This work presents the first known attempt to model the dairy business from a multiscale modelling perspective. The multiscale nature of the dairy industry is examined with emphasis on those key decision making and process scales involved in production. Decision making scales identified range from the investor level to the plant operator level, and encompass business, production, plant, and operational levels. The model considers scales from the production manager to the unit operation scale. The cheese making process is used to demonstrate scale identification in the context of the important phenomena and other natural levels of scrutiny of interest to decision makers. This work was a first step in the establishment of a multiscale system model capable of delivering information for process troubleshooting, scheduling, process and business optimization, and process control decision-making for the dairy industry. Here, only material transfer throughout a process, use of raw materials, and production of manufactured product is modelled. However, an implementation pathway for adding other models (such as the precipitation of milk protein which forms curd) to the system model is proposed. The software implementation of the dairy industry multiscale model presented here tests the validity of the proposed: • object model (object and collection classes) used to model unit operations and integrate them into a process, • mechanisms for modelling material and energy streams, • method to create simulations over variable time horizons. The model was implemented using object oriented programming (OOP) methods in conjunction with technologies such as Visual Basic .NET and CAPE-OPEN. An OOP object model is presented which successfully enabled the construction of a multiscale model of the cheese making process. Material content, unit operation, and raw milk supply models were integrated into the multiscale model. The model is capable of performing simulations over variable time horizons, from 1 second, to multiple years. Mechanisms for modelling material streams, connecting unit operations, and controlling unit operation behaviour were implemented. Simple unit operations such as pumps and storage silos along with more complex unit operations, such as a cheese vat batch, were modelled. Despite some simplifications to the model of the cheese making process, the simulations successfully reproduced the major features expected from the process and its constituent unit operations. Decision making information for process operators, plant managers, production managers, and the dairy business manager can be produced from the data generated. The multiscale model can be made more sophisticated by extending the functionality of existing objects, and incorporating other scale partial models. However, increasing the number of reported variables by even a small number can quickly increase the data processing and storage demands of the model. A unit operation’s operational state of existence at any point of time was proposed as a mechanism for integrating and recalculating lower scale partial models. This mechanism was successfully tested using a unit operation’s material content model and is presented here as a new concept in multiscale modelling. The proposed modelling structure can be extended to include any number of partial models and any number of scales.

Context: Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making. Objectives: The main objectives of this thesis are to provide: • A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner; • A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and • A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context Method: We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our approach by performing a systematic literature review, by assessing it against Zellner’ mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail). Results: The two examples show that through the framework’s iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN provided in this thesis as well as the discussed improvement patterns.

Historically, progress in technology development has continually created new opportunities for criminal activities which, in turn, have triggered the need for the development of new security-sensitive systems. Organisations are now adopting mobile technologies for numerous applications to capitalise on the mobile revolution. They are now able to increase their operational efficiency as well as responsiveness and competitiveness and, most importantly, can now meet new, growing customers’ demands. However, although mobile technologies and applications present many new opportunities, they also present challenges. Threats to mobile phone applications are always on the rise and, therefore, compel organisations to invest money and time, among other technical controls, in an attempt to protect them from incurring losses. The computerisation of core activities (such as mobile banking in the banking industry, for example) has effectively exposed organisations to a host of complex fraud challenges that they have to deal with in addition to their core business of providing services to their end consumers. Fraudsters are able to use mobile devices to remotely access enterprise applications and subsequently perform fraudulent transactions. When this occurs, it is important to effectively investigate and manage the cause and findings, as well as to prevent any future similar attacks. Unfortunately, clients and consumers of these organisations are often ignorant of the risks to their assets and the consequences of the compromises that might occur. Organisations are therefore obliged, at least, to put in place measures that will not only minimise fraud but also be capable of detecting and preventing further similar incidents. The goal of this research was to develop a unified fraud management and digital forensic framework to improve the security of Information Technology (IT) processes and operations in organisations that make available mobile phone applications to their clients for business purposes. The research was motivated not only by the increasing reliance of organisations on mobile applications to service their customers but also by the fact that digital forensics and fraud management are often considered to be separate entities at an organisational level. This study proposes a unified approach to fraud management and digital forensic analysis to simultaneously manage and investigate fraud that occurs through the use of mobile phone applications. The unified Fraud Management and Digital Forensic (FMDF) framework is designed to (a) determine the suspicious degree of fraudulent transactions and (b) at the same time, to feed into a process that facilitates the investigation of incidents. A survey was conducted with subject matter experts in the banking environment. Data was generated through a participatory self-administered online questionnaire. Collected data was then presented, analysed and interpreted quantitatively and qualitatively. The study found that there was a general understanding of the common fraud management methodologies and approaches throughout the banking industry and the use thereof. However, while many of the respondents indicated that fraud detection was an integral part of their processes, they take a rather reactive approach when it comes to fraud management and digital forensics. Part of the reason for the reactive approach is that many investigations are conducted in silos, with no central knowledge repository where previous cases can be retrieved for comparative purposes. Therefore, confidentiality, integrity and availability of data are critical for continued business operations. To mitigate the pending risks, the study proposed a new way of thinking that combines both components of fraud management and digital forensics for an optimised approach to managing security in mobile applications. The research concluded that the unified FMDF approach was considered to be helpful and valuable to professionals who participated in the survey. Although the case study focused on the banking industry, the study appears to be instrumental in informing other types of organisations that make available the use of mobile applications for their clients in fraud risk awareness and risk management in general. / Computing / M. Sc. (Computing)

Context: Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making. Objectives: The main objectives of this thesis are to provide: • A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner; • A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and • A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context Method: We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our approach by performing a systematic literature review, by assessing it against Zellner’ mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail). Results: The two examples show that through the framework’s iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN provided in this thesis as well as the discussed improvement patterns.

This thesis explores the potential to unify three emerging disciplines: enterprise engineering, enterprise architecture and enterprise ontology. The current fragmentation that exists in literature on enterprise alignment and design constrains the development and growth of the emerging disciplines. Enterprises need to use a multi-disciplinary approach when they continuously align, design and re-design the enterprise. Although enterprises need to be aligned internally (across various enterprise facets), as well as externally (with the environment), most alignment approaches still focus on business-IT alignment, i.e. aligning the business operations with the information and communication technologies and systems of the enterprise. This study focuses on a popular business-IT alignment approach,called the foundation for execution approach, and its associated artefact, called the operating model. The study acknowledges the theoretical contribution of the operating model to establish the required level of business process integration and standardisation at an enterprise in delivering goods and services to customers. Highlighting the practical problems in selecting an operating model for an enterprise, and more specifically the practical problems of identifying process reuse potential at an enterprise, a thesis statement is formulated: The operating model concept, as part of a business-IT alignment approach, can be enhanced with a process reuse identification framework, when a business-IT alignment contextualisation is used. The study is divided into two research questions. The first research question addresses the current fragmentation that exists in the literature, which impairs reuse of the existing business-IT alignment knowledge base. An inductive literature review develops the Business-IT Alignment Model to provide a common contextualisation for current business-IT alignment approaches. The second research question addresses the practical problems of the operating model regarding the identification of process reuse potential at an enterprise. Applying the newly developed Business-IT Alignment Model as a contextualisation instrument, the study demonstrates the use of design research in developing the Process Reuse Identification Framework. The conclusion after the investigation of the two research questions is that the thesis statement was confirmed, i.e. the operating model concept, as part of a business-IT alignment approach, can be enhanced with a process reuse identification framework, when a business-IT contextualisation is used. / Thesis (PhD)--University of Pretoria, 2013. / Industrial and Systems Engineering / Unrestricted

A detailed and comprehensive description of the state of the art in the field of flood forecasting opens this work. Advantages and shortcomings of currently available methods are identified and discussed. Amongst others, one important aspect considers the most exigent weak point of today’s forecasting systems: The representation of all the fundamentally different event specific patterns of flood formation with one single set of model parameters. The study exemplarily proposes an alternative for overcoming this restriction by taking into account the different process characteristics of flood events via a dynamic parameterisation strategy. Other fundamental shortcomings in current approaches especially restrict the potential for real time flash flood forecasting, namely the considerable computational requirements together with the rather cumbersome operation of reliable physically based hydrologic models. The new PAI-OFF methodology (Process Modelling and Artificial Intelligence for Online Flood Forecasting) considers these problems and offers a way out of the general dilemma. It combines the reliability and predictive power of physically based, hydrologic models with the operational advantages of artificial intelligence. These operational advantages feature extremely low computation times, absolute robustness and straightforward operation. Such qualities easily allow for predicting flash floods in small catchments taking into account precipitation forecasts, whilst extremely basic computational requirements open the way for online Monte Carlo analysis of the forecast uncertainty. The study encompasses a detailed analysis of hydrological modeling and a problem specific artificial intelligence approach in the form of artificial neural networks, which build the PAI-OFF methodology. Herein, the synthesis of process modelling and artificial neural networks is achieved by a special training procedure. It optimizes the network according to the patterns of possible catchment reaction to rainstorms. This information is provided by means of a physically based catchment model, thus freeing the artificial neural network from its constriction to the range of observed data – the classical reason for unsatisfactory predictive power of netbased approaches. Instead, the PAI-OFF-net learns to portray the dominant process controls of flood formation in the considered catchment, allowing for a reliable predictive performance. The work ends with an exemplary forecasting of the 2002 flood in a 1700 km² East German watershed.

With development and impact of ICT, the method of work in many organizations has been becoming more collaborative and communicative, under which a number of organizations start to take corresponding strategies to achieve business goals and create more values. Managing Business Process is an effective and efficient way to improve productivity and performance of business activities from an organizational level. Business Process model, as a representation of Business Process, provides a big picture of Business Process, allowing organizations to acquire understanding on logical relationships among different business activities and to improve Business Process by various approaches. This study discusses the application of Business Process models on weakness identification which is related to the problem of excess inventory in supply chain with a qualitative method. It adopts three Business Process Modelling techniques to build Business Process models for a planning team involved with demand and supply planning, where four experts from the team participate in interview. The models are analyzed according to selected weakness patterns in order to identify the process weaknesses and link them with the causes of excess inventory. The result of this study gives a positive answer, which means the Business Process Models are capable to identify causes for the concrete problem of excess inventory by identifying process weaknesses.

L'évolution rapide dans les environnements métier d'aujourd'hui impose de nouveaux défis pour la gestion efficace et rentable des processus métiers. Dans un tel environnement très dynamique, la conception des processus métiers devient une tâche fastidieuse, source d'erreurs et coûteuse. Par conséquent, l'adoption d'une approche permettant la réutilisation et l'adaptabilité devient un besoin urgent pour une conception de processus prospère. Les modèles de processus configurables récemment introduits représentent l'une des solutions recherchées permettant une conception de processus par la réutilisation, tout en offrant la flexibilité. Un modèle de processus configurable est un modèle générique qui intègre de multiples variantes de procédés d'un même processus métier à travers des points de variation. Ces points de variation sont appelés éléments configurables et permettent de multiples options de conception dans le modèle de processus. Un modèle de processus configurable doit être configuré selon une exigence spécifique en sélectionnant une option de conception pour chaque élément configurable.Les activités de recherche récentes sur les modèles de processus configurables ont conduit à la spécification des langages de modélisation de processus configurables comme par exemple configurable Event-Driven Process Chain (C-EPC) qui étend la notation de l'EPC avec des éléments configurables. Depuis lors, la question de la conception et de la configuration des modèles de processus configurables a été étudiée. D'une part, puisque les modèles de processus configurables ont tendance à être très complexe avec un grand nombre d'éléments configurables, de nombreuses approches automatisées ont été proposées afin d'assister leur conception. Cependant, les approches existantes proposent de recommander des modèles de processus configurables entiers qui sont difficiles à réutiliser, nécessitent un temps complexe de calcul et peuvent confondre le concepteur du processus. D'autre part, les résultats de la recherche sur la conception des modèles de processus configurables ont mis en évidence la nécessité des moyens de soutien pour configurer le processus. Par conséquent, de nombreuses approches ont proposé de construire un système de support de configuration pour aider les utilisateurs finaux à sélectionner les choix de configuration souhaitables en fonction de leurs exigences. Cependant, ces systèmes sont actuellement créés manuellement par des experts du domaine qui est sans aucun doute une tâche fastidieuse et source d'erreurs .Dans cette thèse, nous visons à automatiser le soutien de la variabilité dans les modèles de processus configurables. Notre objectif est double: (i) assister la conception des processus configurables d'une manière à ne pas confondre les concepteurs par des recommandations complexes et (i) assister la création des systèmes de soutien de configuration afin de libérer les analystes de processus de la charge de les construire manuellement. Pour atteindre le premier objectif, nous proposons d'apprendre de l'expérience acquise grâce à la modélisation des processus passés afin d'aider les concepteurs de processus avec des fragments de processus configurables. Les fragments proposés inspirent le concepteur du processus pour compléter la conception du processus en cours. Pour atteindre le deuxième objectif, nous nous rendons compte que les modèles de processus préalablement conçus et configurés contiennent des connaissances implicites et utiles pour la configuration de processus. Par conséquent, nous proposons de bénéficier de l'expérience acquise grâce à la modélisation et à la configuration passées des processus afin d'aider les analystes de processus dans la construction de leurs systèmes de support de configuration. / Today's fast changing environment imposes new challenges for effective management of business processes. In such a highly dynamic environment, the business process design becomes time-consuming, error-prone, and costly. Therefore, seeking reuse and adaptability is a pressing need for a successful business process design. Configurable reference models recently introduced were a step toward enabling a process design by reuse while providing flexibility. A configurable process model is a generic model that integrates multiple process variants of a same business process in a given domain through variation points. These variation points are referred to as configurable elements and allow for multiple design options in the process model. A configurable process model needs to be configured according to a specific requirement by selecting one design option for each configurable element.Recent research activities on configurable process models have led to the specification of configurable process modeling notations as for example configurable Event-Driven Process Chain (C-EPC) that extends the EPC notation with configurable elements. Since then, the issue of building and configuring configurable process models has been investigated. On the one hand, as configurable process models tend to be very complex with a large number of configurable elements, many automated approaches have been proposed to assist their design. However, existing approaches propose to recommend entire configurable process models which are difficult to reuse, cost much computation time and may confuse the process designer. On the other hand, the research results on configurable process model design highlight the need for means of support to configure the process. Therefore, many approaches proposed to build a configuration support system for assisting end users selecting desirable configuration choices according to their requirements. However, these systems are currently manually created by domain experts which is undoubtedly a time-consuming and error-prone task.In this thesis, we aim at automating the support of the variability in configurable process models. Our objective is twofold: (i) assisting the configurable process design in a fin-grained way using configurable process fragments that are close to the designers interest and (ii) automating the creation of configuration support systems in order to release the process analysts from the burden of manually building them. In order to achieve the first objective, we propose to learn from the experience gained through past process modeling in order to assist the process designers with configurable process fragments. The proposed fragments inspire the process designer to complete the design of the ongoing process. To achieve the second objective, we realize that previously designed and configured process models contain implicit and useful knowledge for process configuration. Therefore, we propose to benefit from the experience gained through past process modeling and configuration in order to assist process analysts building their configuration support systems. Such systems assist end users interactively configuring the process by recommending suitable configuration decisions.

De plus en plus les organisations adoptent les systèmes d'informations sensibles aux processus basés sur Cloud en tant qu'un environnement pour gérer et exécuter des processus dans le Cloud dans l'objectif de partager et de déployer leurs applications de manière optimale. Cela est particulièrement vrai pour les grandes organisations ayant des succursales opérant dans des différentes régions avec des processus considérablement similaires. Telles organisations doivent soutenir de nombreuses variantes du même processus en raison de la culture locale de leurs succursales, de leurs règlements, etc. Cependant, le développement d'une nouvelle variante de processus à partir de zéro est sujet à l'erreur et peut prendre beaucoup du temps. Motivés par le paradigme "la conception par la réutilisation", les succursales peuvent collaborer pour développer de nouvelles variantes de processus en apprenant de leurs processus similaires. Ces processus sont souvent hétérogènes, ce qui empêche une interopérabilité facile et dynamique entre les différentes succursales. Une variante de processus est un ajustement d'un modèle de processus afin de s'adapter d'une façon flexible aux besoins spécifiques. De nombreuses recherches dans les universités et les industries visent à faciliter la conception des variantes de processus. Plusieurs approches ont été développées pour aider les concepteurs de processus en recherchant des modèles de processus métier similaires ou en utilisant des modèles de référence. Cependant, ces approches sont lourdes, longues et sujettes à des erreurs. De même, telles approches recommandent des modèles de processus pas pratiques pour les concepteurs de processus qui ont besoin d'ajuster une partie spécifique d'un modèle de processus. En fait, les concepteurs de processus peuvent mieux développer des variantes de processus ayant une approche qui recommande un ensemble bien défini d'activités à partir d'un modèle de processus défini comme un fragment de processus. Les grandes organisations multi-sites exécutent les variantes de processus BP dans l'environnement Cloud pour optimiser le déploiement et partager les ressources communes. Cependant, ces ressources Cloud peuvent être décrites en utilisant des différents standards de description des ressources Cloud ce qui empêche l'interopérabilité entre les différentes succursales. Dans cette thèse, nous abordons les limites citées ci-dessus en proposant une approche basée sur les ontologies pour peupler sémantiquement une base de connaissance commune de processus et de ressources Cloud, ce qui permet une interopérabilité entre les succursales de l'organisation. Nous construisons notre base de connaissance en étendant les ontologies existantes. Ensuite, nous proposons une approche pour exploiter cette base de connaissances afin de supporter le développement des variantes BP. De plus, nous adoptons un algorithme génétique pour allouer d'une manière optimale les ressources Cloud aux BPs. Pour valider notre approche, nous développons deux preuves de concepts et effectuons des expériences sur des ensembles de données réels. Les résultats expérimentaux montrent que notre approche est réalisable et précise dans des cas d'utilisation réels / More and more organizations are adopting cloud-based Process-Aware Information Systems (PAIS) to manage and execute processes in the cloud as an environment to optimally share and deploy their applications. This is especially true for large organizations having branches operating in different regions with a considerable amount of similar processes. Such organizations need to support many variants of the same process due to their branches' local culture, regulations, etc. However, developing new process variant from scratch is error-prone and time consuming. Motivated by the "Design by Reuse" paradigm, branches may collaborate to develop new process variants by learning from their similar processes. These processes are often heterogeneous which prevents an easy and dynamic interoperability between different branches. A process variant is an adjustment of a process model in order to flexibly adapt to specific needs. Many researches in both academics and industry are aiming to facilitate the design of process variants. Several approaches have been developed to assist process designers by searching for similar business process models or using reference models. However, these approaches are cumbersome, time-consuming and error-prone. Likewise, such approaches recommend entire process models which are not handy for process designers who need to adjust a specific part of a process model. In fact, process designers can better develop process variants having an approach that recommends a well-selected set of activities from a process model, referred to as process fragment. Large organizations with multiple branches execute BP variants in the cloud as environment to optimally deploy and share common resources. However, these cloud resources may be described using different cloud resources description standards which prevent the interoperability between different branches. In this thesis, we address the above shortcomings by proposing an ontology-based approach to semantically populate a common knowledge base of processes and cloud resources and thus enable interoperability between organization's branches. We construct our knowledge base built by extending existing ontologies. We thereafter propose an approach to mine such knowledge base to assist the development of BP variants. Furthermore, we adopt a genetic algorithm to optimally allocate cloud resources to BPs. To validate our approach, we develop two proof of concepts and perform experiments on real datasets. Experimental results show that our approach is feasible and accurate in real use-cases

En regardant de près les outils juridiques et autres leviers, pour la conservation de la biodiversité, il semblerait que les invertébrés, et notamment les insectes, soient minoritaires ou absents. Ce constat est d’autant plus paradoxal lorsque l’on sait que 2/3 de la diversité biologique est composée par des insectes. Comment cette diversité essentielle pour le fonctionnement des écosystèmes se retrouve-t-elle dans l’angle mort de la conservation ?La première réponse avancée est le manque d’outils techniques pour étudier ces organismes petits et relativement insaisissables. La rencontre avec les nouvelles méthodes techniques pour la détection et l’étude des insectes est plus que jamais nécessaire. En effet, ces leviers permettront de faciliter l’étude de ces organismes, d’augmenter les connaissances et ainsi de développer une conservation plus adéquate. Nous évoquerons deux approches en particulier : la détection avec des outils moléculaires et l’utilisation de modèles statistiques pour l’exploration de la distribution potentielle des espèces.Mais les connaissances sont également fondées sur la demande sociétale. Et les connaissances alimentent elles-mêmes les outils de protection et de conservation de la biodiversité. À l’échelle des invertébrés, des disparités existent, privilégiant les « grands papillons bleus » aux « petits diptères marrons ». De fait, l’enjeu le plus important pour déverrouiller la conservation des insectes réside dans l’humain et la perception qu’il a de cette biodiversité. À travers une approche de psychologie de la conservation, nous sonderons la perception du grand public sur les insectes. De même, avec une approche de recherche-action-participative, nous tenterons d’engager divers acteurs vers la conservation d’un groupe d’insectes ordinaires : les coléoptères coprophages. Notre volonté est de proposer des moyens pour sensibiliser, éduquer et engager la société dans cet enjeu majeur qu’est la conservation de l’entomofaune. / Looking closely at the legal tools and other levers for preserving biodiversity, it would seem that invertebrates, in particular insects, are in a minority, or absent. This observation is all the more paradoxical when we know that 2/3 of the biological diversity consists of insects. How does this diversity, essential for the functioning of the ecosystems, find itself in the dead angle of conservation?The first answer that is usually put forward is lack of technical tools to study these small and relatively elusive animals. Getting to know and use new technical methods for the detection and the study of insects is more than ever necessary. Indeed, these levers will facilitate the study of these animals, and will thus increase knowledge, which will lead to developing more adequate conservation strategies. We shall evoke two approaches in particular: detection with molecular tools and use of statistical models to explore the potential distribution of the species.But knowledge is also based on what society asks for. Public interest orients the tools of protection and preservation of biodiversity. Among invertebrates, disparities exist, favoring the “big blue butterflies” over the “small brown dipterans”. A simple coincidence? No. Actually, the decisive factor to unlock the preservation of insects rests in human beings and how they perceive this biodiversity. Using a conservation psychology approach, we will explore how the general public perceives insects. We will also draw on participatory action research to see how various conservation actors can be committed towards preserving a group of ordinary insects: coprophagous beetles. Our aim is to propose ways to raise awareness, educate and engage society to this major issue: preserving entomofauna.