Preservation and Redeployment of Sensor Acquisition Processes from a

Dam Safety Information System

Anja Bachmann

, Martin Alexander Neumann

, Hossein Miri

, Jos

e Barateiro

, Gonc¸alo Antunes

and Artur Caetano

Karlsruhe Institute of Technology (KIT), TECO, Vincenz-Prießnitz-Straße 1, 76131 Karlsruhe, Germany

Laborat

orio Nacional De Engenharia Civil (LNEC), Avenida Brasil 101, 1700 Lisbon, Portugal

Instituto de Engenharia de Sistemas e Computadores - Investigac¸

ao e Desenvolvimento (INESC-ID),

Rua Alves Redol 9, 1000-029 Lisbon, Portugal

Keywords:

Digital Preservation, Sensor Networks, Civil Engineering.

Abstract:

There is a growing demand for digital preservation of, not only static objects and ﬁles, e.g. text documents

and images, but also dynamic objects and whole processes, such as interactive media and entire business

and scientiﬁc processes. This paper investigates the problem of digitally preserving monitoring processes of

a dam safety information system. Monitoring processes are a crucial element in ensuring structural safety;

the interpretation of the past data produced by such processes as well as the prediction of potential future

behaviour facilitates an earlier detection of possible dam failure. After a successful preservation performance,

relevant data can be used or re-produced without the need for the original system to still exist; merely by

re-playing the preserved information and data. This enables several possibilities in the scope of a water dam

system. The retracing of former situations and structural behaviour decades later is one of them. Furthermore,

the interpretation of past data and subsequent prediction of future behaviour that could facilitate an earlier

detection of a fault or possible dam failure. This work presents a methodology for preserving the obtained

sensor data (readings, measurements, and meta-data) from a dam safety information system, whose involved

processes include: data acquisition, the preservation process itself, and the re-playing and redeployment of the

preserved data.

1 INTRODUCTION

Within the last few years, there has been a growing

demand for digital preservation. This is caused by the

need to protect information of enduring value in in-

terrelation with the drastically increasing number of

documents and digital objects that are being produced

every day (Conway, 1996; Borghoff et al., 2006). This

amount has not only to be managed, but also pre-

served for later use and contextual interpretation, in

consideration of the preservation of the value com-

prised in this data.

Digital preservation research is concerned with

providing long-term access to and intelligibility of

digital objects, regardless of their complexity (Lee

et al., 2002; Giaretta, 2011). The focus is on preserv-

ing digital objects along with their meta-data (con-

textual or proxy information) required to achieve this

goal (Day, 2001). In the past, static objects were con-

sidered the main focus of digital preservation. These

objects do not expose any behaviour, i.e. they do not

perform any activity or interaction with their environ-

ment that is visible externally. Such objects, which

are to be preserved digitally, are primarily text and

multimedia documents. Notably, in order to be gen-

erated and interpreted, digital objects require a tech-

nological context deﬁned by speciﬁc software and in

some cases even by speciﬁc hardware (Chou et al.,

2012).

Recent research in the area of digital preservation

has focused on extending established preservation ap-

proaches to dynamic objects. These are objects that

actually perform behaviour over time. Examples are

video games (Guttenbrunner et al., 2010), interactive

art (McHugh et al., 2010; Becker et al., 2007) and

computational environments, such as scientiﬁc work-

ﬂows (Roure et al., 2011). Additionally, more and

more static digital objects are replaced by dynami-

cally generated ones, e.g. generated meta-data and dy-

namic websites. Digital business processes are a spe-

490

Bachmann A., Neumann M., Miri H., Barateiro J., Antunes G. and Caetano A..

Preservation and Redeployment of Sensor Acquisition Processes from a Dam Safety Information System.

DOI: 10.5220/0004625404900495

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge

Management and Information Sharing (KMIS-2013), pages 490-495

ISBN: 978-989-8565-75-4

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

cial and more complex type of dynamic object, which

present big challenges to preservation due to the mul-

titude of dependencies that have to be kept in mind.

As a result, there is a necessity to not only preserve

the object but rather the process describing the con-

text of the digital object to fulﬁl the aim of preserving

this object fully.

Here, important civil engineering structures like

dams are considered. The monitoring of their be-

haviour is important to guarantee is faultless function.

The data required for analyzing the behaviour is ac-

quired by a set of sensors placed along the dam. To fa-

cilitate the monitoring and guarantee its functionality

in the future, the monitoring and data acquisition pro-

cess have to be preserved. It is important to preserve

data about the water dams so that later on it is possi-

ble to retrace former situations and behaviours. Pre-

served data can be used for example to build a model

and predict future behaviour or to detect anomalies.

It also facilitates to track a possible problem source

down to speciﬁc system behaviour. To preserve data,

we need to keep both, sensor data and contextual in-

formation, in a preservable format.

The remainder of this paper is structured as fol-

lows. Section 2 gives an overview of the background

of our use case and the monitoring processes of the

water dam. In Section 3, we present a typical moni-

toring architecture for water dams and name relevant

aspects to be preserved. This is followed by our pro-

posed methodology to preserve these contextual as-

pects in Section 4. This extends our general architec-

ture for preservation of business processes which has

been previously published in (Galushka et al., 2012).

The preserved data can, then, be re-played for contex-

tual interpretation. Section 5 describes this process

in detail. Finally, Section 6 discusses the usefulness

and suitability of our architecture and model in the

context of preserving and re-playing water dam mon-

itoring processes, and also gives an outlook to future

work.

2 MONITORING OF WATER DAM

BEHAVIOUR

We perceive a monitoring process of a water dam as

such a kind of process to be preserved. In this paper,

we present our perspectives on what, why and how it

is to be preserved, in the context of such water dam

monitoring processes. Our general formal model to

capture what to preserve has been presented in (Neu-

mann et al., 2012).

Here, we present our specialization of this model

to the domain of water dam monitoring processes, and

will discuss its usefulness in preserving these moni-

toring processes.

2.1 Use Case Scenario

Within the last years, the interest in the area of civil

engineering raised. The focus is on “conservation of

the environment, the welfare and safety of the individ-

ual” and there is a need to “manage the prevailing nat-

ural and manmade risks in a conscious, consistent and

rational manner” (Faber, 2007). Wieland and Mueller

(Wieland and Mueller, 2009) advise their readers of

the far-reaching consequences of a possible failure of

large civil engineering structures. Especially in sys-

tems like dams or nuclear power plants a failure could

endanger the life of human beings and the nature, but

also damage important infrastructures like electricity

or water supply. There are several preventive mea-

sures and mitigation strategies to reduce the risk. An

often applied methodology is to detect anomalies and

errors as early as possible to increase the time to react

to them and prevent from the worst consequences.

In this use case, the focus will be on large civil

engineering structures like dams. They have a crit-

ical infrastructure that can face the risks mentioned

above. Some countries established legal obligations

for national research institutions to monitor the be-

haviour of a civil engineering system. Portugal for ex-

ample enforced a strict Dam Safety Legislation which

are stating some general regulations for water dams,

among others. This legislation also instructed the

Laborat

orio Nacional De Engenharia Civil (LNEC)

to monitor the structural safety of a water dam system.

The system, considered here, encompasses 117 water

dams in Portugal. Each of them is equipped with a

different number of sensors, most with less than 50,

some even with more than 1000. Overall there are

31479 sensors that are taken into account.

To preserve all data relevant for the preservation

process in the context of this use case, it is neces-

sary to gather information about the sensor, its envi-

ronment and its measurements (Kutter et al., 2002).

Such information has to be exchanged between sev-

eral programs, e.g. the data collector and the evalua-

tion system. Hence, it is necessary to select a suitable

and interoperable ﬁle format to perform this task. The

resulting tasks are therefore to acquire process infor-

mation, sensor data and environmental information,

convert the data into a preservable format and, when-

ever required by the user, replay this data for contex-

tual interpretation.

To perform this task it is necessary to install sev-

eral sensors in the dam structure. It is possible to

http://www.lnec.pt/

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491

manually or automatically gather the required infor-

mation and later on convert this data for further anal-

ysis. Data is acquired manually, e.g. by a human op-

erator using measuring devices at different locations

within the water dam. For automatic data collection

it is advisable to establish a wireless network to di-

rectly get the data from the sensor and store it. If such

a connection is established, the sensor can commu-

nicate directly and automatically with the dam safety

information system and transfer the information.

2.2 The Need for Preservation

Monitoring a dam and understanding its structure are

inseparable from each other. It is possible to evalu-

ate and validate measurements, taken from sensors in-

stalled at strategic points within the observed system,

and interpret correlations between them. The result-

ing information can further be used for the prediction

of future behaviour, the estimation of missing values

or the detection of anomalies. This underlies the re-

quirement of reliable data and measurements. De-

vices like plumb lines and piezometers are installed

at different locations within a water dam to gather in-

formation about it and monitor its behaviour (Amer-

ican Society of Civil Engineers, 2000; International

Commission on Large Dams, 1999). The number of

used sensors can range from less than 100 to more

than 1000, depending on the dimension of the dam.

Process information, meta-data and sensor mea-

surement – all this data has to be preserved. In

this way, contextual interpretation is possible even

decades after the data was gathered and even without

the existence of the original system. Occurring prob-

lems are that the former processes are not existing any

more and cannot be retraced or that former data is

missing and cannot be reproduced easily. Preserva-

tion countervails these problems.

Altogether, the motivational factors for digital

preservation of sensor data acquisition processes in

the scope of civil engineering can be summarized as

follows.

• Compliance with Legal Requirements – Fulﬁlling

the legal obligations and the agreements estab-

lished with the owners of the structures.

• Replay of Processes – Enabling a later replay of

former scenarios for retracing the scenario and the

original behaviour of the underlying system. En-

suring the authenticity, correctness and validation

of the execution.

• Re-use of Processes – Gaining new insights by

modifying parts of the execution process. Re-

execution of former processes with modiﬁed pa-

rameters or execution of a modiﬁed process with

the original data.

• Assessment of the Costs of Retention – Consider-

ing whether to keep a big dataset of gathered data

or to only keep the process that generate the data

with respect to the corresponding costs.

In consideration of these factors, the preservation of

processes in the scope of civil engineering monitoring

activities is a challenge that must be addressed.

3 DATA ACQUISITION

In digital preservation it is not sufﬁcient to only store

measurements that were gathered by sensors. For a

later understanding of a situation or a scenario it is

inevitable to also store information about the process

and meta-data about the environment and interrela-

tion of sensors. This environment encompasses the

location of the sensor (position, location), but also en-

vironmental information like weather, temperature or

humidity. Additionally, there is also contextual in-

formation about the process itself, e.g. dependencies

among the software or hardware components.

The focus of this project is on process preserva-

tion. It is inevitable to preserve all this data which

incorporates information about the process or repre-

sents inﬂuencing factors. Environmental components

(temperature, location, position) inﬂuence the func-

tionality of a sensor, the measuring and monitoring

processes. Dependencies and interrelations of soft-

ware or hardware components are relevant to model

the process.

3.1 Acquisition of Sensor Data

As mentioned before, we require sensor data and

meta-data, among others. The sensors have to be ac-

cessed and the measurements have to be gathered and

afterwards stored in a database. This data consists of

the identiﬁcation number of the sensor, the identiﬁ-

cation number of the measuring device, the measured

value, information about the temperature and so on.

It is possible to acquire data by manual or automatic

measurement. For example it is possible to automat-

ically get location information by checking the GPS

signal. Values like temperature can also be measured

automatically. Information about the monitoring pro-

cess itself or dependencies will have to be added semi-

automatically in the next step.

Currently, the Laborat

orio Nacional De Engen-

haria Civil is handling 35 types of manual sensors

and 25 types of automatic data gathering devices. The

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492

Figure 1: UML Class diagram describing the classes of the Sensor DSO.

processes of gathering and transforming the measure-

ments and all involved data transformations and algo-

rithms are what has to be preserved within this use

case.

3.2 Water Dam Context Model

Several aspects in the context of a business process

have to be taken into account for digital preserva-

tion to ensure successful redeployment of that process

(Neumann et al., 2012). The authors also deﬁne two

kinds of aspects that have to be taken into account

for the preservation of a process: coarse-granular as-

pects (abstract and not only relevant for the process

but its entire domain) and ﬁne-granular aspects (more

speciﬁc to the process itself and its sub-processes).

If such knowledge about a process is machine-

interpretable formalized and stored directly with the

digital objects, semantic reasoners can immediately

incorporate it into decision making. There is no need

to consult external systems, repositories or human

preservation operators. Furthermore, if the formal

system is not a specialized language, but a generic

knowledge representation language such as OWL

(Web Ontology Language), the preservation-relevant

advantage arises that the knowledge kept close to

the digital objects may even be object-speciﬁc. This

means that the model may be speciﬁc to a preserved

business process, but still is machine-interpretable

(Neumann et al., 2012).

There are two Context Model instances (DIO and

DSO) related to this use case which will only be

shortly described here. A domain-independent ontol-

ogy (DIO) represents a neutral, domain-independent

language that is able to represent the core concepts

of our Context Model. It is designated domain-

independent since it does not address any speciﬁc

domain-dependent concerns. A domain-speciﬁc on-

tology (DSO) represents a domain-speciﬁc language

that addresses a particular set of concerns. Our Con-

text Model will comprise a core DIO and a set of

DSOs. They represent object classes and depen-

dencies and interrelations between them in order to

describe the process to be preserved and its con-

text. Contextual information and the dependencies

between processes are extracted by using different ex-

tractors. One of the DSOs is related to this use case

– the Sensor DSO. It aims at capturing particular as-

pects about sensors and other related information. It

gives an overview of the relations between acquisition

algorithms and transformations and the sensors, their

types and properties. The UML class diagram in Fig.

1 describes the different classes of the Sensor DSO.

4 DATA PRESERVATION

The focus of this project is on digital process preser-

vation. To achieve this the subsequently described

methodology to preserve civil engineering processes

was performed. As explained in Section 3 the ﬁrst

step is to acquire data. This consists of the acquisition

of measurements, but also (meta) information about

the sensors and the associated processes. To model a

process it is also important to detect and cover depen-

dencies between data and sub-processes. Such con-

textual information is added to the content data and

stored together for preservation issues.

In todays IT domain there is a burden of coping

with the diversity and heterogeneity of data sources,

dynamically selecting appropriate data sources, and

scaling of data from mobile, distributed data sources

(Cohen et al., 2001). This causes the need for an inter-

operable, easily manageable, ﬂexibly extensible and

common data format that facilitates the access of and

further work on a document (Bloechle et al., 2006).

An interesting candidate is XML with a sensor data

PreservationandRedeploymentofSensorAcquisitionProcessesfromaDamSafetyInformationSystem

493

extension, e.g. Unisens

, SenML

or SensorML

They provide various features relevant to our use case.

The presented preservation approach divides the

set of required data into three parts: sensor mea-

surements, sensor meta-data and process meta-data.

A separation of these components was chosen to al-

low working on one component without inﬂuencing

or endangering the other ones. Two different data

ﬁle types are used to preserve this data. The sen-

sor measurements and the environmental sensor meta-

data (e.g. temperature) are stored in one of the men-

tioned XML-based formats. The process meta-data

(e.g. dependencies and interrelations of processes or

sensors) is stored in the Context Model, introduced in

Subsection 3.2. A speciﬁc part of the Context Model,

the OWL ﬁle “SensorDSO”, is capturing dependen-

cies and properties of sensors. All entities, depen-

dencies and properties of the DSO can be mapped to

the acquired sensor data and meta-data. The advan-

tage: OWL is not only interoperable, it also allows

a reasoning on the data to induces ﬁndings about de-

pendencies and properties. Thereby, the incorporated

meta-data will be contextual interpretable and human

understandable.

5 DATA REPLAY

One of the main purposes of the digital preserva-

tion of business process is to enable a later contex-

tual interpretation of a former scenario and to retrace

what has happened before. For realizing this, pre-

served data is replayed. As stated by van der Ho-

even et al. the focus is not only “on the digital ob-

ject, but on the hardware and software environment

in which the object is rendered”, moreover the aim is

to “(re)creating the environment in which the digital

object was originally created” (van der Hoeven et al.,

2007). In our case, this means to preserve and replay

the monitoring process.

The replay of a former scenario is important to

retrace what had happened before or to make further

use of this historic data. There are several alternatives

to perform an emulation of past sensor data.

• One method: as input data, a certain time and date

combination and the ID of a sensor are given. Af-

terwards it is possible to track down this one sen-

sor to the time back then and retrieve the past mea-

surement.

http://www.unisens.org/

http://tools.ietf.org/html/draft-jennings-senml-08

http://www.opengeospatial.org/standards/sensorml

• A second way: again, time and date for the

emulation are given. This time, a whole water

dam/sensor system is considered and not a sin-

gle sensor. Then, by using the past measurements

and the contextual information, it is possible to get

an overview of the former behaviour of this water

dam.

• It is also possible to apply machine learning tech-

niques to the sensor data and contextual informa-

tion. A model is trained and learned. Later on

this model is able to reproduce information about

former behaviour based on given input.

Based on the concrete application and its com-

plexity, a different method has to be chosen. Prob-

ably, there are even more possible replaying methods,

the presented ones are only giving a ﬁrst insight. A

preserved process has been redeployed successfully

if the replayed process shows the same behaviour as

the original one.

It is not only possible to use the preserved data

to replay former scenarios and interpret them. The

data can also be used as historic input data for sev-

eral stream mining algorithms. It is possible to pre-

dict the future behaviour of a water dam based on this

data. Moreover it is possible to learn a model to de-

tect anomalies. It is also possible to compare different

scenarios.

6 CONCLUSIONS AND

OUTLOOK

The relevance of digital preservation in areas such as

civil engineering is rising due to the evolvement of big

data and the increasing importance of safety civil in-

frastructures. This paper underlined the signiﬁcance

of digital preservation of monitoring such an infras-

tructures. It further presented a methodology for pre-

serving complex dynamic processes of a water dam

system. Still, there are open tasks and starting points

for further investigations.

Digital preservation is challenging. Its aim is not

only to store cultural heritage but also to enable con-

textual interpretation and retracing of the past. We are

investigating a use case situated in the area of civil en-

gineering. One aim of civil engineering is to increase

safety and reduce the risks of a failure by a better

monitoring and by future anomaly detection. Here,

the focus is on one step before: the preservation of

data gathering and monitoring processes, containing

information of what already happened and is happen-

ing. We presented a methodology for this preserva-

tion, bringing civil engineering one ﬁrst step closer to

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risk mitigation and failure avoidance.

The considerations presented in this paper are a

good basis, but requesting further research. First of

all it is inevitable to develop a conversion tool for

preservation. This tool is supposed to take sensor

and contextual information as input and convert them

into a preservable format. Of course, it is necessary

to check whether the preserved data contains all rel-

evant information that was given by the input data.

The preserved data will be replayed some time later

on. To perform this action a replaying algorithm is

required. This algorithm has to be examined regard-

ing its replay accuracy and usefulness. It is important

to check whether the replayed data behaves like the

original data did. As mentioned before in Subsection

4 there are additional possibilities to work with pre-

served data in the future than just replay it. There is

also the option to use the preserved, historic data to

predict future behaviour, e.g. by performing stream

mining algorithms. Especially in the framework of

civil engineering, where the focus is often on enhanc-

ing the safety and reducing a risk by avoiding failures,

the early detection errors or uncommon behaviour is

important. This can be achieved for example by learn-

ing a model by applying machine learning techniques

on the data. It is considerable to simulate emergency

situations, e.g. the bursting or the failure of a water

dam, by altering some parameters. The task for the

developed system is to detect this anomaly as soon as

possible to raise an alarm. The question is whether

the system is able to detect such a failure in time.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the funding

by the European Commission under the ICT project

“TIMBUS” (Project No. 269940, FP7-ICT-2009-6)

within the 7th Framework Programme. This work

was also supported by national funds through FCT

Fundac¸

ao para a Ci

encia e a Tecnologia, under project

PEst-OE/EEI/LA0021/2013.

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