Enabling Model-Driven Live Analytics For Cyber-Physical Systems: The Case of Smart Grids

244 Pages·2016·4.39 MB·English

PhD-FSTC-2016-43
The Faculty of Science, Technology and Communication
Dissertation
Defence held on 08/11/2016 in Luxembourg
to obtain the degree of
Docteur de l’Université du Luxembourg
en Informatique
by
Thomas Hartmann
Born on 13th November 1981 in Kaufbeuren (Germany)
Enabling Model-Driven Live Analytics
For Cyber-Physical Systems:
The Case of Smart Grids
Dissertation defence committee
Prof. Dr. Nicolas Navet, chairman
Professor, University of Luxembourg, Luxembourg, Luxembourg
Dr. François Fouquet, vice-chairman
Research Associate, University of Luxembourg, Luxembourg, Luxembourg
Prof. Dr. Yves Le Traon, supervisor
Professor, University of Luxembourg, Luxembourg, Luxembourg
Prof. Dr. Jordi Cabot, member
Professor, Universitat Oberta de Catalunya, Castelldefels (Barcelona), Spain
Prof. Dr. François Taïani, member
Professor, Université de Rennes 1, Rennes Cedex, France
Dr. Jacques Klein, expert
Senior Research Scientist, University of Luxembourg, Luxembourg, Luxembourg
Abstract
Advances in software, embedded computing, sensors, and networking technologies will
lead to a new generation of smart cyber-physical systems that will far exceed the capa-
bilities of today’s embedded systems. They will be entrusted with increasingly complex
tasks like controlling electric grids or autonomously driving cars. These systems have
the potential to lay the foundations for tomorrow’s critical infrastructures, to form the
basis of emerging and future smart services, and to improve the quality of our everyday
lives in many areas. In order to solve their tasks, they have to continuously monitor
and collect data from physical processes, analyse this data, and make decisions based
on it. Making smart decisions requires a deep understanding of the environment, in-
ternal state, and the impacts of actions. Such deep understanding relies on e cient
data models to organise the sensed data and on advanced analytics. Considering that
cyber-physical systems are controlling physical processes, decisions need to be taken
very fast. This makes it necessary to analyse data in live, as opposed to conventional
batch analytics. However, the complex nature combined with the massive amount of
data generated by such systems impose fundamental challenges. While data in the
context of cyber-physical systems has some similar characteristics as big data, it holds
a particular complexity. This complexity results from the complicated physical phe-
nomena described by this data, which makes it dicult to extract a model able to
explain such data and its various multi-layered relationships. Existing solutions fail to
provide sustainable mechanisms to analyse such data in live.
This dissertation presents a novel approach, named model-driven live analytics.
The main contribution of this thesis is a multi-dimensional graph data model that
brings raw data, domain knowledge, and machine learning together in a single model,
which can drive live analytic processes. This model is continuously updated with the
sensed data and can be leveraged by live analytic processes to support decision-making
of cyber-physical systems. The presented approach has been developed in collaboration
with an industrial partner and, in form of a prototype, applied to the domain of smart
grids. The addressed challenges are derived from this collaboration as a response to
shortcomings in the current state of the art. More specifically, this dissertation provides
solutions for the following challenges:
First, data handled by cyber-physical systems is usually dynamic—data in motion as
opposed to traditional data at rest—and changes frequently and at di↵erent paces.
Analysing such data is challenging since data models usually can only represent a
snapshot of a system at one specific point in time. A common approach consists in
a discretisation, which regularly samples and stores such snapshots at specific times-
tamps to keep track of the history. Continuously changing data is then represented as
a finite sequence of such snapshots. Such data representations would be very ine cient
to analyse, since it would require to mine the snapshots, extract a relevant dataset,
and finally analyse it. For this problem, this thesis presents a temporal graph data
model and storage system, which consider time as a first-class property. A time-relative
navigation concept enables to analyse frequently changing data very eciently.
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Secondly, making sustainable decisions requires to anticipate what impacts certain
actions would have. Considering complex cyber-physical systems, it can come to sit-
uations where hundreds or thousands of such hypothetical actions must be explored
before a solid decision can be made. Every action leads to an independent alternative
from where a set of other actions can be applied and so forth. Finding the sequence
of actions that leads to the desired alternative, requires to e ciently create, represent,
and analyse many di↵erent alternatives. Given that every alternative has its own his-
tory, this creates a very high combinatorial complexity of alternatives and histories,
which is hard to analyse. To tackle this problem, this dissertation introduces a multi-
dimensional graph data model (as an extension of the temporal graph data model)
that enables to e ciently represent, store, and analyse many di↵erent alternatives in
live.
Thirdly, complex cyber-physical systems are often distributed, but to fulfil their tasks
these systems typically need to share context information between computational en-
tities. This requires analytic algorithms to reason over distributed data, which is a
complex task since it relies on the aggregation and processing of various distributed
and constantly changing data. To address this challenge, this dissertation proposes
an approach to transparently distribute the presented multi-dimensional graph data
model in a peer-to-peer manner and defines a stream processing concept to eciently
handle frequent changes.
Fourthly, to meet future needs, cyber-physical systems need to become increasingly
intelligent. To make smart decisions, these systems have to continuously refine be-
havioural models that are known at design time, with what can only be learned from
live data. Machine learning algorithms can help to solve this unknown behaviour
by extracting commonalities over massive datasets. Nevertheless, searching a coarse-
grained common behaviour model can be very inaccurate for cyber-physical systems,
which are composed of completely di↵erent entities with very di↵erent behaviour. For
these systems, fine-grained learning can be significantly more accurate. However, mod-
elling, structuring, and synchronising many fine-grained learning units is challenging.
To tackle this, this thesis presents an approach to define reusable, chainable, and in-
dependently computable fine-grained learning units, which can be modelled together
with and on the same level as domain data. This allows to weave machine learning
directly into the presented multi-dimensional graph data model.
In summary, this thesis provides an e cient multi-dimensional graph data model to
enable live analytics of complex, frequently changing, and distributed data of cyber-
physical systems. This model can significantly improve data analytics for such systems
and empower cyber-physical systems to make smart decisions in live. The presented so-
lutions combine and extend methods from model-driven engineering,
Acknowledgments
This work has been funded by the National Research Fund Luxembourg (grant
6816126) and Creos Luxembourg S.A. under the SnT-Creos partnership program.
The PhD experience goes beyond research, experimentations, and paper writing. It is
indeed a challenging life experience that started in August 2013 and which outcome
owes much to the support and help of many people.
First of all, I want to express my sincere thanks to my supervisor Prof. Dr. Yves
Le Traon for giving me the opportunity to pursue my PhD studies within his group
and under his supervision. He always encouraged me, had a permanent confidence in
me, and supported me throughout these years. I have learned a lot from his rigorous
scientific guidance as a researcher and from his positive, motivating, and open-minded
attitude as a team leader. I am equally grateful to my co-supervisor Dr. Jacques Klein
for his advice, optimism, countless discussions, guidance, and for always emphasising
the bright side of things.
My special thanks goes to my daily advisor Dr. Fran¸cois Fouquet for his patience,
advice, and flawless guidance throughout the sometimes daunting world of academia.
He taught me how to do research, conduct rigorous experiments, write scientific papers,
and always pushed me a step further. I am very happy about the friendship we have
built up during the years.
I am grateful to the members of my dissertation committee, Prof. Dr. Jordi Cabot,
Prof. Dr. Fran¸cois Ta¨ıani, and Prof. Dr. Nicolas Navet, for their time to review my
work and for providing interesting and valuable feedback.
My sincere thanks also goes to Yves Reckinger and Robert Graglia from Creos for the
many fruitful discussions and the time they found to collaborate with us. It was really
useful and rewarding to me to be able to apply my research on a concrete industrial
case.
I would also like to express my warm thanks to all current and former members of
the SerVal team for the plenty good co↵ee breaks, discussions, and the great times
we had. I wish all of them the very best. In particular, I want to thank Dr. Assaad
Moawad, Dr. Gr´egory Nain, and Matthieu Jimenez for their continuous feedback and
proofreading. I also want to express my thanks to all my co-authors and to the people I
have been in touch with during my PhD journey and that are not explicitly mentioned
here.
Finally, and more personally, I would like to express my heartfelt thanks to my family
and my friends for their continuous and unconditional support during these last years.
The role they all played in this journey is more important than they can possibly know.
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Contents
List of abbreviations and acronyms xi
List of figures xv
List of tables xvii
List of algorithms and listings xviii
1 Introduction 1
1.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 The smart grid case study . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.1 The smart grid vision . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.2 Smart grids in the context of this thesis . . . . . . . . . . . . . . 6
1.3 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4.2 Challenges addressed in this thesis . . . . . . . . . . . . . . . . 11
1.5 Approach: model-driven live analytics . . . . . . . . . . . . . . . . . . . 14
1.6 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.7 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
I Background and state of the art 19
2 Background 21
2.1 Data analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.1.1 Taxonomy of data analytics . . . . . . . . . . . . . . . . . . . . 22
2.1.2 Batch and (near) real-time analytics . . . . . . . . . . . . . . . 23
2.1.3 Complex event processing . . . . . . . . . . . . . . . . . . . . . 24
2.1.4 Extract-transform-load and extract-load-transform . . . . . . . 24
2.1.5 OLAP and OLTP . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2 Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.1 Model-driven engineering . . . . . . . . . . . . . . . . . . . . . . 26
2.2.2 MOF: The Meta Object Facility . . . . . . . . . . . . . . . . . . 27
2.2.3
Contents
2.4 Machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3 State of the art 41
3.1 Analysing data of cyber-physical systems . . . . . . . . . . . . . . . . . 42
3.2 Data analytics platforms . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2.1 Online analytical processing (OLAP) . . . . . . . . . . . . . . . 42
3.2.2 The Hadoop stack . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2.3 The Spark stack . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.3 Stream processing frameworks . . . . . . . . . . . . . . . . . . . . . . . 48
3.4 Graph processing frameworks . . . . . . . . . . . . . . . . . . . . . . . 53
3.5 Graph databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.6 Analysing data in motion . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.6.1 Temporal databases . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.6.2 Temporal RDF and OWL . . . . . . . . . . . . . . . . . . . . . 64
3.6.3 Model versioning . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.6.4 Time series databases . . . . . . . . . . . . . . . . . . . . . . . . 65
3.6.5 Temporal graph processing . . . . . . . . . . . . . . . . . . . . . 66
3.7 Exploring hypothetical actions . . . . . . . . . . . . . . . . . . . . . . . 70
3.8 Reasoning over distributed data in motion . . . . . . . . . . . . . . . . 72
3.9 Combining domain knowledge and machine learning . . . . . . . . . . . 74
3.10 Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
II Analysing data in motion and what-if analysis 79
4 A continuous temporal data model to eciently analyse data in mo-
tion 81
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.2 Time as a first-class property . . . . . . . . . . . . . . . . . . . . . . . 84
4.3 Continuous validity of model elements . . . . . . . . . . . . . . . . . . 85
4.4 Navigating in time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.4.1 Selecting model element versions . . . . . . . . . . . . . . . . . 87
4.4.2 Time-relative navigation . . . . . . . . . . . . . . . . . . . . . . 87
4.5 Storing temporal data . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.6 Implementation details and API . . . . . . . . . . . . . . . . . . . . . . 90
4.7 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.7.1 KPI-1: Model updates . . . . . . . . . . . . . . . . . . . . . . . 92
4.7.2 KPI-2: Navigating the context model in time . . . . . . . . . . 94
4.7.3 KPI-3: Storing temporal data . . . . . . . . . . . . . . . . . . . 95
4.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5 A multi-dimensional graph data model to support what-if analysis 99
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.2 Motivating example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.3 Many-world graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.3.1 Key concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.3.2 Many-world graph semantics . . . . . . . . . . . . . . . . . . . . 105
5.3.3 Base graph (BG) . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.3.4 Temporal graph (TG) . . . . . . . . . . . . . . . . . . . . . . . 106
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Contents
5.3.5 Many-world graph (MWG) . . . . . . . . . . . . . . . . . . . . . 108
5.4 MWG implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.4.1 Mapping graph nodes to state chunks . . . . . . . . . . . . . . . 110
5.4.2 Indexing and resolving state chunks . . . . . . . . . . . . . . . . 112
5.4.2.1 Index time tree (ITT) . . . . . . . . . . . . . . . . . . 112
5.4.2.2 World index maps (WIM) . . . . . . . . . . . . . . . . 113
5.4.2.3 Chunk resolution algorithm . . . . . . . . . . . . . . . 114
5.4.3 Scaling the processing of graphs . . . . . . . . . . . . . . . . . . 115
5.4.4 Querying and traversing graphs . . . . . . . . . . . . . . . . . . 115
5.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.5.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.5.2 Base graph benchmarks . . . . . . . . . . . . . . . . . . . . . . 117
5.5.3 Temporal graph benchmarks . . . . . . . . . . . . . . . . . . . . 118
5.5.4 MWG benchmarks of a node . . . . . . . . . . . . . . . . . . . . 119
5.5.5 MWG benchmarks of a graph . . . . . . . . . . . . . . . . . . . 120
5.5.6 Deep what-if simulations . . . . . . . . . . . . . . . . . . . . . . 121
5.5.7 Smart grid case study . . . . . . . . . . . . . . . . . . . . . . . 122
5.5.8 Discussion and perspectives . . . . . . . . . . . . . . . . . . . . 123
5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
III Reasoning over distributed data and combining domain
knowledge with machine learning 125
6 A peer-to-peer distribution and stream processing model 127
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
6.2 Reactive distributed
Contents
7.2.5.1 Weaving learned attributes into domain classes . . . . 154
7.2.5.2 Defining a learning scope for coarse-grained learning in
domain models . . . . . . . . . . . . . . . . . . . . . . 154
7.2.5.3 Modelling relations between learning units and domain
classes . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
7.2.5.4 Decomposing complex learning tasks into several micro
learning units . . . . . . . . . . . . . . . . . . . . . . . 155
7.2.6 Framework implementation details . . . . . . . . . . . . . . . . 156
7.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
7.3.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . 157
7.3.2 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
7.3.3 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
7.4 Discussion: meta learning and meta modelling . . . . . . . . . . . . . . 161
7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
IV Industrial application and conclusion 163
8 Industrial application: electric overload prediction and warning 165
8.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
8.1.1 The Creos partnership . . . . . . . . . . . . . . . . . . . . . . . 166
8.1.2 The REASON project . . . . . . . . . . . . . . . . . . . . . . . 166
8.2 Smart grid meta model . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
8.3 Electric overload prediction and warning . . . . . . . . . . . . . . . . . 168
8.4 Electric load approximation . . . . . . . . . . . . . . . . . . . . . . . . 170
8.4.1 General considerations . . . . . . . . . . . . . . . . . . . . . . . 170
8.4.2 Topology scenarios . . . . . . . . . . . . . . . . . . . . . . . . . 173
8.4.2.1 Single cable . . . . . . . . . . . . . . . . . . . . . . . . 173
8.4.2.2 Cabinet connecting several cables . . . . . . . . . . . . 173
8.4.2.3 Parallel cables . . . . . . . . . . . . . . . . . . . . . . 174
8.4.3 Considering active and reactive energy . . . . . . . . . . . . . . 175
8.4.4 Deriving the electric load . . . . . . . . . . . . . . . . . . . . . . 175
8.4.5 Integration into the smart grid meta model . . . . . . . . . . . . 177
8.5 Predicting consumption behaviour . . . . . . . . . . . . . . . . . . . . . 177
8.5.1 General considerations . . . . . . . . . . . . . . . . . . . . . . . 177
8.5.2 Live machine learning . . . . . . . . . . . . . . . . . . . . . . . 178
8.5.3 Gaussian mixture models . . . . . . . . . . . . . . . . . . . . . . 178
8.5.4 Profiling power consumption . . . . . . . . . . . . . . . . . . . . 179
8.5.5 Integration into the smart grid meta model . . . . . . . . . . . . 180
8.6 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
8.6.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . 181
8.6.2 Performance of electric load approximation . . . . . . . . . . . . 182
8.6.3 Accuracy of electric load approximation . . . . . . . . . . . . . 182
8.6.4 Eciency of electric consumption prediction . . . . . . . . . . . 183
8.6.5 Accuracy of electric consumption prediction . . . . . . . . . . . 184
8.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
9 Conclusion 187
9.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
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