Runway exit designs for capacity improvement demonstrations. Phase 2: Computer model development

The development is described of a computer simulation/optimization model to: (1) estimate the optimal locations of existing and proposed runway turnoffs; and (2) estimate the geometric design requirements associated with newly developed high speed turnoffs. The model described, named REDIM 2.0, represents a stand alone application to be used by airport planners, designers, and researchers alike to estimate optimal turnoff locations. The main procedures are described in detail which are implemented in the software package and possible applications are illustrated when using 6 major runway scenarios. The main output of the computer program is the estimation of the weighted average runway occupancy time for a user defined aircraft population. Also, the location and geometric characteristics of each turnoff are provided to the user

Compressing DNA sequence databases with coil

Background: Publicly available DNA sequence databases such as GenBank are large, and are growing at an exponential rate. The sheer volume of data being dealt with presents serious storage and data communications problems. Currently, sequence data is usually kept in large "flat files," which are then compressed using standard Lempel-Ziv (gzip) compression – an approach which rarely achieves good compression ratios. While much research has been done on compressing individual DNA sequences, surprisingly little has focused on the compression of entire databases of such sequences. In this study we introduce the sequence database compression software coil. Results: We have designed and implemented a portable software package, coil, for compressing and decompressing DNA sequence databases based on the idea of edit-tree coding. coil is geared towards achieving high compression ratios at the expense of execution time and memory usage during compression – the compression time represents a "one-off investment" whose cost is quickly amortised if the resulting compressed file is transmitted many times. Decompression requires little memory and is extremely fast. We demonstrate a 5% improvement in compression ratio over state-of-the-art general-purpose compression tools for a large GenBank database file containing Expressed Sequence Tag (EST) data. Finally, coil can efficiently encode incremental additions to a sequence database. Conclusion: coil presents a compelling alternative to conventional compression of flat files for the storage and distribution of DNA sequence databases having a narrow distribution of sequence lengths, such as EST data. Increasing compression levels for databases having a wide distribution of sequence lengths is a direction for future work

Boosting Perturbation-Based Iterative Algorithms to Compute the Median String

[Abstract] The most competitive heuristics for calculating the median string are those that use perturbation-based iterative algorithms. Given the complexity of this problem, which under many formulations is NP-hard, the computational cost involved in the exact solution is not affordable. In this work, the heuristic algorithms that solve this problem are addressed, emphasizing its initialization and the policy to order possible editing operations. Both factors have a significant weight in the solution of this problem. Initial string selection influences the algorithm’s speed of convergence, as does the criterion chosen to select the modification to be made in each iteration of the algorithm. To obtain the initial string, we use the median of a subset of the original dataset; to obtain this subset, we employ the Half Space Proximal (HSP) test to the median of the dataset. This test provides sufficient diversity within the members of the subset while at the same time fulfilling the centrality criterion. Similarly, we provide an analysis of the stop condition of the algorithm, improving its performance without substantially damaging the quality of the solution. To analyze the results of our experiments, we computed the execution time of each proposed modification of the algorithms, the number of computed editing distances, and the quality of the solution obtained. With these experiments, we empirically validated our proposal.This work was supported in part by the Comisión Nacional de Investigación Científica y Tecnológica - Programa de Formación de Capital Humano Avanzado (CONICYT-PCHA)/Doctorado Nacional/2014-63140074 through the Ph.D. Scholarship, in part by the European Union's Horizon 2020 under the Marie Sklodowska-Curie under Grant 690941, in part by the Millennium Institute for Foundational Research on Data (IMFD), and in part by the FONDECYT-CONICYT under Grant 1170497. The work of ÓSCAR PEDREIRA was supported in part by the Xunta de Galicia/FEDER-UE refs under Grant CSI ED431G/01 and Grant GRC: ED431C 2017/58, in part by the Office of the Vice President for Research and Postgraduate Studies of the Universidad Católica de Temuco, VIPUCT Project 2020EM-PS-08, and in part by the FEQUIP 2019-INRN-03 of the Universidad Católica de TemucoXunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2017/58Chile. Comisión Nacional de Investigación Científica y Tecnológica; 2014-63140074Chile. Comisión Nacional de Investigación Científica y Tecnológica; 1170497Universidad Católica de Temuco (Chile); 2020EM-PS-08Universidad Católica de Temuco (Chile); 2019-INRN-0

Runway Exit Designs for Capacity Improvement Demonstrations. Phase 1: Algorithm Development

A description and results are presented of a study to locate and design rapid runway exits under realistic airport conditions. The study developed a PC-based computer simulation-optimization program called REDIM (runway exit design interactive model) to help future airport designers and planners to locate optimal exits under various airport conditions. The model addresses three sets of problems typically arising during runway exit design evaluations. These are the evaluations of existing runway configurations, addition of new rapid runway turnoffs, and the design of new runway facilities. The model is highly interactive and allows a quick estimation of the expected value of runway occupancy time. Aircraft populations and airport environmental conditions are among the multiple inputs to the model to execute a viable runway location and geometric design solution. The results presented suggest that possible reductions on runway occupancy time (ROT) can be achieved with the use of optimally tailored rapid runway designs for a given aircraft population. Reductions of up to 9 to 6 seconds are possible with the implementation of 30 m/sec variable geometry exits

Designing labeled graph classifiers by exploiting the R\'enyi entropy of the dissimilarity representation

Representing patterns as labeled graphs is becoming increasingly common in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowadays available and tested for various datasets of labeled graphs. However, the design of effective learning procedures operating in the space of labeled graphs is still a challenging problem, especially from the computational complexity viewpoint. In this paper, we present a major improvement of a general-purpose classifier for graphs, which is conceived on an interplay between dissimilarity representation, clustering, information-theoretic techniques, and evolutionary optimization algorithms. The improvement focuses on a specific key subroutine devised to compress the input data. We prove different theorems which are fundamental to the setting of the parameters controlling such a compression operation. We demonstrate the effectiveness of the resulting classifier by benchmarking the developed variants on well-known datasets of labeled graphs, considering as distinct performance indicators the classification accuracy, computing time, and parsimony in terms of structural complexity of the synthesized classification models. The results show state-of-the-art standards in terms of test set accuracy and a considerable speed-up for what concerns the computing time.Comment: Revised versio

Optimizing the weighted sequence alignment algorithm for large-scale text similarity computation

Peer reviewe

Online Analysis of Dynamic Streaming Data

Die Arbeit zum Thema "Online Analysis of Dynamic Streaming Data" beschäftigt sich mit der Distanzmessung dynamischer, semistrukturierter Daten in kontinuierlichen Datenströmen um Analysen auf diesen Datenstrukturen bereits zur Laufzeit zu ermöglichen. Hierzu wird eine Formalisierung zur Distanzberechnung für statische und dynamische Bäume eingeführt und durch eine explizite Betrachtung der Dynamik von Attributen einzelner Knoten der Bäume ergänzt. Die Echtzeitanalyse basierend auf der Distanzmessung wird durch ein dichte-basiertes Clustering ergänzt, um eine Anwendung des Clustering, einer Klassifikation, aber auch einer Anomalieerkennung zu demonstrieren. Die Ergebnisse dieser Arbeit basieren auf einer theoretischen Analyse der eingeführten Formalisierung von Distanzmessungen für dynamische Bäume. Diese Analysen werden unterlegt mit empirischen Messungen auf Basis von Monitoring-Daten von Batchjobs aus dem Batchsystem des GridKa Daten- und Rechenzentrums. Die Evaluation der vorgeschlagenen Formalisierung sowie der darauf aufbauenden Echtzeitanalysemethoden zeigen die Effizienz und Skalierbarkeit des Verfahrens. Zudem wird gezeigt, dass die Betrachtung von Attributen und Attribut-Statistiken von besonderer Bedeutung für die Qualität der Ergebnisse von Analysen dynamischer, semistrukturierter Daten ist. Außerdem zeigt die Evaluation, dass die Qualität der Ergebnisse durch eine unabhängige Kombination mehrerer Distanzen weiter verbessert werden kann. Insbesondere wird durch die Ergebnisse dieser Arbeit die Analyse sich über die Zeit verändernder Daten ermöglicht

Approximate string matching methods for duplicate detection and clustering tasks

Approximate string matching methods are utilized by a vast number of duplicate detection and clustering applications in various knowledge domains. The application area is expected to grow due to the recent significant increase in the amount of digital data and knowledge sources. Despite the large number of existing string similarity metrics, there is a need for more precise approximate string matching methods to improve the efficiency of computer-driven data processing, thus decreasing labor-intensive human involvement. This work introduces a family of novel string similarity methods, which outperform a number of effective well-known and widely used string similarity functions. The new algorithms are designed to overcome the most common problem of the existing methods which is the lack of context sensitivity. In this evaluation, the Longest Approximately Common Prefix (LACP) method achieved the highest values of average precision and maximum F1 on three out of four medical informatics datasets used. The LACP demonstrated the lowest execution time ensured by the linear computational complexity within the set of evaluated algorithms. An online interactive spell checker of biomedical terms was developed based on the LACP method. The main goal of the spell checker was to evaluate the LACP method’s ability to make it possible to estimate the similarity of resulting sets at a glance. The Shortest Path Edit Distance (SPED) outperformed all evaluated similarity functions and gained the highest possible values of the average precision and maximum F1 measures on the bioinformatics datasets. The SPED design was inspired by the preceding work on the Markov Random Field Edit Distance (MRFED). The SPED eradicates two shortcomings of the MRFED, which are prolonged execution time and moderate performance. Four modifications of the Histogram Difference (HD) method demonstrated the best performance on the majority of the life and social sciences data sources used in the experiments. The modifications of the HD algorithm were achieved using several re- scorers: HD with Normalized Smith-Waterman Re-scorer, HD with TFIDF and Jaccard re-scorers, HD with the Longest Common Prefix and TFIDF re-scorers, and HD with the Unweighted Longest Common Prefix Re-scorer. Another contribution of this dissertation includes the extensive analysis of the string similarity methods evaluation for duplicate detection and clustering tasks on the life and social sciences, bioinformatics, and medical informatics domains. The experimental results are illustrated with precision-recall charts and a number of tables presenting the average precision, maximum F1, and execution time