Mobile Development Meets Semantic Technology

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?


Agenda

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


Premise


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


Goal

Use
semantic technology
to mitigate mobile
platform constraints
at runtime

MOBILE CONSTRAINTS


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


CPU

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


Bandwidth

10baseT

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


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


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


SEMANTIC TECHNOLOGY
COMPONENTS


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


Ontology

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

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


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


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


LEVERAGING SEMANTIC
TECHNOLOGY, MOBILE STYLE


Levers to Minimize Power Consumption

Remote Data Access

Local Computation


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


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
?

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)


Simple Ontology and Data


Initial Triple Count and Combined Inferred Assertions


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


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


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/


USING REASONING FOR
TUNING


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, …


Simple Example of Real-time Tuning

In our fuel consumption application…

Partial Data
and Ontology Component Full Data

Network

Local CPU

Server CPU


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


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.


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


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


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);


Process the Ontology (Run the Reasoner)

inputStream =
new StringReader(“Your Ontology”);

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


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


USING QUERYING FOR
TUNING


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)


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


Query Execution Behavior – Server Decides

Server Client

Request
? 1 Data
Query

Queries 2 3

Query Results
Data 3 4
Engine


Query Execution Behavior – Client Decides

Server Client

? 2 Queries

1 Data
3

Query Results
Data 3 4
Engine


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


Setup a SPARQL Query Against Local Model

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

qe = QueryExecutionFactory.create(
query, model);


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


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


Use Case Q-3 (Federated SPARQL Endpoints) - Alt

• If significantly complex, makes sense to proxy

Client

Query Results
Engine

Queries


Setup a SPARQL Query Against Remote Endpoint

QueryExecution qe;
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);
}


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


Setup a SPARQL Query Against Remote Graph

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

QueryExecution qe;

qe = QueryExecutionFactory.create(query);


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


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();

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

Summary

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


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


Mobile Development Meets Semantic Technology

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