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REDUCTION
Eco Routing and Eco Driving— The REDUCTION Project
Josif Grabocka Umer Khan Lars Schmidt-Thieme
Information Systems and Machine Learning Lab (ISMLL),University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth,November 15, 2012
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 0 / 31
REDUCTION
Outline
1. The REDUCTION Project
2. Eco Routing I: Estimating Fuel Consumption from GPS Data
3. Eco Routing II: Contextualized Estimation of Fuel Consumption
4. Eco Driving: Acceleration/Deacceleration Recommendations
5. Distributed Data Mining in Vehicular Networks
6. About ISMLL
7. Conclusion
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 1 / 31
REDUCTION 1. The REDUCTION Project
Outline
1. The REDUCTION Project
2. Eco Routing I: Estimating Fuel Consumption from GPS Data
3. Eco Routing II: Contextualized Estimation of Fuel Consumption
4. Eco Driving: Acceleration/Deacceleration Recommendations
5. Distributed Data Mining in Vehicular Networks
6. About ISMLL
7. Conclusion
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 1 / 31
REDUCTION 1. The REDUCTION Project
REDUCTION Overview
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 1 / 31
REDUCTION 1. The REDUCTION Project
REDUCTION Vision
I Enable fleet managers in EU to “go green” by using advanced ICTsolutions for substantially reducing mileage and fuel consumption.Based on historic and real-time data about driving behaviour, routinginformation and emission measurements.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 2 / 31
REDUCTION 1. The REDUCTION Project
REDUCTION Partners
I Project Duration: 01.09.2011 – 31.08.2014
I Project Homepage: http://reduction-project.eu
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 3 / 31
REDUCTION 1. The REDUCTION Project
REDUCTION Field TrialsI. flex routes (taxis): FlexDenmark II. fixed routes (busses): CTL,
Nikosia
III. Multi-modality (trains & taxis): TrainOSE
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 4 / 31
REDUCTION 2. Eco Routing I: Estimating Fuel Consumption from GPS Data
Outline
1. The REDUCTION Project
2. Eco Routing I: Estimating Fuel Consumption from GPS Data
3. Eco Routing II: Contextualized Estimation of Fuel Consumption
4. Eco Driving: Acceleration/Deacceleration Recommendations
5. Distributed Data Mining in Vehicular Networks
6. About ISMLL
7. Conclusion
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 5 / 31
REDUCTION 2. Eco Routing I: Estimating Fuel Consumption from GPS Data
Data-Source Types
High frequent GPS: Trajectory-to-Eco
Work on Eco Routing I has been carried out by University of Aalborg, Universityof Aarhus and FlexDenmark.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 5 / 31
REDUCTION 2. Eco Routing I: Estimating Fuel Consumption from GPS Data
Data-Source Types
High frequent GPS: Trajectory-to-Eco
Work on Eco Routing I has been carried out by University of Aalborg, Universityof Aarhus and FlexDenmark.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 5 / 31
REDUCTION 2. Eco Routing I: Estimating Fuel Consumption from GPS Data
Framework of EcoMark
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 7 / 31
REDUCTION 2. Eco Routing I: Estimating Fuel Consumption from GPS Data
Spatial Network Lifting
I MotivationI Eco-routes are highly correlated to the elevation of road segments.I TomTom states ITS using 3D spatial network will result in an annual fuel
saving of 6 Billion USD in American. [L. Sugarbaker et. al. 2012]
I 3D spatial network is constructed by using a laser scan point cloud for lifting
a 2D spatial networkI Yields a model with high accuracy
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 8 / 31
REDUCTION 2. Eco Routing I: Estimating Fuel Consumption from GPS Data
Comparison and Analysis
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 11 / 31
REDUCTION 2. Eco Routing I: Estimating Fuel Consumption from GPS Data
Comparison: Aggregated Models - 1
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 13 / 31
REDUCTION 2. Eco Routing I: Estimating Fuel Consumption from GPS Data
Comparison: Aggregated Models - 2
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 14 / 31
REDUCTION 3. Eco Routing II: Contextualized Estimation of Fuel Consumption
Outline
1. The REDUCTION Project
2. Eco Routing I: Estimating Fuel Consumption from GPS Data
3. Eco Routing II: Contextualized Estimation of Fuel Consumption
4. Eco Driving: Acceleration/Deacceleration Recommendations
5. Distributed Data Mining in Vehicular Networks
6. About ISMLL
7. Conclusion
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 15 / 31
REDUCTION 3. Eco Routing II: Contextualized Estimation of Fuel Consumption
Eco-Routing
I Routing provides the fastest route from a start point to a destination,i.e., the one with the shortest travel time.
I Eco Routing provides the most fuel efficient route from a start point toa destination,i.e., the one with the lowest fuel consumption.
I Routing requires Speed/Travel Time Maps that provide estimatedtravel times for each road segment.
I Eco Routing requires Fuel/Eco Maps that provide estimated fuelconsumption for each road segment.
I Travel times and fuel consumption can be estimatedI from heuristics: taking into account length of the road segment,
elevations, speed limits etc.I from observed data: take the average.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 15 / 31
REDUCTION 3. Eco Routing II: Contextualized Estimation of Fuel Consumption
Contextualized Eco-RoutingI More accurate estimations can be made when taking context into
account such asI vehicle type,I driver,I time of day,I weather,I road conditions,I traffic conditions, etc.
I from observed data, taking averages:I build a map for each context value,
e.g., a map each for mornings, afternoons and nights.I problem: observations in each cell become sparse,
esp. when combining several contexts.
REDUCTION solution:
estimate travel time and fuel consumption for a road segmentand some context using a probabilistic model.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 16 / 31
REDUCTION 3. Eco Routing II: Contextualized Estimation of Fuel Consumption
Contextualized Eco-RoutingI More accurate estimations can be made when taking context into
account such asI vehicle type,I driver,I time of day,I weather,I road conditions,I traffic conditions, etc.
I from observed data, taking averages:I build a map for each context value,
e.g., a map each for mornings, afternoons and nights.I problem: observations in each cell become sparse,
esp. when combining several contexts.
REDUCTION solution:
estimate travel time and fuel consumption for a road segmentand some context using a probabilistic model.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 16 / 31
REDUCTION 3. Eco Routing II: Contextualized Estimation of Fuel Consumption
Example (1/3)I 3 road segments: S1,S2,S3.I 3 vehicles: V 1,V 2,V 3 (context).
Figure: Driver 1 travels across all the three segments 1, 2 and 3, while the Driver2 passes through segments 1 and 2. Finally Driver 3 has an itinerary involvingonly segment 3.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 17 / 31
REDUCTION 3. Eco Routing II: Contextualized Estimation of Fuel Consumption
Example (2/3)
Observed travel times can be represented in a vehicle-vs-segment matrix:
T =
S1 S2 S3
V1 6 4 8V2 3 2 ?V3 ? ? 6
I How long will vehicle 2 need to pass road segment 3 ?
I How long will vehicle 3 need to pass road segment 1 or 2 ?
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 18 / 31
REDUCTION 3. Eco Routing II: Contextualized Estimation of Fuel Consumption
Example (3/3)Find a low rank / low norm matrix factorization to explain the observeddata:
T = Φ ·Ψ, T ∈ R3×3,Φ ∈ R3×1,Ψ ∈ R1×3
T =
6 4 83 2 ?? ? 6
≈ 2
11.5
· ( 3 2 4)
Use the factorization to predict unobserved cells:
T̂2,3 = Φ2,1 ∗Ψ1,3 = 4
T̂ =
6 4 83 2 44.5 3 6
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 19 / 31
REDUCTION 3. Eco Routing II: Contextualized Estimation of Fuel Consumption
Matrix Factorization
Reconstruct original matrix, X ∈ RN×M , via lower dimensionality matrices,Φ ∈ RN×K , Ψ ∈ RK×M , while K << M.
X ≈O Φ ·ΨO = { (i , j) | Xi ,j 6= ?}
Optimize the regularized loss function:
argminΦ,Ψ
L(Φ,Ψ;X , λΦ, λΨ) := ||X − Φ ·Ψ||2O + λΦ||Φ||2 + λΨ||Ψ||2
Factorization models can efficiently be learnt via Stochastic GradientDescent.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 20 / 31
REDUCTION 3. Eco Routing II: Contextualized Estimation of Fuel Consumption
ExperimentsVery preliminary results:
I infati dataI 20 cars xI 837 road segments yI 2741 car/road segment travel time t observations (xi , yi , ti ):
t ∈ [1s, 2023s]
I split randomly 50:50 into train and test set
I trained on train set, evaluated via RMSE on test set:
RMSE(t̂, t) :=
√√√√1
n
n∑i=1
(t̂i − ti )2, t̂i := t̂(xi , yi )
method RMSE [s]
averages per road segment 167.7averages per road segment and vehicle 143.7
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 21 / 31
REDUCTION 3. Eco Routing II: Contextualized Estimation of Fuel Consumption
Predicting Fuel Consumption
I Fuel consumption can be estimated by the same type of modelsI with fuel consumption as target variable instead of travel times.
I requires per segment measurement of fuel consumptionI e.g., via CanBUS data
I several context attributes can be taken into account by moving fromMatrix Factorization to Tensor Factorization.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 22 / 31
REDUCTION 4. Eco Driving: Acceleration/Deacceleration Recommendations
Outline
1. The REDUCTION Project
2. Eco Routing I: Estimating Fuel Consumption from GPS Data
3. Eco Routing II: Contextualized Estimation of Fuel Consumption
4. Eco Driving: Acceleration/Deacceleration Recommendations
5. Distributed Data Mining in Vehicular Networks
6. About ISMLL
7. Conclusion
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 23 / 31
REDUCTION 4. Eco Driving: Acceleration/Deacceleration Recommendations
Different Driving Behaviors
[www.toyota.com.au]
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 23 / 31
REDUCTION 4. Eco Driving: Acceleration/Deacceleration Recommendations
Eco Driving Aspects
I Eco Analytics:I provide insight into dependency of fuel consumption on different
contexts(driver behavior, vehicles, technical conditions, etc.)
I Local Eco RoutingI lane changeI finding parking lotsI . . .
I Acceleration/Deacceleration recommendationsI traffic light aheadI speed limit aheadI slow cars ahead etc.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 24 / 31
REDUCTION 4. Eco Driving: Acceleration/Deacceleration Recommendations
Acceleration/Deacceleration RecommendationsGiven
I a planned path P (including elevations),I a current velocity v0,I a sequence A of acceleration/deacceleration actions, andI a context description C (vehicle, driver, weather, road etc.)
predict the velocity at the end of path P:
v̂(P, v0,A,C )
Such a model can be used to search for the sequence A ofacceleration/deacceleration actions that
1. leads to a target velocity vend at the end of the pathI stop, speed limit
2. is most fuel efficientI using a prediction model for fuel consumption
A special case: recommend when to coast (A :≡ 0).
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 25 / 31
REDUCTION 4. Eco Driving: Acceleration/Deacceleration Recommendations
Acceleration/Deacceleration RecommendationsGiven
I a planned path P (including elevations),I a current velocity v0,I a sequence A of acceleration/deacceleration actions, andI a context description C (vehicle, driver, weather, road etc.)
predict the velocity at the end of path P:
v̂(P, v0,A,C )
Such a model can be used to search for the sequence A ofacceleration/deacceleration actions that
1. leads to a target velocity vend at the end of the pathI stop, speed limit
2. is most fuel efficientI using a prediction model for fuel consumption
A special case: recommend when to coast (A :≡ 0).Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 25 / 31
REDUCTION 5. Distributed Data Mining in Vehicular Networks
Outline
1. The REDUCTION Project
2. Eco Routing I: Estimating Fuel Consumption from GPS Data
3. Eco Routing II: Contextualized Estimation of Fuel Consumption
4. Eco Driving: Acceleration/Deacceleration Recommendations
5. Distributed Data Mining in Vehicular Networks
6. About ISMLL
7. Conclusion
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 26 / 31
REDUCTION 5. Distributed Data Mining in Vehicular Networks
Distributed Data Mining
I Distributed predictive models, which are communication efficient,scalable, asynchronous, and robust to dynamic topology, whichachieve accuracy as close as possible to centralized ones.
Figure: DDM in Vanets
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 26 / 31
REDUCTION 5. Distributed Data Mining in Vehicular Networks
Reduced Support Vector Machinesfor Distributed Data Mining (Khan et al. [2012])
I Reduced Support Vector Machines (RSVM) for distributedvehicle-to-vehicle data mining in VANETS
I Allow only a subset of data as support vectors and solve a smalleroptimization problem
I Considering the SVM optimization problem:
minimize1
2αT (Q +
I
2C)α− eTα
subject to:
yTα = 0, and αi ≥ 0, i = 1, ..., l
I Where e is the vector of all ones, Q is an l by l positive semi-definitematrix, Qij = yiyjK (xi , xj), and K (xi , xj) = Φ(xi )
TΦ(xj)
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 28 / 31
REDUCTION 6. About ISMLL
Outline
1. The REDUCTION Project
2. Eco Routing I: Estimating Fuel Consumption from GPS Data
3. Eco Routing II: Contextualized Estimation of Fuel Consumption
4. Eco Driving: Acceleration/Deacceleration Recommendations
5. Distributed Data Mining in Vehicular Networks
6. About ISMLL
7. Conclusion
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 29 / 31
REDUCTION 6. About ISMLL
Research Goals
supervised prediction:learn models from data where the correct decision hasalready been observed in the past↔ unsupervised learning: detect pattern, groups etc. in data
complex data:learn models from data that cannot naturally be described asjust instances with attributes,
e.g., sequences, images, relational data, etc.
complex decisions:learn models for complex decisions that cannot naturally bedescribed as a scalar,
e.g., multi-label classification, object identification, etc.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 29 / 31
REDUCTION 6. About ISMLL
From Application Problems to ML Problems
predictionrating
contextualized
predictionfuel consumption
database recorddeduplicaton
referencerecognition
...topicclassification
identifying productsin offers
spam filteringissue identification
...PROBEMSAPPLICATION
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 30 / 31
REDUCTION 6. About ISMLL
From Application Problems to ML Problems
database recorddeduplicaton
referencerecognition
...topicclassification
identifying productsin offers
spam filteringissue identification
...PROBEMSAPPLICATION
sparse two−moderegression
predictionrating ...
contextualized
predictionfuel consumption
relationalclassification identification
object ...MACHINE LEARNINGPROBLEMS
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 30 / 31
REDUCTION 6. About ISMLL
From Application Problems to ML Problems
?exhaustivecategorisation?
database recorddeduplicaton
referencerecognition
...topicclassification
identifying productsin offers
spam filteringissue identification
...APPLICATIONPROBEMS
sparse two−moderegression
predictionrating ...
contextualized
predictionfuel consumption
MACHINE LEARNINGPROBLEMS
relationalclassification identification
object ...
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 30 / 31
REDUCTION 7. Conclusion
Outline
1. The REDUCTION Project
2. Eco Routing I: Estimating Fuel Consumption from GPS Data
3. Eco Routing II: Contextualized Estimation of Fuel Consumption
4. Eco Driving: Acceleration/Deacceleration Recommendations
5. Distributed Data Mining in Vehicular Networks
6. About ISMLL
7. Conclusion
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 31 / 31
REDUCTION 7. Conclusion
Conclusions
I Eco Routing and Eco Driving are the fundamental building blocks foreco-friendly transport addressed in REDUCTION.
I Estimating Fuel Consumption from GPS Data can be greatlyimproved by elevation maps.
I Contextualized Estimation of Fuel Consumption can be efficientlyaccomplished by factorization models.
I Acceleration/Deacceleration recommendations can solve many ecodriving tasks and require a velocity and fuel consumption prediction.
I Distributed Data Mining in Vehicular Networks can help to bringmachine learning methods to vehicular networks.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 31 / 31
REDUCTION
References IM. Umer Khan, Alexandros Nanopoulos, and Lars Schmidt-Thieme. Experimental evaluation of reduced support vector
machines and relevance vector machines for communication efficient distributed classification in peer-to-peer networks. Insubmitted, 2012.
Josif Grabocka, Umer Khan, Lars Schmidt-Thieme, ISMLL, University of Hildesheim, Germany
Arbeitskreis Informationstechnologie, Delphi, Bad Salzdetfurth, November 15, 2012 32 / 31