Selected problems in cardinality estimation

Cardinality estimation remains a critical task in query processing. Query optimizers rely on the accuracy of cardinality estimates when generating execution plans, and, in approximate query answering, estimated cardinalities affect the quality of query results. In this thesis, we present multiple new cardinality estimation techniques. The techniques differ vastly by the query under consideration. For single relation queries, we use the principle of maximum entropy to combine information extracted from samples and histograms. For join size estimation, we rely on a model that requires one to find estimates for the intersection size of join attributes. For queries with multiple joins, sketches serve as compact representations of join results that are combined via a data structure that approximates the joint frequency distribution of join attributes. In addition, we present a technique to transform selection predicates into a representation that allows estimators based on machine learning to effectively learn query result cardinalities. For each cardinality estimator presented in this thesis, we precisely define its problem scope, the construction process, and how to obtain estimates. Then, we compare to state-of-the-art cardinality estimators and run a thorough evaluation with queries over multiple data sets. Based on our observations, we analyze the strengths and limitations of each of our cardinality estimators and identify its preferred use case

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Anales del XIII Congreso Argentino de Ciencias de la Computación (CACIC)

Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterogéneas Redes de Avanzada Redes inalámbricas Redes móviles Redes activas Administración y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad informática y autenticación, privacidad Infraestructura para firma digital y certificados digitales Análisis y detección de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integración (Web Services o .Net)Red de Universidades con Carreras en Informática (RedUNCI