Evaluating holistic aggregators efficiently for very large datasets

In data warehousing applications, numerous OLAP queries involve the processing of holistic aggregators such as computing the “top n,” median, quantiles, etc. In this paper, we present a novel approach called dynamic bucketing to efficiently evaluate these aggregators. We partition data into equiwidth buckets and further partition dense buckets into sub-buckets as needed by allocating and reclaiming memory space. The bucketing process dynamically adapts to the input order and distribution of input datasets. The histograms of the buckets and subbuckets are stored in our new data structure called structure trees. A recent selection algorithm based on regular sampling is generalized and its analysis extended. We have also compared our new algorithms with this generalized algorithm and several other recent algorithms. Experimental results show that our new algorithms significantly outperform prior ones not only in the runtime but also in accuracy

Towards autonomous robotic systems: seamless localization and trajectory planning in dynamic environments

Evolucionar hacia una sociedad más automatizada y robotizada en la que podamos convivir con sistemas robóticos que desempeñen tareas poco atractivas o peligrosas para el ser humano, supone plantearnos, entre otras cuestiones, qué soluciones existen actualmente y cuáles son las mejoras a incorporar a las mismas. La mayoría de aplicaciones ya desarrolladas son soluciones robustas y adecuadas para el fin que se diseñan. Sin embargo, muchas de las técnicas implantadas podrían funcionar de manera más eficiente o bien adaptarse a otras necesidades. Asimismo, en la mayoría de aplicaciones robóticas adquiere importancia el contexto en el que desempeñan su función. Hay entornos estructurados y fáciles de modelar, mientras que otros apenas presentan características utilizables para obtener información de los mismos.Esta tesis se centra en dos de las funciones básicas que debe tener cualquier sistema robótico autónomo para desplazarse de forma robusta en cualquier tipo de entorno: la localización y el cálculo de trayectorias seguras. Además, los escenarios en los que se desea poner en práctica la investigación son complejos: un parque industrial con zonas cuyas características de entorno (usualmente geométricas) son utilizadas para que un robot se localice, varían; y entornos altamente ocupados por otros agentes móviles, como el vestíbulo de un teatro, en los que se debe considerar las características dinámicas de los demás para calcular un movimiento que sea seguro tanto para el robot como para los demás agentes.La información que se puede percibir de los escenarios con ambientes no homogéneos, por ejemplo de interior y exterior, suele ser de características diferentes. Cuando la información que se dispone del entorno proviene de sensores diferentes hay que definir un método que integre las medidas para tener una estimación de la localización del robot en todo momento. El tema de la localización se ha investigado intensamente y existen soluciones robustas en interior y exterior, pero no tanto en zonas mixtas. En las zonas de transición interior-exterior y viceversa es necesario utilizar sensores que funcionan correctamente en ambas zonas, realizando una integración sensorial durante la transición para evitar discontinuidades en la localización o incluso que el robot se pierda. De esta manera la navegación autónoma, dependiente de la correcta localización, funcionará sin discontinuidades ni movimientos bruscos.En entornos dinámicos es esencial definir una forma de representar la información que refleje su naturaleza cambiante. Por ello, se han definido en la literatura diferentes modelos que representan el dinamismo del entorno, y que permiten desarrollar una planificación de trayectorias directamente sobre las variables que controlan el movimiento del robot, en nuestro caso, las velocidades angular y lineal para un robot diferencial. Los planificadores de trayectorias y navegadores diseñados para entornos estáticos no funcionan correctamente en escenarios dinámicos, ya que son puramente reactivos. Es necesario tener en cuenta la predicción del movimiento de los obstáculos móviles para planificar trayectorias seguras sin colisión. Los temas abordados y las contribuciones aportadas en esta tesis son:• Diseño de un sistema de localización continua en entornos de interior y exterior, poniendo especial interés en la fusión de las medidas obtenidas de diferentes sensores durante las transiciones interior-exterior, aspecto poco abordado en la literatura. De esta manera se obtiene una estimación acotada de la localización durante toda la navegación del robot. Además, la localización se integra con una técnica reactiva de navegación, construyendo un sistema completo de navegación. El sistema integrado se ha evaluado en un escenario real de un parque industrial, para una aplicación logística en la que las transiciones interior-exterior y viceversa suponían un problema fundamental a resolver.• Definición de un modelo para representar el entorno dinámico del robot, llamado Dynamic Obstacle Velocity-Time Space (DOVTS). En este modelo aparecen representadas las velocidades permitidas y prohibidas para que el robot evite las colisiones con los obstáculos de alrededor. Este modelo puede ser utilizado por algoritmos de navegación ya existentes, y sirve de base para las nuevas técnicas de navegación desarrolladas en la tesis y explicadas en los siguientes puntos. • Desarrollo de una técnica de planificación y navegación basada en el modelo DOVTS. En este modelo se identifica un conjunto de situaciones relativas entre el robot y los obstáculos. A cada situación se asocia una estrategia de navegación, que considera la seguridad del robot para evitar colisiones, a la vez que intenta minimizar el tiempo al objetivo.• Implementación de una técnica de planificación y navegación basada en el modelo DOVTS, que utiliza explícitamente la información del tiempo para la planificación del movimiento. Se desarrolla un algoritmo A*-like que planifica los movimientos de los siguientes instantes, incrementando la maniobrabilidad del robot para la evitación de obstáculos respecto al método del anterior punto, a costa de un mayor tiempo de cómputo. Se analizan las diferencias en el comportamiento global del robot con respecto a la técnica anterior.Los diferentes aspectos que se han investigado en esta tesis tratan de avanzar en el objetivo de conseguir robots autónomos que puedan adaptarse a nuestra vida cotidiana en escenarios que son típicamente dinámicos de una forma natural y segura.<br /

Road transport and emissions modelling in England and Wales: A machine learning modelling approach using spatial data

An expanding street network coupled with an increasing number of vehicles testifies to the significance and reliance on road transportation of modern economies. Unfortunately, the use of road transport comes with drawbacks such as its contribution to greenhouse gases (GHG) and air pollutant emissions, therefore becoming an obstacle to countries’ objectives to improve air quality and a barrier to the ambitious targets to reduce Greenhouse Gas emissions. Unsurprisingly, traffic forecasting, its environmental impacts and potential future configurations of road transport are some of the topics which have received a great deal of attention in the literature. However, traffic forecasting and the assessment of its determinants have been commonly restricted to specific, normally urban, areas while road transport emission studies do not take into account a large part of the road network, as they usually focus on major roads. This research aimed to contribute to the field of road transportation, by firstly developing a model to accurately estimate traffic across England and Wales at a granular (i.e., street segment) level, secondly by identifying the role of factors associated with road transportation and finally, by estimating CO2 and air pollutant emissions, known to be responsible for climate change as well as negative impacts on human health and ecosystems. The thesis identifies potential emissions abatement from the adoption of novel road vehicles technologies and policy measures. This is achieved by analysing transport scenarios to assess future impacts on air quality and CO2 emissions. The thesis concludes with a comparison of my estimates for road emissions with those from DfT modelling to assess the methodological robustness of machine learning algorithms applied in this research. The traffic modelling outputs reveal traffic patterns across urban and rural areas, while traffic estimation is achieved with high accuracy for all road classes. In addition, specific socioeconomic and roadway characteristics associated with traffic across all vehicle types and road classes are identified. Finally, CO2 and air pollution hot spots as well as the impact of open spaces on pollutants emissions and air quality are explored. Potential emission reduction with the employment of new vehicle technologies and policy implementation is also assessed, so as the results can support urban planning and inform policies related to transport congestion and environmental impacts mitigation. Considering the disaggregated approach, the methodology can be used to facilitate policy making for both local and national aggregated levels

Database Learning: Toward a Database that Becomes Smarter Every Time

In today's databases, previous query answers rarely benefit answering future queries. For the first time, to the best of our knowledge, we change this paradigm in an approximate query processing (AQP) context. We make the following observation: the answer to each query reveals some degree of knowledge about the answer to another query because their answers stem from the same underlying distribution that has produced the entire dataset. Exploiting and refining this knowledge should allow us to answer queries more analytically, rather than by reading enormous amounts of raw data. Also, processing more queries should continuously enhance our knowledge of the underlying distribution, and hence lead to increasingly faster response times for future queries. We call this novel idea---learning from past query answers---Database Learning. We exploit the principle of maximum entropy to produce answers, which are in expectation guaranteed to be more accurate than existing sample-based approximations. Empowered by this idea, we build a query engine on top of Spark SQL, called Verdict. We conduct extensive experiments on real-world query traces from a large customer of a major database vendor. Our results demonstrate that Verdict supports 73.7% of these queries, speeding them up by up to 23.0x for the same accuracy level compared to existing AQP systems.Comment: This manuscript is an extended report of the work published in ACM SIGMOD conference 201

Exploring the cultural, managerial and organizational implications on mergers and acquisitions outcomes

Background/Purpose: This work is focused on the variables that influence the outcomes of mergers and acquisitions, by learning from past mistakes, adopt better strategies and make wiser decisions to enhance the outcomes of their mergers and acquisitions. Using a qualitative approach, this research contributes to existing knowledge on mergers and acquisitions performance by exploring the cultural, managerial and organizational factors dimensions through an integrative approach using multiple perspectives. Methodology: Fifteen interviews were conducted with experienced professionals in multiple areas of mergers and acquisitions. Content analysis was used to interpret the results. This enabled to achieve a more complete set of answers and potential solutions while comparing opinions on the same problems from slightly different angles. Results: Results show the existence of managerial hubris, emotional attachment and over-optimism in mergers and acquisitions. There was a relative support towards standardizing the process of mergers and acquisitions deals, but respondents advise to keep some creativity and flexibility. Conclusion: The article concludes by addressing key issues for mergers and acquisitions performance: capabilities and experience; organizational communication; internal coordination; and, key issues for decision-making.info:eu-repo/semantics/publishedVersio

Memory resource balancing for virtualized computing

Virtualization has become a common abstraction layer in modern data centers. By multiplexing hardware resources into multiple virtual machines (VMs) and thus enabling several operating systems to run on the same physical platform simultaneously, it can effectively reduce power consumption and building size or improve security by isolating VMs. In a virtualized system, memory resource management plays a critical role in achieving high resource utilization and performance. Insufficient memory allocation to a VM will degrade its performance dramatically. On the contrary, over-allocation causes waste of memory resources. Meanwhile, a VM’s memory demand may vary significantly. As a result, effective memory resource management calls for a dynamic memory balancer, which, ideally, can adjust memory allocation in a timely manner for each VM based on their current memory demand and thus achieve the best memory utilization and the optimal overall performance. In order to estimate the memory demand of each VM and to arbitrate possible memory resource contention, a widely proposed approach is to construct an LRU-based miss ratio curve (MRC), which provides not only the current working set size (WSS) but also the correlation between performance and the target memory allocation size. Unfortunately, the cost of constructing an MRC is nontrivial. In this dissertation, we first present a low overhead LRU-based memory demand tracking scheme, which includes three orthogonal optimizations: AVL-based LRU organization, dynamic hot set sizing and intermittent memory tracking. Our evaluation results show that, for the whole SPEC CPU 2006 benchmark suite, after applying the three optimizing techniques, the mean overhead of MRC construction is lowered from 173% to only 2%. Based on current WSS, we then predict its trend in the near future and take different strategies for different prediction results. When there is a sufficient amount of physical memory on the host, it locally balances its memory resource for the VMs. Once the local memory resource is insufficient and the memory pressure is predicted to sustain for a sufficiently long time, a relatively expensive solution, VM live migration, is used to move one or more VMs from the hot host to other host(s). Finally, for transient memory pressure, a remote cache is used to alleviate the temporary performance penalty. Our experimental results show that this design achieves 49% center-wide speedup