data 3 4 Engine 42 © Blue Slate Solutions 2012
  • 44. Use Case Q-1 (Local Model) • Query executes against local model • If query requires a lot of CPU for (sorting, filtering) could be better off re-architecting to server Client Data Queries Query Results Engine 43 © Blue Slate Solutions 2012
  • 45. Setup a SPARQL Query Against Local Model QueryExecution qe; String query = “Your SPARQL Query”; qe = QueryExecutionFactory.create( query, model); 44 © Blue Slate Solutions 2012
  • 46. Use Case Q-2 (Single SPARQL Endpoint) • Query executes against one SPARQL endpoint • Have client device execute this directly • One consideration: significant latency Server Client Query SPARQL Query Results Engine Endpoint Engine Data Queries 45 © Blue Slate Solutions 2012
  • 47. Use Case Q-3 (Federated SPARQL Endpoints) • Query executes against several SPARQL endpoints • Requires communications with all the endpoints and integration of the results Client Query Results Engine Queries 46 © Blue Slate Solutions 2012
  • 48. Use Case Q-3 (Federated SPARQL Endpoints) - Alt • If significantly complex, makes sense to proxy Client Query Results Engine Queries 47 © Blue Slate Solutions 2012
  • 49. Setup a SPARQL Query Against Remote Endpoint QueryExecution qe; String query = “Your SPARQL Query”; String queryUri = “Some SPARQL Endpoint URI”; String queryDefaultGraphUri = “An Optional Graph Uri”; if (queryDefaultGraphUri.length() > 0) { qe = QueryExecutionFactory. sparqlService(queryUri, query, queryDefaultGraphUri); } else { qe = QueryExecutionFactory. sparqlService(queryUri, query); } 48 © Blue Slate Solutions 2012
  • 50. Use Case Q-4 (Raw Data Sources) • Query executes against semantic data sources • Server aggregates results since the entire graph typically needs to be brought across the network Server Client Data Queries Server Query SPARQL Query Results Engine Endpoint Engine 49 © Blue Slate Solutions 2012
  • 51. Setup a SPARQL Query Against Remote Graph /* e.g. query contains FROM or SERVICE */ QueryExecution qe; String query = “Your SPARQL Query”; qe = QueryExecutionFactory.create(query); 50 © Blue Slate Solutions 2012
  • 52. Query Processing Behavior is Unchanged for Any Use Case • The query execution syntax differs for local versus remote data sources • The result set, however, is processed in the same manner for any select query • Results in a small amount of code to execute the correct query form (the three preceding code snippets) but all downstream code is consistent 51 © Blue Slate Solutions 2012
  • 53. Retrieve the SPARQL Results ResultSet resultSet = qe.execSelect(); List<String> colNames=results.getResultVars(); for (String colName : colNames) { while (results.hasNext()) { QuerySolution solution = results.next(); for (String var : columnNames) { if (solution.get(var) != null) { if (solution.get(var).isLiteral()) { solution.getLiteral(var).toString(); } else { solution.getResource(var).getURI(); } } } } qe.close(); 52 © Blue Slate Solutions 2012
  • 54. Caching • Caching is a good option with mobile devices – Cache the data received, assertions reasoned and results obtained • Typically have access to “private” storage space, often located on removable storage (SD card) • Doesn’t interfere with basic phone data (local, phone) storage • Not as limited as native storage – For BlackBerry this is the only reasonable way to break out of the 8MB application data limitation • Mature set of caching libraries do most of the interesting work for you 53 © Blue Slate Solutions 2012
  • 55. Summary Semantic technology, by virtue of inferencing, platform independence, consistent syntax and standard protocols, enables dynamic intra-tier tuning without significant coding and configuration. 54 © Blue Slate Solutions 2012
  • 56. Thank You • I appreciate your taking the time to attend this session • Contact and Business – David.Read@blueslate.net – www.blueslate.net • Reference Information – Semantic technology thoughts and work • http://monead.com/semantic – Sparql Droid • https://play.google.com/store/apps/details?id=com.monead.sema ntic.android.sparql&hl=en 55 © Blue Slate Solutions 2012