I takt med att organisationers omgivande miljö beskrivs som alltmer dynamisk, ökar kritiken mot den budgeteringen, då budget anses var ett statiskt ekonomistyrningsverktyg. Trots medhållet som kritiken får visar empiriska studier att få företag faktiskt överger budget som huvudsakligt planerings- och kontrollverktyg (Sandalgaard 2012; Ekholm & Wallin 2000; Dokulil, Zlámalová & Popesko 2017). Till följd av Covid-19-pandemin under år 2020 förlorande många marknader sin förutsägbarhet vilket resulterade i osäkra omgivningar för många organisationer och branscher. Bilförsäljningen visade en hög variation under pandemiåret. Först tappade marknaden 40% av försäljningen och några månader senare fick den uppgång som täckte upp för de föregående förlorade intäkterna. Marknaden upplevde därmed en berg-och-dal-bana som ger insikt i hur budgetens praktiska användning sammanfaller med kritiserade svagheter. Genom att använda fem av de främsta argumenten i kritiken mot budgetering undersöker studien hur budgeten påverkas hos svenska bilåterförsäljare. Detta illustrerar behovet som bilåterförsäljarna har i en bransch vars omgivning påverkats av en oväntad osäkerhet. Sju bilåterförsäljare kontaktades och intervjuades i syfte att tillhandahålla den empiriska datan som i efterhand kompletterades genom de utvalda återförsäljarnas årsredovisningar. Genom detta urvalet utforskar studien en variation i behovet av budgeten. Studien kommer bland annat fram till att budgeten och dess komplement samspelar för att tillgodose de observerade bilåterförsäljarnas individuella behov av planering och kontroll. / The critique of budgeting has been growing louder during recent years as the influencing factors in the organizational environment is increasingly described as dynamic, while budgeting itself is seen as a static appliance in organizational management. However, despite the support this critique receives, empirical evidence shows that few companies actually abandon budgeting as one of their main tools for planning and control in financial management (Sandalgaard 2012; Ekholm & Wallin 2000; Dokulil, Zlámalová & Popesko 2017). Due to the Covid-19 pandemic year 2020, the wide consumer market lost its predictability, thus making it an uncertain environment for a majority of companies to act in. Sales of cars showed a wide variety during the pandemic year. First a decline of 40% in sales in the overall market and then an upswing which made up for previous loss. The car market had therefore experienced an interchangeable environment which contribute to giving insight to how the praxis of the budget coincide with the criticism. Using five of the main points in the critique against budgeting, the research area of this study, car retailers, were chosen to illuminate the need for the budget of retailers in an industry unexpectedly affected by uncertainty in the environment. Seven car retailer companies were contacted and interviewed to provide the empirical data which were later supplemented by the chosen companies’ annual reports. Through this selection this study explores a variety in the need for the budget. The results include that there is an interaction between the budget and the budget complement to satisfy the observed car retailers' individual needs of planning and control. This study is written in Swedish.

[ES] Esta tesis describe la investigación para obtener información sobre soluciones de middleware y soluciones sensibles al contexto que amplían la perspectiva de entornos estáticos a entornos dinámicos generalizados. La motivación detrás de esta investigación surgió de la necesidad de reconsiderar y reemplazar las soluciones sensibles al contexto actuales con soluciones más inteligentes para dar cuenta de los entornos dinámicos y los cambios de preferencias de los usuarios en el tiempo de ejecución. En este sentido, el objetivo final es centrarse en ofrecer soluciones inteligentes sensibles al contexto que puedan abordar la evolución automática del modelo de contexto y la generación de nuevas decisiones de acuerdo con los cambios de contexto en tiempo de ejecución. Con este fin, en la tesis actual ilustramos un enfoque híbrido denominado IConAS, que combina las ventajas prácticas de la evolución del contexto con la adaptación en la toma de decisiones. Esta combinación conduce a soluciones inteligentes sensibles al contexto que podrían reflejar los cambios que ocurren en sus entornos dinámicos en tiempo de ejecución. La tesis se concentra en las tres contribuciones importantes de la siguiente manera: ¿ Definición del enfoque IConAS que combina dos enfoques principales. Este enfoque híbrido tiene como objetivo ofrecer soluciones inteligentes sensibles al contexto mediante la extensión de una solución middleware existente. El propósito de esta extensión consiste en dar soporte en tiempo de ejecución a la evolución automática del contexto y la adaptación de la toma de decisiones para reflejar los cambios en entornos dinámicos; ¿ Introducción de la primera parte de nuestro enfoque híbrido: el enfoque CoE. Este enfoque tiene como objetivo establecer una evolución de modelo de contexto a partir de una ontología basada en un enfoque de aprendizaje no supervisado. Por lo tanto, desarrolla automáticamente un modelo de contexto basado en dicha ontología de acuerdo con los cambios de contexto que ocurren en los entornos dinámicos en tiempo de ejecución; ¿ Introducción de la segunda parte de nuestro enfoque híbrido: el enfoque DMA. Este enfoque tiene como objetivo aprender y generar automáticamente reglas de decisión y, posteriormente, enriquecer una base de conocimientos de reglas en tiempo de ejecución para hacer frente a los cambios y modelos de contexto basados en modelos de ontología evolucionados. Se basa en el uso de técnicas de Machine Learning y el uso de un Algoritmo Genético. Estas contribuciones se validan desde diferentes perspectivas: Primero, la evaluación del enfoque CoE se realiza utilizando enfoques de evaluación basados en características, criterios, expertos y preguntas de competencia; ¿ En segundo lugar, la evaluación del enfoque DMA se establece evaluando su eficacia en términos de número de reglas, rendimiento y tiempo computacional; ¿ Finalmente, la evaluación del enfoque IConAS se lleva a cabo a través de un estudio de caso de atención médica para personas mayores junto con enfoques de reconocimiento de actividad y evaluación de la satisfacción del usuario. / [CA] Aquesta tesi descriu la recerca per obtenir informació sobre solucions middleware i solucions sensibles al context que amplien la perspectiva d'entorns estàtics a entorns dinàmics generalitzats. La motivació darrere aquesta investigació va sorgir de la necessitat de reconsiderar i reemplaçar les solucions sensibles al context actuals amb solucions més intelligents per donar compte dels entorns dinàmics i els canvis de preferències dels usuaris en el temps d'execució. En aquest sentit, l'objectiu final es centrar en oferir solucions intelligents sensibles al context que puguin abordar l'evolució automàtica del model de context i la generació de noves decisions d'acord amb els canvis de context en temps d'execució. Amb aquesta finalitat, a la tesi actual illustrem un enfocament híbrid anomenat IConAS, que combina els avantatges pràctics de l'evolució del context amb l'adaptació a la presa de decisions. Aquesta combinació condueix a solucions intelligents sensibles al context que podrien reflectir els canvis que tenen lloc als seus entorns dinàmics en temps d'execució. La tesi es concentra en les tres contribucions importants de la manera següent: Definició de l'enfocament IConAS que combina dos aspectes principals. Aquest enfocament híbrid té com a objectiu oferir solucions intel¿ligents sensibles al context mitjançant l'extensió d'una solució middleware existent. El propòsit d'aquesta extensió consisteix a donar suport en temps d'execució a l'evolució automàtica del context l'adaptació de la presa de decisions per reflectir els canvis en entorns dinàmics; Introducció de la primera part del nostre enfocament híbrid: enfocament CoE. Aquest enfocament té com a objectiu establir una evolució de model de context a partir duna ontologia basada en un enfocament d'aprenentatge no supervisat. Per tant, desenvolupa automàticament un model de context basat en aquesta ontologia d'acord amb els canvis de context que ocorren en els entorns dinàmics en temps d'execució; Introducció de la segona part del nostre enfocament híbrid: enfocament DMA. Aquest enfocament té com a objectiu aprendre i generar automàticament regles de decisió i, posteriorment, enriquir una base de coneixements de regles en temps d'execució per fer front als canvis i models de context basats en models d'ontologia evolucionats. Es basa en l'ús de tècniques de Machine Learning i l'ús d'un algoritme genètic. Aquestes contribucions es validen des de diferents perspectives: Primer, l'avaluació de l'enfocament CoE es realitza utilitzant tècniques d'avaluació basades en característiques, criteris, experts i preguntes de competència; En segon lloc, l'avaluació de l'enfocament DMA s'estableix avaluant la seva eficacia en termes de nombre de regles, rendiment i temps computacional; inalment, l'avaluació de l'enfocament IConAS es duu a terme a través d'un estudi de cas d'atenció mèdica per a gent gran juntament amb enfocaments de reconeixement d'activitat i avaluació de la satisfacció de l'usuari. / [EN] This thesis describes research to gain insight into pervasive middleware solutions and context-aware solutions that expand their perspective from static to dynamic pervasive environments. The motivation behind this research arose from a need to reconsider and replace today's context-aware solutions with more intelligent solutions to account for dynamic environments and users' preferences changes at runtime. In this context, the end goal is to focus on offering intelligent context-aware solutions that could deal with the automatic context model evolution and new decisions generation according to context changes at runtime. To do so, in the current thesis, we illustrate a hybrid approach termed IConAS - a means of combining the practical advantages of context evolution with the decision-making adaptation. This combination leads to intelligent context-aware solutions that could reflect changes occurring in their surrounding dynamic environments at runtime. The thesis concentrates on the three important contributions as follows: Definition of the IConAS approach that combines two main approaches. This hybrid approach aims to offer intelligent context-aware solutions through augmenting an existing middleware. The purpose of this augmentation is to support runtime and automatic context evolution and decision-making adaptation in order to reflect changes in dynamic environments; Introduction of the first part of our hybrid approach: the CoE approach. This approach aims to establish an ontology-based context model evolution based on an unsupervised ontology learning approach. Therefore, it automatically evolves an ontology-based context model according to context changes occurring in surrounding dynamic environments at runtime; Introduction of the second part of our hybrid approach: the DMA approach. This approach aims to automatically learn and generate decision rules and subsequently, enrich a rules knowledge base at runtime to cope with changes and evolved ontologybased context models. It is relying on the use of Machine Learning and a Genetic Algorithm. These contributions are validated through different perspectives: First, the evaluation of the CoE approach is performed using feature-based, criteriabased, expert-based and competency question-based evaluation approaches; Second, the evaluation of the DMA approach is established through assessing its effectiveness in terms of number of rules, performance and computational time; Finally, the evaluation of the IConAS approach is conducted through an elderly healthcare case study together with activity recognition and user satisfaction evaluation approaches. / Jabla, R. (2023). Towards an Approach for Intelligent Adaptation Decision-Making of Pervasive Middleware [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191878

Data mining (DM) is the process of finding patterns and relationships in databases. The breakthrough in computer technologies triggered a massive growth in data collected and maintained by organisations. In many applications, these data arrive continuously in large volumes as a sequence of instances known as a data stream. Mining these data is known as stream data mining. Due to the large amount of data arriving in a data stream, each record is normally expected to be processed only once. Moreover, this process can be carried out on different sites in the organisation simultaneously making the problem distributed in nature. Distributed stream data mining poses many challenges to the data mining community including scalability and coping with changes in the underlying concept over time. In this thesis, the author hypothesizes that learning classifier systems (LCSs) - a class of classification algorithms - have the potential to work efficiently in distributed stream data mining. LCSs are an incremental learner, and being evolutionary based they are inherently adaptive. However, they suffer from two main drawbacks that hinder their use as fast data mining algorithms. First, they require a large population size, which slows down the processing of arriving instances. Second, they require a large number of parameter settings, some of them are very sensitive to the nature of the learning problem. As a result, it becomes difficult to choose a right setup for totally unknown problems. The aim of this thesis is to attack these two problems in LCS, with a specific focus on UCS - a supervised evolutionary learning classifier system. UCS is chosen as it has been tested extensively on classification tasks and it is the supervised version of XCS, a state of the art LCS. In this thesis, the architectural design for a distributed stream data mining system will be first introduced. The problems that UCS should face in a distributed data stream task are confirmed through a large number of experiments with UCS and the proposed architectural design. To overcome the problem of large population sizes, the idea of using a Neural Network to represent the action in UCS is proposed. This new system - called NLCS { was validated experimentally using a small fixed population size and has shown a large reduction in the population size needed to learn the underlying concept in the data. An adaptive version of NLCS called ANCS is then introduced. The adaptive version dynamically controls the population size of NLCS. A comprehensive analysis of the behaviour of ANCS revealed interesting patterns in the behaviour of the parameters, which motivated an ensemble version of the algorithm with 9 nodes, each using a different parameter setting. In total they cover all patterns of behaviour noticed in the system. A voting gate is used for the ensemble. The resultant ensemble does not require any parameter setting, and showed better performance on all datasets tested. The thesis concludes with testing the ANCS system in the architectural design for distributed environments proposed earlier. The contributions of the thesis are: (1) reducing the UCS population size by an order of magnitude using a neural representation; (2) introducing a mechanism for adapting the population size; (3) proposing an ensemble method that does not require parameter setting; and primarily (4) showing that the proposed LCS can work efficiently for distributed stream data mining tasks.

Data mining (DM) is the process of finding patterns and relationships in databases. The breakthrough in computer technologies triggered a massive growth in data collected and maintained by organisations. In many applications, these data arrive continuously in large volumes as a sequence of instances known as a data stream. Mining these data is known as stream data mining. Due to the large amount of data arriving in a data stream, each record is normally expected to be processed only once. Moreover, this process can be carried out on different sites in the organisation simultaneously making the problem distributed in nature. Distributed stream data mining poses many challenges to the data mining community including scalability and coping with changes in the underlying concept over time. In this thesis, the author hypothesizes that learning classifier systems (LCSs) - a class of classification algorithms - have the potential to work efficiently in distributed stream data mining. LCSs are an incremental learner, and being evolutionary based they are inherently adaptive. However, they suffer from two main drawbacks that hinder their use as fast data mining algorithms. First, they require a large population size, which slows down the processing of arriving instances. Second, they require a large number of parameter settings, some of them are very sensitive to the nature of the learning problem. As a result, it becomes difficult to choose a right setup for totally unknown problems. The aim of this thesis is to attack these two problems in LCS, with a specific focus on UCS - a supervised evolutionary learning classifier system. UCS is chosen as it has been tested extensively on classification tasks and it is the supervised version of XCS, a state of the art LCS. In this thesis, the architectural design for a distributed stream data mining system will be first introduced. The problems that UCS should face in a distributed data stream task are confirmed through a large number of experiments with UCS and the proposed architectural design. To overcome the problem of large population sizes, the idea of using a Neural Network to represent the action in UCS is proposed. This new system - called NLCS { was validated experimentally using a small fixed population size and has shown a large reduction in the population size needed to learn the underlying concept in the data. An adaptive version of NLCS called ANCS is then introduced. The adaptive version dynamically controls the population size of NLCS. A comprehensive analysis of the behaviour of ANCS revealed interesting patterns in the behaviour of the parameters, which motivated an ensemble version of the algorithm with 9 nodes, each using a different parameter setting. In total they cover all patterns of behaviour noticed in the system. A voting gate is used for the ensemble. The resultant ensemble does not require any parameter setting, and showed better performance on all datasets tested. The thesis concludes with testing the ANCS system in the architectural design for distributed environments proposed earlier. The contributions of the thesis are: (1) reducing the UCS population size by an order of magnitude using a neural representation; (2) introducing a mechanism for adapting the population size; (3) proposing an ensemble method that does not require parameter setting; and primarily (4) showing that the proposed LCS can work efficiently for distributed stream data mining tasks.

Data mining (DM) is the process of finding patterns and relationships in databases. The breakthrough in computer technologies triggered a massive growth in data collected and maintained by organisations. In many applications, these data arrive continuously in large volumes as a sequence of instances known as a data stream. Mining these data is known as stream data mining. Due to the large amount of data arriving in a data stream, each record is normally expected to be processed only once. Moreover, this process can be carried out on different sites in the organisation simultaneously making the problem distributed in nature. Distributed stream data mining poses many challenges to the data mining community including scalability and coping with changes in the underlying concept over time. In this thesis, the author hypothesizes that learning classifier systems (LCSs) - a class of classification algorithms - have the potential to work efficiently in distributed stream data mining. LCSs are an incremental learner, and being evolutionary based they are inherently adaptive. However, they suffer from two main drawbacks that hinder their use as fast data mining algorithms. First, they require a large population size, which slows down the processing of arriving instances. Second, they require a large number of parameter settings, some of them are very sensitive to the nature of the learning problem. As a result, it becomes difficult to choose a right setup for totally unknown problems. The aim of this thesis is to attack these two problems in LCS, with a specific focus on UCS - a supervised evolutionary learning classifier system. UCS is chosen as it has been tested extensively on classification tasks and it is the supervised version of XCS, a state of the art LCS. In this thesis, the architectural design for a distributed stream data mining system will be first introduced. The problems that UCS should face in a distributed data stream task are confirmed through a large number of experiments with UCS and the proposed architectural design. To overcome the problem of large population sizes, the idea of using a Neural Network to represent the action in UCS is proposed. This new system - called NLCS { was validated experimentally using a small fixed population size and has shown a large reduction in the population size needed to learn the underlying concept in the data. An adaptive version of NLCS called ANCS is then introduced. The adaptive version dynamically controls the population size of NLCS. A comprehensive analysis of the behaviour of ANCS revealed interesting patterns in the behaviour of the parameters, which motivated an ensemble version of the algorithm with 9 nodes, each using a different parameter setting. In total they cover all patterns of behaviour noticed in the system. A voting gate is used for the ensemble. The resultant ensemble does not require any parameter setting, and showed better performance on all datasets tested. The thesis concludes with testing the ANCS system in the architectural design for distributed environments proposed earlier. The contributions of the thesis are: (1) reducing the UCS population size by an order of magnitude using a neural representation; (2) introducing a mechanism for adapting the population size; (3) proposing an ensemble method that does not require parameter setting; and primarily (4) showing that the proposed LCS can work efficiently for distributed stream data mining tasks.

Aim of this thesis is to search the market for suitable autonomous robot to be used by smart house. The research in this work is partly done on the range of abilities of smart houses in matter of sensor systems, ability of data processing and their use by mobile robots. The output of this thesis is robotics application written using Microsoft Robotics Developer Studio (C#) and simulated using Visual Simulation Environment. Main feature of this robotic application is the interface between robot and smart house, and robot and user. This interface enables employer to directly control robot's movement or to use automated pathfinding. The robot is able to navigate in dynamic environment and to register, interact and eventually forget temporary obstacles.

Aim of this thesis is to search the market for suitable autonomous robot to be used by smart house. The research in this work is partly done on the range of abilities of smart houses in matter of sensor systems, ability of data processing and their use by mobile robots. The output of this thesis is robotics application written using Microsoft Robotics Developer Studio (C#) and simulated using Visual Simulation Environment. Main feature of this robotic application is the interface between robot and smart house, and robot and user. This interface enables employer to directly control robot's movement or to use automated pathfinding. The robot is able to navigate in dynamic environment and to register, interact and eventually forget temporary obstacles.