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?
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4.
Agenda
• Premise and Goal
• Mobile constraints
– not focused on the UI
• Semantic technology components
• Leveraging semantic flexibility
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5.
Premise
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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
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7.
Goal
Use
semantic technology
to mitigate mobile
platform constraints
at runtime
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8.
MOBILE CONSTRAINTS
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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
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11.
Bandwidth
10baseT
Sources: http://www.diffen.com/difference/3G_vs_4G
and http://www.hostile.org/coredump/bandwidth.html
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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
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14.
SEMANTIC TECHNOLOGY
COMPONENTS
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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
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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
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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
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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
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20.
Levers to Minimize Power Consumption
Remote Data Access
Local Computation
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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)
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24.
Simple Ontology and Data
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25.
Initial Triple Count and Combined Inferred Assertions
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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