THE ADIWA PROJECT

On the Way to Just-in-Time Process Dynamics based on Events

from the Internet of Things

Markus Schief, Christian Kuhn, Birgit Zimmermann, Philipp R

osch

SAP Research, Bleichstraße 8, Darmstadt, Germany

Walter Waterfeld, Jens Schimmelpfennig, Dirk Mayer

Software AG, Darmstadt, Germany

Heiko Maus

German Research Center for AI (DFKI GmbH), Kaiserslautern, Germany

orn Eichler

Fraunhofer SIT, Darmstadt, Germany

Keywords:

Event-based system, Event processing, Context awareness, Dynamic business processes, Internet of things.

Abstract:

In this paper, we introduce a concept, which focuses on innovative commercial system implementations re-

ﬂecting process-embedded events from the Internet of Things. The developed concepts are derived from

experiences applying recent research advances to industry scenarios. The rationale behind the overall con-

cept is twofold: while transparency is increased by event-based methodologies in the context of the Internet

of Things, the agility of business processes is fostered by enhanced business process models, orchestration

support, execution control, and user assistance.

1 INTRODUCTION

In recent years, the speed of change in business envi-

ronments has been accelerating. To enable agile busi-

ness processes, transparency is a crucial prerequisite.

While a great deal of research has been performed in

the ﬁeld of dynamic business processes, a lack of ac-

ceptance can be constituted as, up to now, the data

basis has not been sufﬁcient (Pesic and van der Aalst,

2006). Our concept enables dynamic design, plan-

ning, and execution of business processes based on

the identiﬁcation, processing, and usage of real world

events from the Internet of Things (IoT).

By analyzing four industry scenarios in the area

of logistics, business services, retail, and production,

we discovered requirements for our technical compo-

nents and the concept architecture. With regards to

transparency, the challenge is to identify the relevant

real world events and transform them into consum-

able information pieces for business processes, e.g.

(Heusler et al., 2006). A systematic and consistent

concept is required to transform the IoT information

and, hence, to increase the information value within

business processes. In terms of business processes,

information can be leveraged within the generic de-

sign of business processes as well as during the plan-

ning and execution of speciﬁc business process in-

stances. Internal triggers, such as business process de-

viations, as well as external triggers, such as changed

environmental factors, need to be covered by the tar-

geted event-based information system.

To alleviate these challenges, an extension of busi-

ness process modeling and execution is needed, which

integrates IoT data as well as the description of pro-

cess variance. Orchestration tooling as well as knowl-

edge worker support have to be provided in order to

371

Schief M., Kuhn C., Zimmermann B., Rösch P., Waterfeld W., Schimmelpfennig J., Mayer D., Maus H. and Eichler J..

THE ADIWA PROJECT - On the Way to Just-in-Time Process Dynamics based on Events from the Internet of Things.

DOI: 10.5220/0003417503710377

In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 371-377

ISBN: 978-989-8425-56-0

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

realize the full potential of IoT information. Support-

ing users is important for two main aspects: To en-

sure that humans are involved in business processes

that are closer to the real world and, secondly, in or-

der to foster process dynamics, process participants’

know-how from the actual process execution needs to

be exploited. The second aspect ensures a steady pro-

cess evolution and adaptation to the real world.

2 FUNCTIONALITIES

2.1 Event Processing

Event processing plays a central role in the overall

ADiWa approach of connecting business processes

to the real world via IoT technologies. It is essen-

tial to complement the traditional top-down realiza-

tion of business processes with a bottom-up approach

of events. Only in this way can business processes

cope with the unpredictability of the real world.

The connecting of business processes with IoT

technologies results in a wide spread of the technol-

ogy stack in ADiWa. It reaches from business pro-

cess management, via service and event management,

to IoT sensors and devices. Events occurring at all

layers are often intertwined. Thus, there is a strong

need to process and aggregate low-level IoT events

to high level business events (Schmidt and Schief,

2010). This challenge usually requires not only a rel-

atively complex multi-step processing of events into

business events but also complex structured events

during and as the result of the processing. Thus,

there is a strong need for a complex event process-

ing (CEP) component. In our event concept, we dis-

tinguish between two main event processing compo-

nents: Firstly, the CEP component generates and pro-

cesses complex structured events and, secondly, the

event bus provides a scalable distribution via publish-

subscribe-mechanisms.

Our work leverages state of the art research in

these areas. The ﬁeld of complex event processing

has been initiated by (Luckham, 2002). (Hinze et al.,

2009) gives an overview of the currently-developed

technologies and identiﬁes characteristics of a large

number of applications. It shows that there exist two

approaches for deﬁning event processing language

(EPL) rules: streaming queries, e.g., according to the

SQL-style, and rule-based approaches. (von Ammon

et al., 2008) describes an integration of event process-

ing with Business Process Management (BPM) and

gives examples of practical applications. Although

it describes the ﬁrst approach for the integration of

event processing with BPM, it does not address the in-

tegration with IoT technologies. There is also no ap-

proach for an integrated event tooling for the business

user from BPM to the IoT. Based on the Java Messag-

ing Service (Hapner et al., 2002), a lot of work has

been done on message bus functionality for the dis-

tributing and brokering of events. (Muhl et al., 2006)

gives an overview of many relevant systems. Though

quite a large number of techniques exists in different

systems, they focus on the level of a messaging sys-

tems and not completely on events.

Beyond the general complex event processing, we

additionally consider several quality criteria as well

as analysis capabilities to further enhance the value of

information provided by the complex events. To un-

derstand and trust the output of the system, the user

requires reliability and guarantees. In ADiWa, we

achieve these goals by a holistic quality determination

and propagation approach. We consider four different

layers associated with quality. At the lowest layer,

the event producers emit events with a certain pro-

duction quality. Typical event producers are sensors

or devices (e.g., RFID readers); corresponding qual-

ity dimensions are, for example, accuracy and com-

pleteness of the measured values. Next, the events are

transmitted and processed with a certain notiﬁcation

and processing quality, respectively. We determine

the notiﬁcation quality based on a large set of fea-

tures of the event bus (Hinze et al., 2009). Examples

for such features and their impact on the quality are:

does the event bus support validity intervals of events,

and what is the granularity? Is the event bus capable

of early ﬁltering, and how efﬁcient and accurate is this

ﬁltering? Is there support for distributed processing?

Is there support for privacy? The processing quality

mainly targets the properties of the CEP engine like

the above-mentioned throughput, latency, or accuracy

of complex event detection. Finally, the events af-

fect dynamic business processes with a certain con-

trolling quality. Here, key performance indicators

(KPIs) given by the business process management in-

dicate the success of the dynamic adaptation of busi-

ness processes utilizing events.

To realize the holistic quality determination and

propagation, we make use of an extended metadata

schema. There, we track the individual quality cri-

teria as well as lineage information to additionally

provide an uninterrupted and persistent trail of events

for downstream analyses. For efﬁciency reasons, only

the event-speciﬁc metadata are directly attached to the

events (such as its age), while the remaining informa-

tion is stored in external repositories (quality criteria

of the event producers and event bus). Further, by

benchmarking and monitoring, we evaluate, observe,

and ensure the quality of the event transport.

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

372

With several analysis capabilities, we provide both

the event processing and the user with valuable sup-

plementary functionalities. These functions include

the handling of missing events as well as the de-

tection of new complex events. Further, we enable

the proactive detection of occurring complex events;

here, a user is notiﬁed with an alert-event if the occur-

rence probability of a speciﬁc complex event exceeds

a given threshold. This alert allows the user to dynam-

ically initiate appropriate actions at an early stage.

2.2 Process Modeling and Execution

Usually, only a low degree of ﬂexibility is supported

in business process management systems. There are

a few approaches dealing with the question of how to

enhance the ﬂexibility of business process execution

(Dadam and Reichert, 2009; Hallerbach et al., 2009).

In addition to offering change operations, rules can

be used to offer a more ﬂexible business process ex-

ecution. An overview of the current state of the art

can be found in (Paschke and Kozlenkov, 2009) and

(Graessle et al., 2006). Based on the enhanced trans-

parency provided by IoT events, ADiWa enables com-

panies to support ﬂexible adaptation and dynamic ex-

ecution of business processes. Four phases in the life-

cycle of a process are considered in our concept: de-

sign, planning, execution and controlling.

The lifecycle of business processes starts with

the design phase. In this phase, business processes

are documented as semi-formal process models. In

ADiWa, we design business processes using an ex-

tended event-driven process chain (Keller et al., 1992)

method, allowing the deﬁnition of complex events

and providing a means of integrating real-world IoT

events into business processes. This approach allows

process owners to deﬁne process-relevant events and

to specify appropriate process responses. The result-

ing process models are transformed into technical rep-

resentations and are used as templates for both the

planning of individual process instances and the con-

ﬁguration of a process-controlling component.

In the planning phase, results of the modeling

phase are instantiated (respectively adjusted) for spe-

ciﬁc process instances. Planning is an iterative pro-

cess in which a large amount of intermediate results is

created and interpreted. For example, all data occur-

ring for one process instance and the matching rules

have to be taken into account. In addition, it must be

possible to adjust plans at a later time, for example,

to adapt them to a suddenly-occurring event. Plan-

ers must be able to specify the required operations.

They expect immediate reactions in order to be able

to continue with planning directly after the adapta-

tion. To enable this rapid interaction, the planning

component must be able to read, analyze, and write

data as quickly as possible. The goal is to enable users

to plan processes ﬂexibly. Thus, it must be possible

to deﬁne several alternative planning sequences based

on potential variations of (strategic and operative) ob-

jectives. Likewise, required activities and resources

required need to be adjusted to reﬂect IoT events.

In the execution phase, the focus is on a ﬂexible

process execution in terms of IoT intelligence. To be

able to offer this functionality, an execution environ-

ment is needed that is based on the results of both the

design and the planning phase. In addition, the ex-

ecution environment must provide a rule-based pro-

cess execution as well as the interpretation of events

from the IoT. All this information must be combined

in order to respond to events showing up during run-

time. As such, during runtime, the execution of a pro-

cess instance can be adapted to its actual context. If

an upcoming event necessitates a change of planning

or even execution of certain process steps, the exe-

cution environment supports this ﬂexibility, for ex-

ample, by offering change operations similar to the

ones deﬁned by (Hallerbach et al., 2009). Adaptations

in process execution or process planning are commu-

nicated as process-relevant events. According to the

event context, three kinds of business process-relevant

events can be distinguished: execution-, planning-,

and design-relevant events. If the execution or plan-

ning of a certain process needs to be changed fre-

quently during runtime, this fact may indicate that

the original process design, on which planning and

execution is based upon, is insufﬁcient or outdated.

Thus, the design needs to be adjusted to ideally ﬁt to

the runtime requirements. Such optimization poten-

tial is detected by a CEP engine (Section 2.1) react-

ing to both execution- and planning-relevant events,

which are aggregated to the above-described design-

relevant events. Design-relevant events trigger gover-

nance processes, which support the process owner in

the optimization of business process models, provid-

ing an automated change management approach for

execution-driven process evolution.

Another key stage for ﬂexible processes is the

controlling phase. The results of monitoring and

controlling affect all process lifecycle phases. The

ADiWa monitoring and controlling component col-

lects event data from the execution platform as well

as identiﬁed business process-relevant events from the

CEP engine. It reassembles single as-is process in-

stances and visualizes them as event-driven process

chains. By combining and aggregating the data of IoT

objects belonging to business process instances with

other process-related data (e.g., semantic data), the

THE ADIWA PROJECT - On the Way to Just-in-Time Process Dynamics based on Events from the Internet of Things

373

controlling component is the process memory. Pro-

cess analysts and process managers are able to an-

alyze single process instances as well as aggregated

instances and even process types. In case of devia-

tions in process performance, an alert-event is gener-

ated and communicated to the person in charge (Sec-

tion 2.3) or other subscribers. The employee in charge

can react to the identiﬁed issues to control the process

in a ﬂexible way, or change the process.

2.3 User Process Interaction

One major topic in ADiWa is the process-embedded

support of users and the preservation of their process

know-how. Leveraging the users’ know-how from

daily process work is one of the challenges for busi-

ness process management (Riss et al., 2005). There-

fore, several projects tackle this issue, such as Apos-

dle

, ACTIVE

, and Nepomuk (Riss et al., 2007), to

name a few. However, no approach has yet to con-

sider exploiting know-how from processes traceable

via the IoT.

Nonetheless, supporting users through the IoT,

such as smart items in instrumented environments,

has recently gained momentum. Applications range

from product tracing, such as in SemProM (Schnei-

der and Kroener, 2008), to knowledge-based assis-

tance, such as memory externalization (Kawamura

et al., 2007). The novelty in ADiWa consists of the

comprehensive approach to the use of the IoT to let

users intuitively participate in, intervene, and modify

business processes. This approach is complemented

by means for process-embedded information support

based on IoT-enriched user and process context.

Investigating the project’s industry scenarios, we

ﬁnd processes which are often coarsely modeled (if at

all), partly unspeciﬁc, dynamic, and human-centric,

as well as human-individual parts. In those cases, pro-

cess participants must react to new situations (e.g.,

those which can be tracked by events from the IoT)

and often have to adapt a running process. Partici-

pants range from ofﬁce workers to process-embedded

mobile workers interacting with smart objects in an

instrumented environment. ADiWa empowers users

with tools to understand processes and provides de-

tailed information on involved objects in order to al-

low controlling, optimization, or different kinds of

intervention such as in exceptional cases. It sup-

ports users in coping with the vast amount of newly-

available information from the IoT.

ADiWa’s interactive user workspace (see Fig-

ure 1) enables process participants to ﬂexibly work on

www.aposdle.tu-graz.at

www.active-project.eu

process steps by introducing agile task management,

collaboration, and knowledge work support. Here,

a conﬁgurable process information cockpit provides

required information, such as process instance data,

data from legacy systems, and occurred events rel-

evant to the user. This approach is combined with

proactive information delivery to satisfy the presumed

information need inferred from the process and user

context. The workspace presents relevant informa-

tion objects (such as documents, notes, guidelines,

and data from smart objects and their product mem-

ory) as well as alternative actions from process know-

how (such as process guidelines, former process in-

stances, and similar problem solutions). Besides sup-

porting knowledge reuse, the environment is designed

to automatically capture process knowledge during

process execution.

ADiWa explicitly focuses on enabling users to ex-

ecute processes in reality while tracking their work-

ing activities automatically, and thus, keeping up-to-

date process information in the system. This func-

tion ranges from giving feedback (e.g., noting reasons

for exceptions) and supporting solution-ﬁnding in the

task management environment (e.g., making a check-

list for handling an exception) to directly interacting

with smart objects (e.g., to solve an exception). More-

over, it is also tracked as process audit data. For in-

stance, in the retail scenario, rearranging goods in a

store due to an exception handling does not require

a manual reporting of transferred goods, because this

is already traced by the IoT events sent by the instru-

mented shelves. Comparing the process model to the

actual execution based on traced data, provides a valu-

able basis for performance analysis and optimization.

ADiWa provides a platform for multi-modal inter-

action with a wide range of usage paradigms and in-

terfaces. Within the latter, participants see their tasks,

required information, and supposed actions, as well

as any changes in the dynamic processes. They are

able to interact with smart objects, retrieve additional

information, and provide feedback on process steps.

Another innovative paradigm applied is the digital

pen and paper interaction in mobile scenarios. Writ-

ing with the digital pen on a specially-prepared pa-

per can be interpreted by software either after man-

ual synchronization or by direct streaming (via Blue-

tooth). Applications range from simple note-taking

or ﬁlling out a form with handwriting recognition

to selecting commands on a command sheet to con-

trol software. By introducing this as IoT source in

ADiWa, users are able to do their process work on pa-

per while being seamlessly embedded in the business

process and providing direct feedback. In conjunction

with the controlling phase explained in Section 2.2,

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

374

Figure 1: ADiWa big picture of components.

process participants are also able to intuitively com-

bine context data from various sources including IoT

and process data during the planning and execution

phase. They can aggregate performance indicators

and include them in the interactive user workspace as

widgets. These widgets are used as working tools and

can be shared between users and jointly evolved. A

widget conﬁguration can be done without the inter-

vention of IT departments, as it is required in business

process management nowadays.

3 ARCHITECTURE AND

SECURITY

The core architectural paradigm of ADiWa is event-

driven business process management. To enable

this vision, a reference architecture (see Figure 1)

is deﬁned, which deﬁnes and integrates the most

important and inﬂuential architectural concepts and

mechanisms. All modules within the reference archi-

tecture are self-contained and modular entities with

clearly-deﬁned external interfaces. The interoperabil-

ity will be maintained through the development of

Web services conforming to WS-I

guidelines. Part-

ner projects of ADiWa within the Digital Product

Memory Innovation Cluster play an important role.

The SemProM

project focuses on the collection and

representation of information during an object’s life-

cycle. The Aletheia

project provides a holistic view

of an item’s state. The ADiWa reference architecture

consists of some key components which are essential

for event-driven processes.

• The event bus and the CEP enable the routing,

distribution, and the event processing algebra-

based analysis of events.

www.ws-i.org

[http://www.semprom.org/]

[http://www.aletheia-projekt.de/]

• The service bus serves as central orchestration

and integration entity of the different components

based on a system-wide service registry.

• The process modeling environment is the main

component for design-time business process man-

agement, including maintenance of business rules.

• The process execution environment is the com-

ponent for business process run-time, especially

for planning and execution of process instances.

• An interactive user workspace assists the human

with appropriate context information and consum-

able information for effective decision-making.

• The analytical and evaluation component col-

lects information from events and processes to de-

rive meaningful representations.

Dynamic business processes consume a large

number of events from intelligent objects and base

decisions on those events. Planned as well as ad hoc

changes in complex environments must not violate se-

curity policies. The architecture needs to enforce se-

curity polices despite of weak security mechanisms of

intelligent objects and dynamic environments. Secu-

rity properties of events must be checked in spite of

their large number and high frequency.

There are several business process-based ap-

proaches for the analysis and speciﬁcation of secu-

rity requirements and controls. Rodriquez et al. (Ro-

driguez et al., 2007) present their method for secure

business process speciﬁcation to annotate UML activ-

ity diagrams with an UML proﬁle. Modeling security

goals in business processes more explicitly enables

Wolter et al. (Wolter et al., 2009) to adapt their policy

transformation process to different target security in-

frastructures. Basin et al. (Basin et al., 2006) present

with SecureUML a ﬂexible approach developing spe-

cial purpose UML dialects for security speciﬁcation

and analysis. Neither of them seamlessly integrates

multiple models horizontally or incorporates support

for cross-phase modeling as is proposed in ADiWa.

Accordingly, we deﬁne a guideline for the sys-

tematic analysis of security requirements within the

context of dynamic business processes. Further, we

derive a framework for an integrated security mod-

eling of dynamic business processes. Using weav-

ing models (Bezivin et al., 2006), we link security-

related artifacts of dynamic business processes. The

interconnectivity allows for the analysis of security-

related dependencies and serves as a basis for im-

mediate impact analysis, policy derivation, and se-

curity status evaluation. Moreover, we formulate a

method to infer and enforce secure interactions with

smart objects at runtime. Low-level security polices

are decoupled from security expert knowledge and

THE ADIWA PROJECT - On the Way to Just-in-Time Process Dynamics based on Events from the Internet of Things

375

(frequently changing) implementation details utiliz-

ing description logics and ontologies. Thus, we are

developing a process security evaluation engine pro-

viding operators with insight concerning the current

security status of a process. The engine analyzes the

events consumed or generated by the dynamic busi-

ness process using the integrated security model and

the derived security policies. It is capable of simu-

lating possible execution paths and detecting poten-

tial security breaches in the near future. Trustworthi-

ness of the events to be processed is at the heart of

secure event processing. In ADiWa, we are develop-

ing methods to establish trusted relationships between

event processing components to avoid the overhead

of authenticating each event individually. Plausibility

checks are used to detect compromised components.

4 OUTLOOK

In this paper, we provide a very brief overview of

the motivation, progress, and goals of the ADiWa

project. In line with the overall concept, the differ-

ent facets of the research activities deliver a holistic

solution framework that covers the various challenges

in the ﬁeld of event-based system implementations.

While the described event processing functionalities

increase the needed transparency, the enhanced busi-

ness model design and execution improve the ﬂexibil-

ity of business process adaptations. By that, we aim to

close the gap between technical possibilities and their

business usage and, thus, to demonstrate the business

value of our overall concept. In this respect a deep and

thorough evaluation of the concept and the functional-

ities is our benchmark. Leveraged by our research ac-

tivities, we intend to raise the research ﬁeld of event-

based system implementations one step higher and to

provide a fertile ground for future research projects.

ACKNOWLEDGEMENTS

The project was funded by means of the German Fed-

eral Ministry of Education and Research under the

promotional reference 01IA08006. The authors take

the responsibility for the contents.

REFERENCES

Basin, D., Doser, J., and Lodderstedt, T. (2006). Model

driven security: From uml models to access control

infrastructures. ACM Transactions on Software Engi-

neering and Methodology, 15(1):39–91.

Bezivin, J., Bouzitouna, S., Del Fabro, M., Gervais, M.,

Jouault, F., Kolovos, D., Kurtev, I., and Paige, R.

(2006). A canonical scheme for model composition.

Lecture Notes in Computer Science, 4066:346–360.

Dadam, P. and Reichert, M. (2009). The ADEPT project:

a decade of research and development for robust and

ﬂexible process support. Computer Science-Research

and Development, 23(2):81–97.

Graessle, P., Schacher, M., Grc, P., et al. (2006). Agile Un-

ternehmen Durch Business Rules. Springer.

Hallerbach, A., Bauer, T., and Reichert, M. (2009). Conﬁg-

uration and management of process variants. Hand-

book on BPM.

Hapner, M., Burridge, R., Sharma, R., Fialli, J., and Stout,

K. (2002). Java Message Service Speciﬁcation v1. 1.

Sun Microsystems, Inc.

Heusler, K., Stolzle, W., and Bachman, H. (2006). Sup-

ply Chain Event Management Grundlagen, Funk-

tionen und potenzielle Akteure. Wirtschaftswis-

senschaftliches Studium, 35(1):19.

Hinze, A., Sachs, K., and Buchmann, A. (2009). Event-

based applications and enabling technologies. In Pro-

ceedings of the Third ACM International Conference

on Distributed Event-Based Systems, page 1.

Kawamura, T., Fukuhara, T., Takeda, H., Kono, Y., and Ki-

dode, M. (2007). Ubiquitous memories: a memory ex-

ternalization system using physical objects. Personal

and Ubiquitous Computing, 11(4):287–298.

Keller, G., Nuettgens, M., and Scheer, A. (1992). Semantis-

che prozessmodellierung auf der grundlage ereignis-

gesteuerter prozessketten (epk). Veroeffentlichungen

des Instituts fuer Wirtschaftsinformatik, 89.

Luckham, D. (2002). The power of events: an introduction

to complex event processing in distributed enterprise

systems. Springer.

Muhl, G., Fiege, L., and Pietzuch, P. (2006). Distributed

Event-Based Systems. Springer.

Paschke, A. and Kozlenkov, A. (2009). Rule-Based Event

Processing and Reaction Rules. In Proceedings of the

2009 International Symposium on Rule Interchange

and Applications, pages 53–66. Springer.

Pesic, M. and van der Aalst, W. (2006). A declarative ap-

proach for ﬂexible business processes management.

LNCS, 4103:169.

Riss, U., Rickayzen, A., Maus, H., and van der Aalst, W.

(2005). Challenges for Business Process and Task

Management. Journal of Universal Knowledge Man-

agement, 0(2):77–100.

Riss, U. V., Jarodzka, H. M., and Grebner, O. (2007).

Pattern-based task management & implicit knowl-

edge. In 4th Conference on Professional Knowledge

Management. GITO Verlag Berlin.

Rodriguez, A., Fernandez-Medina, E., and Piattini, M.

(2007). M-BPSec: A method for security requirement

elicitation from a UML 2.0 business process speciﬁca-

tion. Lecture Notes in Computer Science, 4802:106.

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

376

Schmidt, B. and Schief, M. (2010). Towards Agile Business

Processes Based on the Internet of Things. Advanced

Manufacturing and Sustainable Logistics.

Schneider, M. and Kroener, A. (2008). The smart pizza

packing: An application of object memories. In IE’08.

von Ammon, R., Greiner, T., Paschke, A., Springer, F.,

and Wolff, C. (2008). Event-Driven Business Process

Management. OBJEKTSpektrum.

Wolter, C., Menzel, M., Schaad, A., Miseldine, P., and

Meinel, C. (2009). Model-driven business process se-

curity requirement speciﬁcation. Journal of Systems

Architecture, 55(4):211–223.

THE ADIWA PROJECT - On the Way to Just-in-Time Process Dynamics based on Events from the Internet of Things

377