Query processing of geometric objects with free form boundarie sin spatial databases

The increasing demand for the use of database systems as an integrating factor in CAD/CAM applications has necessitated the development of database systems with appropriate modelling and retrieval capabilities. One essential problem is the treatment of geometric data which has led to the development of spatial databases. Unfortunately, most proposals only deal with simple geometric objects like multidimensional points and rectangles. On the other hand, there has been a rapid development in the field of representing geometric objects with free form curves or surfaces, initiated by engineering applications such as mechanical engineering, aviation or astronautics. Therefore, we propose a concept for the realization of spatial retrieval operations on geometric objects with free form boundaries, such as B-spline or Bezier curves, which can easily be integrated in a database management system. The key concept is the encapsulation of geometric operations in a so-called query processor. First, this enables the definition of an interface allowing the integration into the data model and the definition of the query language of a database system for complex objects. Second, the approach allows the use of an arbitrary representation of the geometric objects. After a short description of the query processor, we propose some representations for free form objects determined by B-spline or Bezier curves. The goal of efficient query processing in a database environment is achieved using a combination of decomposition techniques and spatial access methods. Finally, we present some experimental results indicating that the performance of decomposition techniques is clearly superior to traditional query processing strategies for geometric objects with free form boundaries

10381 Summary and Abstracts Collection -- Robust Query Processing

Dagstuhl seminar 10381 on robust query processing (held 19.09.10 - 24.09.10) brought together a diverse set of researchers and practitioners with a broad range of expertise for the purpose of fostering discussion and collaboration regarding causes, opportunities, and solutions for achieving robust query processing. The seminar strove to build a unified view across the loosely-coupled system components responsible for the various stages of database query processing. Participants were chosen for their experience with database query processing and, where possible, their prior work in academic research or in product development towards robustness in database query processing. In order to pave the way to motivate, measure, and protect future advances in robust query processing, seminar 10381 focused on developing tests for measuring the robustness of query processing. In these proceedings, we first review the seminar topics, goals, and results, then present abstracts or notes of some of the seminar break-out sessions. We also include, as an appendix, the robust query processing reading list that was collected and distributed to participants before the seminar began, as well as summaries of a few of those papers that were contributed by some participants

Managing Distributed Cloud Applications and Infrastructure

The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities

Codon usage adaptation in prokaryotic genomes

La tesi esta basada en l'adaptació de l'ús de codons a genomes procariotes, especialment l'adaptació de l'ús de codons a una alta expressió. Hi ha un grup de genomes procariotes, els quals estan sota una selecció traduccional, que tenen un grup de gens amb un ús de codons esbiaixat de la resta de gens del genoma i adaptats a l'abundància dels tRNA. Hem desenvolupat un nou algoritme per a avaluar si un genoma esta sota selecció traduccional i predir els gens altament expressat de tots els genomes sota selecció traduccional. Aquestes prediccions són públiques a la base de dades HEG-DB (http://genomes.urv.cat/HEG-DB), la qual s'ha publicat a la revista Nucleic Acids Research. Les prediccions de gens altament expressats s'han fet servir com a filtre en les prediccions de gens adquirits per transferència horitzontal, ja que els gens altament expressats molts cops son predits com a falsos positius en la predicció de gens adquirits. Amb les dades de la predicció de gens altament expressats, també hem desenvolupat una nova eina Bioinformàtica, anomenada OPTIMIZER (http://genomes.urv.cat/OPTIMIZER) i publicada al Nucleic Acids Research, per tal d'optimitzar l'ús de codons d'un gen per a incrementar la seva expressió en experiments d'expressió heteròloga de proteïnes. També hem estudiat un cas particular d'adaptació de l'ús de codons. El cas de l' 'amelioration', que és l'adaptació de l'ús de codons que pateix un gen inserit en un genoma hoste. Aquest cas l'hem estudiat amb els gens mitocondrials que varen saltar al genoma nuclear i varen haver d'adaptar el seu us de codons mitocondrial a l'ús de codons del genoma nuclear. Per tal d'estudiar l''amelioration', hem desenvolupat un nou índex anomenat CAI esperat (eCAI) i una nova eina Bioinformàtica anomenada CAIcal (http://genomes.urv.cat/CAIcal), que està en procés de revisió a la revista BMC Bioinformatics. Analitzant l'anàlisi de l'ús de codons dels genomes completament sequenciats vàrem realitzar una troballa que s'aparta una mica del tema central de la tesi. Vàrem veure que els genomes que estan adaptats a la (hiper)termofília tenen un patró de l'ús de codons i d'aminoàcids diferent a la resta de genomes (mesòfils). Aquest fet ens ha permès descobrir casos de guany i pèrdua (recents i antics) de la capacitat d'adaptació termofílica en genomes procariotes. Aquests resultats han donat lloc a una publicació a la revista Trends in Genetics. Durant la tesi he realitzat una estada de 4 mesos (Febrer - Juny, 2006) en el laboratori de bioinformàtica del departament de biologia de la universitat nacional d'Irlanda a Maynooth sota la supervisió del Dr James McInerney on vaig desenvolupar un nou programa per a la comparació d'arbres filogenètics anomenat TOPD/FMTS (http://genomes.urv.cat/topd) el qual està publicat a la revista Bioinformatics.This thesis is based in codon usage adaptation in prokaryotic genomes, especially the codon usage adaptation to a high expression. In genomes under translational selection, the group of highly expressed genes has a codon usage adapted to the most abundant tRNA species. We have developed a new iterative algorithm which predicts a group of highly expressed genes in genomes under translational selection by using the Codon Adaptation Index and the group of ribosomal protein genes as a seed. We have developed a new genomic database, called HEG-DB, to store genes that are predicted as highly expressed in prokaryotic complete genomes under strong translational selection. The database is freely available at http://genomes.urv.cat/HEG-DB and it has been published in Nucleic Acids Research. The predicted highly expressed genes are used as an initial filter to reduce the number of false positives of the Horizontal Gene Transfer Database, due to highly expressed genes are usually false positive in predictions of acquired genes. We have developed a new web sever, called OPTIMIZER (http://genomes.urv.cat/OPTIMIZER), which has been published in Nucleic Acids Research, to optimize the codon usage of DNA or RNA sequences. This new web server can be used to predict and optimize the level expression of a gene in heterologous gene expression or to express new genes that confer new metabolic capabilities in a given species. We have also analyzed an especial case of codon usage adaptation, which is called 'amelioration'. The 'amelioration' is the adaptation of foreign genes to a new genome. This is the case of mitochondrial genes encoded in the human nuclear genome and originally encoded in the proto-mitochondria. To test the 'amelioration' process we have developed an expected value of CAI (eCAI) to find out whether the differences in the CAI are statistically significant or whether they are the product of biased nucleotide and/or amino acid composition and a new bioinformatics tool called CAIcal (http://genomes.urv.cat/CAIcal). We have also analyzed the evolution of thermophilic adaptation in prokaryotes and we suggest that the amino acid composition signature in thermophilic organisms is a consequence of or an adaptation to living at high temperatures, not its cause. Our findings suggest that there have been several cases where the capacity for thermophilic adaptation has been gained or lost throughout the evolution of prokaryotes. These results have been published in Trends in Genetics. During my thesis I have worked for four months in the Bioinformatics Laboratory of the Biology Department at the National University of Ireland under the supervision of Dr James O. McInerney where I developed a new software program to compare phylogenetic trees called TOPD/FMTS (http://genomes.urv.cat/topd), that has been published in Bioinformatics

Managing Distributed Cloud Applications and Infrastructure

The emergence of the Internet of Things (IoT), combined with greater heterogeneity not only online in cloud computing architectures but across the cloud-to-edge continuum, is introducing new challenges for managing applications and infrastructure across this continuum. The scale and complexity is simply so complex that it is no longer realistic for IT teams to manually foresee the potential issues and manage the dynamism and dependencies across an increasing inter-dependent chain of service provision. This Open Access Pivot explores these challenges and offers a solution for the intelligent and reliable management of physical infrastructure and the optimal placement of applications for the provision of services on distributed clouds. This book provides a conceptual reference model for reliable capacity provisioning for distributed clouds and discusses how data analytics and machine learning, application and infrastructure optimization, and simulation can deliver quality of service requirements cost-efficiently in this complex feature space. These are illustrated through a series of case studies in cloud computing, telecommunications, big data analytics, and smart cities

PFNs Are Flexible Models for Real-World Bayesian Optimization

In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible surrogate for Bayesian Optimization (BO). PFNs are neural processes that are trained to approximate the posterior predictive distribution (PPD) for any prior distribution that can be efficiently sampled from. We describe how this flexibility can be exploited for surrogate modeling in BO. We use PFNs to mimic a naive Gaussian process (GP), an advanced GP, and a Bayesian Neural Network (BNN). In addition, we show how to incorporate further information into the prior, such as allowing hints about the position of optima (user priors), ignoring irrelevant dimensions, and performing non-myopic BO by learning the acquisition function. The flexibility underlying these extensions opens up vast possibilities for using PFNs for BO. We demonstrate the usefulness of PFNs for BO in a large-scale evaluation on artificial GP samples and three different hyperparameter optimization testbeds: HPO-B, Bayesmark, and PD1. We publish code alongside trained models at http://github.com/automl/PFNs4BO.Comment: Accepted at ICML 202