Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13]. / This is an extended version of the article in: 15th International Conference on Data Engineering, Sydney, Australia, 1999.

Energy storage systems with many Lithium Ion battery cells per string require sophisticated balancing hardware due to individual cells having manufacturing inconsistencies, different self discharge rates, internal resistances and temperature variations. For capacity maximization, safe operation, and extended lifetime, battery balancing is required. Redistributive Non-Dissipative balancing further improves the pack capacity and efficiency over a Dissipative approach where energy is wasted as heat across shunt resistors. Redistribution techniques dynamically shuttle charge to and from weak cells during operation such that all of the stored energy in the stack is utilized. This thesis identifies and develops different balancing control methods. These methods include a unconstrained optimization problem using a Linear Quadratic Regulator (LQR) and a constrained optimization problem using Model Predictive Control (MPC). These methods are benchmarked against traditional rule based (RB) balancing. The control systems are developed using MATLAB/Simulink and validated experimentally on a multiple transformer individual cell to stack topology. The implementation uses a DC2100A Demo-board from Linear Technology with bi-directional flyback converters to transfer the energy between the cells. The results of this thesis show that the MPC control method has the highest balancing efficiency and minimum balancing time. / Thesis / Master of Applied Science (MASc)

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Den ökade globalisering som råder gynnar internationell handel på många plan, vilket även har bidragit till att internprissättning blivit en allt mer central fråga. Däremot ställs det högre krav på att adressera de problem som systemet resulterar i. Detta med hänsyn till att ett internpris är av påverkande karaktär på organisationens resultat på grund av skattemässiga motiv och incitament från ledningen. Även om OECD:s riktlinjer TPG är utformade av det politiskt styrande organet i respektive medlemsland är dessa riktlinjer inte tvingande. Systemet möjliggör subjektivitet i internprissättning. Problematiken belyses i synnerhet när redovisningstraditioner och deras eventuella påverkan på internprissättningen i Sverige och Storbritannien tas i beaktning.Studien har som syfte att genom en komparativ analys undersöka hur Sveriges och Storbritanniens redovisningstraditioner påverkar respektive lands tolkning av TPG. Ländernas skattemyndigheter, Skatteverket och HMRC, har gett ut en handledning respektive en intern manual rörande internprissättning där myndighetens tolkning av TPG framgår.Studien är en innehållsanalys av kvalitativ karaktär och datamaterialet som har använts är textutdrag från myndigheternas handledning och manual samt respektive lands lagstiftning på vissa områden. Studien har som ambition att undersöka fyra områden avseende internprissättning, myndigheternas referering till gällande rätt, armlängdsprincipen, metoder för prissättning och bevisförfarande. Tolkningsmodellen som har använts i studien har utvecklats fram ifrån Johanssons (2010) modell avseende substance over form. Tolkningsmodellen tillsammans med den teoretiska referensramen har fungerat som ett verktyg i analysen kring hur redovisningstraditionerna i respektive land påverkar tolkningen av TPG.Studiens slutsats indikerar att det finns inslag av redovisningstraditionerna i respektive lands tolkning av TPG. Både Storbritannien och Sverige refererar till OECD kontinuerligt men studien har resulterat i vissa skillnader som belyser det faktum att länderna har olika redovisningstraditioner. I Skatteverkets handledning identifierar resultatet vissa inslag av den kontinentala redovisningstradition som Sverige präglas av, det påträffas även inslag av det regelbaserade synsättet samt spår av det rättssystem som karaktäriserar Sverige, code law. Resultatet visar även att Storbritanniens redovisningstraditioner framträder i HMRC:s tolkning av TPG. Det återfinns inslag av den anglosaxiska traditionen, det principbaserade synsättet och även här syns spår av deras rättssystem, common law. Studien bidrar med en ökad förståelse kring hur nationella redovisningstraditioner påverkar tolkningen av internationella riktlinjer. Den bidrar även med en praktisk kunskap för företag, myndigheter och internationella verkställande organ. / Increased globalization stimulates international trade in many ways. It has led to increased importance of transfer pricing. However, there is a need to address several issues that have arisen since a transfer price affects the organization's profit or loss due to tax consequences and incentives from management. Although the guidelines known as TPG are drafted by the governments in members’ states, these guidelines are not mandatory. The system allows for subjectivity in transfer pricing. The problem is can be demonstrated by comparing the accounting traditions and their possible impact on transfer pricing in Sweden and the UK.The purpose of the study is to investigate how Sweden's and Britain's accounting traditions affect the countries' interpretation of the TPG. The tax authorities of these countries, the Swedish Tax Agency and HMRC, have each issued a guide and an internal manual on transfer pricing, which explains the authorities’ interpretation of the TPG. The aim of the study is to make a comparative analysis of these guides and internal manuals to see if and how the countries’ interpretation of the TPG differ due to the countries’ accounting traditions. Further, the study is a content analysis of qualitative nature and the data used has been extracted from the authorities' guide and internal manual, as well as the legislation of each country in certain areas. The aim of the study is to investigate four areas of transfer pricing, the authorities' reference to current law, the arm's length principle, pricing methods and evidence procedures. The research model used in this study has been developed from Johanssons (2010) model regarding substance over form. The resarch model together with the theoretical reference framework have served as a tool in the analysis of how accounting traditions in each country affect the interpretation of TPG.The study’s conclusion indicates that there are elements of accounting traditions in each country’s interpretation of TPG. Both the UK and Sweden refer to the OECD repeatedly but the study has resulted in some differences highlighting the fact that the countries have different accounting traditions. In the Swedish Tax Agency’s guidance, the result identifies certain elements of the continental accounting tradition that Sweden is characterized by, it also finds elements of the rule-based approach as well as traces of the legal system code law that characterizes Sweden. Likewise, the result also shows that Britain's accounting traditions appear in HMRC's interpretation of TPG. There are elements of Anglo-Saxon tradition, the principle-based approach, and also traces of their legal system common law. The study contributes to an increased understanding of how national accounting traditions affect the interpretation of international guidelines. It also provides useful information for companies, authorities and international executive agencies.This paper is hereinafter written in Swedish.

A l’avenir, l'agriculture et l'environnement vont pouvoir bénéficier de plus en plus de données hétérogènes collectées par des réseaux de capteurs sans fil (RCSF). Ces données alimentent généralement des outils d’aide à la décision (OAD). Dans cette thèse, nous nous intéressons spécifiquement aux systèmes sensibles et adaptatifs au contexte basés sur un RCSF et un OAD, dédiés au suivi de phénomènes naturels. Nous proposons ainsi une formalisation pour la conception et la mise en œuvre de ces systèmes. Le contexte considéré se compose de données issues du phénomène étudié mais également des capteurs sans fil (leur niveau d’énergie par exemple). Par l’utilisation des ontologies et de techniques de raisonnement, nous visons à maintenir le niveau de qualité de service (QdS) des données collectées (en accord avec le phénomène étudié) tant en préservant le fonctionnement du RCSF. Pour illustrer notre proposition, un cas d'utilisation complexe, l'étude des inondations dans un bassin hydrographique, est considéré. Cette thèse a produit un logiciel de simulation de ces systèmes qui intègre un système de simulation multi-agents (JADE) avec un moteur d’inférence à base de règles (Jess). / In the future, agriculture and environment will rely on more and more heterogeneous data collected by wireless sensor networks (WSN). These data are generally used in decision support systems (DSS). In this dissertation, we focus on adaptive context-aware systems based on WSN and DSS, dedicated to the monitoring of natural phenomena. Thus, a formalization for the design and the deployment of these kinds of systems is proposed. The considered context is established using the data from the studied phenomenon but also from the wireless sensors (e.g., their energy level). By the use of ontologies and reasoning techniques, we aim to maintain the required quality of service (QoS) level of the collected data (according to the studied phenomenon) while preserving the resources of the WSN. To illustrate our proposal, a complex use case, the study of floods in a watershed, is described. During this PhD thesis, a simulator for context-aware systems which integrates a multi-agent system (JADE) and a rule engine (Jess) has been developed.Keywords: ontologies, rule-based inferences, formalization, heterogeneous data, sensors data streams integration, WSN, limited resources, DSS, adaptive context-aware systems, QoS, agriculture, environment.

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.

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Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal