Thesis
Reference
Conceptual framework for the steering of Information Systems Evolution
OPPRECHT, Wanda
Abstract
The main challenge for steering IS evolution is to cope with the uncertainty inherent to any IS change, while taking into consideration its complexity due to the entanglement of its multiple dimensions: regulation, information, activity and technology. We observe that i) steering IS evolution requires understanding its IS domain, ii) its impacts are difficult to predict and iii) the guidance for IS evolution steering is almost nonexistent. Consequently, the main goal of this thesis is to provide an approach to reduce uncertainty by exploiting the information available in the IS and by considering its multiple dimensions. In particular, we intend to reach four interrelated sub-goals: 1) to propose a steering information kernel which integrates the multiple IS dimensions into one model, 2) to provide a generic model of IS evolution, 3) to provide analysis perspectives for the evolution and 4) to provide guidance for IS evolution steering.
OPPRECHT, Wanda. Conceptual framework for the steering of Information Systems Evolution. Thèse de doctorat : Univ. Genève, 2014, no. SES 838
URN : urn:nbn:ch:unige-354083
DOI : 10.13097/archive-ouverte/unige:35408
Available at:
Conceptual Framework for the Steering of Information Systems Evolution
THÈSE
présentée à la Faculté des sciences économiques et sociales de l’Université de Genève
par
Wanda OPPRECHT
sous la direction de prof. Michel LEONARD et
prof. Jolita RALYTE
pour l’obtention du grade de
Docteur ès sciences économiques et sociales mention systèmes d’information
Membres du jury de thèse:
M. Eric DUBOIS, Professeur, Centre Henri Tudor, Luxembourg M. Abdelaziz KHADRAOUI, Chargé de cours
M. Dimitri KONSTANTAS, Professeur, président du jury M. Jean-Henry MORIN, Professeur
Thèse no 838
Genève, 28 mars 2014
La Faculté des sciences économiques et sociales, sur préavis du jury, a autorisé l’impression de la présente thèse, sans entendre, par là, n’émettre aucune opinion sur les propositions qui s’y trouvent énoncées et qui n’engagent que la responsabilité de leur auteur.
Genève, le 28 mars 2014
Le doyen
Bernard MORARD
Impression d’après le manuscrit de l’auteur
Abstract
The main challenge for steering IS evolution is to cope with the uncertainty inherent to any IS change, while taking into consideration its complexity due to the entanglement of its multiple dimensions:
regulation, information, activity and technology. The actors responsible of IS steering face paradoxical tensions which they must be able to manage, such as between exploration which is seeking for flexibility and exploitation which is seeking for stability.
We observe that i) steering IS evolution requires understanding its IS domain, ii) its impacts are difficult to predict and iii) the guidance for IS evolution steering is almost nonexistent. Consequently, the main goal of this thesis is to provide an approach to reduce the uncertainty inherent to the IS evolution, by exploiting the information available in the IS and by considering its multiple dimensions.
In particular, we intend to reach four interrelated sub-goals: 1) to propose a steering information kernel which integrates from the outset the multiple IS dimensions into one model, 2) to provide a generic model of IS evolution, 3) to provide analysis perspectives for the evolution and 4) to provide guidance for IS evolution steering.
To this purpose, we build a metamodel for IS evolution steering (IS-SM) which homogeneously integrates the activity, regulation and information IS dimensions. IS-SM is an information kernel which is generic to any organisation, and which dynamically supports the potential steering of several IS in the organisation as well as their integration with services.
In order to understand the evolution and identify its impacts, we develop three models (structural, dynamical and impact-related) which apply to generic situations of evolution. They are based on IS-SM entities and on their primitive methods which we consider as the elementary particles of any evolution.
However, the impact analysis of an evolution is often too complex to conduct due to the number of entities and possible points of view it implies. Consequently, we define two perspectives based on responsibility for facilitating this analysis by reducing its complexity. The first perspective looks at the information impacts (Ispace), while the second perspective looks at the regulatory impacts (Rspace).
Finally, we propose a method for guiding the steering actors which comprehends a product model for IS evolution steering (INFORM-ES) as well as a process model for the steering of evolution (GUID-ES).
The former integrates IS-SM and the evolution models and is extended in order to serve the purposes of GUID-ES which is an intention-driven process described with formal guidelines.
Our contribution provides a concrete guidance for steering IS evolution, which is applicable to any type of organisation. It unveils the strong potentialities of IS models exploitation where information represents a mean both to address strategic concerns, and provide related operational support for decision-making in the context of IS evolution.
Résumé
Le principal enjeu du pilotage de l’évolution d’un SI est de gérer l’incertitude qui est inhérente à tout changement, tout en considérant la complexité due à l’intrication de ses dimensions: régulation, information, activité et technologie. Les acteurs responsables du pilotage du SI sont confrontés à des tensions paradoxales qu’ils doivent être capable de gérer, telle que l’exploration et l’exploitation: l’une à la recherche de la flexibilité et l’autre de la stabilité du SI.
Nous constatons que 1) le pilotage d’un SI nécessite la compréhension de son domaine métier, 2) ses impacts sont difficiles à prévoir et 3) le pilotage de l’évolution d’un SI est presque inexistant. Par conséquent, l’objectif principal de cette thèse est de fournir une approche pour réduire l’incertitude inhérente à une evolution de SI, en exploitant l’information à disposition dans le SI et en prenant en compte ses multiples dimensions.
En particulier, nous entendons atteindre les quatre sous-objectifs suivants: 1) proposer un noyau informationnel pour le pilotage qui intègre, en amont, les multiples dimensions du SI dans un même modèle, 2) fournir un modèle générique d’évolution SI, 3) fournir des perspectives facilitatrices pour l’analyse de l’évolution et 4) fournir un guidage pour le pilotage de l’évolution de SI.
Dans ce but, nous construisons un méta modèle pour le pilotage de l’évolution SI (IS-SM) qui intègre de façon homogène les dimensions activité, régulation et information. IS-SM est un noyau informationnel générique qui peut s’appliquer à n’importe quel type d’organisation et qui supporte de façon dynamique le pilotage de plusieurs SI dans une organisation ainsi que leur intégration avec des services.
Pour comprendre l’évolution et identifier ses impacts, nous développons trois modèles (structurel, dynamique et impact) qui s’appliquent à n’importe quelle situation d’évolution. Ils sont basés sur les entités d’IS-SM et sur leurs méthodes primitives que nous considérons comme les particules élémentaires de n’importe quelle évolution.
Cependant, l’analyse de l’impact d’une évolution est souvent trop complexe à conduire et ceci dû au grand nombre d’entités impliquées et de points de vue possibles. Par conséquent, nous définissons deux perspectives basées sur la responsabilité pour faciliter cette analyse et réduire sa complexité. La première perspective s’intéresse aux impacts informationnels (Ispace) tandis que la seconde perspective s’intéresse aux impacts régulatoires (Rspace).
Finalement, nous proposons une méthode pour guider les acteurs du pilotage. Elle est composée d’un modèle produit pour le pilotage de l’évolution SI (INFORM-ES) et d’un modèle de processus pour le pilotage de l’évolution (GUID-ES). INFORM-ES intègre et étend IS-SM et les modèles d’évolution pour répondre aux objectifs de GUID-ES qui est un processus dirigé par les intentions et qui est décrit avec des directives formelles.
Notre contribution fournit un guidage concret pour le pilotage de l’évolution SI, qui est applicable à n’importe quel type d’organistion. Elle permet d’illustrer le fort potentiel des modèles SI où l’information représente à la fois un moyen de répondre à des problématiques stratégiques, mais aussi de fournir un support opérationnel dédié à la prise de décision dans le contexte de l’évolution SI .
Acknowledgments
The conducting and the writing of a PhD thesis is not an easy project. However, during this time, I have been offered very good research conditions and received support and help from many people to whom I would like to address here my gratitude.
First and foremost I would like to thank my supervisors to whom I am profoundly indebted:
Prof. Michel Léonard and Prof. Jolita Ralyté.
Prof. Michel Léonard taught me the exploitation of powerful conceptual tools and strategies to analyse IS situations from an information perspective. He directed my work with inciting a very challenging and innovative approach.
Prof. Jolita Ralyté taught me the particular significance of situational methods development and use for IS engineering and evolution. She directed my work with inciting very relevant questioning about goals and quality achievement.
I am very grateful to the members of the jury: Prof. Eric Dubois, Dr. Abdelaziz Khadraoui, Prof. Dimitri Konstantas (President of Jury) and Prof. Jean-Henry Morin who accepted to assess my work. They greatly contributed to it by their expertise, advises and encouragement.
I would like to like to thank my past and present colleagues at CUI who generously shared with me their invaluable experience: Nicolas Arni-Bloch, Giovanna Di Marzo Serugendo, Verena Kantere, Laurent Moccozet, Mehdi Snene, Kate Wac and Anastasiya Yurchyshyna.
For their friendly and unique accompagnying, I want to thank very much: Hélène de Ribaupierre, Katerina Stamou, Camille Tardy and Christiana Tsiourti.
I also wish to thank the librarians and administrative assistants of CUI for their diligent and proactive support during the course of my PhD journey.
Finally, but not least, I wish to express a special and immense gratitude to Judith, Olga, my family and my friends who are an important and unlimited source of support.
To Laurent, Maxime and Morgane;
To PEM, Véra and Yola.
Table of Content
Abstract ... 3
Résumé ... 5
Acknowledgments ... 7
1. Introduction ... 17
1.1. Research Settings... 18
1.1.1. Information System ... 18
1.1.2. IS Dimensions ... 19
1.1.3. IS Evolution Steering ... 20
1.1.4. Span of IS Steering ... 20
1.1.5. IS Conceptual Model ... 20
1.2. Observations ... 21
1.3. Underlying Assumptions ... 22
1.4. Goals and Requirements ... 23
1.5. Nature of the Contribution ... 24
1.6. Overview of the Contribution ... 24
1.7. Running Example ... 26
1.7.1. Description of the Organisation ... 26
1.7.2. IS Architecture ... 27
1.7.3. Situation of the Evolution ... 28
1.8. Research Process ... 29
1.9. Outline ... 29
1.10. Quality of models... 30
1.11. Notation ... 31
1.11.1. Static modelling ... 31
1.11.2. Lifecycle modeling ... 32
2. State of the Art ... 33
2.1. Framework ... 33
2.2. Subject world ... 35
2.2.1. Steering system ... 35
2.2.2. Steering structures ... 37
2.2.3. Steering processes ... 38
2.3. Usage world ... 39
2.3.1. Define evolution ... 39
2.3.2. Guide evolution ... 41
2.3.3. Identify evolution risks ... 44
2.4. System world ... 45
2.4.1. Evolution metamodels ... 45
2.4.2. Enterprise architecture models ... 48
2.4.3. Alignment models ... 50
2.5. Development world ... 53
2.5.1. Evolution methods ... 54
2.5.2. Risks identification methods ... 56
2.5.3. Tools ... 58
2.6. Synthesis and position of our approach ... 60
3. IS Steering Metamodel ... 65
3.1. Model Language ... 66
3.1.1. Existential Dependency ... 67
3.1.2. Specialisation Relationship ... 67
3.2. Megamodel ... 68
3.3. Activity Model ... 71
3.3.1. Activity model description ... 71
3.4. Regulatory Model ... 73
3.4.1. Regulatory model description ... 73
3.5. Information Model ... 74
3.5.1. Generic IS level Metamodel ... 75
3.5.2. Generic Level Metamodel Description ... 76
3.5.3. IS Level Metamodel ... 78
3.5.4. Service Level Metamodel ... 81
3.5.5. Metamodel Class Description ... 82
3.6. Models Inter-Relations ... 84
3.6.1. Role ... 84
3.6.2. Hinge Between Information and Activity Dimensions ... 86
3.6.3. Hinge Between Information and Regulation Dimensions ... 86
3.6.4. Hinge Between Activity and Regulation Dimensions ... 88
3.7. Primitive Methods ... 90
3.8. Conclusion ... 91
4. Ispace and Rspace ... 93
4.2. Language ... 95
4.3. Information Space (Ispace) ... 95
4.3.1. Ispace of a set of X ... 96
4.3.2. Ispace of a role r ... 96
4.3.3. Ispace of a person p ... 98
4.3.4. Ispace of a position po ... 99
4.3.5. Ispace of an activity a ... 100
4.3.6. Ispace of a service s ... 101
4.4. Regulatory Space (Rspace) ... 103
4.4.1. Rspace of a set of x ... 103
4.4.2. Strict Rspace of a role r ... 104
4.4.3. Info-Regulatory Space of a role r ... 104
4.4.4. Rspace of a role r ... 106
4.4.5. Rspace of a person p (through Activity)... 107
4.4.6. Rspace of a person p (through Position)... 108
4.4.7. Rspace of a person p ... 109
4.4.8. Strict Rspace of a position po ... 110
4.4.9. Rspace of a position po (through Activity) ... 111
4.4.10. Info-Regulatory Space of a position po ... 112
4.4.11. Rspace of a position po ... 113
4.4.12. Strict Rspace of an activity a ... 114
4.4.13. Extended Rspace of activity a ... 115
4.4.14. Info-Regulatory Space of activity a ... 116
4.4.15. Rspace of an activity a ... 118
4.4.16. Rspace of service s (through Role) ... 119
4.4.17. Rspace of service s (through Integrity_Rule) ... 120
4.4.18. Rspace of service s ... 121
4.5. Conclusion ... 122
5. The Evolution Models ... 125
5.1. Evolution Structural Model ... 126
5.2. Evolution Lifecycle ... 131
5.3. Primitives lifecycle ... 133
5.4. Coordination protocols ... 134
5.4.1. General coordination rules ... 134
5.4.2. Cardinality ... 135
5.5. Transactional models ... 136
5.5.1. The evolution-primitive coordination ... 136
5.5.2. The composite-atomic primitives coordination ... 137
5.5.3. The subsequent evolution-triggering evolution coordination... 137
5.6. Definition of the evolution impact space ... 138
5.6.1. Decision-making space : DM_Space ... 139
5.6.2. Dynamic space : Dyn_Space ... 139
5.7. Evolution structural and impact models related ... 141
5.8. Concurrent evolutions ... 142
5.8.1. Disjoint impact spaces ... 142
5.8.2. Overlap situations ... 142
5.9. Conclusion ... 143
6. Guidance for IS Evolution Steering ... 145
6.1. Modelling Language ... 146
6.2. Information Model for Evolution Steering (INFORM-ES)... 148
6.3. Guidance for Evolution Steering (GUID-ES) ... 153
6.3.1. Build evolution ... 154
6.3.2. Assess organisational risks ... 171
6.3.3. Do the transition ... 186
6.3.4. Operate the evolution ... 192
6.4. Conclusion ... 197
7. Conclusion and Perspectives ... 199
7.1. Contributions ... 199
7.2. Evaluation ... 201
7.3. Implications for research ... 201
7.4. Implications for practice ... 202
7.5. From limitations to perspectives ... 202
7.5.1. Technology dimension of IS-SM ... 202
7.5.2. Portfolio of evolutions ... 203
7.5.3. Regulatory and information impacts ... 203
7.5.4. Library of evolution steering patterns ... 203
7.5.5. Initiatives as source of evolution ... 204
7.6. Informating rather than automating ... 204
References ... 205
Appendix A - Ascendant / Descendant algorithm ... 213
Appendix B - Evolution Model Integrity Rules ... 215
Appendix D - IS-SM Activity Specification ... 219
Appendix E - IS-SM Regulatory Specification ... 227
Appendix F - IS-SM Generic IS Specification ... 231
Appendix G - IS-SM IS Specification ... 257
Appendix H - IS-SM Service Specification ... 281
Appendix I - IS-SM Metamodels Relationships Specification ... 307
Table of Figures ... 315
List of Tables ... 323
1. Introduction
A state without the means of some change is without the means of its conservation.
(Edmund Burke, 1790)1
The main challenge for the steering of information system (IS) evolution is to cope with the uncertainty which is inherent to any IS change, while taking into consideration its complexity due to the entanglement of its multiple dimensions: regulation, information, activity and technology.
Besides operational importance, an information system has also strategic significance for the organisation2. Indeed, it holds key information for the organisation, and represents a strategic resource which underpins its key functions and decision-making processes. This is particularly true for service-oriented organisations whose number constantly grows. These organisations are characterised by transdisciplinary and collaborative work where actors are often both knowledge providers and consumers. The strategic decisions are generally made in situations which are distinguished by their uniqueness and by their uncertainty (e.g. in case of business innovation). These decisions may have serious consequences and could jeopardise the organisation's sustainability [Lesca and Mancret 2007].
In order to ensure IS sustainability (and hence, its information sustainability), its evolution must be understood and supported. Indeed, evolution is inherent to any IS and evolving is its permanent condition. This is due to its ever-changing environment where contingency may arise from any dimension of the IS: activity (e.g. establishment of new business processes, re-organisation of business units, companies mergers or acquisitions), technology (e.g.
introduction of a new hard or soft technology), or regulation (e.g. law abrogation or modification, adoption of new industrial standards). IS evolution is necessary but also presents several risks towards its sustainability and further changes. Some of the problems
1 Burke, Edmund. Reflections on The Revolution in France, 1st November 1790 (Letter) – Transcript available at: http://eudocs.lib.byu.edu/index.php/France:_1789_-_1871 [last accessed: 20/02/2014]
2 In the following, we use the term "organisation" to refer to a political group, an association, a commercial enterprise, a governmental or non-governmental institution.
could be: inconsistency with IS requirements, loss of regulatory compliance, conflicting evolutions, hindrance to undoing, information loss and the need to change the whole system when only part of it is impacted.
IS steering has become more and more complex, and its actors face today a number of major challenges presenting potential risks but also opportunities which they must understand. These challenges include actors' mobility, multiplicity of IS (due to the difficulty to evolve legacy systems, to integrate the organisation into partnerships, etc.), service orentation, massive IT outsourcing, advent of Cloud computing services, organisation involvement into social networks, adaptation to big data.
The actors responsible of IS steering need guidance to undertake IS evolution taking into consideration the multiple IS concerns. They must be able to look for the right balance between the political, technological and financial concerns for delivering a sustainable value to the organisation with the information as a starting point.
1.1. Research Settings
In this section, we define the relevant concepts applicable to this thesis. We focus on the following notions: information system, IS dimensions, IS evolution steering, span of IS steering and conceptual model.
1.1.1. Information System
Multiple convergent (or divergent) points of view and definitions of IS have been produced by various researchers since the mid-sixties [Hirschheim and Klein 2012] [Alter 2008]
[Carvalho 2000] [Davis 2000]. After [Carvalho 2000], many definitions of IS have in common the idea of dealing with information, to be related to organisations or to the work carried out in organisations, and to be related to the information technology. Some definitions emphasise on social or organisational concerns while others emphasise on technical or mathematical ones [Alter 2008].
The FRISCO Report [Falkenberg et al. 1998] distinguishes two meanings:
in the narrow sense: "A computerised information sub-system is a sub-system of an information system, whereby all actions within that sub-system are performed by one or several computer(s)."
in the broad sense: "An information system is a sub-system of an organisational system, comprising the conception of how the communication- and information–
oriented aspects of an organisation are composed (e.g. of specific communicating, information-providing and/or information-seeking actors, and specific information- oriented acts and how they operate, thus describing the (explicit and/or implicit) communication-oriented and information–providing actions and arrangements
For the subject we intend to develop in this thesis, we use the following definition produced by [Bodart and Pigneur 1989] which we adapt in English3:
An IS is a structure formed by: a set of information which partly represents organisational facts; treatments which comprise the processes of acquisition, memorisation, transformation, search, presentation and communication of information; organisational rules which regulate the execution of informational treatments; and human and technical resources which are required to operate the IS.
The goal of an IS is to support the execution of human activities within an organisation.
These activities have an operational, tactical or strategic nature.
1.1.2. IS Dimensions
The definition of an IS relies on several complementary dimensions. Specifically, we consider the four following dimensions: regulation, information, activity and technology. The regulatory dimension [Khadraoui 2007] relates to the repositories gathering and linking necessary, unquestionable and invariant concepts such as those originating form legal bases or industrial standards (examples of entities are: concept, regulatory role or regulatory rule).
The information dimension relates to the information exploitation of the regulatory elements through four sub-dimensions [Arni-Bloch 2009]: static, dynamic, rules and responsibilities.
The activity dimension concerns the activities supported by the information elements (its entities are for example activity, business role, business process, business rule). Finally, the technology dimension relates to the technical implementation of the IS (human-computer interface, database, and algorithm are examples of entities in this dimension). Each of these dimensions can be represented by a dedicated model.
The integration of these dimensions is a main concern for both IS practitioners and researchers. It is notably expressed by the need for the alignment between the activity (business) and the technology (IT) dimensions (see below 2.4.3), with for example, frameworks of Enterprise Architecture integrating these dimensions (see below 2.4.2).
However, this integration is difficult to achieve and to retain partly due to the permanent evolution of their entities.
The actors of IS evolution steering have an important responsibility: they must take into account the intertwinement of several IS dimensions. Indeed, the failure to take into account any of these aspects would inevitably lead to the failure of the evolution project and even more importantly to IS inconsistency. In this thesis, we focus our attention on the regulation, information and activity dimensions. The technology dimension is not in the scope of this thesis.
3 In French: [Un SI est une] "construction formée: d'ensembles d'informations qui sont des représentations partielles de faits qui intéressent l'institution; de traitements, qui constituent des procédés d'acquisition, de mémorisation, de transformation, de recherche, de présentation et de communication d'informations; de règles d'organisation qui régissent l'exécution de traitements informationnels; de ressources humaines et techniques requises pour le fonctionnement du SI".
1.1.3. IS Evolution Steering
We define, after [Mélèse 1991], the steering as a set of processes, which enables for the control and guides the transformations of a system4. As already said, the ultimate goal of IS steering is the sustainability of information which is of prime value for the organisation.
Sustainability here refers to a dynamic equilibrium balancing the need for both exploration and exploitation where the organisation's unity and identity 5 are preserved without compromising its further evolutions. Thus, the intertwinement of the IS and organisation concerns creates the necessity for the organisation and the IS steering actors to consider, altogether, the situations of evolution.
1.1.4. Span of IS Steering
Often, there are not only one IS, but several IS to be taken into account in the same organisation. Either wholly (or partly) dependent or independent from each other, they support the activities of the organisation at different organisational levels (i.e. strategic, tactic, operational). Some of them have been developed and evolve in silos and therefore testify to the consequences of the organisational restructuration (e.g. a merger of two businesses, a fusion of two departments or two political counties), the evolution of the organisation activities (the development of a portfolio of B2B services for example), or the involvement of the organisation into partnerships. This situation engenders important issues regarding the IS interoperability at the information, technical and organisational levels, and it is particularly manifest when the organisation aims to adopt a service-oriented paradigm [Khadraoui et al. 2011]. Therefore, we assume in our research that several IS are potentially at stake for the IS steering in any organisation.
1.1.5. IS Conceptual Model
An IS conceptual model is commonly referred to as an abstract representation of the elements in a particular domain, which is expressed in a semi-formal or formal visual language. Since IS nature is complex, several models are usually needed in order to describe it with rigour, among which: the activity, regulatory, technological and information models [Léonard 2006]. The goal of a conceptual model is to gather and structure knowledge on the IS domain in order to allow its engineering and/or evolution. A conceptual model must be considered as the key element of an IS.
Multiple languages (metamodels) have been developed in order to build conceptual models, among which the most known are Entity-Relationships (E/R) [Chen 1976] and Unified Modelling Language (UML) [OMG 2011]. [Krogstie 2012] identifies the key quality aspects of the IS conceptual model: physical (persistence, protection, currency, availability), empirical (variety of elements distinguished, error frequencies, ergonomics), syntactic (syntax compliance), semantic (validity and completeness of the correspondence between the model and the modelling domain), pragmatic (comprehension of the model by participants),
social (agreements in the knowledge, interpretation and model) and deontic (costs and benefits of modelling). These quality aspects are important because conceptual models include subjectivity (they are not unbiased), and IS engineering and evolution decisions of actions are largely based on them.
1.2. Observations
IS Steering requires to take strategic decisions which are characterised by a state of information deficiency, in other words by uncertainty [ISO 2009]. Indeed, the model of the IS domain may be missing or without sufficient adequacy (correspondence between the model and the reality it represents).
It would be over-simplistic and misleading to strive to utterly remove any uncertainty.
Indeed, after [Paquet 2013], no individual or organisation has the resources, the power and the required information to define a simple but transcendent goal, and the pursuit of simplistic goals would lead to negative effects. However, uncertainty situations may have paralysing effects, and thus, go so far as to threaten the organisation. Consequently, we only intend to reduce uncertainty.
Hence, we make three further observations:
the steering of IS evolution requires understanding its IS domain;
the impacts of IS evolutions are difficult to predict;
the guidance for IS evolution steering is almost nonexistent.
To begin, controlling the transformation of an IS through steering requires understanding it.
But the understanding of an IS evolution is complex for several reasons. One of the reasons is that an IS evolution is not easy to observe. This is mainly due to the intertwinement of the IS domain and the artificial world which is inherent to any IS. Another reason is that the origins and impacts of an IS evolution are to be found in multiple interrelated IS dimensions (information, activity, regulation, technology) for which no IS stakeholder has the entire knowledge, nor a comprehensive model. Although several evolution models have been produced (see below 2.4.1), there is no commonly accepted definition or model of an IS evolution which takes into account various IS dimensions and which is generic.
Consequently, it is difficult to understand a given situation of IS evolution.
Our second observation is that the impacts of an IS evolution are difficult to predict. One reason is that from direct and identified impacts, there are ripple effects. Another reason is that there is no symmetry between direct and indirect impacts. Where a direct impact is low, its ripple effects may be heavy. Moreover, an IS evolution has implications on the IS domain and, conversely, the IS domain has implications on the IS evolution. Consequently, it becomes impossible to be dispensed from the use of the IS during its evolution (analogous to the works in a train station where trains must continue to pass).
Finally, our third observation is that the guidance for IS evolution steering is almost nonexistent. Indeed, IS steering has received little attention from IS researchers. Existing ad hoc approaches often refer to good practices.
1.3. Underlying Assumptions
In the context of the steering of IS evolution, our research assumptions acknowledge the following:
the domain information and the domain knowledge are key elements for the actors in charge of IS steering;
the use of conceptual models is the sole and most reliable way to know the IS;
the best IS steering system is the one based on its model.
Knowledge is of prime importance in the steering of IS evolution as it provides the basis for the action [Davis 2000]. Knowledge relies on information elements which it connects together in order to construct a meaning. Although it is impossible for anybody to know the whole IS (even more when there are several IS and services), we believe that the key source for IS evolution steering is the information extracted from the IS itself and which can be analysed through specific lenses.
We share the point of view of [Olivé 2005] who conveys the message that conceptual models (in [Olivé 2005] "schemas") should be the centre of the IS development. In line with this statement, we argue that conceptual models should be the centre of the IS evolution steering, too. IS and its evolutions are complex artefacts which can be expressed in a meaningful way with the help of conceptual models. This is particularly relevant for the understanding of the intertwinement of the several IS dimensions [Léonard 2006] which cannot be undertaken otherwise. The conceptual models are necessary for ensuring the IS unity. However, a conceptual model represents only an image of the perceived reality that is a hypothesis. In the case of IS, we go even a step further and claim that this hypothesis may never be validated.
Finally, inspired by [Conant and Ross Ashby 1970], we assume that a sound and reliable IS steering must be based on the same model as the IS itself. This assumption is even stronger when the steering needs to cope with a highly complex system. It further implies that a steering system needs to continuously learn from its environment to achieve its sustainability.
1.4. Goals and Requirements
In line with the aforementioned assumptions, the main goal of this thesis is to facilitate the IS evolution with reducing the uncertainty inherent in the IS evolution by exploiting the information produced by the IS, and with considering the multiple IS dimensions.
In particular, we intend to reach the four interrelated sub-goals:
to propose a kernel of information for the steering of IS evolution with the integration of the multiple IS dimensions;
to provide analysis perspectives for the evolution;
to provide a generic model of IS evolution;
to provide guidance for IS evolution steering.
That arises four corresponding research questions:
I. Which kernel of information is required for the steering of IS evolution?
There we rely on the assumption that IS evolution steering requires the information knowledge which is at stake in the IS evolution. We aim to capture and interrelate the regulatory, activity and information knowledge, as well as the specific knowledge related to the IS under consideration in order to lower the level of uncertainty of the actors responsible of this IS evolution steering.
II. How to analyse a situation of IS evolution?
The situations of IS evolution are complex to analyse due to the large number of elements at stake. Consequently, we aim here to provide a set of perspectives of analysis which identifies key elements whose comparison, before and after the evolution simulation, makes possible the detection of potential risks.
III. How to understand an IS evolution?
In order to respond to this question, we aim at providing a model for a generic IS evolution, regardless its origin (regulation, activity, information, technology or mixed), span (local, global or glocal6), granularity (at the service or IS level) and temporal aspects (execution and validity time). The model of IS evolution must be defined on the IS model in order to enable the simulation of such an evolution.
IV. How to steer an IS evolution?
Having reached the three preceding sub-goals with the integration of the multiple IS dimensions in one model, the perspective of analysis and the definition of a generic evolution, this last sub-goal consists in providing a guidance for the IS evolution steering.
This guidance must be adaptable to any organisational context, and to any IS.
6 We use the term "glocal" for the entanglement of a local and a global situation.
The four aforementioned objectives have several general requirements. First of all, the language used to develop the conceptual models of the approach must be: simple, explicit, independent of any disciplinary knowledge, executable in a production environment and able to evolve [Olivé 2005]. Moreover, the developed approach must be generic in order to be applied in the IS evolution steering of any type of organisation, and it has to be appropriately formalised to be supported by technical tools. Finally, the developed approach must be able to cope with the problem of conceptual discrepancy i.e. to understand these high level objectives, and, hence, to translate them operationally.
1.5. Nature of the Contribution
In order to address the goals of this thesis, we offer a guidance to the actors of IS evolution steering in the form of a situational method. A method is defined in [Prakash 1999] as a couple of two interrelated models: a product model and a process model (Figure 1). The product model defines the set of concepts and constraints which are used by an engineer for defining a product together with their properties and relationships. A method is composed of one or several product model(s). The process model defines how to use the concepts defined within a product model.
Figure 1 - Method metamodel [Adapted from Ralyte_2001]
A situational method is a development method adapted to the situation of the project at hand [Harmsen et al. 1994]. It can be applied in various IS domains such as strategic alignment [Etien 2006] [Thevenet 2009], governance [Gericke et al. 2009] [Claudepierre 2010] or service engineering [Arni-Bloch 2009] for example. It fits to the nature of IS evolution steering which must adapt to any organisational context and to any occurring IS evolution.
1.6. Overview of the Contribution
This thesis contributes to the IS research and practice by proposing a novel conceptual framework for the steering of IS evolution. This framework is based on several dedicated models. Figure 2 summarises our comprising with its four constitutive and inter-related elements (a-d):
a) IS Steering Metamodel
With the IS steering metamodel, we define an IS conceptual model which represents a kernel of information for the IS evolution steering. Indeed, it comprehends the activity, regulation and information dimensions and seeks to treat their diverse elements in a
Product_Model P_M Method M_P Process_Model
Binex metamodel [Turki 2005]. However, the issues around and the method for the model population and the legacy system transformation are out of the scope of the present work.
b) Ispace/Rspace Definitions
We introduce the definitions of Ispace/Rspace on the basis of the IS steering metamodel in order to reduce the complexity pertaining to a situation of evolution. Ispace/Rspace are perspectives of analysis based on the concept of responsibility (toward information and regulation) which allow to identify possible risks after the evolution simulation. The formalisation of the Ispace/Rspace definitions is inspired from the relational algebra.
c) Evolution Models
In order to define the various aspects of a generic IS evolution, we develop a set of models.
The static aspects are expressed with the Binex metamodel. The dynamic aspects are represented with a set of lifecycle models whose formulation uses the IASDO metamodel [Thi 2005]. Finally, the evolution impact aspects are modelled with a set of impact models which rely on the entities of the IS steering metamodel.
d) Evolution Steering Method
We build a method for the actors in charge of the IS evolution steering in order to support their activity with i) an information model for the evolution steering (INFORM-ES) and ii) a guidance for the evolution steering (GUID-ES). This method exploits the Ispace/Rspace definitions as well as the set of evolution models. Its guidelines altogether form a situational guidance which adapts to specific situations and may be easily supported by a tool. They use the Map metamodel.
Figure 2 – Overview of the contribution
Although some authors debate on the relevance of pursuing the benefits of both the scientific and the practice communities [DeNisi 1994], we argue that our work reaches both of them. Our approach contributes to the scientific knowledge by providing a novel framework which rigorously defines models and processes for the IS evolution steering thus allowing addressing its complexity. It builds on previous research works which have developed the conceptual modelling languages (Binex, IASDO and Map) and the situational methods. While consistently addressing the actual concerns of the IS steering actors with regard to the inherent and complex IS changes, we also provide relevant answers to the practitioners throughout systematic illustrations. Moreover, the formalisation of our method allows associating it to a CASE (Computer-Aided Software Engineering) tool (e.g. IBM Rational Rose7, Sybase Powerbuilder8) or a meta-CASE tool (e.g. MetaCase MetaEdit9) or to build a CAGE (Computer-Aided Governance Environment).
1.7. Running Example
We now introduce a running example named "Convergence SA" which will be used to illustrate in the different chapters of this thesis. This example is used throughout the thesis to support and clarify our propositions. The design of this example is made simple and free from unnecessary details. First, we introduce the running example by describing the
"Convergence SA" company. Then, we outline its "HR-IS" architecture and we finish with the presentation of the evolution situation.
1.7.1. Description of the Organisation
Based in Geneva (Switzerland), "Convergence SA" is the corporate headquarter of an international company operating in the ITC sector. This example focuses on the company's HR department "HR-Dept" which is structured in several sections among which recruitment and personal development sections. "HR-Dept" responsibilities include the recruitment of employees and the personal development. John Doe holds the position of Assistant in the recruitment section.
The company "Convergence SA" must comply with a regulatory framework which includes, among others, the Swiss Labour Law (LTr10).
7IBM Rational Rose - http://www-03.ibm.com/software/products/us/en/ratirosefami/ - [last accessed:
15 dec. 2013]
8 Sybase Powerbuilder - http://www.sybase.fr/products/modelingdevelopment/powerbuilder - [last accessed: 15 dec. 2013]
9 MetaCase MetaEdit - http://www.metacase.com/mep - [last accessed: 15 dec. 2013]
1.7.2. IS Architecture
"Convergence SA" exploits several IS such as "Finance_IS", "Marketing_IS" and "HR_IS". The activities of the "HR-Dept" are supported by the "HR_IS" and by several IS services11:
"Training_Service" and "Recruiting_Service".
The conceptual class model of "HR_IS" is given in Figure 3. It has been designed with a minimal set of concepts in order to meet the needs of our illustrations.
Figure 3 - Convergence SA HR_IS conceptual model
A person (entity "Person") has one or more professional or private address-es (entity
"Person_Address"). He/she is candidate (entity "Candidate") and/or employee (entity
"Employee"). To a candidate is associated one or more competence-s (entity
"Candidate_Competence"). A competence (entity "Competence") relates to linguistic, technical or managerial skills.
A candidate can apply to one or more vacancy(-ies) (entity "Candidate_Vacancy") each of them related to a position (entity "Position") such as "Sales assistant", "Sales manager" in an organisational unit (entity "Organisational_Unit"), for example: "EU_Sales_Division",
"Asia_Sales_Division" or "Certification_Project". To a position is associated one or more competence-s (entity "Position_Competence") for which a competence level is defined.
11In our approach, a service shares the same metamodel as an IS: it is a specialised IS which is based on one or several IS (see Section 3.5.4, p. 80)
An employee (entity "Employee") has one or more position-s (entity "Position_Employee") for each of which he/she has a work contract (entity "Work_Contract") which defines the type of wages ("hourly rates", "monthly rates", "fixed-rate"), the amount, the percentage, the contract type ("permanent" or "temporary") and the validity. He/she has one or more competence-s (entity "Employee_Competence"). It is required for an employee to have at least half of the required competences for the position he/she is (or is going to be) affected (entity
"Employee_Position_Competence").
For their career development, employees participate to one or more training program-s (entity "Employee_Training_Program") which are organised either internally or externally and resulting in an evaluation (either "pass" or "fail"). Programs are constituted by one or more training course-s (entity "Training_Course") which may have different duration.
Finally, an employee can participate to a project in the organisation (entity
"Employee_Project_Competence") for which he/she endorses a role ("manager", "team member", "tech. support"). Each project is defined with a minimum and maximum number of required members, one or more competence-s (entity "Project_Competence") and validity.
The participation of an employee to a project requires for the employee to have at least one of the competences required for the project.
Two services are implemented: the recruiting and the training services. The recruiting service supports the corporate activities related to the filling of vacancies. This service is defined on the following "HR_IS" classes: "Address", "Candidate", "Candidate_Competence",
"Candidate_Vacancy", "Competence", "Employee" "Employee_Competence",
"Employee_Position_Competence", "Organisational_Unit", "Person", "Person_Address",
"Position", "Position_Competence", "Position_Employee", "Vacancy", "Work_Contract".
The training service supports the activities related to lifelong learning. This service is defined on the following "HR_IS" classes: "Competence", "Employee", "Employee_Competence",
"Employee_Training_Program", "Person", "Training_Program", "Training_Program_Course",
"Training_Course".
Consequently, the two services share several classes: "Competence", "Employee",
"Employee_Competence" and "Person".
1.7.3. Situation of the Evolution
The IS evolution at stake is the change of position of a person inside the same organisational unit. For example JohnDoe leaves the position of assistant in the recruitment section to hold the position of manager in the same section. It is a single and expected evolution which originates from the activity dimension of the IS (Chap. 5 presents in detail the construct of evolution).
For simplicity reasons, we do not discuss here how and by whom this evolution has been initiated. This evolution situation may cause several side effects, such as no more people in charge of a position, no more people in charge of an activity at the same position, no more
the activities among the remaining people in charge of the position) or a new recruitment.
Moreover, there may also be a need for a knowledge transfer, the transmission of "know- how" (e.g. business processes, business rules execution) and "know-what" (e.g. local regulations) type of knowledge.
1.8. Research Process
The selection of a research process is dependent on the research settings and goals. In this thesis we apply a research process which is partly inspired by the design-science. Indeed, the goal of a study in the design science paradigm is to create things that serve humain purposes, therefore gain some utility [March and Smith 1995]. In this research, our aim is to create a framework for facilitating the steering of IS evolution, consequently to serve IS steering actors purposes. However, we only partly comply to design-science guidelines [Hevner et al. 2004] because we do not conduct a deep evaluation of our artefacts (evaluation is discussed below in Section 7.2, p. 201).
Each of our four objectives (see above Section 1.4) has been reached and completed before beginning the next one. The first objective to reach was to propose a kernel of information for the steering of IS evolution, then the second one was to provide analysis perspectives for the evolution, then the third one was to provide a generic model of IS evolution, finally, the last one was to provide guidance for IS evolution steering .
The construction of our artefacts has been conducted through meta modelling, i.e. through a process of abstraction from our study domain. It has fully exploited the knowledge refered to and created in the literature.
1.9. Outline
The thesis is divided into seven chapters as it is illustrated in Figure 4.
Chapter II (p. 33) is dedicated to the state of the art on IS evolution steering for which there is no consensus on its definition, goals, models or methods. It is at the crossroads of several IS research area concerns, among which: Enterprise Architecture (EA), Enterprise Modelling, Business/IT alignment, IS Governance and Risks Management.
Chapter III (p. 65) presents the IS steering metamodel which we have developed for the purposes of IS evolution steering as an information kernel and which is used by our following models.
Chapter IV (p. 93) presents the concepts of informational and regulatory spaces which are later used by the steering guidelines (see Chap. 6). These two constructs are built on the steering metamodel. They aim at providing a perspective facilitating the analysis of an evolution simulation.
Chapter V (p. 125) is dedicated to the definition of the structural, dynamic and impact views of IS evolution. The structural view of an evolution is expressed by an evolution class model.
Its dynamic view is represented by a set the evolution lifecycles and the impact view is formalised by the evolution impact model.
Chapter VI (p. 145) presents guidelines for supporting the steering actor in the process of a planed and permanent evolution with four phases. It uses the IS Steering Metamodel (see Chap. 1), the concepts of Ispace/Rspace (see Chap. 4 and the Evolutions Metamodels (see Chap. 5).
Chapter VII (p. 199) concludes the thesis by summarising its contribution to the IS research and practice and gives insight and directions on the potential future research works.
Figure 4 - Thesis outline and contribution
1.10. Quality of models
Multiple approaches intent to assess the quality of conceptual models such as [Krogstie 2012] and [Thi and Helfert 2007]. The former develops the SEQUAL Framework with the following main inter-related concepts: "Goals of the modelling task", "Audience", "Language extension", "Externalised model", "Modelling domain", "Relevant explicit knowledge of the audience", "Social audience interpretation" and "technical audience interpretation". While the latter differentiates the conceptual model quality and the logical model quality.
For the purpose of IS evolution steering, we consider that the following quality aspects have to be targeted: i) evolution capacity, ii) ease of automation, iii) communication appropriateness and iv) IS complexity comprehension. The evolution capacity of a metamodel (i) positively influences the evolution capacity of its instances. Consequently, we see this aspect as prominent in the discussion about the steering model quality. The ease of
straightforward and where the model causes a minimal mismatch with its technical enactment. The communication appropriateness (iii) relates to the human aspect of the pragmatic quality defined in [Krogstie 2012]. It represents the fit between the model and the audience's interpretation of it. Finally, the IS complexity (iv) stems, amongst others, from its multiple dimensions (activity, regulation and information) as well as from its architecture (IS plurality, services definition) and must be exempt from the steering models.
These quality aspects are assessed in the conclusion of Chapters III-VI.
1.11. Notation
1.11.1. Static modelling
Static modeling is used at the model and the instance levels as it is illustrated in Figure 5.
At the model level, static models are represented with graph of classes. A class is described with a name (and when applicable) a set of attributes and a set of methods. The set of attributes which form the object identifier is followed by two slashes ("//"). Two classes are related either by an existential dependency (plain arrow) or a generalisation/specialisation link (empty arrow).
At the instance level, static models are represented with graph of objects. An object is described with a name and a classification. Two objects are related by an existential dependency link (plain arrow). The instantiation link between an object and a class is represented by a dotted line.
Figure 5 - Notation for the static modelling
The existential dependency link is imperative12 : when Class_C existentialy depends on Class_D, then the existence of object obj2 depends on the existence of one (and one only) object of Class_D. This dependency is permanent throughout the objects lifecycle and when obj1 is inactivated, all its dependent objects are inactivated, too.
1.11.2. Lifecycle modeling
Lifecycle models are represented with bi-partite graphs which are composed of two types of node: object state and transaction [Léonard 2005] as it is illustrated in
Figure 6. When an object changes of state, it may keep (or not) its previous state (with the mechanism of object activation/inactivation).
A transaction uses at least one class object(s) as input (dotted line) and one class object(s) as output (dotted line), and may be defined by a pre-condition (transaction triggering condition) and/or a post-condition (transaction termination condition) which are textually specified.
Figure 6 Notation for dynamic modelling
2. State of the Art
This chapter presents the state of the art on IS evolution steering13. In this chapter we demonstrate that there is no consensus on IS steering definition, goals, models and methods. This domain is at the crossroads of several IS research areas such as: Enterprise Architecture (EA), Enterprise Modelling, Business/IT alignment, IS Governance and Risks Management. With our literature review, we have identified refered approaches which contribute to the understanding of IS evolution steering stakes.
The chapter is organised as follows: in the next section we present a framework for related literature analysis. This framework allows to structure, in the following sections, the review and discussion of the literature. Finally, in the last section our approach is positioned with regards to the reviewed research works.
2.1. Framework
In order to structure our state of the art, we use the four-world framework (Figure 7) 14 which was originally developed for classifying software components [Prieto-Díaz 1991] and which was later applied for the understanding of various IS engineering disciplines (for example: [Jarke et al. 1992] [Rolland et al. 1998] [Nurcan et al. 2002]). We consider IS evolution steering from four different but complementary view points ("worlds" hereafter):
subject, system, usage and development. A set of facets is associated to each world in order to study it in its diversity. The facets are described with keywords identified in the literature review. Their values may be of simple, set or enum(-eration) type. The values of each facet are summarised in a table at the end of the facet description.
13 Although in English the term "control" is more often used, we prefer using the term "steering"
because the former carries a connotation of automation.
14 All the figures are re-drawn by the author.
Figure 7 - Four-world metamodel (after [Claudepierre 2010])
For the purposes of this analysis, the subject world presents IS evolution steering as our subject of analysis and identifies its intrinsic characteristics. The usage world concerns the goals and responsibilities of the subject. The system world represents the information kernel which is used for the IS evolution steering activities. Finally, the development world focuses on the methods and tools for the IS evolution steering (Figure 8). Each of their facets is detailed in the following sections.
Figure 8 - IS evolution steering four-world framework
Framework World
Facet Value_Domain
Simple Set Enum
cl_a cl_b
4 1
1..*
1 1
specialises is associated with
composes
2.2. Subject world
IS and steering are associated since the mid sixties in the concept of MIS (Management of Information System) [Tardieu and Theys 1987]. MIS is described in [Davis and Olson 1985]
as a pyramid structure in which the bottom layer consists of information for transaction processing, the next level of information resources which support the day-to-day operation management, the third level of IS resources which support the tactical planning and decision making for management control, and the top level of information resources which support the planning and policy making by higher levels of management.
For [Conant and Ross Ashby 1970], every good regulator of a system must be a model of that system. Their theorem shows that, under very broad conditions, any regulator that is maximally both successful and simple must be isomorphic with the system being regulated.
In [Tardieu and Theys 1987]'s analogy, steering is conceived as conducting a complex machine, for which managers chose a target and then control its trajectory through the obstacles of the real world. For these two authors, steering is a recursive concept as it both relates to a process (create and execute plans of action) and to a result (actions as they are understood by the other actors).
We chose to define steering after [Mélèse 1991] as a set of processes, which allow the control and guidance of the transformations of a system.
The aspects of the subject world are detailed with the three following facets: Steering system, Steering structure and Steering processes.
2.2.1. Steering system
In the cybernetic field, the concept of steering is related to the definition and the organisation of the inter-relations between two systems: a physical system (also called operating system) and a decision system (also called steering system) which relies on a feedback loop from the physical system [Mélèse 1991], [Le Moigne 1994] (Figure 9). This feedback-based approach is based on the fundamental assumption that it is impossible to know in detail the behaviour of the operating systems due to data uncertainty, data imprecision and the perturbations which affect them [Trentesaux 2002].
Figure 9 - Steering and operating systems
In [Mélèse 1991], the steering system relies on the core variable values which are defined as indicators for the targeted goals evaluation in order to implement its two functions: i) the controller which determines the action variables value based on the core variables ones and
Operating system Steering system
ii) the regulator which acts, with the feedback loops, on the input variables in order to maintain the output variables at the desired value (Figure 10).
Figure 10 - Steering function [Mélèse 1991]
In the OID canonical model15 of [Le Moigne 1994], three IS sub-systems are interrelated, the operating system, the information system and the steering system (Figure 11). The operating system transforms the inputs and outputs according to a given purpose. The information system acts as an interface between the steering and the operating systems. It acquires, memorises, and transmits: a) the operating system behaviour to the steering system and b) the actions to be taken by the operating system which are defined by the steering system. Finally, the steering system develops commands (decisions of action) according to the information monitoring.
Figure 11 - OID model [Le Moigne 1994]
Since the sixties [Tardieu and Theys 1987], the steering function is often structured in three hierarchical layers (Figure 12): i) the strategic layer gathering all decisions allowing the definition of long- and very long-term horizon goals, ii) the tactical layer taking into account the system organisation in a medium-term horizon and finally iii) the operational layer
describing the activities on a short- and very short-term horizon and integrating the technical constraints.
Figure 12 - Steering hierarchy [Tardieu and Theys 1987]
The "Steering System" facet can be summarised with the following values:
Facet name Type Attribute Value domain
Steering
system Set
Steering components
Enum {steering system, IS, operating system}
Steering layers Enum {Strategic steering, Tactical steering, Operational steering}
2.2.2. Steering structures
The steering structures and their evolution are studied in numerous works: particularly in the area of manufacturing and production IS [Blanc dit Jolicoeur 2004], [Chaudet 2002], [Zwegers 1998], [Trentesaux 1996]. They have evolved toward a reduction of the aggregated information use and a relaxation of the master-slave relations.
The centralised form (
Figure 13 - a) is characterised by a unique centre of control which oversees and coordinates the execution centres. Then, the proper hierarchical form (
Figure 13 - b) emerges in order to reduce the complexity of the centralised form with the distribution of the control functionality over several hierarchically-organised control centres.
Based on this structure, the modified hierarchical form (
Figure 13 - c) is developed in order to provide a higher autonomy level to the control centres.
It induces a peer-to-peer coordination between control centres. In the heterarchical structure (
Figure 13 - d), there is only one level of control which is distributed among each control centre which is autonomous and co-operates with one another. Finally, the holonic form (
Figure 13 - e) is proposed in order to combine both the advantages of the hierarchical and heterarchical structures. With this form, each control centre is autonomous, but can request support to the higher level centre of control.
Figure 13 – The evolution of the steering structures
The "Steering Structures" facet can be summarised with the following values:
Facet name Type Values Steering
Structures
Enum centralised, proper hierarchical, modified hierarchical, heterarchical, holonic
2.2.3. Steering processes
Several typologies of steering processes are developed [Trentesaux 2002]: either based on their finality, event type, time horizon or decision type. Two types of steering are identified by their finality: the first one involves system improvement and the second one regards the system regulation with the maintenance of a satisfactory level.
According to their capacity of event anticipation, a distinction is made between the reactive steering processes, which are exclusively based on past events, and the anticipatory steering processes, which are based on the prevision of future events.
Based on time horizon, there are the operational (for short-term decisions), the tactical (for medium term decisions) or the strategic (long-term decisions) steering processes.
At the decisional level, three categories of steering processes are identified: one is based on the decision type (choice-based, sorting-based), another on the presence of an operator (i.e.
automated or not) and the last on the level of structuring.
The "Steering Processes" Facet can be summarised with the following values:
Facet name Type Values Steering
processes
Enum by finality, by event type, by time horizon, by decision type
2.3. Usage world
The aspects of the usage world are described according to the three following facets: Define evolution, Guide evolution and Identify evolution risks.
2.3.1. Define evolution
The importance of IS evolution and the mechanisms for supporting it highly depend on its different facets or dimensions [Comyn-Wattiau et al. 2003]. Several works suggest an IS evolution (or change) taxonomy, particularly in the data management [Roddick et al. 2000] or business process modelling [Regev_2006] domains.
The term evolution is commonly used with two meanings in the domain of software evolution [Lehman et al. 2000]: a noun or a verb. When it refers to a noun (less frequently), it concerns the question "what": the understanding of the software evolution phenomenon and its properties. On the other hand, when it refers to a verb (more frequently), it concerns the question "how": namely the theories, abstractions, languages, activities and tools which are required to evolve a software.
After [Aier and Buckl and et al. 2011], there is a distinction to be made between two changes: the so-called evolution and the transformation. On the one hand, the evolution is an incremental change, which is usually bottom-up driven. On the other hand, the transformation is a fundamental change, which is usually top-down driven.
The taxonomy in [Regev et al. 2006] intends to understand the notion of business process flexibility with three orthogonal dimensions: the abstraction level, the subject of change and the properties of change. The abstraction level is usually type or instance. The subject of change has five perspectives: functional (the goal), operational (the activities), behavioural (the preconditions), information (information exchanged between activities) and organisational (people and roles). The properties of change are: the extent (incremental or revolutionary), the duration (temporary or permanent), the swiftness (immediate or deferred) and the anticipation (ad hoc or planned).
[Roddick et al. 2000] reports the fundamental characteristics of change in information and database systems which were handled in a workshop on evolution and change in data management16. These characteristics are grouped in the following aspects: subject, type, cause, effect, response, temporal issues and spatial issues. The subject aspect considers
16 The First International Workshop on Evolution and Change in Data Management, held with the International Conference on Conceptual Modelling (ER'99) in Paris (Nov. 1999).
