Data query operations inside programming languages presently perform their functions through the use of domain-specific, declarative expressions and by way of course-grain, API library calls. These methods of operation are practiced by relational databases as well as semistructured XML data stores. Layers of translation, which are necessary to transform data and instructions from the domain of programming languages to data query systems, negtatively effect the performance of data query operations. Skuery resolves this impedance by adopting XML as a native data type with a native representation (SXML). Likewise, query operations are defined in a general purpose programming language (Scheme in this case) not in an external data query environment. Skuery increases programmer productivity by abstracting layers of translation and unifying computational and data query operations under the auspices of a general purpose programming language.

Probabilistic reasoning in databases has been an active area of research during the last twodecades. However, the previously proposed database approaches, including the probabilistic relationalapproach and the probabilistic object approach, are not good fits for storing and managingdiverse probability distributions along with their auxiliary information.The work in this dissertation extends significantly the initial semistructured probabilistic databaseframework proposed by Dekhtyar, Goldsmith and Hawkes in [20]. We extend the formal SemistructuredProbabilistic Object (SPO) data model of [20]. Accordingly, we also extend the SemistructuredProbabilistic Algebra (SP-algebra), the query algebra proposed for the SPO model.Based on the extended framework, we have designed and implemented a Semistructured ProbabilisticDatabase Management System (SPDBMS) on top of a relational DBMS. The SPDBMS isflexible enough to meet the need of storing and manipulating diverse probability distributions alongwith their associated information. Its query language supports standard database queries as wellas queries specific to probabilities, such as conditionalization and marginalization. Currently theSPDBMS serves as a storage backbone for the project Decision Making and Planning under Uncertaintywith Constraints 1‡ , that involves managing large quantities of probabilistic information. Wealso report our experimental results evaluating the performance of the SPDBMS.We describe an extension of the SPO model for handling interval probability distributions. TheExtended Semistructured Probabilistic Object (ESPO) framework improves the flexibility of theoriginal semistructured data model in two important features: (i) support for interval probabilitiesand (ii) association of context and conditionals with individual random variables. An extended SPO1 This project is partially supported by the National Science Foundation under Grant No. ITR-0325063.(ESPO) data model has been developed, and an extended query algebra for ESPO has also beenintroduced to manipulate probability distributions for probability intervals.The Bayesian Network Development Suite (BaNDeS), a system which builds Bayesian networkswith full data management support of the SPDBMS, has been described. It allows expertswith particular expertise to work only on specific subsystems during the Bayesian network constructionprocess independently and asynchronously while updating the model in real-time.There are three major foci of our ongoing and future work: (1) implementation of a queryoptimizer and performance evaluation of query optimization, (2) extension of the SPDBMS to handleinterval probability distributions, and (3) incorporation of machine learning techniques into theBaNDeS.

Wireless Sensor Networks (WSNs) are deployed widely for various applications. A variety of useful data are generated by these deployments. Since WSNs have limited resources and unreliable communication links, traditional data management techniques are not suitable. Therefore, designing effective data management techniques for WSNs becomes important. In this dissertation, we address three key issues of data management in WSNs. For data collection, a scheme of making some nodes sleep and estimating their values according to the other active nodes’ readings has been proved energy-efficient. For the purpose of improving the precision of estimation, we propose two powerful estimation models, Data Estimation using a Physical Model (DEPM) and Data Estimation using a Statistical Model (DESM). Most of existing data processing approaches of WSNs are real-time. However, historical data of WSNs are also significant for various applications. No previous study has specifically addressed distributed historical data query processing. We propose an Index based Historical Data Query Processing scheme which stores historical data locally and processes queries energy-efficiently by using a distributed index tree. Area query processing is significant for various applications of WSNs. No previous study has specifically addressed this issue. We propose an energy-efficient in-network area query processing scheme. In our scheme, we use an intelligent method (Grid lists) to describe an area, thus reducing the communication cost and dropping useless data as early as possible. With a thorough simulation study, it is shown that our schemes are effective and energy- efficient. Based on the area query processing algorithm, an Intelligent Monitoring System is designed to detect various events and provide real-time and accurate information for escaping, rescuing, and evacuation when a dangerous event happened.

Background: Advances in information technology have paved the way to facilitate accessibility to population-level health data through web-based data query systems (WDQSs). Despite these advances in technology, US state agencies face many challenges related to the dissemination of their local health data. It is essential for the public to have access to high-quality data that are easy to interpret, reliable, and trusted. These challenges have been at the forefront throughout the COVID-19 pandemic. Objective: The purpose of this study is to identify the most significant challenges faced by state agencies, from the perspective of the Behavioral Risk Factor Surveillance System (BRFSS) coordinator from each state, and to assess if the coordinators from states with a WDQS perceive these challenges differently. Methods: We surveyed BRFSS coordinators (N=43) across all 50 US states and the District of Columbia. We surveyed the participants about contextual factors and asked them to rate system aspects and challenges they faced with their health data system on a Likert scale. We used two-sample t tests to compare the means of the ratings by participants from states with and without a WDQS. Results: Overall, 41/43 states (95%) make health data available over the internet, while 65% (28/43) employ a WDQS. States with a WDQS reported greater challenges (P=.01) related to the cost of hardware and software (mean score 3.44/4, 95% CI 3.09-3.78) than states without a WDQS (mean score 2.63/4, 95% CI 2.25-3.00). The system aspect of standardization of vocabulary scored more favorably (P=.01) in states with a WDQS (mean score 3.32/5, 95% CI 2.94-3.69) than in states without a WDQS (mean score 2.85/5, 95% CI 2.47-3.22). Conclusions: Securing of adequate resources and commitment to standardization are vital in the dissemination of local-level health data. Factors such as receiving data in a timely manner, privacy, and political opposition are less significant barriers than anticipated.

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隨著地籍管理工作在廣度深度上的增加，使得地籍、土地利用等歷史資料的查詢與檢索日益頻繁，但現階段地籍管理系統之建置方法對於歷史資料查詢之效率較差。本文在探討含時間維度資料庫之建置方法，並利用異動時間與異動註記等欄位及異動紀錄表之建立，令目前地籍管理系統可達到以時間維度，或指定地號，進行地籍資料查詢與檢索。 / The more cadastral management is developed, the more historic data query of cadastre and land use are important. The primary purpose of this study is concentrated on the recoveries of parcel linage and areas on any time profile. The targets of this research are placed on design of spatio-temporal data model, and the analysis of efficiency of data recovery and query.

Ce travail de thèse s'inscrit dans le cadre du projet < personnalisation des environnements informatiques pour l'apprentissage humain (EIAH) > financé par la Région Rhône-Alpes. La personnalisation des EIAH est essentiellement dépendante de la capacité à produire des traces pertinentes et exploitables des activités des apprenants interagissant avec un EIAH. Dans ce domaine, l'exploitation des traces relève explicitement plusieurs problématiques allant de sa représentation de manière normalisée et intelligible à son traitement et interprétation en temps différé ou en temps réel au moment même de l'apprentissage. La multiplication des pratiques et des usages des traces requiert des outils génériques pour soutenir leurs exploitations. L'objectif de cette thèse est de définir les fondements théoriques de tels outils génériques permettant l'exploitation des traces d'interaction. Ceci nous a amené à définir la notion de Systèmes à Base de Trace modélisées : une classe de systèmes à base de connaissances facilitant le raisonnement et l'exploitation des traces modélisées. L'approche théorique proposée pour construire de tels systèmes s'articule autour de deux contributions : (1) La définition d'un cadre conceptuel définissant les concepts, l'architecture et les services mobilisés par les SBT. (2) La définition d'un cadre formel pour les systèmes à base de traces modélisées. Plus précisément, la proposition d'un langage pour l'interrogation et la transformation de trace modélisées à base de règles permettant des évaluations ponctuelles et continues. La sémantique formelle de ce langage est définie sous forme d'une théorie des modèles et d'une théorie de point fixe, deux formalismes habituellement utilisés pour décrire la sémantique formelle des langages de représentation de connaissances / This thesis is funded by the Rhône-Alpes Region as a part of the project < Personalisation of Technology-Enhanced Learning (TEL) Systems >. Personalising TEL Systems is, above all, dependent on the capacity to produce relevant and exploitable traces of individual or collaborative learning activities. In this field, exploiting interaction traces addresses several problems ranging from its representation in a normalised and intelligible manner to its processing and interpretation in continuous way during the ongoing TEL activities. The proliferation of trace-based exploitations raises the need of generic tools to support their representation and exploitation. The main objective of this thesis is to define the theoretical foundations of such generic tools. To do that, we define the notion of Trace-Based System (TBS) as a kind of Knowledge-based system whose main source of knowledge is a set of trace of user-system interactions. This thesis investigates practical and theoretical issues related to TBS, covering the spectrum from concepts, services and architecture involved by such TBS (conceptual framework) to language design over declarative semantics (formal framework). The central topic of our framework is the development of a high-level trace transformation language supporting deductive rules as an abstraction and reasoning mechanism for traces. The declarative semantics for such language is defined by a (Tarski-style) model theory with accompanying fixpoint theory