This thesis deals with transformation of network data reporting process in a Telecom company. The current process is MS Excel based and is inadequate and inefficient. The goal is to find the right tools and to implement them. The thesis is divided into three parts. First part is focused on theoretical background of reporting, i.e. Business Intelligence and other approaches. Second part explains general Network reporting principles and trends. In contrast with these theoretical recommendations, the actual level of the company's process is depicted. The last part of this thesis covers a practical implementation of selected applications. First, a choice is made within a variety of tools based on department's needs then the architecture is proposed and applications are implemented. The final part of the thesis provides an assessment of the benefits attained by this project.

Long wait times are a global issue in the healthcare sector, particularly in Canada. Despite numerous research findings on wait time management, the issue persists. This is partly because for a given hospital, the data required to conduct wait times analysis is currently scattered across various information systems. Moreover, such data is usually not accurate (because of possible human errors), imprecise and late. The whole situation contributes to the current state of wait times. This thesis proposes a location-aware business process management system for real-time care process monitoring. More precisely, the system enables an improved visibility of process execution by gathering, as processes execute, accurate and granular process information including wait time measurements. The major contributions of this thesis include an architecture for the system, a prototype taking advantages of commercial real-time location system combined with a business process management system to accurately measure wait times, as well as a case study based on a real cardiology process from an Ontario hospital.

Long wait times are a global issue in the healthcare sector, particularly in Canada. Despite numerous research findings on wait time management, the issue persists. This is partly because for a given hospital, the data required to conduct wait times analysis is currently scattered across various information systems. Moreover, such data is usually not accurate (because of possible human errors), imprecise and late. The whole situation contributes to the current state of wait times. This thesis proposes a location-aware business process management system for real-time care process monitoring. More precisely, the system enables an improved visibility of process execution by gathering, as processes execute, accurate and granular process information including wait time measurements. The major contributions of this thesis include an architecture for the system, a prototype taking advantages of commercial real-time location system combined with a business process management system to accurately measure wait times, as well as a case study based on a real cardiology process from an Ontario hospital.

La complexité et la dynamique de l'environnement dans lequel évolue chaque entreprise requiert de la part de ses managers la capacité de prendre des décisions pertinentes dans un laps de temps très court afin de s'accroître. Pour cela, l'analyse des données générées par l'activité de l'entreprise peut être une précieuse source d'information. L'Intelligence Opérationnelle (IO) est une classe de systèmes d'aide à la décision permettant aux managers d'avoir une très bonne compréhension de la situation de l'entreprise, à travers l'analyse de l'activité passée et présente. Dans ce contexte, les notions de temps et de traçabilité sont primordiales dans la compréhension de l'évolution de l'activité de l'entreprise à travers le temps. Dans cette thèse, nous présentons Axway Decision Insight (ADI), une solution d'IO développée par Axway. Son composant clé est un SGBD orienté-colonnes et bi-temporel développé en interne par l'entreprise pour répondre aux besoins spécifiques de l'IO. Ses capacités bi-temporelles lui permettent de gérer nativement aussi bien l'évolution des données dans la réalité modélisée (temps de validité) que l'évolution des données dans la base de données (temps de transaction). Nous commencerons par présenter la solution ADI en nous focalisant sur deux éléments importants: 1) l'interface graphique qui permet la conception et l'utilisation d'ADI sans écrire la moindre ligne de code. 2) L'approche adoptée pour modéliser les données bi-temporelles. Ensuite, nous présenterons un benchmark bi-temporel destiné ADI.Après cela, nous présenterons deux optimisations pour ADI. La première permet de pré-calculer et matérialiser les opérations d'agrégation, ce qui permet de réduire le temps nécessaire à la mise à jour de interface graphique d'ADI. La deuxième optimisation ordonne l'exécution des opérateurs de jointure des plans de requêtes en utilisant un modèle coût basé sur des statistiques sur des données bi-temporelles. Pour ces optimisations, nous avons effectué des expérimentations en utilisant notre benchmark, et qui ont démontré leurs intérêts. / Nowadays, every company is operating in very dynamic and complex environments which require from its managers to have a deep understanding of its business in order to take rapid and relevant decisions, and thus maintain or improve their company's activities. They can rely on analyzing the data deluge generated by the company's activities. A new class of systems has emerged in the decision support system galaxy called "Operational Intelligence" (OI) to meet this challenge. The objective is to enable operational managers to understand what happened in the past as well as what is currently happening in their business. In this context, the notions of time and traceability turns out to play a crucial role to understand what happened in the company and what is currently happening in the company. In this thesis, we present "Axway Decision Insight" (ADI), an "Operational Intelligence" solution developed by Axway. ADI's key component is a proprietary bi-temporal and column-oriented DBMS that has specially been designed to meet OI requirements. Its bi-temporal capabilities enable to catch both data evolution in the modeled reality (valid time) and in the database (transaction time).We first introduce ADI by focusing on two topics: 1) the GUI that makes the platform "code-free". 2) The adopted bi-temporal modeling approaches. Then we propose a performance benchmark that meets ADI's requirements. Next, we present two bi-temporal query optimizations for ADI. The first one consists in redefining a complex bi-temporal query into: 1) a set of continuous queries in charge of computing aggregation operations as data is collected. 2) A bi-temporal query that accesses the continuous queries' results and feeds the GUI. The second one is a cost-based optimization that uses statistics on bi-temporal data to determine an "optimal" query plan. For these two optimizations, we conducted some experiments, using our benchmark, which show their interests.