<p> Intermittently connected sensor networks are a subset of wireless sensor networks that have a high data volume and suffer from the problem of infrequent data offloading. When the generated data exceeds the storage capacity of the network between offloading opportunities, there must be some method of data preservation. This requires two phases: compressing the data and redistributing it. The use of data aggregation with consideration given to minimizing total energy is examined as a solution for the compression problem. Simulations of the optimal solution and an approximation heuristic are compared.</p>

XML, the eXtended Markup Language, is well believed to be the most common tool of the future for all data manipulation and data transmission. As a result, a lot of research and work have been done on XML. However, the current efforts on XML only focus on data queries. So far, there does not exist an XML data manipulation language that has reached its maturity to be accepted widely. Most of the existing XML databases only implement some kind of XML data query language and do not support data update operations. This thesis tries to make up the absence of XML data updates, and it begins with a through study on topics such as XML language features, its history and development, the existing XML query languages and update languages, XML database management systems, and XML indexing structures, etc. Based on the research, McXML, a native XML database management system, is proposed, which supports both data queries and data updates, with emphasis on data updates. In order to optimize the performance of McXML, some indexing structures are developed on it, which overcome the difficulties incurred by the support of data updates and make McXML work more efficiently and flexibly.

Hypertext systems have traditionally been characterized as collections of data connected by "links," or navigational paths between explicit anchors in the data; links from hypertext systems to other resources or to computation engines have been afterthoughts. The thesis of this dissertation is that a model that views hypertext "links" as queries can encompass not only the traditional definition of hypertext, but also integrated access to remote resources, as well as computation and intelligent retrieval of data from within and without the hypertext system. The Virtual Object Model is presented in support of this thesis. The Virtual Object Model provides a comprehensive model that includes naming and typing of data in local and remote information sources and computations, thus facilitating both human and automatic search and retrieval in hypertext. An implementation of the model, called "Amanuensis," includes query engines that support query of diverse remote resources including MEDLINE (TexSearch), the Unified Medical Language System (UMLS) Metathesaurus, and SNOMED nomenclature, in addition to two distributed hypertext systems, the Virtual Notebook System (VNS), and World-Wide Web. Interfaces to Amanuensis were created from the VNS and Mosaic a Web viewer. The utility of Amanuensis has been demonstrated by the development of the prototype of a Distributed Medical Dictionary that provides for intelligent construction of medical terms from a hierarchy of vocabulary resources including the UMLS Metathesaurus. Experience with Amanuensis demonstrates that the Virtual Object Model simplifies naming and searching across diverse domains. It simplifies the user interface because objects from diverse sources can share similar behaviors. This can encourage the use of network bandwidth in place of local storage, reducing users' personal or institutional needs for disk space and lessening the individual burdens of data management. Searching in a large, diverse information space is still inherently difficult, but the Virtual Object Model integrates the information space better than previous hypertext models, simplifying the task of constructing intelligent agents that aid the user in information gathering.

<p> Cross-organizational business processes are common in today&rsquo;s economy. Of necessity, enterprises conduct their business in cooperation to create products and services for the marketplace. Thus business processes inherently involve autonomous partners with heterogeneous software designs and implementations. The existing business modeling approaches that employ high-level abstractions are difficult to operationalize, and the approaches that employ low-level abstractions lead to highly rigid processes that lack business semantics. We propose a novel business model based on multiagent abstractions. Unlike existing approaches, our model gives primacy to the contractual relationships among the business partners, thus providing a notion of business-level correctness, and offers flexibility to the participants. Our approach employs reusable patterns as building blocks to model recurring business scenarios. A step-by-step methodology guides a modeler in constructing a business model. Our approach employs temporal logic to formalize the correctness properties of a business model, and model checking to verify if a given operationalization satisfies those properties. Developer studies found that our approach yields improved model quality compared to the traditional approaches from the supply chain and healthcare domains.</p><p> Commitments capture how an agent relates with another agent, whereas goals describe states of the world that an agent is motivated to bring about. It makes intuitive sense that goals and commitments be understood as being complementary to each other. More importantly, an agent&rsquo;s goals and commitments ought to be coherent, in the sense that an agent&rsquo;s goals would lead it to adopt or modify relevant commitments and an agent&rsquo;s commitments would lead it to adopt or modify relevant goals. However, despite the intuitive naturalness of the above connections, they have not yet been studied in a formal framework. This dissertation provides a combined operational semantics for goals and commitments. Our semantics yields important desirable properties, including convergence of the configurations of cooperating agents, thereby delineating some theoretically well-founded yet practical modes of cooperation in a multiagent system.</p><p> We formalize the combined operational semantics of achievement commitments and goals in terms of hierarchical task networks (HTNs) and show how HTN planning provides a natural representation and reasoning framework for them. Our approach combines a domain-independent theory capturing the lifecycles of goals and commitments, generic patterns of reasoning, and domain models. We go beyond existing approaches by proposing a first-order representation that accommodates settings where the commitments and goals are templatic and may be applied repeatedly with differing bindings for domain objects. Doing so not only leads to a more perspicuous modeling, it also enables us to support a variety of practical patterns.</p>

The topic of this thesis revolves around the organization of geographical information in permanent memory. Our premise is that a recognized and fully documented direct access storage technique called Multidimensional Paging or Multipaging would provide a well balanced storing structure for this category of data. Since multipaging uses a multidimensional perspective on the information to allocate it to pages in secondary storage then spatial data, which is fundamentally multidimensional by nature, would surely offer a suitable profile. / First, we attempt to evaluate the efficiency of multipaging on static files and to suggest possible modifications to the standard algorithm to better serve spatial data. / Our solution to this problem consists in compressing the pages that overflow. Because geographical information is often a representation of occurences of Nature, we hypothesize that Fractal Geometry, which serves to formalize a mathematical description of such elements, could provide the theoretical background to derive an efficient fractal-based compression algorithm. An appreciable improvement is obtained by compressing the pages of the multipaged administrative regions data that exceed their capacity: $ alpha=0.7272$ and $ pi=1.0$. / The outcome of these experiments led us to elaborate a mixed system based on two relatively different approaches: multipaging and fractal-based data compression. The first part consisted in the implementation of the standard static multipaging algorithm using a relational database management system named Relix. The other approach was developed using the C programming language to accommodate some particularities of the multipaged spatial data. The preliminary results were encouraging and allowed us to establish the parameters for a more formal implementation. Also, it brought out the limits of the compression method in view of the intended usage of the data. (Abstract shortened by UMI.)

<p> By clustering one seeks to partition a given set of points into a number of clusters such that points in the same cluster are similar and are dissimilar to points in other clusters. In the virtue of this goal, data of relational nature become typical for clustering. The similarity and dissimilarity relations between the data points are supposed to be the nuts and bolts for cluster formation. Thus, the task is driven by the notion of similarity between the data points. In practice, the similarity is usually measured by the pairwise distances between the data points. Indeed, the objective function of the two widely used clustering algorithms, namely, <i>k</i>-means and fuzzy <i> c</i>-means, appears in terms of the pairwise distances between the data points. </p><p> The clustering task is complicated by the choice of the distance measure and estimating the number of clusters. Fuzzy c-means is convenient when there are uncertainties in allocating points, in overlapping areas, to clusters. The k-means algorithm allocates the points unequivocally to clusters; overlooking the similarities between those points in overlapping areas. The fuzzy approach allows a point to be a member in as many clusters as necessary; thus it provides better insight into the relations between the points in overlapping areas. </p><p> In this thesis we develop a relational framework that is inspired by the silhouette measure of clustering quality. The framework asserts the relations between the data points by means of logical reasoning with the cluster membership values. The original description of computing the silhouettes is limited to crisp partitions. A natural generalization of silhouettes, to fuzzy partitions is given within our framework. Moreover, two notions of silhouettes emerge within the framework at different levels of granularity, namely, point-wise silhouette and center-wise silhouette. Now by the generalization, each silhouette is capable of measuring the extent to which a crisp, or fuzzy, partition has fulfilled the clustering goal at the level of the individual points, or cluster centers. The partitions are evaluated by the silhouette measure in conjunction with point-to-point or center-to-point distances. </p><p> By the generalization, the average silhouette value becomes a reasonable device for selecting between crisp and fuzzy partitions of the same data set. Accordingly, one can find about which partition is better in representing the relations between the data points, in accordance with their pairwise distances. Such powerful feature of the generalized silhouettes has exposed a problem with the partitions generated by fuzzy c-means. We have observed that defuzzifying the fuzzy c-means partitions always improves the overall representation of the relations between the data points. This is due to the inconsistency between some of the membership values and the distances between the data points. This inconsistency was reported, by others, in a couple of occasions in real life applications. </p><p> Finally, we present an experiment that demonstrates a successful application of the generalized silhouette measure in feature selection for highly imbalanced classification. A significant improvement in the classification for a real data set has resulted from a significant reduction in the number of features. </p>

<p> Spatio-temporal research frequently results in analyzing large sets of data (i.e., a data set larger than will reside in common PC main memory). Currently, many analytical techniques used to analyze large data sets begin by sampling the data such that it can all reside in main memory. Depending upon the research question posed, information can be lost when outliers are discarded. For example, if the focus of the analysis is on clusters of automobiles, the outliers may not be represented in the sampled dataset. The purpose of this study is to use similarity measures to detect anomalies. The clustering algorithm that is used in this thesis research is DBSCAN. Synthetic data is generated and then analyzed to evaluate the effectiveness of detecting anomalies using similarity measures. Results from this study support the hypothesis, "If similarity measures can be developed, then DBSCAN can be used to find anomalies in trajectory data using time slices." Synthetic data is analyzed using DBSCAN to address the research question -"Can DBSCAN be used to find anomalies in trajectory data using time slices?"</p>

<p> The purpose of this study was to construct a child-driven metadata schema by understanding children's cognitive processes and behaviors during book selection. Existing knowledge organization systems including metadata schemas and previous literature in the metadata domain have shown that there is a no specialized metadata schema that describes children's resources that also is developed by children. It is clear that children require a new or alternative child-driven metadata schema. Child-driven metadata elements reflected the children's cognitive perceptions that could allow children to intuitively and easily find books in an online cataloging system. The literature of development of literacy skills claims that the positive experiences of selecting books empower children's motivation for developing literacy skills. Therefore, creating a child-driven metadata schema not only contributes to the improvement of knowledge organization systems reflecting children's information behavior and cognitive process, but also improves children's literacy and reading skills. </p><p> Broader research questions included what metadata elements do children like to use? What elements should a child-driven metadata schema include? In order to answer these research questions, a triangulated qualitative research design consisting of questionnaires, paired think-aloud, interview, and diaries were used with 22 child participants between the ages of 6 and 9. A holistic understanding of the children's cognitive processes during book selection as a foundation of a child-driven metadata schema displays an early stage of an ontological contour for a children's knowledge organization system. A child-driven metadata schema constructed in this study is apt to include different metadata elements from those metadata elements existing in current cataloging standards. A child-driven metadata schema includes five classes such as story/subject, character, illustration, physical characteristics, and understandability, and thirty three metadata elements such as character's names and images, book cover's color, shape, textured materials, engagement element, and tone. In addition, the analysis of the relationship between emergent emotional vocabularies and cognitive factors and facets illustrated the important role of emotion and attention in children's information processing and seeking behaviors.</p>

<p> Recommender systems are increasingly driving user experiences on the Internet. In recent years, online social networks have quickly become the fastest growing part of the Web. The rapid growth in social networks presents a substantial opportunity for recommender systems to leverage social data to improve recommendation quality, both for recommendations intended for individuals and for groups of users who consume content together. This thesis shows that incorporating social indicators improves the predictive performance of group-based and individual-based recommender systems. We analyze the impact of social indicators through small-scale and large-scale studies, implement and evaluate new recommendation models that incorporate our insights, and demonstrate the feasibility of using these social indicators and other contextual data in a deployed mobile application that provides restaurant recommendations to small groups of users.</p>

Motivated by Geographical Information Systems (GIS) applications, we introduce a new data model for mutually nested objects. Combining features from relational as well as object-oriented database systems, our data model is efficient for queries involving multiple access patterns. By maintaining symmetrical relationships between entities, we allow nesting to be formulated dynamically at the query level rather than the data model level, thus dissociating the data structure from the access method. In addition, we do not favor one access pattern over another by clustering data in one particular manner, giving therefore flexibility and performance to our system. / In order to integrate nesting into the relational algebra, we propose an extension to Relix, which is an academic database management system. We then show how those modifications can be used in a wide variety of queries, provide an algorithm to translate nested queries into flat relational expressions, and finally show that similar improvements can be applied to SQL allowing nested queries to be expressed more naturally.