APPLICATIONS OF MACHINE LEARNING IN MICROBIAL FORENSICS

Microbial ecosystems are complex, with hundreds of members interacting with each other and the environment. The intricate and hidden behaviors underlying these interactions make research questions challenging – but can be better understood through machine learning. However, most machine learning that is used in microbiome work is a black box form of investigation, where accurate predictions can be made, but the inner logic behind what is driving prediction is hidden behind nontransparent layers of complexity. Accordingly, the goal of this dissertation is to provide an interpretable and in-depth machine learning approach to investigate microbial biogeography and to use micro-organisms as novel tools to detect geospatial location and object provenance (previous known origin). These contributions follow with a framework that allows extraction of interpretable metrics and actionable insights from microbiome-based machine learning models. The first part of this work provides an overview of machine learning in the context of microbial ecology, human microbiome studies and environmental monitoring – outlining common practice and shortcomings. The second part of this work demonstrates a field study to demonstrate how machine learning can be used to characterize patterns in microbial biogeography globally – using microbes from ports located around the world. The third part of this work studies the persistence and stability of natural microbial communities from the environment that have colonized objects (vessels) and stay attached as they travel through the water. Finally, the last part of this dissertation provides a robust framework for investigating the microbiome. This framework provides a reasonable understanding of the data being used in microbiome-based machine learning and allows researchers to better apprehend and interpret results. Together, these extensive experiments assist an understanding of how to carry an in-silico design that characterizes candidate microbial biomarkers from real world settings to a rapid, field deployable diagnostic assay. The work presented here provides evidence for the use of microbial forensics as a toolkit to expand our basic understanding of microbial biogeography, microbial community stability and persistence in complex systems, and the ability of machine learning to be applied to downstream molecular detection platforms for rapid and accurate detection

MOVING OBJECTS MANAGEMENT FOR LOCATION-BASED SERVICES

Ph.DDOCTOR OF PHILOSOPH

Enabling Things to Talk

Information Systems Applications (incl. Internet); Business IT Infrastructure; Computer Appl. in Administrative Data Processing; Operations Management; Software Engineering; Special Purpose and Application-Based Systems; Business Information Systems; Ubiquitous Computing; Reference Architecture; Spatio-Temporal Systems; Smart Objects; Supply Chain Management; IoT; SCM; Web Applications; Internet of Things; Smart Homes; RFI

Efficient Reorganisation of Hybrid Index Structures Supporting Multimedia Search Criteria

This thesis describes the development and setup of hybrid index structures. They are access methods for retrieval techniques in hybrid data spaces which are formed by one or more relational or normalised columns in conjunction with one non-relational or non-normalised column. Examples for these hybrid data spaces are, among others, textual data combined with geographical ones or data from enterprise content management systems. However, all non-relational data types may be stored as well as image feature vectors or comparable types. Hybrid index structures are known to function efficiently regarding retrieval operations. Unfortunately, little information is available about reorganisation operations which insert or update the row tuples. The fundamental research is mainly executed in simulation based environments. This work is written ensuing from a previous thesis that implements hybrid access structures in realistic database surroundings. During this implementation it has become obvious that retrieval works efficiently. Yet, the restructuring approaches require too much effort to be set up, e.g., in web search engine environments where several thousands of documents are inserted or modified every day. These search engines rely on relational database systems as storage backends. Hence, the setup of these access methods for hybrid data spaces is required in real world database management systems. This thesis tries to apply a systematic approach for the optimisation of the rearrangement algorithms inside realistic scenarios. Thus, a measurement and evaluation scheme is created which is repeatedly deployed to an evolving state and a model of hybrid index structures in order to optimise the regrouping algorithms to make a setup of hybrid index structures in real world information systems possible. Thus, a set of input corpora is selected which is applied to the test suite as well as an evaluation scheme. To sum up, it can be said that this thesis describes input sets, a test suite including an evaluation scheme as well as optimisation iterations on reorganisation algorithms reflecting a theoretical model framework to provide efficient reorganisations of hybrid index structures supporting multimedia search criteria

Gestion efficace et partage sécurisé des traces de mobilité

Nowadays, the advances in the development of mobile devices, as well as embedded sensors have permitted an unprecedented number of services to the user. At the same time, most mobile devices generate, store and communicate a large amount of personal information continuously. While managing personal information on the mobile devices is still a big challenge, sharing and accessing these information in a safe and secure way is always an open and hot topic. Personal mobile devices may have various form factors such as mobile phones, smart devices, stick computers, secure tokens or etc. It could be used to record, sense, store data of user's context or environment surrounding him. The most common contextual information is user's location. Personal data generated and stored on these devices is valuable for many applications or services to user, but it is sensitive and needs to be protected in order to ensure the individual privacy. In particular, most mobile applications have access to accurate and real-time location information, raising serious privacy concerns for their users.In this dissertation, we dedicate the two parts to manage the location traces, i.e. the spatio-temporal data on mobile devices. In particular, we offer an extension of spatio-temporal data types and operators for embedded environments. These data types reconcile the features of spatio-temporal data with the embedded requirements by offering an optimal data presentation called Spatio-temporal object (STOB) dedicated for embedded devices. More importantly, in order to optimize the query processing, we also propose an efficient indexing technique for spatio-temporal data called TRIFL designed for flash storage. TRIFL stands for TRajectory Index for Flash memory. It exploits unique properties of trajectory insertion, and optimizes the data structure for the behavior of flash and the buffer cache. These ideas allow TRIFL to archive much better performance in both Flash and magnetic storage compared to its competitors.Additionally, we also investigate the protect user's sensitive information in the remaining part of this thesis by offering a privacy-aware protocol for participatory sensing applications called PAMPAS. PAMPAS relies on secure hardware solutions and proposes a user-centric privacy-aware protocol that fully protects personal data while taking advantage of distributed computing. For this to be done, we also propose a partitioning algorithm an aggregate algorithm in PAMPAS. This combination drastically reduces the overall costs making it possible to run the protocol in near real-time at a large scale of participants, without any personal information leakage.Aujourd'hui, les progrès dans le développement d'appareils mobiles et des capteurs embarqués ont permis un essor sans précédent de services à l'utilisateur. Dans le même temps, la plupart des appareils mobiles génèrent, enregistrent et de communiquent une grande quantité de données personnelles de manière continue. La gestion sécurisée des données personnelles dans les appareils mobiles reste un défi aujourd’hui, que ce soit vis-à-vis des contraintes inhérentes à ces appareils, ou par rapport à l’accès et au partage sûrs et sécurisés de ces informations. Cette thèse adresse ces défis et se focalise sur les traces de localisation. En particulier, s’appuyant sur un serveur de données relationnel embarqué dans des appareils mobiles sécurisés, cette thèse offre une extension de ce serveur à la gestion des données spatio-temporelles (types et operateurs). Et surtout, elle propose une méthode d'indexation spatio-temporelle (TRIFL) efficace et adaptée au modèle de stockage en mémoire flash. Par ailleurs, afin de protéger les traces de localisation personnelles de l'utilisateur, une architecture distribuée et un protocole de collecte participative préservant les données de localisation ont été proposés dans PAMPAS. Cette architecture se base sur des dispositifs hautement sécurisés pour le calcul distribué des agrégats spatio-temporels sur les données privées collectées