Efficient processing of large-scale spatio-temporal data

Millionen Geräte, wie z.B. Mobiltelefone, Autos und Umweltsensoren senden ihre Positionen zusammen mit einem Zeitstempel und weiteren Nutzdaten an einen Server zu verschiedenen Analysezwecken. Die Positionsinformationen und übertragenen Ereignisinformationen werden als Punkte oder Polygone dargestellt. Eine weitere Art räumlicher Daten sind Rasterdaten, die zum Beispiel von Kameras und Sensoren produziert werden. Diese großen räumlich-zeitlichen Datenmengen können nur auf skalierbaren Plattformen wie Hadoop und Apache Spark verarbeitet werden, die jedoch z.B. die Nachbarschaftsinformation nicht ausnutzen können - was die Ausführung bestimmter Anfragen praktisch unmöglich macht. Die wiederholten Ausführungen der Analyseprogramme während ihrer Entwicklung und durch verschiedene Nutzer resultieren in langen Ausführungszeiten und hohen Kosten für gemietete Ressourcen, die durch die Wiederverwendung von Zwischenergebnissen reduziert werden können. Diese Arbeit beschäftigt sich mit den beiden oben beschriebenen Herausforderungen. Wir präsentieren zunächst das STARK Framework für die Verarbeitung räumlich-zeitlicher Vektor- und Rasterdaten in Apache Spark. Wir identifizieren verschiedene Algorithmen für Operatoren und analysieren, wie diese von den Eigenschaften der zugrundeliegenden Plattform profitieren können. Weiterhin wird untersucht, wie Indexe in der verteilten und parallelen Umgebung realisiert werden können. Außerdem vergleichen wir Partitionierungsmethoden, die unterschiedlich gut mit ungleichmäßiger Datenverteilung und der Größe der Datenmenge umgehen können und präsentieren einen Ansatz um die auf Operatorebene zu verarbeitende Datenmenge frühzeitig zu reduzieren. Um die Ausführungszeit von Programmen zu verkürzen, stellen wir einen Ansatz zur transparenten Materialisierung von Zwischenergebnissen vor. Dieser Ansatz benutzt ein Entscheidungsmodell, welches auf den tatsächlichen Operatorkosten basiert. In der Evaluierung vergleichen wir die verschiedenen Implementierungs- sowie Konfigurationsmöglichkeiten in STARK und identifizieren Szenarien wann Partitionierung und Indexierung eingesetzt werden sollten. Außerdem vergleichen wir STARK mit verwandten Systemen. Im zweiten Teil der Evaluierung zeigen wir, dass die transparente Wiederverwendung der materialisierten Zwischenergebnisse die Ausführungszeit der Programme signifikant verringern kann.Millions of location-aware devices, such as mobile phones, cars, and environmental sensors constantly report their positions often in combination with a timestamp to a server for different kinds of analyses. While the location information of the devices and reported events is represented as points and polygons, raster data is another type of spatial data, which is for example produced by cameras and sensors. This Big spatio-temporal Data needs to be processed on scalable platforms, such as Hadoop and Apache Spark, which, however, are unaware of, e.g., spatial neighborhood, what makes them practically impossible to use for this kind of data. The repeated executions of the programs during development and by different users result in long execution times and potentially high costs in rented clusters, which can be reduced by reusing commonly computed intermediate results. Within this thesis, we tackle the two challenges described above. First, we present the STARK framework for processing spatio-temporal vector and raster data on the Apache Spark stack. For operators, we identify several possible algorithms and study how they can benefit from the underlying platform's properties. We further investigate how indexes can be realized in the distributed and parallel architecture of Big Data processing engines and compare methods for data partitioning, which perform differently well with respect to data skew and data set size. Furthermore, an approach to reduce the amount of data to process at operator level is presented. In order to reduce the execution times, we introduce an approach to transparently recycle intermediate results of dataflow programs, based on operator costs. To compute the costs, we instrument the programs with profiling code to gather the execution time and result size of the operators. In the evaluation, we first compare the various implementation and configuration possibilities in STARK and identify scenarios when and how partitioning and indexing should be applied. We further compare STARK to related systems and show that we can achieve significantly better execution times, not only when exploiting existing partitioning information. In the second part of the evaluation, we show that with the transparent cost-based materialization and recycling of intermediate results, the execution times of programs can be reduced significantly

Learning for Optimization with Virtual Savant

Optimization problems arising in multiple fields of study demand efficient algorithms that can exploit modern parallel computing platforms. The remarkable development of machine learning offers an opportunity to incorporate learning into optimization algorithms to efficiently solve large and complex problems. This thesis explores Virtual Savant, a paradigm that combines machine learning and parallel computing to solve optimization problems. Virtual Savant is inspired in the Savant Syndrome, a mental condition where patients excel at a specific ability far above the average. In analogy to the Savant Syndrome, Virtual Savant extracts patterns from previously-solved instances to learn how to solve a given optimization problem in a massively-parallel fashion. In this thesis, Virtual Savant is applied to three optimization problems related to software engineering, task scheduling, and public transportation. The efficacy of Virtual Savant is evaluated in different computing platforms and the experimental results are compared against exact and approximate solutions for both synthetic and realistic instances of the studied problems. Results show that Virtual Savant can find accurate solutions, effectively scale in the problem dimension, and take advantage of the availability of multiple computing resources.Los problemas de optimización que surgen en múltiples campos de estudio demandan algoritmos eficientes que puedan explotar las plataformas modernas de computación paralela. El notable desarrollo del aprendizaje automático ofrece la oportunidad de incorporar el aprendizaje en algoritmos de optimización para resolver problemas complejos y de grandes dimensiones de manera eficiente. Esta tesis explora Savant Virtual, un paradigma que combina aprendizaje automático y computación paralela para resolver problemas de optimización. Savant Virtual está inspirado en el Sı́ndrome de Savant, una condición mental en la que los pacientes se destacan en una habilidad especı́fica muy por encima del promedio. En analogı́a con el sı́ndrome de Savant, Savant Virtual extrae patrones de instancias previamente resueltas para aprender a resolver un determinado problema de optimización de forma masivamente paralela. En esta tesis, Savant Virtual se aplica a tres problemas de optimización relacionados con la ingenierı́a de software, la planificación de tareas y el transporte público. La eficacia de Savant Virtual se evalúa en diferentes plataformas informáticas y los resultados se comparan con soluciones exactas y aproximadas para instancias tanto sintéticas como realistas de los problemas estudiados. Los resultados muestran que Savant Virtual puede encontrar soluciones precisas, escalar eficazmente en la dimensión del problema y aprovechar la disponibilidad de múltiples recursos de cómputo.Fundación Carolina Agencia Nacional de Investigación e Innovación (ANII, Uruguay) Universidad de Cádiz Universidad de la Repúblic

A survey on active simultaneous localization and mapping: state of the art and new frontiers

Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics

Algorithm Libraries for Multi-Core Processors

By providing parallelized versions of established algorithm libraries, we ease the exploitation of the multiple cores on modern processors for the programmer. The Multi-Core STL provides basic algorithms for internal memory, while the parallelized STXXL enables multi-core acceleration for algorithms on large data sets stored on disk. Some parallelized geometric algorithms are introduced into CGAL. Further, we design and implement sorting algorithms for huge data in distributed external memory

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Conservative From-Point Visibility.

Visibility determination has been an important part of the computer graphics research for several decades. First studies of the visibility were hidden line removal algorithms, and later hidden surface removal algorithms. Today’s visibility determination is mainly concentrated on conservative, object level visibility determination techniques. Conservative methods are used to accelerate the rendering process when some exact visibility determination algorithm is present. The Z-buffer is a typical exact visibility determination algorithm. The Z-buffer algorithm is implemented in practically every modern graphics chip. This thesis concentrates on a subset of conservative visibility determination techniques. These techniques are sometimes called from-point visibility algorithms. They attempt to estimate a set of visible objects as seen from the current viewpoint. These techniques are typically used with real-time graphics applications such as games and virtual environments. Concentration is on the view frustum culling and occlusion culling. View frustum culling discards objects that are outside of the viewable volume. Occlusion culling algorithms try to identify objects that are not visible because they are behind some other objects. Also spatial data structures behind the efficient implementations of view frustum culling and occlusion culling are reviewed. Spatial data structure techniques like maintaining of dynamic scenes and exploiting spatial and temporal coherences are reviewed.1. Introduction.............................................................................................................1 2. Visibility Problem...................................................................................................3 3. Scene Organization...............................................................................................10 3.1. Bounding Volume Hierarchies and Scene Graphs.................................10 3.2. Spatial Data Structures ...............................................................................13 3.3. Regular Grids...............................................................................................14 3.4. Quadtrees and Octrees ...............................................................................15 3.5. KD-Trees.......................................................................................................20 3.6. BSP-Trees......................................................................................................23 3.7. Exploiting Spatial and Temporal Coherence ..........................................27 3.8. Dynamic Scenes...........................................................................................30 3.9. Summary ......................................................................................................34 4. View Frustum Culling .........................................................................................35 4.1. View Frustum Construction ......................................................................36 4.2. View Frustum Test......................................................................................37 4.3. Hierarchical View Frustum Culling .........................................................41 4.4. Optimizations ..............................................................................................42 4.5. Summary ......................................................................................................44 5. Occlusion Culling .................................................................................................45 5.1. Fundamental Concepts...............................................................................45 5.2. Occluder Selection.......................................................................................46 5.3. Hardware Occlusion Queries....................................................................49 5.4. Object-Space Methods ................................................................................50 5.5. Image-Space Methods ................................................................................55 5.6. Summary ......................................................................................................64 6. Conclusion.............................................................................................................66 References .................................................................................................................... 7