Patient wait times and care service times are key performance measures for care processes in hospitals. Managing the quality of care delivered by these processes in real-time is challenging. A key challenge is to correlate source medical events to infer the care process states that define patient wait times and care service times. Commercially available complex event processing engines do not have built in support for the concept of care process state. This makes it unnecessarily complex to define and maintain rules for inferring states from source medical events in a care process. Another challenge is how to present the data in a real-time BI dashboard and the underlying data model to use to support this BI dashboard. Data representation architecture can potentially lead to delays in processing and presenting the data in the BI dashboard. In this research, we have investigated the problem of real-time monitoring of care processes, performed a gap analysis of current information system support for it, researched and assessed available technologies, and shown how to most effectively leverage event driven and BI architectures when building information support for real-time monitoring of care processes. We introduce a state monitoring engine for inferring and managing states based on an application model for care process monitoring. A BI architecture is also leveraged for the data model to support the real-time data processing and reporting requirements of the application’s portal. The research is validated with a case study to create a real-time care process monitoring application for an Acute Coronary Syndrome (ACS) clinical pathway in collaboration with IBM and Osler hospital. The research methodology is based on design-oriented research.

Patient wait times and care service times are key performance measures for care processes in hospitals. Managing the quality of care delivered by these processes in real-time is challenging. A key challenge is to correlate source medical events to infer the care process states that define patient wait times and care service times. Commercially available complex event processing engines do not have built in support for the concept of care process state. This makes it unnecessarily complex to define and maintain rules for inferring states from source medical events in a care process. Another challenge is how to present the data in a real-time BI dashboard and the underlying data model to use to support this BI dashboard. Data representation architecture can potentially lead to delays in processing and presenting the data in the BI dashboard. In this research, we have investigated the problem of real-time monitoring of care processes, performed a gap analysis of current information system support for it, researched and assessed available technologies, and shown how to most effectively leverage event driven and BI architectures when building information support for real-time monitoring of care processes. We introduce a state monitoring engine for inferring and managing states based on an application model for care process monitoring. A BI architecture is also leveraged for the data model to support the real-time data processing and reporting requirements of the application’s portal. The research is validated with a case study to create a real-time care process monitoring application for an Acute Coronary Syndrome (ACS) clinical pathway in collaboration with IBM and Osler hospital. The research methodology is based on design-oriented research.

Detta examensarbete undersöker användningen av datadrivna metoder för efterfrågan prognoser och lagerstyrning inom ramen för Ericssons supply chain management. Studien fokuserar på integrationen av maskininlärning, demand shaping och realtidsdata för att förbättra noggrannheten och effektiviteten inom dessa avgörande områden. Studien utforskar effekten av maskininlärningstekniker på efterfråganprognoser och betonar betydelsen av exakta förutsägelser för att vägleda produktion, lagerhantering och distributionsstrategier. För att implementera detta föreslår studien integrationen av realtidsdataströmmar och Internet of Things (IoT)-enheter, vilket möjliggör insamling av aktuell information. Denna integration underlättar snabba svar på varierande efterfrågemönster och optimerar därmed supply chain-operationer. Studien ger värdefulla insikter för Ericsson för att förbättra sina förmågor inom efterfråganprognoser och för att optimera lagerhanteringen i en datadriven miljö. / This master's thesis investigates the utilization of data-driven approaches for demand forecasting and inventory control in the context of Ericsson's supply chain management. The study focuses on the integration of machine learning, demand shaping, and real-time data to enhance accuracy and efficiency in these critical areas. The research explores the impact of machine learning techniques on demand forecasting, highlighting the significance of precise predictions in guiding production, inventory management, and distribution strategies. To address this, the study proposes the integration of real-time data streams and Internet of Things (IoT) devices, enabling the capture of up-to-date information. This integration facilitates prompt responses to evolving demand patterns, thereby optimizing supply chain operations.The research provides valuable insights for Ericsson to enhance its demand forecasting capabilities and optimize inventory management in a data-driven environment.

Un système d'aide à la décision (SIAD) est un système d'information qui assiste lesdécideurs impliqués dans les processus de décision complexes. Les SIAD modernesont besoin d'exploiter, en plus de données numériques et symboliques, des donnéeshétérogènes (données texte, données multimédia, ...) et provenant de sources diverses(comme le Web). Nous qualifions ces données complexes. Les entrepôts dedonnées forment habituellement le socle des SIAD. Ils permettent d'intégrer des données provenant de diverses sources pour appuyer le processus décisionnel. Cependant, l'avènement de données complexes impose une nouvelle vision de l'entreposagedes données, y compris de l'intégration des données, de leur stockage et de leuranalyse. En outre, les exigences d'aujourd'hui imposent l'intégration des donnéescomplexes presque en temps réel, pour remplacer le processus ETL traditionnel(Extraction, Transformation et chargement). Le traitement en temps réel exige unprocessus ETL plus actif. Les tâches d'intégration doivent réagir d'une façon intelligente, c'est-à-dire d'une façon active et autonome pour s'adapter aux changementsrencontrés dans l'environnement d'intégration des données, notamment au niveaudes sources de données.Dans cette thèse, nous proposons des solutions originales pour l'intégration dedonnées complexes en temps réel, de façon active et autonome. En eet, nous avons conçu une approche générique basé sur des métadonnées, orientée services et orienté évènements pour l'intégration des données complexes. Pour prendre en charge lacomplexité des données, notre approche stocke les données complexes à l'aide d'unformat unie en utilisant une approche base sur les métadonnées et XML. Nous noustraitons également la distribution de données et leur l'interopérabilité en utilisantune approche orientée services. Par ailleurs, pour considérer le temps réel, notreapproche stocke non seulement des données intégrées dans un référentiel unie,mais présente des fonctions d'intégration des données a la volée. Nous appliquonségalement une approche orientée services pour observer les changements de donnéespertinentes en temps réel. En outre, l'idée d'intégration des données complexes defaçon active et autonome, nous proposons une méthode de fouille dans les évènements.Pour cela, nous proposons un algorithme incrémentiel base sur XML pourla fouille des règles d'association a partir d’évènements. Ensuite, nous denissonsdes règles actives a l'aide des données provenant de la fouille d'évènements an deréactiver les tâches d'intégration.Pour valider notre approche d'intégration de données complexes, nous avons développé une plateforme logicielle, à savoir AX-InCoDa ((Active XML-based frameworkfor Integrating Complex Data). AX-InCoDa est une application Web implémenté à l'aide d'outils open source. Elle exploite les standards du Web (comme les services Web et XML) et le XML actif pour traiter la complexité et les exigences temps réel. Pour explorer les évènements stockés dans base d'évènement, nous avons proposons une méthode de fouille d'évènements an d'assurer leur autogestion.AX-InCoDa est enrichi de règles actives L'ecacite d'AX-InCoDa est illustrée par une étude de cas sur des données médicales. En, la performance de notre algorithme de fouille d'évènements est démontrée expérimentalement. / A decision support system (DSS) is an information system that supports decisionmakers involved in complex decision-making processes. Modern DSSs needto exploit data that are not only numerical or symbolic, but also heterogeneouslystructured (e.g., text and multimedia data) and coming from various sources (e.g,the Web). We term such data complex data. Data warehouses are casually usedas the basis of such DSSs. They help integrate data from a variety of sourcesto support decision-making. However, the advent of complex data imposes anothervision of data warehousing including data integration, data storage and dataanalysis. Moreover, today's requirements impose integrating complex data in nearreal-time rather than with traditional snapshot and batch ETL (Extraction, Transformationand Loading). Real-time and near real-time processing requires a moreactive ETL process. Data integration tasks must react in an intelligent, i.e., activeand autonomous way, to encountered changes in the data integration environment,especially data sources.In this dissertation, we propose novel solutions for complex data integration innear real-time, actively and autonomously. We indeed provide a generic metadatabased,service-oriented and event-driven approach for integrating complex data.To address data complexity issues, our approach stores heterogeneous data into aunied format using a metadata-based approach and XML. We also tackle datadistribution and interoperability using a service-oriented approach. Moreover, toaddress near real-time requirements, our approach stores not only integrated datainto a unied repository, but also functions to integrate data on-the-y. We also apply a service-oriented approach to track relevant data changes in near real-time.Furthermore, the idea of integrating complex data actively and autonomously revolvesaround mining logged events of data integration environment. For this sake,we propose an incremental XML-based algorithm for mining association rules fromlogged events. Then, we de ne active rules upon mined data to reactivate integrationtasks.To validate our approach for managing complex data integration, we develop ahigh-level software framework, namely AX-InCoDa (Active XML-based frameworkfor Integrating Complex Data). AX-InCoDa is implemented as Web application usingopen-source tools. It exploits Web standards (e.g., XML and Web services) andActive XML to handle complexity issues and near real-time requirements. Besidewarehousing logged events into an event repository to be mined for self-managingpurposes, AX-InCoDa is enriched with active rules. AX-InCoDa's feasibility is illustratedby a healthcare case study. Finally, the performance of our incremental eventmining algorithm is experimentally demonstrated.