Fabio A. Schreiber from Politecnico di Milano presented "Pervasive Data Management In Future Networked Enterprises" (FInES Workshop at Aalborg)

1. PERVASIVE DATA MANAGEMENT
IN FUTURE NETWORKED ENTERPRISES
Fabio A. Schreiber and Letizia Tanca
Politecnico di Milano

2. The Networked Enterprise Background
1
 Increasing international competition
 Flexibility needs
 Rapid evolution of the environments (market needs and available
technologies)
 Reduction of the time-to-market
 Need of personalising the offer
A new organisational model not limited to the production supply
chain but involving also the design and innovation processes : the
networked enterprise
Networked enterprise
Traditional model
Network based interdependent
companies
providers Aimed at cooperation and coordination
purchase production distribution customers
FInES Workshop Aalborg 2012

3. Pervasive, Context-aware Systems
2
 Networked enterprises need information gathered
from multiple heterogeneous sources
 Information noise must be filtered by context in
order to provide only the right data to the right
person in the right situation
 Pervasive languages can be effectively used to
design and implement complex, self-adapting
applications
FInES Workshop Aalborg 2012

4. The Artdeco project vision
3
To develop models, methods
and a platform to support
exchange of information Product X
arriving
through various enterprises
tomorrow
part of a supply chain
• Sensors and RFIDs placed
everywhere allowing the system Product X
to monitor the position of
Artdeco
on truck Y
products and their state in the middleware on A1 highway
supply chain
• Mechanisms to autonomically
reconfigure a sensors network
based on environmental
conditions
• Context awareness allowing the
various actors of the enterprise
to access the right information at
the right time
FInES Workshop Aalborg 2012

5. Context-aware information and service
tailoring
4
• observables • instantiation • context-aware
Context Context Context- data
Modelling • context Sensing • validation Aware
schema Behaviour • context-aware
• reasoning operations
design-time run-time
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6. Case Study: The Wine LifeCycle
5
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7. Design Time Modeling
6
The Context Dimension Tree
risk
 Context is hierarchically modelled in terms of observable parameters that have
a symbolic internal representation within the CDT context schema
 The CD model is completely independent of the specific formalism adopted.
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8. Context-aware data tailoring
7
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9. Context-aware system architecture
8
DATA VIEW FOR
CONTEXT C
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10. Some contextual views
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Sample contexts:
C1=< phase=harvesting, *, role=agronomist >
C2 =< phase=ageing, *, role=agronomist >
C3=< phase=harvesting, *, role=qlity manager >
C4 =< phase=transport, *, role=qlity manager >
FInES Workshop Aalborg 2012

11. Some contextual queries
10
The agronomist during the harvesting phase
(context C1) wants to collect all the available
information coming from sensors:
SELECT m.date_time,m.value,s.s_id, s.meas_unit
FROM C1.sensor s, C1.measure_data m
WHERE s.s_id=m.s_id;
S/he obtains only the information from sensors
placed in the vineyards (see Rel(C1))
FInES Workshop Aalborg 2012

12. Some contextual queries
11
The quality manager during the harvesting phase
(context C3) wants to collect all the available
information about bottles of “Aglianico” wine:
SELECT *
FROM C3.bottle b
WHERE b.appellation="aglianico";
But the query is out of context, in the context C3
only information about vineyard and grapevine
are available for the qlity manager
FInES Workshop Aalborg 2012

13. A fragment of the CDT model
12
Role Phase
Risk
Overheat Disease
Agronomist Driver QtyMngr Growth Ageing Transport
Type Affected
hectares
$ID $Square_meters
 The environment is modelled in terms of dimension, concept
nodes and attributes
 Intuitively a valid context is a particular subtree of the CDT
(formally defined in IJWET09)
FInES Workshop Aalborg 2012

14. Context management at runtime
13
sensors as • Numerical
tuple observables
context
providers • Symbolic
recognition observables
context • Context activation
sensing switch • Real-time C-A
behaviour
A middleware and a
querying language to manage
pervasive systems hiding
the complexity of handling
different technologies
FInES Workshop Aalborg 2012

15. Context-aware WSN
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Apply the sensor query
only to the sensors in
context:
phase = ‘growth'
AND
risk='overheat‘
AND
orientation=‘west
ward’
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16. The context language (1/2)
15
Example: Given the previous CDT
CREATE DIMENSION Role
CREATE CONCEPT Agronomist
WHEN get_user_role()=‘agronomist’ Role
CREATE CONCEPT QtyMngr Risk
WHEN get_user_role()=‘qtyMngr’
CREATE CONCEPT Driver Overheat Disease
WHEN get_user_role()=‘driver’
CREATE DIMENSION Risk Agronomist Driver QtyMngr
CREATE CONCEPT Disease Type Affected
WHEN get_interest_topic()=‘disease’ hectares
CREATE CONCEPT Overheat
WHEN temperature > 30 AND
brightness > 0.75; $ID $square_
….. meters
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17. The context Language (2/2)
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Role Risk Phase
Agronomist Overheat Growth
CREATE CONTEXT Growth_Monitoring
ACTIVE IF phase = ‘growth' AND role=‘agronomist’ AND Risk='overheat'
ON ENABLE(Growth_Monitoring):
SELECT temperature,humidity
SAMPLING EVERY 120 s
EXECUTE IF location = ‘vineyard'
SET PARAMETER 'alarm' = TRUE;
ON DISABLE(Growth_Monitoring):
SET PARAMETER 'alarm' = FALSE;
REFRESH EVERY 24 h;
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18. The context manager
17
The CM associates to every dimension of the CDT a table that
contains the values of every numeric observable sampled from the
environment.
OVERHEAT: Role Risk Phase
temperature > 30 AND brightness > 0.75
Agronomist Overheat Growth
ID Temperature Brightness The context is active for all the
sensors for which the rule is true,
1 28 0.60
and the context-aware actions will
3 31 0.71
be performed only on them.
4 33 0.80
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19. Concluding remarks
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 Pervasive systems are widely adopted to monitor many kinds
of physical phenomena and to seamlessly accommodate
information coming from all kinds of heterogeneous data
sources (e.g. social networks as sensors)
 Context-awareness plays a fundamental role since it allows,
through the perception of the environment, to make the system
autonomic w.r.t. environmental situations and changes.
 Further data personalization can be obtained considering the
user’s preferences
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20. Acknowledgements
19
Cristiana Romolo Giorgio
Bolchini Camplani Orsi
Elisa Fabio A. Letizia
Quintarelli Schreiber Tanca
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21. Q&A
20
Q&A
(If you see this slide we’ve not run out of time)
FInES Workshop Aalborg 2012