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Report 04/12
May 2012
A CYCLIC PROCESS MODEL
FOR MONITORING MOBILE
CYBER-PHYSICAL SYSTEMS
TINO NOACK
INGO SCHMITT
Computer Science Reports
Brandenburg University of Technology Cottbus
ISSN: 1437-7969
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BTU Cottbus
Institut für Informatik
Postfach 10 13 44
D-03013 Cottbus
Tino Noack, Ingo Schmitt
[email protected], [email protected],
http://dbis.informatik.tu-cottbus.de
A Cyclic Process Model
for Monitoring Mobile Cyber-Physical Systems
Computer Science Reports
04/12
May 2012
Brandenburg University of Technology Cottbus
Faculty of Mathematics, Natural Sciences and Computer Science
Institute of Computer Science
Computer Science Reports
Brandenburg University of Technology Cottbus
Institute of Computer Science
Head of Institute:
Prof. Dr. Hartmut König
BTU Cottbus
Institut für Informatik
Postfach 10 13 44
D-03013 Cottbus
Research Groups:
Computer Engineering
Computer Network and Communication Systems
Data Structures and Software Dependability
Database and Information Systems
Programming Languages and Compiler Construction
Software and Systems Engineering
Theoretical Computer Science
Graphics Systems
Systems
Distributed Systems and Operating Systems
Internet-Technology
CR Subject Classification (1998): H.2.8, H.1
Printing and Binding: BTU Cottbus
ISSN: 1437-7969
[email protected]
Headed by:
Prof. Dr. H. Th. Vierhaus
Prof. Dr. H. König
Prof. Dr. M. Heiner
Prof. Dr. I. Schmitt
Prof. Dr. P. Hofstedt
Prof. Dr. C. Lewerentz
Prof. Dr. K. Meer
Prof. Dr. D. Cunningham
Prof. Dr. R. Kraemer
Prof. Dr. J. Nolte
Prof. Dr. G. Wagner
A Cyclic Process Model for Monitoring
Mobile Cyber-Physical Systems
Tino Noack
Ingo Schmitt
Institute of Computer Science
TU Cottbus
Institute of Computer Science
TU Cottbus
[email protected]
[email protected]
ABSTRACT
behaviour continuously. The monitoring process requires
knowledge about the CPS, the physical environment and the
system behaviour. Such knowledge needs to be discovered
continuously because of continuous changes like wear and
tear. We distinguish between abrupt and long-term change
in system behaviour. Abrupt change of system behaviour
must be immediately followed by an appropriate action. For
example, it is impossible to exclude abrupt changes such
like crashes. Conditions of the physical environment can
also induce long-term change and wearout effects. Longterm wearout effects can cause abrupt change in system behaviour. Monitoring long-term wear and tear is a prerequisite to forecast respectively to predict such abrupt change in
system behaviour. Hence, monitoring mobile CPSs is essential and necessary to ensure reliability and to avoid damage.
Basic approaches of mobile cyber-physical systems (CPS)
such like cars, trains, aeroplanes or spaceships are parts of
our daily life. Mobile CPSs are embedded into a physical environment which is usually harsh and uncertain. Monitoring
mobile CPSs is essential to ensure reliability and to avoid
damage. Monitoring mobile CPSs presupposes knowledge
about the CPS, the physical environment and the system
behaviour. Such knowledge needs to be discovered continuously because of continuous changes like wear and tear. Our
contribution is the definition of an abstract process cycle for
monitoring mobile CPSs. We call this abstract process cycle
the Knowledge Discovery Cycle (KDC). Our KDC is based
on common characteristics of mobile CPSs and key challenges for monitoring mobile CPSs. We particularly discuss
common characteristics and key challenges by means of the
German Inter City Express and the International Space Station. Based on this, we examine five overall considerations
for monitoring mobile CPSs. Further on, we identify processing steps for our KDC and we associate existing concepts
and technologies with the identified processing steps. This
involves knowledge discovery in databases, knowledge discovery from data streams and complex event processing.
Monitoring mobile CPSs is very challenging. Key challenges
are (i.) data processing, (ii.) continuity and (iii.) autonomy.
Data processing is a key challenge, because it is indispensable for monitoring mobile CPSs to process transient as well
as persistent data. Continuity is a key challenge, because the
monitoring process presupposes knowledge about the CPS,
the physical environment and the system behaviour. Environmental conditions and the system behaviour are changing
over time. Hence, it is necessary to gain knowledge continuously. Autonomy is a key challenge, because the monitoring
solution must be able to act autonomously in case of the
interruption of the external network connection.
Categories and Subject Descriptors
H.2.8 [DATABASE MANAGEMENT]: Database Applications—Data mining; H.1 [MODELS AND PRINCIPLES]: Miscellaneous
1.
Our contribution is the motivation, introduction and definition of a Knowledge Discovery Cycle (KDC). Our KDC is
an abstract process cycle for monitoring mobile CPSs. We
motivate, introduce and define our KDC with special emphasis on the mentioned key challenges for monitoring mobile CPSs. (i.) The KDC reflects dynamic streams of data
and events as well as static and historical data and events.
(ii.) The KDC is an active, dynamic, cyclic and continuous
process. For that reason, it is possible to gain knowledge
about the CPS, the physical environment as well as the system behaviour over time. (iii.) We subdivide our KDC
into two subcycles. This involves an online subcycle and an
offline subcycle. The online subcycle is an automatic and
autonomic process. The offline subcycle is a semiautomatic
process which needs to be supported by human experts.
INTRODUCTION
Basic approaches of cyber-physical systems (CPS) [38, 39]
are widely disseminated in application domains such like
manufacturing, home entertainment, power grids, healthcare, transportation or aerospace. In this paper we focus on
mobile CPSs. Examples are cars, trains such like the German Inter City Express (ICE), submarines, aeroplanes or
spaceships such like the International Space Station (ISS).
Mobile CPSs are embedded into a physical environment and
they are location-independent. Mobile CPSs consist of embedded devices. A mobile CPS interacts with the physical
environment by means of sensors and actuators. Furthermore, an external network connects the mobile CPS with
external information systems.
The changing system behaviour is one of the main facets
for monitoring mobile CPSs. The physical environment is
usually harsh and uncertain. Conditions of the physical environment such like heat or humidity influence the system
The motivation, introduction and definition of our KDC also
considers the characteristics of mobile CPSs. Monitoring
mobile CPSs is very interdisciplinary because of complex
1
characteristics of today’s mobile CPSs. We identify processing steps for our KDC and we associate the identified processing steps with existing concepts and technologies. The
usage of existing concepts and technologies can help to improve the adaptability and reusability of the monitoring process. Moreover, the usage of existing concepts and technologies can also help to decrease production and implementation costs for future monitoring applications. In particular,
we use existing concepts and technologies from knowledge
discovery in databases (KDD) [24, 21, 36, 43], knowledge
discovery from data streams (KDDS) [29, 27] and complex
event processing (CEP) [41, 20].
provide query languages such like the continuous query language (CQL) [3] or the stream query algebra (SQuAl) [1]
for data stream processing. Data stream processing induced
new storage strategies such like stream warehouses [30, 32].
Data stream mining [26, 61, 7] concepts are considered as
well. Data stream mining is the application of data mining respectively machine learning algorithms directly onto
data streams. Several data stream mining frameworks such
like MOA [6], VEDAS [35] and SMM [59] were developed.
All these concepts can be grouped together under the term
knowledge discovery from data streams (KDDS) [29, 27].
In recent times, data stream processing is reconsidered under
a certain perspective. This certain perspective is intended
by the extension of stream query languages by means of
pattern definitions and action parts. This endeavour is already known from active data bases as the event condition
action (ECA) paradigm [15, 50]. The combination of the
ECA paradigm and data stream processing is called complex event processing (CEP) [41, 20, 18]. CEP is the deduction of complex events from fundamental or underlying
events in a data stream context. Standards for CEP and
reaction rules are discussed in [49]. A variety of academic
and commercial CEP engines such like SASE [65], Cayuga
[14] or Esper [19] were already developed. CEP induced new
storage strategies such like event data warehouses [54].
The rest of the paper is organized into two main parts.
The first main part addresses the motivation for monitoring mobile CPSs. In Section 2, we discuss related work.
This discussion encompasses literature about CPSs, existing concepts, system monitoring and available applications.
In Section 3, we introduce an abstract architecture of mobile
CPSs. Additionally, we investigate common characteristics
of mobile CPSs. In Section 4, we provide a discussion of
characteristics of mobile CPSs by means of two real world
scenarios. The first real world scenario is related to the German ICE. Whereas, the second real world scenario is related
to the ISS Columbus module. A closer look on key challenges
for monitoring mobile CPSs is provided in Section 5. In Section 6, we examine five overall considerations for monitoring
mobile CPSs. The second main part addresses our KDC.
In Section 7, we define the acronym KDC in detail. This
detailed definition is based on the common characteristics
of mobile CPSs, the overall considerations and the key challenges for monitoring mobile CPSs. Further on, we identify
processing steps for our KDC in Section 8. In Section 9, we
assign existing concepts and technologies to the identified
processing steps. Finally, we conclude our work in Section
10. While concluding our work, we also consider challenges
for future work.
2.
Amongst others, the term embedded system is explained in
[51, 44], real-time systems are described in [37] and more information about embedded software is located in [64]. A particular view on CPSs is given in [38, 39, 55, 52]. Real-time
system monitoring is considered in [60] and an abstract viewpoint on real-time stream processing is given in [57]. Based
on this abstract viewpoint, eight requirements for real-time
stream processing are delineated. None of the listed references suggest the combination of data stream processing,
KDD, KDDS and CEP for monitoring mobile CPSs in a
cyclic manner as we purpose.
RELATED WORK
However, several approaches exist which refer specialized
application domains. One of such approaches is Odysseus
[9]. Odysseus is a data stream management framework. It
is intended to combine and integrate different techniques
for data stream processing and CEP. A second approach
is VEDAS [35]. VEDAS is a mobile and distributed data
stream mining system for real-time vehicle monitoring. A
third approach is mentioned in [42]. This third approach
describes an architecture for queries over streaming sensor
data. A fourth approach is presented in [40], which reflects
real-time storm detection and weather forecasting by means
of data mining and event processing. A fifth approach is
mentioned in [56]. This fifth approach is related to the application of CEP in real-time situations. A sixth approach
is mentioned in [62], which is related to mining large distributed log data in near real-time. A seventh approach is
called Mini-ME [11]. Mini-ME is a rule-based fault monitoring system for space craft monitoring. It also considers
real-time fault diagnoses. Another approach is called Pharos
[23]. Pharos is a testbed for validation and evaluation of
mobile CPSs. However, the mentioned approaches consider
complex characteristics of mobile CPSs and key challenges
for monitoring mobile CPSs more or less insufficiently.
The related work is structured as follows. We discuss basic approaches of KDD, KDDS and CEP first. Further on,
we list literature which refers embedded systems, CPSs and
real-time system monitoring respectively real-time stream
processing. At least, we discuss related applications which
refers directly to our KDC.
As we assume, knowledge discovery in data bases (KDD)
[24, 21, 36, 43] should be a basement for system monitoring.
KDD is the process of identifying valid, novel and potentially
useful patterns in data. KDD involves storage techniques
such like data bases (DB) [12] and data warehouses (DWH)
[34]. Data mining [16, 58, 8, 63] is the core of KDD.
We assume that data stream processing should be a prerequisite to provide real-time monitoring. CPSs are equipped
with a wide range of sensors. These sensors produce a variety of continuous sequences of data items. These continuous sequences can be construed as sensor data streams.
Data streams are mostly produced in real-time and they
are potentially infinitive [5, 4, 31, 2, 28, 32]. Based on
the definition of data streams, a variety of academic data
stream management systems (DSMS) such like STREAM
[4] or Aurora [10] were developed. In most cases, DSMSs
2
3.
MOBILE CYBER-PHYSICAL SYSTEMS
puting resources. But external information systems are not
always available.
As stated in [39], the acronym CPS is motivated by the
term cybernetics. The term cybernetics was coined over a
half century ago and it describes the conjunction of physical
processes, computation and communication. The acronym
CPS is a redefinition of the term cybernetic system. It is
inspired to bring the term cybernetic system into a context
of today’s technologies. This includes digital computing,
software intensive computation and massive use of networks.
On that score, we define the acronym CPS as a cybernetic
system for monitoring and controlling physical processes by
means of digital computing, software intensive computation
and massive use of networks.
3. Real-Time Constraints: Mobile CPSs are subject to
real-time constraints. Stimuli respectively abrupt changes
such like crashes can occur in real-time. Hence, the computation and the resulting actions must also be performed in
real-time to ensure reliability and to avoid damage.
4. External Network: Due to the mobility of mobile
CPSs, the external network connection is uncertain and subject to bandwidth limitations. Thus, the external network
of mobile CPSs is a temporal connection and it is not always
available.
We distinguish between stationary CPSs and mobile CPSs.
Stationary CPSs are strongly tied to a specific location. Examples are power grids, manufacturing plants or research
facilities like the Large Hadron Collider. Mobile CPS are
location-independent. Examples are cars, trains such like
the German ICE, aeroplanes or spaceships such like the ISS.
4.
The first real world scenario is related to the disastrous railway accident of ICE 884 in Eschede (Germany). The particular focus of the first real world scenario is on the physical
environment, resulting wareout effects and the occurrence of
abrupt change in system behaviour and real-time events.
Figure 1 sketches an abstract architecture of a mobile CPS.
A mobile CPS is embedded into a physical environment and
it consists of embedded devices such like embedded systems
(hardware). Embedded devices consist of electronic assemblies which are connected by an internal network. A mobile
CPS interacts with the physical environment via sensors and
actuators. The embedded devices and the internal network
are usually used for monitoring and controlling of physical
processes. Software is an integral and substantial part of a
mobile CPS. An external network connects a mobile CPS
with external information systems. External information
systems are usually stationary parts of a mobile CPS.
The second real world scenario is related to the ISS Columbus module. The particular focus of the second real world
scenario is on the current implementation of the failure management of the ISS Columbus module.
4.1
Mobile Cyber-Physical System
Embedded Device
Electronic
Assembly
Electronic
Assembly
External
Network
Internal Network
Electronic
Assembly
Sensors
External
Information
Information
Information
Systems
Systems
Systems
Electronic
Assembly
Sensors
Railway Accident of ICE 884 in Eschede
The root cause of the disastrous railway accident of ICE
884 in Eschede was the fracture of a rubber-sprung railway
wheel tyre [53, 22]. The wheel tyre fracture arose because
of long-term physical reactions (e.g. reduction of the wheel
tyre thickness, deformations or corrosion). The particular
analysis of the fracture plane pointed out that long-term
wearout effects led to a crack in the wheel tyre. The fracture was the root cause for the derailment of the ICE. The
described example outlines long-term wear and tear caused
by the physical environment as well as the occurrence of
abrupt changes and real-time events. In the following, we
discuss the mentioned characteristics of mobile CPSs.
Physical Environment
Embedded Device
REAL WORLD SCENARIOS
In this section, we discuss the mentioned characteristics of
mobile CPSs by means of two real world scenarios.
1. Physical Environment: The physical environment is
usually harsh and uncertain. Weather conditions such like
frost, heat or humidity can change within a short time. They
also differ enormously within seasons and geographical locations. Fast train journeys, continual friction and corrosion
induce long-term wear and tear of system components. The
wheel tyre fracture is a real-time stimuli respectively a realtime event which arose because of long-term wear and tear.
Actuators
Actuators
Figure 1: Mobile Cyber-Physical System
Following, we identify four characteristics of a mobile CPS.
2. Restricted Computing Resources: The existing computing resources of ICEs are limited. For that reason, it is
impossible to analyze complex interrelations of environmental and physical conditions, embedded devices and the system behaviour on the ICE directly. For that reason, continual maintenance is used to compensate resource restrictions.
1. Physical Environment: The physical environment is
usually harsh and uncertain. Environmental conditions of
mobile CPSs can change within seconds. Conditions of the
physical environment can also induce long-term wearout.
2. Restricted Computing Resources: Mobile CPSs are
subject to restricted computing resources such like processor
speed or memory and power consumption. External information systems can be used to compensate restricted com-
3. Real-Time Constraints: The fracture of the wheel
tyre and the derailment occurred in a very short time period. The fracture and the derailment must have triggered
3
a significant and measurable change of the normal driving
performance. This significant change should be detected immediately. Furthermore, the detection should be suddenly
followed by an appropriate action such like deceleration.
3. Engineering Support Centre: The engineering support centre is another ground instance. It is used for root
cause analysis and to plan long-term corrective actions. It
works semiautomatically as the ground control. Human experts work offline and they use data analysis tools. Furthermore, they use data and information which is available from
persistent storage and the ground control.
4. External Network: Especially in rural areas, a permanent external network connection cannot be guaranteed.
4.2
4. Assembly, Integration and Test Facility: The assembly, integration and test facility is also a ground instance
and it works semiautomatically. It is used for engineering
tests, for troubleshooting and for validation tests. Troubleshooting of on-board issues and the validation of updated
or new failure detection methods is very important. The
assembly, integration and test facility uses data and information which is available from persistent storage and the
ground control.
ISS Columbus Failure Management
In this section, we provide a closer look on the current implementation of the ISS Columbus failure management. The
ISS Columbus failure management was previously described
in [45, 46]. As depicted in Figure 2, it is distributed over
different instances. Each instance is responsible for specific
functions, capabilities and constraints. We distinguish between on-board instances and ground instances.
The ISS Columbus module is a mobile CPS and it contains
on-board instances. On-board instances are embedded devices and they are connected by an internal network. Onboard instances should work automatically and in real-time.
But on-board instances suffer from resource restrictions.
5. Mission Archive: The mission archive is another ground
instance. It is a persistent storage of all measurements and
data which was transmitted from the ISS Columbus module
to the ground control. Furthermore, it contains annotations
and additional information.
Ground instances are stationary parts of the mobile CPS and
they are external information systems. Ground instances
are applied semiautomatically and without real-time constraints. They can be used for long-term analysis. But
ground instances suffer from bandwidth limitations and the
availability of the wireless network.
On-Board
Instances
1. ISS Columbus
Automatic
Real-Time
Ground
Instances
Wireless
External Network
Semiautomatic
Long-Term
1. ISS Columbus: The ISS Columbus module is a mobile
CPS which is embedded into the physical environment respectively the space. The ISS Columbus module comprises
a set of on-board instances respectively embedded devices
such like scientific laboratories or an air loop as life-support
system. For example, the on-board failure management system is applied for monitoring the air loop. The on-board failure management system is responsible for crew health and
it works automatically. Automatic detection of time critical failures is necessary. But existing on-board instances are
subject to resource restrictions. The ISS Columbus module
consists of a variety of sensors. The on-board failure management acquires approximately 3000 analogue and digital
measurements per second. These measurements can be construed as data streams. But because of resource restrictions,
only a small proportion (about 233) of these measurements
can be adequately monitored on-board and in real-time. All
measurements are sent to the ground control when the wireless network is available. But they cannot be sent directly
in case of a loss of signal (LOS). Hence, they must be stored
temporally on-board.
4. Assembly,
Integration and
Test Facility
2. Ground Control
5. Mission
Archive
3. Engineering Support
Centre
Figure 2: ISS Columbus Failure Management
Following, we discuss the characteristics of mobile CPSs.
1. Physical Environment: The physical environment respectively the space differs totally from terrestrial conditions. This includes zero gravity, atmospheric pressure and
the existence of the earth’s magnetic field. The life support
system of the ISS Columbus module is intended to be at service 24/7. Hence, wear and tear is an existing and overall
problem. For example, the air loop is part of the life support system. The air loop consists of ventilation assemblies.
As described in [45, 48], bearing wareout led to a functional
loss of an existing ventilation assembly.
2. Ground Control: The ground control is one of the
ground instances and it works semiautomatically. Hence,
human experts are needed for manual failure detection and
recovery. As the on-board failure management system, the
ground control is responsible for crew health. Not all onboard measurements are available at the ground control because of the external network and its bandwidth limitations.
Delays can occur from time to time due to LOS. On that
score, on-board acquired measurements arrive at the ground
control not in real-time. The ground control transmits the
acquired data to other ground instances.
2. Restricted Computing Resources: The existing computing resources of the ISS Columbus module are limited.
On that score, only a small proportion of approximately 233
from 3000 measurements can be adequately monitored onboard and in real-time.
4
Real-Time
Long-Term
Local
Time
Known
Knowledge
Unknown
Global
Locality
Overall
Considerations
Sharpness
Crisp
Non-Crisp
System Resources
Restricted
Unrestricted
Figure 3: Overall Considerations [47]
6.
3. Real-Time Constraints: Unforeseeable situations such
like crashes or losses of on-board instances can occur at any
time. Such situations should be immediately followed by an
appropriate action to ensure reliability and to avoid damage.
OVERALL CONSIDERATIONS
According to the presented real world scenarios and the abstract architecture of mobile CPSs, we identify five overall
considerations for monitoring mobile CPSs. This includes
time, locality, knowledge, system resources and sharpness.
We subdivide each consideration into two facts. Figure 3
summarizes the mentioned considerations and facts.
4. External Network: The downlink suffers from bandwidth limitations and it is continuously interrupted.
In this section, we provide a closer look on the mentioned
key challenges for monitoring mobile CPSs. Key challenges
for monitoring mobile CPSs are data processing, continuity
and autonomy. One of the main facets of monitoring mobile
CPSs is the continuously changing system behaviour. Conditions of the physical environment influence the system behaviour continuously. Monitoring of the system behaviour
presupposes knowledge about the CPS, its physical environment and the continuously changing system behaviour.
Time: This consideration refers to the temporal changing of
the target system behaviour. We distinguish between abrupt
change and long-term change in system behaviour. Abrupt
change is also called concept shift in the research area of
change detection. Long-term change is also called concept
drift [7]. Abrupt changes such like collision or crashes can
occur unforeseeable and at any time. It is needed to detect
such abrupt changes in real-time. In order to detect longterm influencing factors and changes such like wear and tear,
long-term analysis is required.
1. Data Processing: Monitoring mobile CPSs requires
processing of transient as well as persistent data. Sensors
produce continuously transient data. These sensor data can
be construed as data streams. It is necessary to process
data streams automatically and in real-time. For long-term
analysis, it is needed to store these transient data into persistent data repositories. Long-term analysis is important to
discover complex interrelations of system components and
for knowledge extraction. Long-term analysis is usually a
semiautomatic process and it is needed to be supported and
fostered by human experts.
Locality: This consideration refers to interrelation effects
of influencing factors and the spatial location of monitoring.
We distinguish between local monitoring and global monitoring. Failures that relate on few system components should
be detected by means of local monitoring and analysis. For
example, local monitoring can be applied by means of intelligent sensors. Complex interrelations and influencing factors
between the physical environment and the system components exists due to complex characteristics of mobile CPSs.
Hence, it is needed to gather and to detect such complex
interrelations by the use of global analysis.
2. Continuity: Monitoring mobile CPSs requires knowledge about the CPS, its physical environment and the system behaviour. Conditions of the physical environment as
well as the system behaviour changes over time. For that
reason, monitoring CPSs must be a continuous process. It is
necessary to gain more and more knowledge over time. Such
knowledge can be used for detection of long-term changes,
for the detection of abrupt changes and for prediction.
Knowledge: This consideration refers to the available information about the CPS, the physical environment and the
system behaviour. We distinguish between known and unknown facts. Known facts refer to the existence of knowledge
and information about the CPS, the physical environment
and the system behaviour. It is necessary to employ available knowledge and information as comprehensively as possible. Because of unknown and unforeseeable conditions a dynamic, flexible and adaptable monitoring process is needed.
This monitoring process should be able to gain continuously
knowledge about the CPS, the physical environment and the
system behaviour to decrease unawareness over time.
5.
KEY CHALLENGES
3. Autonomy: The monitoring facility on the CPS must
be able to act automatically and autonomously in case of a
interruption of the external network.
5
System Resources: This consideration refers to all available resources for monitoring, data processing and analysis.
We distinguish between unrestricted and restricted system
resources. A CPS is subject to resource restrictions. External information systems can be used to compensate resource
restrictions. Online monitoring refers to automatic real-time
monitoring and should be applied on the CPS directly. Offline monitoring refers to semiautomatic long-term analysis
and should be applied by means of external information systems. In particular long-term analysis requires extremely
many system resources. For that reason, a combination of
online and offline monitoring is needed to provide enough
system resources for the intended monitoring approach.
discovery. The offline subcycle is described by means of a
separate and continuous cycle, because it is necessary to
evaluate new derived knowledge by the use of preexisting
knowledge and available persistent data.
At certain times, a synchronization of both subcycles is necessary. For example, this synchronization can be used for
data, knowledge and events transfer. For a complete round
trip of the KDC, two synchronizations are needed.
Real-Time Monitoring
Online
Sharpness: This consideration refers to the interpretation
respectively the processing of conditions. We distinguish between crisp and non-crisp processing. System states must
be detected exactly and reliably by the use of binary processing (Boolean logic). For example, if a threshold value is
reached. Crisp processing is inadequate for particular problems. Hence, it is necessary to generalize binary processing
by means of affiliation degrees between 0 and 1. The value 1
implies full affiliation and the value 0 implies the opposite.
7.
Offline
Long-Term Analysis
KNOWLEDGE DISCOVERY CYCLE
The KDD process is often described by means of a linear
model. But because of the continuously changing conditions of the physical environment and the system behaviour,
monitoring mobile CPSs is a continuous process. Knowledge about the CPS, the physical environment and the system behaviour must be discovered continuously over time.
On that score, we extend the linear KDD process model by
means of an abstract and cyclic process model. We call this
cyclic process model a Knowledge Discovery Cycle (KDC).
The definition of our KDC is based upon the presented real
world scenarios, the mentioned key challenges and the explained overall considerations for monitoring mobile CPSs.
The KDC is a cyclic, dynamic, abstract, data-, knowledgeand event-oriented arrangement of data processing and analysis concepts. This includes dynamic streams of data and
events as well as static and historical data and events. The
KDC is an abstract architectural model. As depicted in
Figure 4, we distinguish between an online subcycle and an
offline subcycle. Automatic real-time monitoring is applied
on the online subcycle. Semiautomatic long-term analysis
is applied on the offline subcycle. A complete cyclic pass
through of the KDC is called a round trip hereinafter.
Figure 4: The Knowledge Discovery Cycle
Dynamic: Target system reconfigurations and unforeseeable changes of the system behaviour like crashes can occur
at any time during runtime. On that score, our KDC is
a dynamic and continuous process which reflects dynamic
change in system behaviour as well as dynamic processing
of data and event streams.
Abstract: The KDC is an abstract model respectively an
abstract infrastructure and we only use concepts for our
KDC. On that score, the KDC is flexible, easily extensible and easily adaptable. Based upon this abstract model,
the KDC can be used to associate existing concepts and
techniques from KDD, KDDS and CEP.
Data-oriented: A KDC requires different and adequate
data storage strategies. On the one hand, it is required
to process data streams which are transient respectively
volatile data. On the other hand, this volatile data must
be stored into non-volatile and persistent data repositories.
Furthermore, adequate data access strategies are needed.
In most cases, the online subcycle reflects streaming data
and the offline subcycle reflects persistent data. At the first
round trips of our KDC, only little persistent data exist.
Over time, more and more persistent data are gathered.
This continuously increasing amount of persistent data can
be used for long-term analysis and for knowledge discovery.
Cyclic: The KDC is a cyclic process which is intended to increase data as well as knowledge about the CPS, the physical
environment and the system behaviour at each round trip.
As depicted in Figure 4, we subdivide our KDC into two
subcycles. Both subcycles are mostly decoupled, independent and asynchronous. This subdivision is based upon the
assumption that the entire KDC is a semiautomatic process.
The online subcycle reflects real-time monitoring, it is applied automatically and the support of human experts is not
needed. The online subcycle is described by means of a separate and continuous cycle, because it is necessary to compare
the current system behaviour with a small set of historical
and previously identified system behaviour. The offline subcycle reflects long-term analysis, it is applied semiautomatically and human experts are needed to support knowledge
Knowledge-oriented: One of the main intentions of the
KDC is to discover, to harvest or to mine more and more
knowledge about the CPS, its physical environment and
the system behaviour during runtime. On that score, data
repositories and data sources are used intensively. At the
first round trips of our KDC, only little knowledge is avail6
able. Knowledge is mostly extracted and stored by the offline subcycle. It is intended to increase knowledge about the
CPS, the physical environment and system behaviour over
time. This implies round trips of the KDC and knowledge
extraction during runtime. In most cases, the offline subcycle is used for knowledge discovery and knowledge mining.
The online subcycle is mostly used to apply the previously
discovered knowledge onto. For example, the application of
knowledge onto the online subcycle can be implemented by
means of rule sets. The applied knowledge is used for online
and real-time monitoring. Whereas, only necessary knowledge is applied onto the online subcycle. Hence, the processing overhead for the existing system resources is minimized.
However, the online subcycle can also be used for knowledge discovery. But the application of knowledge discovery
directly onto the online subcycle increases the processing effort. On both subcycles, adequate knowledge repositories
and knowledge storage strategies are needed.
is related to automatic and online data processing. Hence,
human experts are not needed. We can distinguish between
three operational methods (i.) classification, (ii.) data
stream mining and (iii.) hybrid online analysis. (i.) Classification refers to the application of previously extracted and
available knowledge by means of, for example, rule sets. As
depicted in Figure 5, this first operational method requires
a repository for knowledge or rule sets. Furthermore, this
operational method presupposes previous round trips of the
offline subcycle respectively long-term analysis for knowledge extraction as well as rule sets generation and transfer
to the online subcycle. (ii.) Data stream mining is the
second operational method which reflects online knowledge
extraction without any previously extracted knowledge and
without any previous round trips of the offline subcycle respectively the long-term analysis. (iii.) Hybrid online analysis is the third operational method and it is a combination
of classification and data stream mining.
Event-oriented: Based upon the discovered knowledge,
the KDC is intended to retract and detect events as well
as complex events from data and event streams. On that
score, adequate event storage techniques are necessary. Additionally, it could be useful to create event metrics.
The main intention of the three mentioned operational methods is the description of the current system behaviour by
means of fundamental events. This includes the identification of particular events and the generation of event streams.
Based on these fundamental events, it is intended to deduce
and to detect complex events (e.g. crashes).
8.
PROCESSING STEPS
4. Actions: As depicted in Figure 5, the fourth processing
step is directly related to the previous step and could be part
of the online analysis. The deduction of fundamental and
complex events necessitate the initiation of actions as known
from the ECA paradigm. We distinguish between (i.) hardware actions and (ii.) software actions. (i.) Hardware actions effect hardware directly such like toggling of switches.
(ii.) Software actions do not effect hardware. Amongst others, they can be used for messages sending, for alarm triggering or to provoke a storage strategy for data and events.
Furthermore, software actions could also include the preparation of metrics and the initiation of data or event transfer
from the online subcycle to the offline subcycle.
We distinguish between four types of processing steps repository management, preprocessing, transfer and analysis as
depicted in Figure 5. Repository management includes data
streams and temporal as well as persistent storage techniques. Steps for repository management are depicted by
means of cylinders. Preprocessing steps include data, knowledge or event preprocessing and they are depicted by means
of hexagons. Transfer processing steps include the transfer of data, knowledge or events from one subcycle to another. Transfer processing steps are depicted by means of
ellipses. Analysis includes stream analysis as well as analysis from persistent repositories. Analysis processing steps
are depicted by means of squares. In the following, we identify twelve potential processing steps and we discuss each
processing step in detail.
8.1
5. Temporal Storage: The fifth step includes the temporal storage on the online subcycle. This temporal storage is
a window-based, preliminary and short-term storage. This
includes storage of data items from data streams as well
as the storage of events, complex events and metrics. We
distinguish between two fundamental storage strategies (i.)
complete storage and (ii.) incomplete storage. (i.) Complete storage assures that no data or events are getting lost
and it refers to persistent respectively non-volatile data storage. Several application such like the ISS Columbus module
presupposes complete storage. (ii.) Incomplete storage does
not assure complete storage and it refers to transient respectively volatile data storage. On that score, data and event
sketches respectively samples can be used. Furthermore,
previously stored data can be overwritten or deleted if necessary. Because of restricted system resources, incomplete
storage can be an adequate storage strategy.
Online Processing Steps
In this section, we discuss processing steps of the online subcycle respectively of the real-time monitoring process which
are depicted in Figure 5.
1. Data and Event Streams: The first processing step is
a repository. More precisely, this repository is a continuous
data source. As depicted in Figure 5 data streams and event
streams arrive continuously and asynchronously.
2. Preprocessing: This processing step includes the extraction of relevant data and event streams as well as the
extraction of specific data items and events. Furthermore,
this includes the transformation such like dimensionality reduction, noise reduction, scaling or time stamp standardization and correlation. This includes also the provision of
the extracted and transformed data and events for the next
processing step.
The cyclic process model splits after the fifth processing
step. This split points out that the online subcycle respectively the real-time monitoring process is asynchronous and
decoupled from the offline subcycle respectively from longterm analysis.
3. Online Analysis: The third processing step is the core
of the online subcycle respectively the real-time process. It
7
3. Online
Analysis
Hardware/
Software
Actions
4. Actions
2. Preprocessing
Real-Time Monitoring
Online
1. Data and Event
Streams
5. Temporal
Storage
6. Data and Event
Transfer
12. Knowledge
Transfer
11. Knowledge
Storage
Offline
Long-Term Analysis
7. Persistent
Storage
8. Preprocessing
Annotations
10. Validation
External
Data
9. Offline
Analysis
Figure 5: KDC Processing Steps
6. Data and Events Transfer: The sixth step is the last
step of the online subcycle. It is used for synchronization
and to transmit temporally stored data, events and metrics
from the online subcycle into a persistent memory on the
offline subcycle. The data transmission is optional and not
performed at each pass through. It can be performed from
time to time when the external network is available.
8.2
offline analysis. This evaluation step can be used by human
experts to interpret the discovered and derived knowledge
and to evaluate analysis results.
11. Knowledge Storage: After the evaluation step, new
derived knowledge must be stored, combined and integrated
with already existing knowledge. Furthermore, the annotation of the derived knowledge by means of human experts
with additional information can be useful. We distinguish
between two operational methods (i.) evolutionary method
and (ii.) re-launch method. (i.) The evolutionary method is
the most common operational method and it is an evolutionary process. The evolutionary process is used to increases
knowledge about the CPS, the physical environment and
the system behaviour over time. It can be used to obtain an
overview of existing knowledge and information. Furthermore, already existing knowledge can be used to evaluate
new derived knowledge. (ii.) The re-launch method starts
from the scratch without any knowledge. This operational
method is useful at the very first round trip of the KDC.
Furthermore, it can be useful if the system behaviour differs totally from preexisting knowledge. After the re-launch
method it is possible to switch back to the evolutionary
method to gather new knowledge over time again.
Offline Processing Steps
In this section, we discuss processing steps of the offline
subcycle respectively of the long-term analysis as depicted
in Figure 5.
7. Persistent Memory: As the real-time monitoring process, the long-term analysis starts with a data source. The
persistent storage is the basis for the long-term analysis. It
includes necessary, compacted and integrated data items,
events and metrics. As depicted in Figure 5, it is also possible to import data or events from external data sources.
8. Preprocessing: This processing step reflects the selection and transformation of relevant data and events as
known from the linear KDD process model. This preprocessing is necessary for the following offline analysis step.
The cyclic process model splits after the eleventh processing step. This split points out that the offline subcycle is
asynchronous and decoupled from the online subcycle.
9. Offline Analysis: Offline analysis is the core of the offline subcycle respectively the long-term analysis as known
from the linear KDD process model. The analysis step is
used to discover and to derive knowledge by means of preprocessed data. As already mentioned, the offline subcycle
works semiautomatically. Hence, the offline analysis needs
to be supported and fostered by human experts.
12. Knowledge Transfer: The twelfth processing step is
the last step of the offline subcycle and the KDC. It is used
for synchronization and to transmit knowledge from the offline subcycle to the online subcycle. This step is a kind of
reconfiguration and refinement of the real-time monitoring
process respectively the online subcycle. Knowledge transfer
10. Evaluation: The tenth processing step is directly related to the previous processing step and could be part of the
8
can affect the following processing steps from the online subcycle: preprocessing (2.), online analysis (3.), actions (4.),
temporal storage (5.) or data and event transfer (6.).
concepts refer to the online subcycle respectively to the realtime monitoring process. The concepts assignment is summarized in Figure 6.
9.
9.1
CONCEPTS ASSIGNMENT
Concepts for the Online Subcycle
In this ection, we discuss concepts for the online subcycle
respectively for the real-time monitoring process.
Limit monitoring or limit checking is a very easy monitoring approach which uses thresholds [25, 45]. Such thresholds
are mostly defined by means of one-dimensional functions.
A limit monitoring approach generates a message if an attribute value reaches the previously defined threshold. Intelligent sensors are an example for the implementation of
limit monitoring. Limit monitoring is an appropriate monitoring approach that can be easily applied over a variety
of application domains. But limit monitoring ignores complex interrelations between system components. Hence, this
basic limit monitoring is not always the best approach.
I. Data Stream Management: Starting with the online subcycle, we group the first and the second processing
steps together. This group is a fundamental setup for data
stream management. Hence, we associate this group with
data steam management concepts.
II. Data Stream Analysis and CEP: We associate the
third processing step with data stream analysis and CEP.
As already mentioned, we assume that data stream analysis is an automatic process. For data stream analysis, we
apply the previously derived rules sets by means of a CEP
engine and its query language. The CEP engine is intended
to derive complex events. Based on these derived complex
events it is intended to trigger actions. Further on, it is also
possible to apply automatic data stream mining algorithms.
Model-based monitoring is applied by building a static and
preliminary model of the target system which is intended
to be monitored. For example, a static and preliminary
model could be build by means of a prototype [17, 13, 33].
Model-based monitoring is necessary to provide initial or basic reliability for the target system. But building such static
and preliminary system models is highly expensive and very
time consuming. Model-based monitoring approaches suffer
from the limited knowledge about the CPS, the physical environment and the system behaviour during the design and
test phases. Moreover, model-based monitoring approaches
suffer from the inflexibility of the resulting static and preliminary model. This includes no dynamic, no revision and no
adjustment of the static preliminary model during runtime.
III. Data and Event Stream Warehousing: We associate the fifth processing step with data and event stream
warehousing. Data stream warehouses faces the same challenges as standard DWHs. Additionally, it is required to
consider continuously arriving data streams. Event stream
processing is also necessary. The data and event stream
warehouse approach is subject to the resource restrictions
of mobile CPSs. Moreover, the data and event stream warehouse approach should be able to provide different storage
strategies such like complete and incomplete storage.
Our KDC is not intended to surrogate neither limit monitoring nor model-based monitoring. Both monitoring approaches have their justifications and are necessary to provide initial or basic reliability for a CPS. The KDC is intended to be an additional monitoring approach, which combines and improves both approaches for monitoring mobile
CPSs. Therefore, we use KDD to build a static and preliminary model first. This reflects model-based monitoring. The
resulting static and preliminary model defines limits. This
could be done by means of a prototype during design and
test phases. Further on, we suggest to translate the static
and preliminary model into rule sets. These rule sets represent the preliminary knowledge about the CPS, its physical
environment and the system behaviour. These rule sets are
applied onto the CPS for real-time monitoring by means of
KDDS and CEP concepts. This reflects one-dimensional and
high-dimensional limit monitoring. During runtime, we suggest to collect more and more data. These collected data are
transmitted to the offline subcycle afterwards. The offline
subcycle is used for knowledge discovery. Such knowledge
can be used for revision, adjustment and refinement of the
existing model. The refined model can be translated into
rule sets again. These new rule sets can be used for the refinement of already existing rule sets on the CPS. Hence, the
KDC is a dynamic and continuous process which considers
revision, adjustment and refinement during runtime.
IV. ETL: The sixth processing step initializes the offline
subcycle. At this point, both subcycles are synchronized
and the cyclic KDD process starts. It is a kind of an ETL
process as already known from data warehousing. First,
relevant data and events are selected and extracted from the
temporal storage respectively the data and event warehouse.
Second, the extracted data and events are transmitted to
the persistent storage on the offline subcycle. Finally, the
transmitted data and events are loaded into the persistent
storage respectively a data warehouse.
9.2
Concepts for the Offline Subcycle
In this section, we discuss concepts for the offline subcycle
respectively of the long-term process.
V. Data Warehousing: The data warehousing process
starts with the already mentioned ETL process. The DWH
contains integrated data, events and metrics.
VI. Data Mining and Machine Learning: Data mining and machine learning is used to discover knowledge from
data, events and metrics which are provided by the DWH.
This concept assignment refers to preprocessing, offline analysis and evaluation. Data mining and machine learning as
well as evaluation are supported by human experts.
Based on KDD, KDDS and CEP concepts it is potentially
possible to build a flexible, adaptable, dynamic and continuous monitoring process. KDD concepts refer to the offline
subcycle respectively to long-term analysis. KDDS and CEP
VII. Knowledge Repository: This concept assignment
refers to knowledge storage. This includes the integration
9
II. Data Stream Analysis
and CEP
at a
I. D
m
ea
Str
M
en
em
ag
n
a
t
Ev
en
3. Online
Analysis
4. Actions
KD
DS
2. Preprocessing
Real-Time Monitoring
Online
in
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5. Temporal
Storage
L
IV. ET
6. Data and Events
Transfer
KDD
1. Data and Event
Streams
t S III.
t re D
a m ata
W a nd
ar
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ou
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II.
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8. Preprocessing
dg
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10. Validation
ep
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usi
7. Persistent
Storage
ata
Wa
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o
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emen
Refin
V
11. Knowledge
Storage
Offline
Long-Term Analysis
V.
D
VIII.
12. Knowledge
Transfer
9. Offline
Analysis
or
y
VI. Data Mining and Machine Learning
Figure 6: KDC with Associated Concepts
of evaluated knowledge into a persistent repository. For example, the storage of the integrated knowledge can be done
by the already existing DWH. Additionally, this concept assignment also includes the representation of knowledge by
means of standard CEP vocabularies and semantic ontologies [49]. Moreover, this concept assign also refers to the
annotation of the discovered knowledge by human experts.
mentioned real world scenarios, our KDC can potentially
help to predict similar crashes such like the disastrous railway accident of ICE 884 in Eschede. Moreover, it is potentially possible to strengthen the existing failure management
of the ISS Columbus module.
A first future challenge refers to CEP for monitoring mobile
CPSs. A lightweight CEP engine is needed which reflects
restricted system resources and real-time constraints. Moreover, such a CEP engine should provide data stream mining
algorithms as well as crisp and non-crisp processing of conditions. A second future challenge is related to the temporal
storage. A combined data and event stream warehouse is required which also considers the restricted system resources.
A third future challenge refers to data and event transfer.
An adequate ETL process is needed to synchronize the online subcycle with the offline subcycle. A fourth future challenge is related to knowledge storage. Adequate techniques
are required for knowledge representation and annotations
of human experts. Finally, another future challenge is related to knowledge transfer. Relevant knowledge must be
translated into a specific query language. Moreover, the resulting queries must be transferred to the online subcycle in
a proper way. This also includes the synchronization of the
offline subcycle with the online subcycle.
VIII. Refinement: The refinement refers to the translation of the derived knowledge into a specific query language.
Furthermore, it reflects the transmission of the resulting
queries to the online subcycle.
10.
CONCLUSION
Our contribution is the motivation, introduction and definition of a Knowledge Discovery Cycle (KDC). The KDC
is an architectural model and an abstract process cycle for
monitoring mobile CPSs. We subdivide the KDC into an online subcycle and an offline subcycle. Our KDC is a cyclic,
dynamic, abstract, data-, knowledge- and event-oriented arrangement of data processing and analysis concepts. This
includes dynamic streams of data and events as well as static
and historical data and events. In particular, we associate
our KDC with well known concepts from KDD, KDDS and
CEP. Based on the usage of these well known existing concepts, it is potentially possible to provide a flexible, adaptable and continuous monitoring process. Our KDC is a monitoring approach which combines and improves limit monitoring and model-based monitoring. With respect to the
11.
ACKNOWLEDGMENTS
We wish to thank and acknowledge DLR, ESA and ASTRIUM Space Transportation for their insights and support,
10
with special thanks to Enrico Noack. We would also like to
thank Adrian Giurca for preliminary reading. This work
was supported by the Brandenburg Ministry of Science, Research and Culture as part of the International Graduate
School at Brandenburg University of Technology.
12.
[16]
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