This is the slide set I have used on Dec. 10th, 2014 during my final Ph. D. dissertation.

1. SuNDroPS: Semantic and dyNamic Data in a
Pervasive System
Context-ADDICT Revisited
Doctoral Dissertation of:
Emanuele Panigati
Advisor: Prof. Letizia Tanca
Co-Advisors: Prof. Fabio A. Schreiber, Prof. G. Cugola
Politecnico di Milano { Dipartimento di Elettronica, Informazione e Bioingegneria
December 10th, 2014

2. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions & Future Works
Summary of the content
Introduction and Motivation Go
A motivating scenario: the Green Move project Go
The SuNDroPS system Go
The SuNDroPS legacy blocks Go
Context-ADDICT Go
PerLa & Tesla Go
Hystorical data analysis Go
The SuNDroPS new components Go
New features of PerLa Go
New features of Tesla/TRex: SemTRex Go
MR-Miner & MREClaT Go
Testing SuNDroPS in the Green Move scenario Go
Conclusions and

3. nal remarks Go
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

4. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions & Future Works
Introduction and Motivation
Users are surrounded by a high quantity of heterogeneous data,
often in the form of data streams
Humans cannot fully exploit the whole richness of these data
without digital support for their analysis
Real-time, on-the-
y and historical data processing are equally
necessary to obtain useful knowledge
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

5. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions & Future Works
A Motivating Scenario: the Green Move Project
Green Move is a zero-emission vehicle-sharing system which
supports the users with additional digital services
The Green Move project has been used as a real-world on-

6. eld test,
using several SuNDroPS components
The user experience is

7. nely personalized based on the user context
and on contextual preferences, so the data management process
must consider a contextual tailoring of the data
Data coming as streams from dierent kinds of sensors (e.g.,
on-board vehicle status sensor, environmental sensor, . . . ) must be
processed on-the-
y in order to give to users an immediate feedback
and empower their experience.
Dierent Information Flow Processing systems have been considered
to perform this task (PerLa TRex)
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

8. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
SuNDroPS Architecture Overview
SuNDroPS allows to manage the
ow of (possibly semantically
enriched) information contained
in data streams and to study
how to extract useful knowledge
from it and from the more
traditional, static datasets.
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

9. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
SuNDroPS Architecture Overview
SuNDroPS allows to manage the
ow of (possibly semantically
enriched) information contained
in data streams and to study
how to extract useful knowledge
from it and from the more
traditional, static datasets.
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

10. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Legacy: The Context-ADDICT system
Allows to query dierent
and heterogeneous data
sources providing a
single entry-point for
queries
Automatically tailors the
query according to the
current context of the
user, and rewrites it,
integrating the results
coming from each
dierent data source
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

11. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Legacy: PerLa Tesla
Two information
ow
processing systems
PerLa is based on a DSMS
paradigm
Tesla/TRex is based on a
Complex Event Processing
paradigm
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

12. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
More on Information Flow Processing
Two dierent approaches:
DSMSs, developed by the database community, consider a data
stream as a sequence of tuples, processing them using SQL-like
query languages
CEPs, developed by the distributed software engineering community,
consider the stream as a sequence of events and process them using
rule and/or logic based languages for temporal pattern detection
PerLa is an example of the

13. rst kind of systems while Tesla/TRex
belongs to the second category.
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

14. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
More on Information Flow Processing
Two dierent approaches:
DSMSs, developed by the database community, consider a data
stream as a sequence of tuples, processing them using SQL-like
query languages
CEPs, developed by the distributed software engineering community,
consider the stream as a sequence of events and process them using
rule and/or logic based languages for temporal pattern detection
PerLa is an example of the

15. rst kind of systems while Tesla/TRex
belongs to the second category.
We next compare the features of the two approaches.
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

16. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { PerLa Data
VehicleData
greenBox id Timestamp Speed
A300A 10/12/2014 14:00 0.0
B400B 10/12/2014 14:15 15.0
C100C 10/12/2014 14:01 50.0
TakenOrReleased
greenBox id Timestamp takenReleased
A300A 10/12/2014 7:00 TAKEN
A300A 10/12/2014 8:00 RELEASED
B400B 10/12/2014 10:00 TAKEN
B400B 10/12/2014 11:00 RELEASED
C100C 10/12/2014 12:00 TAKEN
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

17. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { PerLa Queries
PerLa Low Level Query
CREATE SNAPSHOT MostRecentUse (greenBox id String, takenReleased Integer, date[3] Integer)
WITH DURATION 10
AS LOW:
EVERY 30 m
SELECT greenBox id, takenReleased, date[3]
HAVINGdate = MAX(date, 10)
UP TO 30m
SAMPLING ON EVENT takenInCharge Released
PerLa High Level Query
CREATE OUTPUT STREAM Theft (greenBox id String, recentUsage date)
AS HIGH:
EVERY 10 m
SELECT greenBox id, MAX(MostRecentUse.date) as mass
FROM MostRecentUse, TakenOrReleased, VehicleData
WHERE VehicleData.greenBox id = TakenOrReleased.greenBox id AND
TakenOrReleased.date = mass AND TakenOrReleased.takenReleased = 0 AND
VehicleData.speed 0
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

18. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { PerLa Query Results
MostRecentUse
greenBox id takenReleased date
A300A 10/12/2014
8:00
RELEASED
B400B 10/12/2014
11:00
RELEASED
C100C 10/12/2014
12:00
TAKEN
Theft
greenBox id RecentUsage
B400B 10/12/2014 11:00
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

19. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { Tesla Data Rule
Events
event id : 17; greenBox id : A300A; ts : 10=12=2014 14 : 00; speed : 0:0
event id : 17; greenBox id : B400B; ts : 10=12=2014 14 : 15; speed : 15:0
event id : 17; greenBox id : C100C; ts : 10=12=2014 14 : 01; speed : 50:0
event id : 112; greenBox id : A300A; ts : 10=12=2014 7 : 00
event id : 121; greenBox id : A300A; ts : 10=12=2014 8 : 00
event id : 112; greenBox id : B400B; ts : 10=12=2014 10 : 00
event id : 121; greenBox id : B400B; ts : 10=12=2014 11 : 00
event id : 112; greenBox id : C100C; ts : 10=12=2014 12 : 00
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

20. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { Tesla Data Rule
Events
event id : 17; greenBox id : A300A; ts : 10=12=2014 14 : 00; speed : 0:0
event id : 17; greenBox id : B400B; ts : 10=12=2014 14 : 15; speed : 15:0
event id : 17; greenBox id : C100C; ts : 10=12=2014 14 : 01; speed : 50:0
event id : 112; greenBox id : A300A; ts : 10=12=2014 7 : 00
event id : 121; greenBox id : A300A; ts : 10=12=2014 8 : 00
event id : 112; greenBox id : B400B; ts : 10=12=2014 10 : 00
event id : 121; greenBox id : B400B; ts : 10=12=2014 11 : 00
event id : 112; greenBox id : C100C; ts : 10=12=2014 12 : 00
Rule
DEFINE Theft (ID : String)
FROM VehicleData (greenBox id = $id AND speed 0) AND
LAST Release (greenBox id = $id) WITHIN 10day FROM VehicleData AND
NOT Taken (greenBox id = $id) BETWEEN Release AND VehicleData
WHERE ID = VehicleData.greenBox id
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

21. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check if a Vehicle is Being Stolen { Tesla Data Rule
Events
event id : 17; greenBox id : A300A; ts : 10=12=2014 14 : 00; speed : 0:0
event id : 17; greenBox id : B400B; ts : 10=12=2014 14 : 15; speed : 15:0
event id : 17; greenBox id : C100C; ts : 10=12=2014 14 : 01; speed : 50:0
event id : 112; greenBox id : A300A; ts : 10=12=2014 7 : 00
event id : 121; greenBox id : A300A; ts : 10=12=2014 8 : 00
event id : 112; greenBox id : B400B; ts : 10=12=2014 10 : 00
event id : 121; greenBox id : B400B; ts : 10=12=2014 11 : 00
event id : 112; greenBox id : C100C; ts : 10=12=2014 12 : 00
Rule
DEFINE Theft (ID : String)
FROM VehicleData (greenBox id = $id AND speed 0) AND
LAST Release (greenBox id = $id) WITHIN 10day FROM VehicleData AND
NOT Taken (greenBox id = $id) BETWEEN Release AND VehicleData
WHERE ID = VehicleData.greenBox id
Results
event id : 301; greenBox id : B400B; ts : 10=12=2014 14 : 15
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

22. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check User Driving Style w.r.t. Weather Conditions {
PerLa Data
VehicleData
greenBox id GPS Speed
A300A 45.1,15.1 30.0
B400B 45.1,20.2 15.0
C100C 42.2,15.1 50.0
Weather
GPS Climate Limit
45.1,15.1 Normal 130
45.1,20.2 Rain 90
42.2,15.1 Ice 30
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

23. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check User Driving Style w.r.t. Weather Conditions {
PerLa Queries
PerLa Low Level Query
CREATE STREAM WeatherChange (position gps data, climate String)
AS LOW:
EVERY 10 m
SELECT position, climate
SAMPLING ON EVENT WeatherChanged
WHERE climate = Rain OR climate = Ice OR
climate = Snow OR climate = Fog
REFRESH EVERY 5 m
PerLa High Level Query
CREATE OUTPUT SNAPSHOT DangerousDriving (greenBox id String)
WITH DURATION 2 h
AS HIGH:
SELECT greenBox id
FROM VehicleData, WeatherChange, Weather
WHERE VehicleData.gps data = WeatherChange.position AND
WeatherChange.climate = Weather.climate AND
VehicleData.speed Weather.limits
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

24. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check User Driving Style w.r.t. Weather Conditions {
PerLa Query Results
DangerousDriving
position climate
45.1,20.2 Rain
42.2,15.1 Ice
WeatherChange
greenBox id
C100C
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

25. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check User Driving Style w.r.t. Weather Conditions {
Tesla Data
Events
event id : 17; greenBox id : A300A; pos : 45:1; 15:1; speed : 30:0
event id : 17; greenBox id : B400B; pos : 45:1; 20:2; speed : 15:0
event id : 17; greenBox id : C100C; pos : 42:2; 15:1; speed : 50:0
event id : 40; pos : 45:1; 15:1; climate : Normal; temp : 20
event id : 40; pos : 45:1; 20:2; climate : Rain; temp : 17
event id : 40; pos : 42:2; 15:1; climate : Ice; temp : 2
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

26. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Check User Driving Style w.r.t. Weather Conditions {
Tesla Rules Results
Rule (Rain)
DEFINE DangerousDrivingRain(ID : String) FROM VehicleData(speed90) AND LAST
Weather(VehicleData.pos-xposVehicleData.pos+x) WITHIN 1h FROM VehicleData AND Weather.climate=rain
WHERE DangerousDrivingRain.ID = VehicleData.greenBox id
Rule (Ice)
DEFINE DangerousDrivingIce(ID : String) FROM VehicleData(speed50) AND LAST
Weather(VehicleData.pos-xposVehicleData.pos+x and temp0) WITHIN 1h FROM VehicleData WHERE
DangerousDrivingRain.ID = VehicleData.greenBox id
Results
event id : 340; greenBox id : C100C
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

27. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Legacy: Hystorical Data Analysis
Data Mining allows to extract knowledge from the gathered data,
discovering previously unknown facts from them.
Frequent Itemset Mining

28. nds in the database all the sets of items
whose frequency is above a given support threshold
Several algorithms are available to perform this task:
A Priori
Partition
FP-Growth
EClaT
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

29. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Architecture Overview
Legacy: The Context-ADDICT system
Legacy: PerLa Tesla
Legacy: Hystorical Data Analysis
Legacy: Hystorical Data Analysis
Data Mining allows to extract knowledge from the gathered data,
discovering previously unknown facts from them.
Frequent Itemset Mining

30. nds in the database all the sets of items
whose frequency is above a given support threshold
Several algorithms are available to perform this task:
A Priori
Partition
FP-Growth
EClaT
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

31. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
New Components in the Big Data Era
SuNDroPS Adds new features to
Context-ADDICT:
Monitors the environment directly,
using sensors, also reasoning on the
gathered data
Automatically infers (part of) the
user context from the
environmental data that have been
sensed
Integrates historical data processing
with analysis operations,
introducing a new parallel Data
Mining algorithms
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

32. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
New features of PerLa
The PerLa middleware has been completely reengineered to include
asynchronous behaviors of sources (sensors, web services, . . . )
Distributed PerLa allows to exploit the sources (and network)
computation power
PerLa for Context explicitly integrates the context-aware approach of
Context-ADDICT with Context-Oriented Programming COP,
allowing sensors to behave dierently according to their current
context
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

33. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
New features of Tesla/TRex: SemTRex
Original TRex cannot interact with static data
SemTRex adds a RDF static data repository to TRex and new
operators in the Tesla language (IN)
Integrating RDF repositories allows reasoning on the data
IN allows to:
Enrich the events, including into them facts retrieved from the KB
Filter the events using facts included in the KB
Prefetching and Caching of data become necessary to keep
reasonable response times: basic cache, parametric basic cache,
frequent data cache, combined caching strategy.
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

34. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
MapReduce-based Frequent Itemset Mining
MR-Miner supports the mining
processes in SuNDroPS using
MREClaT, an EClaT-based
algorithm exploiting the MapReduce
programming paradigm, that allows
SuNDroPS to analyze the data load
typical of Big Data scenarios
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

35. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
MapReduce-based Frequent Itemset Mining
MR-Miner supports the mining
processes in SuNDroPS using
MREClaT, an EClaT-based
algorithm exploiting the MapReduce
programming paradigm, that allows
SuNDroPS to analyze the data load
typical of Big Data scenarios
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

36. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
MR-Miner MREClaT { Algorithm Details
First step: Mine 1-frequent itemsets
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

37. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
MR-Miner MREClaT { Algorithm Details
Second step: Mine 2-frequent itemsets
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

38. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
MR-Miner MREClaT { Algorithm Details
Third step: Mine k-frequent itemsets
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

39. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
Experiments
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

40. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
The SuNDroPS New Components
New features of PerLa
New features of Tesla/TRex
MapReduce-based Frequent Itemset Mining : MR-Miner MREClaT
Pre

41. x Extension Experiments
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

42. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Testing SuNDroPS in the Green Move Scenario
PerLa context-aware sensors
SemTRex Pervasive and context-aware information push
Context-aware vehicle assignment to user reservation, based on
contextual user preferences
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

43. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Conclusions
The SuNDroPS system helps users to deal with the high information load
that surronds them
Context inference based on the environmental sensor data
ows
Historical data mining using parallel MapReduce algorithms to speed
up processing
Semantic-enhanced complex event processing using cache to reduce
the performance degradation due to the disk bottle-neck
Reengineering of the PerLa middleware allowing its distribution on
the network components and the integration with other
context-oriented paradigms (e.g. Context Oriented Programming)
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

44. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Future Works
Several enhancements are required
Complete implementation of Distributed PerLa (currently a
prototype)
Complete integration of Context Oriented Programming (COP) in
PerLa
Complete the implementation of caches in SemTRex (currently only
the basic and parametric caches are fully implemented)
Further testing on the whole system
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

45. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Thanks
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

46. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Relevant Publication I
1 A. G. Bianchessi, G. Cugola, S. Formentin, A Morzenti, C. Ongini,
E. Panigati, M. Rossi, S. Savaresi, F. Schreiber, L. Tanca, and
E. Vannutelli Depoli
Green move: A platform for highly con

47. gurable, heterogeneous
electric vehicle sharing
Intelligent Transportation Systems Magazine, IEEE, 6(3):96{108,
Fall 2014
2 E. Panigati
Personalized management of semantic, dynamic data in pervasive
systems: Context-addict revisited
In Proc. of the 2014 International Conference on High Performance
Computing Simulation (HPCS 2014), 323-326, 2014
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

48. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Relevant Publication II
3 F. A. Schreiber, E. Panigati
Context-aware software approaches: a comparison and an
integration proposal
In Proc. of the 22nd Italian Symposium on Advanced database
Systems (SEBD), pages 175{184, 2014
4 A. G. Bianchessi, C. Ongini, G. Alli, E. Panigati, S. Savaresi
Vehicle-sharing: Technological infrastructure, vehicles, and
user-side devices - technological review
In Proc. of the 16th International IEEE Conference on Intelligent
Transportation Systems - (ITSC), 2013, pages 1599{1604, Oct 2013
5 E. Panigati, A. Rauseo, F. A Schreiber, L. Tanca
Pervasive data management in the green move system: a progress
report*
In Proc. of the 21st Italian Symposium on Advanced Database
Systems (SEBD), pages 279{288, 2013
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

49. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Relevant Publication III
6 E. Panigati, A. Rauseo, F. A Schreiber, L. Tanca
Aspects of pervasive information management: an account of the
Green Move system
In Proc. of the 10th IEEE/IFIP International Conference on
Embedded and Ubiquitous Computing, Paphos, Cyprus, Dec 2012
7 G. Alli, L. Baresi, A. G. Bianchessi, G. Cugola, A. Margara,
A. Morzenti, C. Ongini, E. Panigati , M. Rossi, S. Rotondi,
S. Savaresi, F. A. Schreiber, A. Sivieri, L. Tanca, E. Vannutelli
Depoli.
Green Move: towards next generation sustainable
smartphone-based vehicle sharing
In Proc. of SustainIT2012, Oct 2012
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

50. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Relevant Publication IV
8 E. Panigati, A. Rauseo, F. A Schreiber, L. Tanca
Context-aware information management in the Green Move system
{ extended abstract
In Proc. of the 5th Interop-VLab Workshop, co-Located with ItAIS
2012, Rome, Sept 2012
9 E. Panigati, F. A. Schreiber, C. Zaniolo
Data Streams and Data Stream Management Systems
Submitted for publication in Data Management in Pervasive
Systems - The Shapes and Dynamics of Information in a Pervasive
World Book, Springer
10 F. A. Schreiber, E. Panigati
Context aware data management and context oriented
programming: is convergence possible?
Technical Report 2014.7 (DEIB)
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

51. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
Relevant Publication V
11 E. Panigati
Methods for Supporting Critical Systems' Failure Diagnosis in the
Railway Scenario
Technical Report 2013.12 (DEIB)
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System

52. Summary
Introduction and Motivation
The Green Move Project
The SuNDroPS Building Blocks
The SuNDroPS System
Real-World Testing
Conclusions Future Works
Conclusions
Future Works
SemTRex Query Example
Send a GM dynamic app to the Green eBox
DEFINE SendApp (String: greenBox id, String:
appUrl, String:class)
FROM VehicleStatus(greenbox id=$a) and ($url,
$class) IN
(SELECT ?url ?class FROM appKB.rdf WHERE f ?a
prop:hasName foo. ?a prop:downloadFromURL ?url.
?a prop:mainClass ?classg)
WHERE SendApp.greenBox id=VehicleStatus.greenBox id
and SendApp.appUrl = $url and SendApp.class= $class
E. Panigati SuNDroPS: Semantic and dyNamic Data in a Pervasive System