Tabulation of multiple responses

Although multiple-response questions are quite common in survey research, Stata’s official release does not provide much capability for an effective analysis of multiple-response variables. For example, in a study on drug addiction an interview question might be, “Which substances did you consume during the last four weeks?” The respondents just list all the drugs they took, if any; e.g., an answer could be “cannabis, cocaine, heroin” or “ecstasy, cannabis” or “none”, etc. Usually, the responses to such questions are stored as a set of variables and, therefore, cannot be easily tabulated. I will address this issue here and present a new module to compute one- and two-way tables of multiple responses. The module supports several types of data structure, provides significance tests, and offers various options to control the computation and display of the results. In addition, tools to create graphs of multiple-response distributions are presented.

The Process-Outcome Mindfulness Effects in Trainees (PrOMET) study: protocol of a pragmatic randomized controlled trial

Background Mindfulness has its origins in an Eastern Buddhist tradition that is over 2500 years old and can be defined as a specific form of attention that is non-judgmental, purposeful, and focused on the present moment. It has been well established in cognitive-behavior therapy in the last decades, while it has been investigated in manualized group settings such as mindfulness-based stress reduction and mindfulness-based cognitive therapy. However, there is scarce research evidence on the effects of mindfulness as a treatment element in individual therapy. Consequently, the demand to investigate mindfulness under effectiveness conditions in trainee therapists has been highlighted. Methods/Design To fill in this research gap, we designed the PrOMET Study. In our study, we will investigate the effects of brief, audiotape-presented, session-introducing interventions with mindfulness elements conducted by trainee therapists and their patients at the beginning of individual therapy sessions in a prospective, randomized, controlled design under naturalistic conditions with a total of 30 trainee therapists and 150 patients with depression and anxiety disorders in a large outpatient training center. We hypothesize that the primary outcomes of the session-introducing intervention with mindfulness elements will be positive effects on therapeutic alliance (Working Alliance Inventory) and general clinical symptomatology (Brief Symptom Checklist) in contrast to the session-introducing progressive muscle relaxation and treatment-as-usual control conditions. Treatment duration is 25 therapy sessions. Therapeutic alliance will be assessed on a session-to-session basis. Clinical symptomatology will be assessed at baseline, session 5, 15 and 25. We will conduct multilevel modeling to address the nested data structure. The secondary outcome measures include depression, anxiety, interpersonal functioning, mindful awareness, and mindfulness during the sessions. Discussion The study results could provide important practical implications because they could inform ideas on how to improve the clinical training of psychotherapists that could be implemented very easily; this is because there is no need for complex infrastructures or additional time concerning these brief session-introducing interventions with mindfulness elements that are directly implemented in the treatment sessions.

Tabulation of multiple responses

Although multiple response questions are quite common in survey research, Stata's official release does not provide much possibility for an effective analysis of multiple response variables. For example, in a study on drug addiction an interview question might be, "Which substances did you consume during the last four weeks?" The respondents just list all the drugs they took if any, e.g., an answer could be "cannabis, cocaine, heroin" or "ecstasy, cannabis" or "none", etc. Usually, the responses to such questions are held as a set of variables and, therefore, cannot be easily tabulated. I will address this issue and present a new module to compute one- and two-way tables of multiple responses. The module supports several types of data structure, provides significance tests, and offers various options to control the computation and display of the results.

Integrated program debugging, verification, and optimization using abstract interpretation (and the Ciao system preprocessor)

The technique of Abstract Interpretation has allowed the development of very sophisticated global program analyses which are at the same time provably correct and practical. We present in a tutorial fashion a novel program development framework which uses abstract interpretation as a fundamental tool. The framework uses modular, incremental abstract interpretation to obtain information about the program. This information is used to validate programs, to detect bugs with respect to partial specifications written using assertions (in the program itself and/or in system libraries), to generate and simplify run-time tests, and to perform high-level program transformations such as multiple abstract specialization, parallelization, and resource usage control, all in a provably correct way. In the case of validation and debugging, the assertions can refer to a variety of program points such as procedure entry, procedure exit, points within procedures, or global computations. The system can reason with much richer information than, for example, traditional types. This includes data structure shape (including pointer sharing), bounds on data structure sizes, and other operational variable instantiation properties, as well as procedure-level properties such as determinacy, termination, nonfailure, and bounds on resource consumption (time or space cost). CiaoPP, the preprocessor of the Ciao multi-paradigm programming system, which implements the described functionality, will be used to illustrate the fundamental ideas.

Enhancing Activity Recognition by Fusing Inertial and Biometric Information

Activity recognition is an active research field nowadays, as it enables the development of highly adaptive applications, e.g. in the field of personal health. In this paper, a light high-level fusion algorithm to detect the activity that an individual is performing is presented. The algorithm relies on data gathered from accelerometers placed on different parts of the body, and on biometric sensors. Inertial sensors allow detecting activity by analyzing signal features such as amplitude or peaks. In addition, there is a relationship between the activity intensity and biometric response, which can be considered together with acceleration data to improve the accuracy of activity detection. The proposed algorithm is designed to work with minimum computational cost, being ready to run in a mobile device as part of a context-aware application. In order to enable different user scenarios, the algorithm offers best-effort activity estimation: its quality of estimation depends on the position and number of the available inertial sensors, and also on the presence of biometric information.

Program development using abstract interpretation (and the ciao system preprocessor)

The technique of Abstract Interpretation has allowed the development of very sophisticated global program analyses which are at the same time provably correct and practical. We present in a tutorial fashion a novel program development framework which uses abstract interpretation as a fundamental tool. The framework uses modular, incremental abstract interpretation to obtain information about the program. This information is used to validate programs, to detect bugs with respect to partial specifications written using assertions (in the program itself and/or in system librarles), to genérate and simplify run-time tests, and to perform high-level program transformations such as múltiple abstract specialization, parallelization, and resource usage control, all in a provably correct way. In the case of validation and debugging, the assertions can refer to a variety of program points such as procedure entry, procedure exit, points within procedures, or global computations. The system can reason with much richer information than, for example, traditional types. This includes data structure shape (including pointer sharing), bounds on data structure sizes, and other operational variable instantiation properties, as well as procedure-level properties such as determinacy, termination, non-failure, and bounds on resource consumption (time or space cost). CiaoPP, the preprocessor of the Ciao multi-paradigm programming system, which implements the described functionality, will be used to illustrate the fundamental ideas.

A tutorial on program development and optimization using the Ciao preprocessor

We present in a tutorial fashion CiaoPP, the preprocessor of the Ciao multi-paradigm programming system, which implements a novel program development framework which uses abstract interpretation as a fundamental tool. The framework uses modular, incremental abstract interpretation to obtain information about the program. This information is used to validate programs, to detect bugs with respect to partial specifications written using assertions (in the program itself and/or in system libraries), to generate and simplify run-time tests, and to perform high-level program transformations such as multiple abstract specialization, parallelization, and resource usage control, all in a provably correct way. In the case of validation and debugging, the assertions can refer to a variety of program points such as procedure entry, procedure exit, points within procedures, or global computations. The system can reason with much richer information than, for example, traditional types. This includes data structure shape (including pointer sharing), bounds on data structure sizes, and other operational variable instantiation properties, as well as procedure-level properties such as determinacy, termination, non-failure, and bounds on resource consumption (time or space cost).

Algoritmos de agrupación para flujos de datos en entornos centralizados y distribuidos

Los avances en el hardware permiten disponer de grandes volúmenes de datos, surgiendo aplicaciones que deben suministrar información en tiempo cuasi-real, la monitorización de pacientes, ej., el seguimiento sanitario de las conducciones de agua, etc. Las necesidades de estas aplicaciones hacen emerger el modelo de flujo de datos (data streaming) frente al modelo almacenar-para-despuésprocesar (store-then-process). Mientras que en el modelo store-then-process, los datos son almacenados para ser posteriormente consultados; en los sistemas de streaming, los datos son procesados a su llegada al sistema, produciendo respuestas continuas sin llegar a almacenarse. Esta nueva visión impone desafíos para el procesamiento de datos al vuelo: 1) las respuestas deben producirse de manera continua cada vez que nuevos datos llegan al sistema; 2) los datos son accedidos solo una vez y, generalmente, no son almacenados en su totalidad; y 3) el tiempo de procesamiento por dato para producir una respuesta debe ser bajo. Aunque existen dos modelos para el cómputo de respuestas continuas, el modelo evolutivo y el de ventana deslizante; éste segundo se ajusta mejor en ciertas aplicaciones al considerar únicamente los datos recibidos más recientemente, en lugar de todo el histórico de datos. En los últimos años, la minería de datos en streaming se ha centrado en el modelo evolutivo. Mientras que, en el modelo de ventana deslizante, el trabajo presentado es más reducido ya que estos algoritmos no sólo deben de ser incrementales si no que deben borrar la información que caduca por el deslizamiento de la ventana manteniendo los anteriores tres desafíos. Una de las tareas fundamentales en minería de datos es la búsqueda de agrupaciones donde, dado un conjunto de datos, el objetivo es encontrar grupos representativos, de manera que se tenga una descripción sintética del conjunto. Estas agrupaciones son fundamentales en aplicaciones como la detección de intrusos en la red o la segmentación de clientes en el marketing y la publicidad. Debido a las cantidades masivas de datos que deben procesarse en este tipo de aplicaciones (millones de eventos por segundo), las soluciones centralizadas puede ser incapaz de hacer frente a las restricciones de tiempo de procesamiento, por lo que deben recurrir a descartar datos durante los picos de carga. Para evitar esta perdida de datos, se impone el procesamiento distribuido de streams, en concreto, los algoritmos de agrupamiento deben ser adaptados para este tipo de entornos, en los que los datos están distribuidos. En streaming, la investigación no solo se centra en el diseño para tareas generales, como la agrupación, sino también en la búsqueda de nuevos enfoques que se adapten mejor a escenarios particulares. Como ejemplo, un mecanismo de agrupación ad-hoc resulta ser más adecuado para la defensa contra la denegación de servicio distribuida (Distributed Denial of Services, DDoS) que el problema tradicional de k-medias. En esta tesis se pretende contribuir en el problema agrupamiento en streaming tanto en entornos centralizados y distribuidos. Hemos diseñado un algoritmo centralizado de clustering mostrando las capacidades para descubrir agrupaciones de alta calidad en bajo tiempo frente a otras soluciones del estado del arte, en una amplia evaluación. Además, se ha trabajado sobre una estructura que reduce notablemente el espacio de memoria necesario, controlando, en todo momento, el error de los cómputos. Nuestro trabajo también proporciona dos protocolos de distribución del cómputo de agrupaciones. Se han analizado dos características fundamentales: el impacto sobre la calidad del clustering al realizar el cómputo distribuido y las condiciones necesarias para la reducción del tiempo de procesamiento frente a la solución centralizada. Finalmente, hemos desarrollado un entorno para la detección de ataques DDoS basado en agrupaciones. En este último caso, se ha caracterizado el tipo de ataques detectados y se ha desarrollado una evaluación sobre la eficiencia y eficacia de la mitigación del impacto del ataque. ABSTRACT Advances in hardware allow to collect huge volumes of data emerging applications that must provide information in near-real time, e.g., patient monitoring, health monitoring of water pipes, etc. The data streaming model emerges to comply with these applications overcoming the traditional store-then-process model. With the store-then-process model, data is stored before being consulted; while, in streaming, data are processed on the fly producing continuous responses. The challenges of streaming for processing data on the fly are the following: 1) responses must be produced continuously whenever new data arrives in the system; 2) data is accessed only once and is generally not maintained in its entirety, and 3) data processing time to produce a response should be low. Two models exist to compute continuous responses: the evolving model and the sliding window model; the latter fits best with applications must be computed over the most recently data rather than all the previous data. In recent years, research in the context of data stream mining has focused mainly on the evolving model. In the sliding window model, the work presented is smaller since these algorithms must be incremental and they must delete the information which expires when the window slides. Clustering is one of the fundamental techniques of data mining and is used to analyze data sets in order to find representative groups that provide a concise description of the data being processed. Clustering is critical in applications such as network intrusion detection or customer segmentation in marketing and advertising. Due to the huge amount of data that must be processed by such applications (up to millions of events per second), centralized solutions are usually unable to cope with timing restrictions and recur to shedding techniques where data is discarded during load peaks. To avoid discarding of data, processing of streams (such as clustering) must be distributed and adapted to environments where information is distributed. In streaming, research does not only focus on designing for general tasks, such as clustering, but also in finding new approaches that fit bests with particular scenarios. As an example, an ad-hoc grouping mechanism turns out to be more adequate than k-means for defense against Distributed Denial of Service (DDoS). This thesis contributes to the data stream mining clustering technique both for centralized and distributed environments. We present a centralized clustering algorithm showing capabilities to discover clusters of high quality in low time and we provide a comparison with existing state of the art solutions. We have worked on a data structure that significantly reduces memory requirements while controlling the error of the clusters statistics. We also provide two distributed clustering protocols. We focus on the analysis of two key features: the impact on the clustering quality when computation is distributed and the requirements for reducing the processing time compared to the centralized solution. Finally, with respect to ad-hoc grouping techniques, we have developed a DDoS detection framework based on clustering.We have characterized the attacks detected and we have evaluated the efficiency and effectiveness of mitigating the attack impact.

Desarrollo de un sistema basado en el conocimiento para la definición de líneas de montaje aeronáuticas en la fase conceptual : Aplicación a una aeroestructura aeronáutica

La presente Tesis persigue la definición y el desarrollo de un sistema basado en el conocimiento que permita la generación de modelos de líneas de montaje durante la fase conceptual de definición de una aeroestructura aeronáutica. Para ello, se propone la definición de un modelo formal del proceso en concurrencia asociado al diseño de líneas de montaje en la fase conceptual, y de un modelo de la estructura de datos básica para soportar dicho proceso. Ambos modelos sirven de base para el desarrollo de una aplicación de prueba de concepto en el entorno del sistema comercial CAX-PLM CATIA v5. Los modelos de línea generados integran las tres estructuras básicas definidas en el modelo propuesto: producto, procesos y recursos. Los modelos generados son estructuras “de montaje”, basadas en estructuras de producto “de fabricación” a su vez derivadas de estructuras “de diseño”. Cada modelo generado se evalúa en términos de cuatro estimaciones básicas: dimensiones máximas del nodo producto, distancia de transporte y medio a utilizar, tiempo total de ejecución y coste total. La generación de modelos de línea de montaje se realiza en concurrencia con la función diseño de producto, teniendo por tanto la oportunidad de influir en la misma e incluir requerimientos de fabricación y montaje al producto en las primeras fases de su ciclo de vida, lo que proporciona una clara ventaja competitiva. El desarrollo propuesto en esta Tesis permite sentar las bases para realizar desarrollos con objeto de asistir a los diseñadores durante la fase conceptual de generación de diseños de líneas de montaje. La aplicación prototipo desarrollada demuestra la viabilidad de la propuesta conceptual que se realiza en la Tesis. ABSTRACT The current thesis proposes the definition and development of a knowledge-based system to generate aircraft components assembly line models during the conceptual phase of the product life cycle. With this objective, the definition of a formal activity model to represent the design of assembly lines during the conceptual phase is proposed; such model considers the concurrence with the product design process. Associated to the activity model, a data structure model is defined to support such process. Both models are the basis for the development of a proof of concept application within the environment of the commercial CAX-PLM system CATIA v5. The generated assembly line models integrate the three basic structures defined in the proposed model: product, processes and resources. The generated models are “As Prepared” structures based on “As Planned” structures derived from “As Designed” structures. Each generated model is evaluated in terms of four basic estimates: maximum dimensions of the product node, transport distance and transport mean to be used, total execution time and total cost. The assembly line models generation is made in concurrence with the product design function. Therefore, it provides the opportunity to influence on it and allows including manufacturing and assembly requirements early in the product life cycle, which gives a clear competitive advantage. The development proposed in this Thesis allows setting the foundation to carry out further developments with the aim of assisting designers during the conceptual phase of the assembly line design process. The developed prototype application shows the feasibility of the conceptual proposal presented in the Thesis.

Reachability-based acyclicity analysis by Abstract Interpretation

In programming languages with dynamic use of memory, such as Java, knowing that a reference variable x points to an acyclic data structure is valuable for the analysis of termination and resource usage (e.g., execution time or memory consumption). For instance, this information guarantees that the depth of the data structure to which x points is greater than the depth of the data structure pointed to by x.f for any field f of x. This, in turn, allows bounding the number of iterations of a loop which traverses the structure by its depth, which is essential in order to prove the termination or infer the resource usage of the loop. The present paper provides an Abstract-Interpretation-based formalization of a static analysis for inferring acyclicity, which works on the reduced product of two abstract domains: reachability, which models the property that the location pointed to by a variable w can be reached by dereferencing another variable v (in this case, v is said to reach w); and cyclicity, modeling the property that v can point to a cyclic data structure. The analysis is proven to be sound and optimal with respect to the chosen abstraction.

Especificaciones eMathTeacher, creación del Modelo Granular Lingüístico de la evaluación del aprendizaje humano, y diseño de sistemas que implementan estos conceptos

El concepto de algoritmo es básico en informática, por lo que es crucial que los alumnos profundicen en él desde el inicio de su formación. Por tanto, contar con una herramienta que guíe a los estudiantes en su aprendizaje puede suponer una gran ayuda en su formación. La mayoría de los autores coinciden en que, para determinar la eficacia de una herramienta de visualización de algoritmos, es esencial cómo se utiliza. Así, los estudiantes que participan activamente en la visualización superan claramente a los que la contemplan de forma pasiva. Por ello, pensamos que uno de los mejores ejercicios para un alumno consiste en simular la ejecución del algoritmo que desea aprender mediante el uso de una herramienta de visualización, i. e. consiste en realizar una simulación visual de dicho algoritmo. La primera parte de esta tesis presenta los resultados de una profunda investigación sobre las características que debe reunir una herramienta de ayuda al aprendizaje de algoritmos y conceptos matemáticos para optimizar su efectividad: el conjunto de especificaciones eMathTeacher, además de un entorno de aprendizaje que integra herramientas que las cumplen: GRAPHs. Hemos estudiado cuáles son las cualidades esenciales para potenciar la eficacia de un sistema e-learning de este tipo. Esto nos ha llevado a la definición del concepto eMathTeacher, que se ha materializado en el conjunto de especificaciones eMathTeacher. Una herramienta e-learning cumple las especificaciones eMathTeacher si actúa como un profesor virtual de matemáticas, i. e. si es una herramienta de autoevaluación que ayuda a los alumnos a aprender de forma activa y autónoma conceptos o algoritmos matemáticos, corrigiendo sus errores y proporcionando pistas para encontrar la respuesta correcta, pero sin dársela explícitamente. En estas herramientas, la simulación del algoritmo no continúa hasta que el usuario introduce la respuesta correcta. Para poder reunir en un único entorno una colección de herramientas que cumplan las especificaciones eMathTeacher hemos creado GRAPHs, un entorno ampliable, basado en simulación visual, diseñado para el aprendizaje activo e independiente de los algoritmos de grafos y creado para que en él se integren simuladores de diferentes algoritmos. Además de las opciones de creación y edición del grafo y la visualización de los cambios producidos en él durante la simulación, el entorno incluye corrección paso a paso, animación del pseudocódigo del algoritmo, preguntas emergentes, manejo de las estructuras de datos del algoritmo y creación de un log de interacción en XML. Otro problema que nos planteamos en este trabajo, por su importancia en el proceso de aprendizaje, es el de la evaluación formativa. El uso de ciertos entornos e-learning genera gran cantidad de datos que deben ser interpretados para llegar a una evaluación que no se limite a un recuento de errores. Esto incluye el establecimiento de relaciones entre los datos disponibles y la generación de descripciones lingüísticas que informen al alumno sobre la evolución de su aprendizaje. Hasta ahora sólo un experto humano era capaz de hacer este tipo de evaluación. Nuestro objetivo ha sido crear un modelo computacional que simule el razonamiento del profesor y genere un informe sobre la evolución del aprendizaje que especifique el nivel de logro de cada uno de los objetivos definidos por el profesor. Como resultado del trabajo realizado, la segunda parte de esta tesis presenta el modelo granular lingüístico de la evaluación del aprendizaje, capaz de modelizar la evaluación y generar automáticamente informes de evaluación formativa. Este modelo es una particularización del modelo granular lingüístico de un fenómeno (GLMP), en cuyo desarrollo y formalización colaboramos, basado en la lógica borrosa y en la teoría computacional de las percepciones. Esta técnica, que utiliza sistemas de inferencia basados en reglas lingüísticas y es capaz de implementar criterios de evaluación complejos, se ha aplicado a dos casos: la evaluación, basada en criterios, de logs de interacción generados por GRAPHs y de cuestionarios de Moodle. Como consecuencia, se han implementado, probado y utilizado en el aula sistemas expertos que evalúan ambos tipos de ejercicios. Además de la calificación numérica, los sistemas generan informes de evaluación, en lenguaje natural, sobre los niveles de competencia alcanzados, usando sólo datos objetivos de respuestas correctas e incorrectas. Además, se han desarrollado dos aplicaciones capaces de ser configuradas para implementar los sistemas expertos mencionados. Una procesa los archivos producidos por GRAPHs y la otra, integrable en Moodle, evalúa basándose en los resultados de los cuestionarios. ABSTRACT The concept of algorithm is one of the core subjects in computer science. It is extremely important, then, for students to get a good grasp of this concept from the very start of their training. In this respect, having a tool that helps and shepherds students through the process of learning this concept can make a huge difference to their instruction. Much has been written about how helpful algorithm visualization tools can be. Most authors agree that the most important part of the learning process is how students use the visualization tool. Learners who are actively involved in visualization consistently outperform other learners who view the algorithms passively. Therefore we think that one of the best exercises to learn an algorithm is for the user to simulate the algorithm execution while using a visualization tool, thus performing a visual algorithm simulation. The first part of this thesis presents the eMathTeacher set of requirements together with an eMathTeacher-compliant tool called GRAPHs. For some years, we have been developing a theory about what the key features of an effective e-learning system for teaching mathematical concepts and algorithms are. This led to the definition of eMathTeacher concept, which has materialized in the eMathTeacher set of requirements. An e-learning tool is eMathTeacher compliant if it works as a virtual math trainer. In other words, it has to be an on-line self-assessment tool that helps students to actively and autonomously learn math concepts or algorithms, correcting their mistakes and providing them with clues to find the right answer. In an eMathTeacher-compliant tool, algorithm simulation does not continue until the user enters the correct answer. GRAPHs is an extendible environment designed for active and independent visual simulation-based learning of graph algorithms, set up to integrate tools to help the user simulate the execution of different algorithms. Apart from the options of creating and editing the graph, and visualizing the changes made to the graph during simulation, the environment also includes step-by-step correction, algorithm pseudo-code animation, pop-up questions, data structure handling and XML-based interaction log creation features. On the other hand, assessment is a key part of any learning process. Through the use of e-learning environments huge amounts of data can be output about this process. Nevertheless, this information has to be interpreted and represented in a practical way to arrive at a sound assessment that is not confined to merely counting mistakes. This includes establishing relationships between the available data and also providing instructive linguistic descriptions about learning evolution. Additionally, formative assessment should specify the level of attainment of the learning goals defined by the instructor. Till now, only human experts were capable of making such assessments. While facing this problem, our goal has been to create a computational model that simulates the instructor’s reasoning and generates an enlightening learning evolution report in natural language. The second part of this thesis presents the granular linguistic model of learning assessment to model the assessment of the learning process and implement the automated generation of a formative assessment report. The model is a particularization of the granular linguistic model of a phenomenon (GLMP) paradigm, based on fuzzy logic and the computational theory of perceptions, to the assessment phenomenon. This technique, useful for implementing complex assessment criteria using inference systems based on linguistic rules, has been applied to two particular cases: the assessment of the interaction logs generated by GRAPHs and the criterion-based assessment of Moodle quizzes. As a consequence, several expert systems to assess different algorithm simulations and Moodle quizzes have been implemented, tested and used in the classroom. Apart from the grade, the designed expert systems also generate natural language progress reports on the achieved proficiency level, based exclusively on the objective data gathered from correct and incorrect responses. In addition, two applications, capable of being configured to implement the expert systems, have been developed. One is geared up to process the files output by GRAPHs and the other one is a Moodle plug-in set up to perform the assessment based on the quizzes results.

Boston, Massachusetts Region, Digital Elevation Model with Bathymetry

This raster layer represents surface elevation and bathymetry data for the Boston Region, Massachusetts. It was created by merging portions of MassGIS Digital Elevation Model 1:5,000 (2005) data with NOAA Estuarine Bathymetric Digital Elevation Models (30 m.) (1998). DEM data was derived from the digital terrain models that were produced as part of the MassGIS 1:5,000 Black and White Digital Orthophoto imagery project. Cellsize is 5 meters by 5 meters. Each cell has a floating point value, in meters, which represents its elevation above or below sea level.

Direct mesh: A multiresolution approach to terrain visualization

Terrain can be approximated by a triangular mesh consisting millions of 3D points. Multiresolution triangular mesh (MTM) structures are designed to support applications that use terrain data at variable levels of detail (LOD). Typically, an MTM adopts a tree structure where a parent node represents a lower-resolution approximation of its descendants. Given a region of interest (ROI) and a LOD, the process of retrieving the required terrain data from the database is to traverse the MTM tree from the root to reach all the nodes satisfying the ROI and LOD conditions. This process, while being commonly used for multiresolution terrain visualization, is inefficient as either a large number of sequential I/O operations or fetching a large amount of extraneous data is incurred. Various spatial indexes have been proposed in the past to address this problem, however level-by-level tree traversal remains a common practice in order to obtain topological information among the retrieved terrain data. A new MTM data structure called direct mesh is proposed. We demonstrate that with direct mesh the amount of data retrieval can be substantially reduced. Comparing with existing MTM indexing methods, a significant performance improvement has been observed for real-life terrain data.

Semantic caching for multiresolution spatial query processing in mobile environments

Spatial data are particularly useful in mobile environments. However, due to the low bandwidth of most wireless networks, developing large spatial database applications becomes a challenging process. In this paper, we provide the first attempt to combine two important techniques, multiresolution spatial data structure and semantic caching, towards efficient spatial query processing in mobile environments. Based on the study of the characteristics of multiresolution spatial data (MSD) and multiresolution spatial query, we propose a new semantic caching model called Multiresolution Semantic Caching (MSC) for caching MSD in mobile environments. MSC enriches the traditional three-category query processing in semantic cache to five categories, thus improving the performance in three ways: 1) a reduction in the amount and complexity of the remainder queries; 2) the redundant transmission of spatial data already residing in a cache is avoided; 3) a provision for satisfactory answers before 100% query results have been transmitted to the client side. Our extensive experiments on a very large and complex real spatial database show that MSC outperforms the traditional semantic caching models significantly

An efficient algorithm for partitioning parameterized polygons into rectangles

In this paper, we propose an algorithm for partitioning parameterized orthogonal polygons into rectangles. The algorithm is based on the plane-sweep technique and can be used for partitioning polygons which contain holes. The input to the algorithm consists of the contour of a parameterized polygon to be partitioned and the constraints for those parameters which reside in the contour. The algorithm uses horizontal cuts only and generates a minimum number of rectangles whose union is the original orthogonal polygon. The proposed algorithm can be used as the basis to build corner stitching data structure for parameterized VLSI layouts and has been implemented in Java programming language. Copyright © 2010 ACM, Inc.