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Copyright 2011 Digital Enterprise Research Institute. All rights reserved.
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Linking the Real World
Manfred Hauswirth
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
DERI’s Mission
Enabling & exploiting
Networked Knowledge
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
About DERI
Founded June 2003 as a CSET (Centre for Science, Engineering and Technology).
Link scientists and engineers / academia and industry
Fundamental research
Development of Irish-based technology companies
Attract industry
Education & outreach
DERI Institute CSET
Commercialization, DAI
EU, EI, direct industry, IRCSET
DERI strategic plan responds to priorities
Local: University focus on Informatics, Physical & Computational Sciences
National: SMART Economy, Program for Government
International: EU Digital Agenda
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
About DERI
Number one in our core space
Research Publications > 1000
Participation in 17 standardisation groups (W3C, OASIS)
Approx. 140 members from 30 nations
57 PhDs /Masters
42 completed PhDs/Masters
Core Industrial Partners:
MNC’s: Cisco, Avaya, Bel-Labs, Ericsson…
SME’s: Storm, Celtrak, OpenLink……
Research: FBK
Total Research Grants: > €60 million
SFI, EU Framework, Enterprise Ireland, Industry
Currently 18 EU project running
Industry funded projects with Fujitsu Labs Japan, Cisco, Google, Renault, EADS, Fidelity,…
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
CSET Partners
Key Industry Collaborations
6
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
DERI Innovation Approach
Research
Excellence
Spin Outs
Open
Source
Prototypes
Commerci-
alisation
Standards
Industry
Collaboration
• Joint projects
• Patents and
Licensing
• DERI Applied
Innovation
• Drupal 7
• Semantic Desktop
• SIREn
• W3C
• OASIS
• SIOC
• VOID, DCAT
• schema.org
• OData
• Atom
• PEPPR
• IVEA
• Seevl
• Sindice.com
• Peracton
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
The DERI House
eBusiness
Financial Services
Health Care
Life Sciences
eLearning Green &
Sustainable IT
eGovernment
Information
Mining
and Retrieval
Natural
Language
Processing
Reasoning and
Querying
Cloud Data
Management
Knowledge
Discovery
Social Software
Service
Oriented
Architecture
Sensor
Middleware
DERI Applied
Research Commercialisation
Data
Visualisation
and Interaction
Cyber
Security
Cloud
Data
Analy
tics
Linked
Data
Security,
Privacy
& Trust
DERI is designed to provide an integrated solution
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Solve which problem?
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Interconnected
Universal
All encompassing
Assists humans,
organizations and systems in
problem solving
Enable global and local
collaboration
A Network of Knowledge
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
A Network of Knowledge
enabling innovation and
increased productivity
Interconnected
Universal
All encompassing
assists humans,
organizations and systems
with problem solving
Linked Data
• Search
• Collaboration
• Information Mining
• Middleware
• Application
Research Domains
• Commercialization
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Enabling networked knowledge 12 of 46
Two Key Ingredients
1. RDF – Resource Description Framework
Graph based Data – nodes and arcs
Identifies objects (URIs)
Interlink information (Relationships)
2. Vocabularies (Ontologies)
provide shared understanding of a domain
organise knowledge in a machine-comprehensible way
give an exploitable meaning to the data
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Why Graphs and Ontologies?
Cities:Dublin
84421km2
Geo:IslandOfIreland
EU:RepublicOfIreland
Geo:locatedOn
Geo:area
Geo:hasCapital
Geo:hasLargestCity
Wikipedia.org
Gov.ie
EU:RepublicOfIreland
Person:EndaKenny
Gov:hasTaoiseach
Gov:hasDepartment
IE:DepartmentOfFinance
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Enabling networked knowledge
Linked Open Data Cloud
14
2008 2007
2008 2008
2008
2009
2009 2010
14
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Enabling networked knowledge
Linked Data Domains
Over 200 open data sets with more than 25 billion facts,
interlinked by 400 million typed links, doubling every 10 month!
http://lod-cloud.net/
Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch.
Media
Government
Geo
Publications
User-generated
Life sciences
Cross-domain
US government
UK government
BBC
New York Times
LinkedGeoData
15
BestBuy
Overstock.com
Powered
by DERI!
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Enabling networked knowledge
Challenges of Big Data
“90% of the data in the world today
has been created in the last two
years alone” – IBM
The bringing together of a vast
amount of data from public and
private sources […] is what Big Data is
all about,” – IDC
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Enabling networked knowledge
Solutions required for
Management and Integration
Abstraction and Reasoning
Analytics and Visualization
Interaction and Collaboration
Domain Knowledge
and
Integration into a coherent
Framework!
How to exploit Big Data?
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Enabling networked knowledge
Knowledge Dashboard
Networked
Data
Abstraction
Reasoning
Analytics
Visualisation
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Enabling networked knowledge
Invests in
human and social capital
traditional/modern infrastructure
that
fuels sustainable economic development
and high quality of life
while
managing natural resources
through
participatory governance
What is a Smart City?
http://ideas.repec.org/p/dgr/vuarem/2009-48.html
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Enabling networked knowledge
A Smart City removes silos moving
towards a connected digital layer.
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Enabling networked knowledge
http://www.mckinsey.com/mgi/publications/ig_data/pdfs/MGI_big_data_full_report.pdf
Untapped Value Silos’ Value
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Enabling networked knowledge
A Smart City driver of change will be Data.
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Enabling networked knowledge
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Enabling networked knowledge
The Problem
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Enabling networked knowledge
Semantic description of sensors, streams,
events, observations, etc.
“Senso ergo sum” – semantic descriptions
down to the sensor level
Web protocols down to the sensor level
SPARQL-like access to streams and sensors
Infrastructure framework
Goal
Streams are just yet another
form/source of linked data
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Enabling networked knowledge
REST
Keep it simple, Stupid!
LOD
Application
Application := Data + Services
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Enabling networked knowledge
KISS revisited
Application
CQELS SPARQL REST
Linked Data
COAP
Sensors
Virtual
Sensors
Linked Streams
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Enabling networked knowledge
Where are we
right now?
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Sensors, streams,
events, observations
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Semantic Sensor Networks ontology to describe sensors
and sensor data
Semantic annotations for OGC’s SWE Sensor Model
Language
Motivations
No existing sensor ontology included all the basic concepts
Ease integration of (some) semantics in more spread languages
and standards (specifically SensorML)
W3C SSN XG
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Enabling networked knowledge
Relation to existing
standards
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Enabling networked knowledge
SSN-XG Ontology Structure
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Enabling networked knowledge
SSN-XG Ontology Structure
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Enabling networked knowledge
SSN Application: SPITFIRE
•DUL: DOLCE+DnS Ultralite
•EventF: Event-Model F
•SSN: SSN-XG
•CC: Contextualised-Cognitive
Concepts on sensor network topology and
devices
Concepts on sensor role, events, sensor project
Event
Datasets
Sensor Datasets
LOD Cloud
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Enabling networked knowledge
SPITFIRE Vocabulary
http://www.spitfire-project.eu
coalesenses
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Enabling networked knowledge
Size matters!
• OS + 6LowPAN + CoAP + Semantic description < 48kB?
• Processing power?
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Enabling networked knowledge
Storage requirements
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Enabling networked knowledge
OK, we can
describe sensors
and their data now
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Enabling networked knowledge
Standardisation
Physical: 802.15.4
Network: IEEE 6LoWPAN, ROLL
Service layer:
– IETF CoRE (Constrained RESTful Environments):
CoAP protocol + extensions (very recent)
– Encoding (Extensible XML interchange - EXI, SensorML)
– Ontologies
CoAP = Constrained Application Protocol
IETF draft, http://tools.ietf.org/id/coap
Core proposal + > 17 extensions
RESTful sensor
interfaces
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Enabling networked knowledge
CoAP = HTTP for sensors
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Enabling networked knowledge
Accessing sensors from we browser using HTTP-CoAP
proxying – SPITFIRE Smart Service Proxy (SSP)
CoAP Example
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Enabling networked knowledge
OK, we can access
sensors via RESTful
interfaces now
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
KISS revisited
Application
CQELS SPARQL REST
Linked Data
COAP
Sensors
Virtual
Sensors
Linked Streams
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
CQELS
Continuous Query Evaluation over
Linked Streams
Scalable processing model for unified
Linked Stream Data and Linked Open
Data
Combines data pre-processing and an
adaptive cost-based query
optimization algorithm
Experimental evaluation shows great
performance (w.r.t. response time
and scalability)
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Enabling networked knowledge
Query rewriter
Orchestrator
Data transformation
SPA
RQ
L-like
Optimizer
Operator implementations
Access methods
Executor
Query
Execution
Overhead
Black Box Approach
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Enabling networked knowledge
Orchestrator
Query Rewriter
SPARQL
Engine
Data
transformation
ESPER
Query Rewriter
Data
transformation
C-SPA
RQ
L
CSPARQL to SPARQL
CSPARQL to EPSER EPL
Orchestrator
Query Rewriter
Prolog Engine
Data
transformation
EP-SPARQL to Prolog
EP-SPARQL
C-SPARQL
RDF to prolog facts
RDF to Java objects
EP-SPA
RQ
L
EP-SPARQL and C-SPARQL
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Enabling networked knowledge
Adaptive Optimizer
Operator implementations
Native Access methods
Adaptive Executor
Query
Adaptive Execution
RDF
dataset
Linked
datastream
White Box Approach
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Enabling networked knowledge
Incoming data will continuously change the costs of query plans
➥Data elements are adaptively routed to processing operators on
equivalent data flows (routing policies)
Enabling adaptivity
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Enabling networked knowledge
Example
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Enabling networked knowledge
Triple-based window operators extracts triples
from RDF stream or dataset that
match a given triple pattern
are valid within in a time window
Relational operators enable employing relational
algebras in the processing model
Streaming operators generate new streams from
output of other operators based on graph
templates
Processing Model:
Operators
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Enabling networked knowledge
Continuous Query Evaluation over
Linked Streams (CQELS)
Adaptive Optimizer
Operator implementations
Native Access methods
Adaptive Executor
Query
Adaptive Execution
RDF
dataset
Linked
datastream
CQELS language (an extension of SPARQL 1.1)
Dictionary
SPO index scheme
Ring Triple-based
indices for
windows
Caching and Indexing
Dynamic Routing Policy
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Enabling networked knowledge
Dictionary encoding
Smaller memory for representing triples
Avoid lookup & decoding overhead for numeric RDF nodes
Caching and Indexing
Caching: avoid re-computing of intermediate results of sub-
queries over non-stream data.
Indexing: facilitate faster access on caches and window data.
Dynamic Routing Policy
Incoming data can be evaluated in multiple equivalent data
flows adaptive to changes
Easy & flexible support to implement routing policies
Techniques
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Enabling networked knowledge
Stream pattern Construct new RDF stream
CQELS Language – an extension to SPARQL 1.1
CQELS query language
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Enabling networked knowledge
Conference scenario: combine linked stream from RFID tags
(physical relationships) with DBLP data (social relationships)
Setup
Systems: CQELS vs ETALIS and C-SPARQL
Datasets
– Replayed RFID data from Open Beacon deployments
– Simulated DBLP by SP2Bench
Queries: 5 query templates with different complexities
– Q1: selection,
– Q2: stream joins, Q3,Q4: Stream and non-stream joins
– Q5: aggregation
Experiments
– Single query: generate 10 query instances of each template by varying the constants
– Vary size of DBLP dataset (104-10
7triples)
– Multiple queries: register 2M parallel instances (0≤M≤10)
Experimental setup
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Enabling networked knowledge
CQELS performs faster by orders of magnitude
Experiment results - Query
execution time
Query 1 Query 2 Query 3 Query 4 Query5
CQELS 0.47 3.90 0.51 0.53 21.83
C-SPARQL 332.46 99.84 331.68 395.18 322.64
ETALIS 0.06 27.47 79.95 469.23 160.83
Simple selection: ETALIS performs best
Stream join:
25 times faster than C-SPARQL
8 times faster than ETALIS
Stream and non-stream joins:
>600 times faster than C-SPARQL
150-850 times faster than ETALIS
Aggregation:
15 times faster than C-SPARQL
8 times faster than ETALIS
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Enabling networked knowledge
Scalability: Static data size
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Enabling networked knowledge
Scalability: # of queries
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Enabling networked knowledge
Optimization
Adaptive cost-based query optimization
Inter-query optimization
Smart and dynamic caching
Adaptive caching
Materialized view maintenance for dynamic data
Scalability: clusters and cloud
Next steps
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Enabling networked knowledge
OK, now we can also
process Linked
Streams and integrate
Linked Data efficiently
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
KISS revisited
Application
CQELS SPARQL REST
Linked Data
COAP
Sensors
Virtual
Sensors
Linked Streams
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
A single, integrated mobile phone data store for all
applications
Intrinsically integrated with the Web (Linked Data)
RDF-on-the-go
http://rdfonthego.googlecode.com/
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Enabling networked knowledge
RDF-on-the-go
http://rdfonthego.googlecode.com/
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Enabling networked knowledge
Business Card Demo
URI to FOAF file
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Enabling networked knowledge
OK, we can do all
that on mobile
phones too
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
KISS revisited
Application
CQELS SPARQL REST
Linked Data
COAP
Sensors
Virtual
Sensors
Linked Streams
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Global Sensor Networks
Heterogeneous platforms
Heterogeneous data
No common abstractions
Very large scale
Decrease the cost and complexity of sensor
network deployment
Abstraction level
Abstract from data
producers
Data level
Semantic description of
sensors and sensor data
Data integration
Large scale
Distributed query
processing and reasoning
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Enabling networked knowledge
Where are we now?
• Uniform, declarative abstractions
• Simple semantic descriptions
• Supports all major platforms
• Fast and simple deployment
• Plug & play
• Zero-programming
• Efficient query processing
Global Sensor Networks
http://gsn.sourceforge.net/
GSN
SOA
WS
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Enabling networked knowledge
GSN’s view of the world
Sensor network +
base computer =
sensor node
Many sensor nodes
produce a lot of data on
the Internet
Questions:
Deployment
Description
Discovery
Integration
Distributed processing
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Enabling networked knowledge
Central abstraction:
Virtual sensors
A virtual sensor can be any kind of data producer
a real sensor, a wireless camera, a mobile phone, etc.
a combination of other local or
remote virtual sensors
Specification
simple semantic descriptions of sensors and data streams
declarative SQL-based specification of the data stream processing
functional properties related to stream quality management, etc.
N input streams
processing
1 (structured) output stream
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Enabling networked knowledge
<virtual-sensor name="room-monitor" priority="11">
<addressing>
<predicate key="geographical">BC143</predicate>
<predicate key="usage">room monitoring</predicate>
</addressing>
<life-cycle pool-size="10" />
<storage permanent="true" history-size="10h" />
<output-structure>
<field name="image" type="binary:jpeg" />
<field name="temp" type="int" />
</output-structure>
<input-streams>
<input-stream name="cam">
<stream-source alias="cam" storage-size="1"
disconnect-buffer-size="10">
<address wrapper="remote">
<predicate key="geographical">BC143</predicate>
<predicate key="type">Camera</predicate>
</address>
<query>select * from WRAPPER</query>
</stream-source>
<stream-source alias="temperature1“
storage-size="1m"
disconnect-buffer-size="10">
<address wrapper="remote">
<predicate key="type">temperature</predicate>
<predicate key="geographical">
BC143-N
</predicate>
</address>
Virtual sensor definition:
XML + SQL
70 of 58
<query>
select AVG(temp1) as T1 from WRAPPER
</query>
</stream-source>
<stream-source alias="temperature2“
storage-size="1m"
disconnect-buffer-size="10">
<address wrapper="remote">
<predicate key="type">
temperature
</predicate>
<predicate key="geographical">
BC143-S
</predicate>
</address>
<query>
select AVG(temp2) as T2
from WRAPPER
</query>
</stream-source>
<query>
select cam.picture as image, temperature.T1
as temp
from cam, temperature1
where temperature1.T1 > 30 AND
temperature1.T1 = temperature2.T2
</query>
</input-stream>
</input-streams>
</virtual-sensor>
Input stream 3:
Temperature
Input stream 2:
Temperature
Input stream 1:
Camera images
Meta-data
System resources
to assign
Structure / data type
of output stream
Query over input
streams to produce
output stream of
the Virtual Sensor
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Enabling networked knowledge
HTTP Generic Wrapper
HTTP GET or POST requests
Serial Forwarder Wrapper
TinyOS compatible motes
USB Camera Wrapper
Local USB connection
Bluetooth Wrapper
MAC and RFCOMM Bluetooth
GPSD Wrapper
More than 60 NMEA-compliant GPS devices
Accessing sensors:
Wrappers
Generic UDP Wrapper
UDP connections
Generic Serial Wrapper
Local RS-232 connections
TI-RFID Wrapper
Texas Instruments Series 6000 S6700 multi-protocol RFID readers
Generic RSS/XML Wrapper
COAP Wrapper
RESTful interface to sensors
Contiki, Coalesenses
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Enabling networked knowledge
Coding efforts
50 RFID reader (TI)
60 Wireless camera (HTTP)
300 Wired camera
180 Generic serial
45 Generic UDP
75 WiseNode
120 TinyOS
Lines of code Wrapper type
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Enabling networked knowledge
Selected Features
SafeStorage
- Safe data backups
Workflow Editor
- Web-based design of
Virtual Sensors
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Enabling networked knowledge
Plug and Play: Zero
Programming
An IEEE 1451-compliant sensor provides a Transducer
Electronic Data Sheet (TEDS) which is stored inside the sensor
TEDS provides a simple semantic description of the sensor
the sensor's properties and measurement characteristic
GSN uses the TEDS self-description feature for dynamic
generation and deployment of virtual sensor descriptions
Next step: store queries not only data in TEDS or RFID tags
New level of data processing in terms of flexibility
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Enabling networked knowledge
Does it really work?
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Enabling networked knowledge
Experimental setup
5 desktop PCs
Pentium 4, 3.2GHz with 1MB
cache, 1GB memory, 100Mbit
Ethernet, Debian 3.1
Linux kernel 2.4.27, MySQL
5.18
SN-1: 10 Mica2 motes with light and
temperature sensors, packet size 15
Bytes, TinyOS
SN-2: 8 Mica2 motes with light,
temperature, acceleration, and
sound sensors, packet size 100
Bytes, TinyOS
SN-3: 4 Shockfish Tiny-Nodes with a
light and two temperature sensors
packet size 29 Bytes, TinyOS
SN-4: 15 wireless 8002.11b cameras
(AXIS 206W), 640x480 JPEG, 5 with
16kB average image size, 5 with
32kB, 5 with 75kB
SN-5: TI Series 6000 S6700 multi-
protocol RFID reader with three
different kind of tags (up to 8KB of
data)
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Enabling networked knowledge
Experimental setup
2 1.8 GHz Centrino laptops with
1GB memory as observers
Each ran up to 250 lightweight
GSN instances.
Each instance produced random
queries with varying table names,
varying filtering condition
complexity, and varying
configuration parameters
3 filtering predicates in the
WHERE clause on average, using
random history sizes from 1
second up to 30 minutes and
uniformly distributed random
sampling rates (seconds) [0.01, 1]
Motes produce random bursts (1-
100 data items) with 25%
probability
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Enabling networked knowledge
Processing time per
client
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Scalability in the number of
clients
79 of 58
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Enabling networked knowledge
OK, now we also have a
nice middleware /
database abstraction
for any sensor type
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Enabling networked knowledge
KISS revisited
Application
CQELS SPARQL REST
Linked Data
COAP
Sensors
Virtual
Sensors
Linked Streams
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Enabling networked knowledge
Putting it all together
Semantics / Linked Data / Real-Time / Streams / GIS
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Enabling networked knowledge
Linking the Real World
QR code points to FOAF profile
Associated RFID tag
Associated mobile
Available Not Available
RFID tag of person is identified
FOAF info is displayed
Availability based on haptic
phone interface
Position of other people
In the demo room
People in DERI scan QR code
with mobile to check into room
Position of people in DERI
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Enabling networked knowledge
Behind the Scenes
Demo roomRFID base station
Screen
where.deri.ie
REST
(position, availability)
REST
(availability)
www.deri.ie
gsn.deri.ie
REST
(position)REST
(position)
REST
(FOAF)
REST
(FOAF)
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Enabling networked knowledge
OK, now let’s make
it bigger and
general-purpose!
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Enabling networked knowledge
CQELS SPARQL REST
Linked Data
COAP
Sensors
Virtual
Sensors
Linked Streams
KISS re-re-visited
Application
Middleware
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Enabling networked knowledge
Linked Sensor
Middleware
Live data
Middleware for the semantic integration of live
real-world data
SPARQL endpoint for querying unified Linked Stream Data
and Linked Data
Sensor mashup composer
Wrappers for collecting and enriching
real-time / stream (sensor) data
Web interface for data
exploration, annotation and
visualisation
Mobile phone applications
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Enabling networked knowledge
LSM Architecture
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Enabling networked knowledge
:dublinAirport
:aHumidity
:aTemperature
:weatherStation
:latestWeather
:readings
:humidValue :tempValue
“18”^xsd:fl
oat “Celcius”
“60”^xsd:fl
oat “%”
ssn:featureOfInterest
ssn:observedBy
ssn:observes
ssn:observes
ssn:isPropertyOf ssn:isPropertyOf
ssm:observedPropery ssm:observedPropery
ssm:value ssm:value ssm:unit ssm:unit
ssn:hasValue ssn:hasValue
ssn:observationResult
Sensor metadata
Stream data snapshot at 2011-07-08T21:32:52
Linked Stream Model
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Enabling networked knowledge
Webcams:24570
Traffic:469 (London,
Ohio)
Roadactivity:575 (Ohio)
Flights: >1000
Railway stations:251
(London)
Bike hire:421(London)
Weather: 82365
Snowfall: 2639
Snow depth: 377
Sea level: 45
Radar:1
Satellite: 12
Over 110,000 live data
sources
… and growing!!!
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Enabling networked knowledge
Deployment
Stream Sources
Data Bus
CQELS
(Stream
Proc.)
Virtuoso Web
server
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Enabling networked knowledge
Mobile Applications
LSM
SPARQL,CQELS
SPARQL-XML/RDF
100-200 lines of code
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Enabling networked knowledge
LSM: Live flights info
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Enabling networked knowledge
LSM: Live train info
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Enabling networked knowledge
LSM: Live traffic info
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Enabling networked knowledge
A demo is worth a thousand words
http://lsm.deri.ie
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
LSM Example
Application
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
LSM Example
Application
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
LSM Example
Application
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
OK, what’s next?
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Across Research Areas
Internet of Things
Cloud Mobile
Semantic Web Linked Data
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Strategic Application
Domains
Enterprise Environments
Telehealth
Smart Cities
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Sensor data management in the Cloud
Sensor data annotation and sharing (portals,
community-based)
Social network analysis (online, mobile, real-world)
Social and opportunistic sensing (mobile phone)
Distributed query processing
Integrating business processes and sensors
Upcoming Research
Areas
10
3
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Conclusions
“Linking the Real World”
requires cross-domain /
cross-layer research
Non-trivial, open research
questions knowledge
management, Semantic
Web, databases, Cloud,
sensor networks, etc.
Running systems and
experiments!
Digital Enterprise Research Institute www.deri.ie
Enabling networked knowledge
Danh Le-Phuoc
Anh Le Tuan
Myriam Leggieri
Hoan Nguyen Mau Quoc
Josiane Xavier Parreira
Martin Serrano
Christian von der Weth
Acknowledgements
Recommended