Spectra in taxonomic evidence in databases III : Application in celestial bodies. Asteroids families

Numerical Taxonomy aims to group in clusters, using so-called structure analysis of operational taxonomic units (OTUs or taxons or taxa) through numerical methods. These clusters constitute families. Structural analysis, based on their phenotypic characteristics, exhibits the relationships, in terms of degrees of similarity, between two or more OTUs. Entities formed by dynamic domains of attributes, change according to taxonomical requirements: Classification of objects to form families or clusters. Taxonomic objects are here represented by application of the semantics of the Dynamic Relational Database Model. Families of OTUs are obtained employing as tools i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix. The main contribution of the present work is to introduce the concept of spectrum of the OTUs, based in the states of their characters. The concept of families’ spectra emerges, if the superposition principle is applied to the spectra of the OTUs, and the groups are delimited through the maximum of the Bienaymé-Tchebycheff relation, that determines Invariants (centroid, variance and radius). Applying the integrated, independent domain technique dynamically to compute the Matrix of Similarity, and, by recourse to an iterative algorithm, families or clusters are obtained. A new taxonomic criterion is thereby formulated. An astronomic application is worked out. The result is a new criterion for the classification of asteroids in the hyperspace of orbital proper elements (the well-known Families of Hirayama). Using an updated database of asteroids we ascertain the robustness of the method. Thus, a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining. The Informatics (Data Mining and Computational Taxonomy), is always the original objective of our researches.Eje: Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI

Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families

The Numeric Taxonomy aims to group operational taxonomic units in clusters (OTUs or taxons or taxa), using the denominated structure analysis by means of numeric methods. These clusters that constitute families are the purpose of this series of projects and they emerge of the structural analysis, of their phenotypical characteristic, exhibiting the relationships in terms of grades of similarity of the OTUs, employing tools such as i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix and in this way the significant concept of spectrum of the OTUs is introduced, being based the same one on the state of their characters. A new taxonomic criterion is thereby formulated and a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining, when apply of Machine Learning techniques, in particular to the C4.5 algorithms, created by Quinlan, the degree of efficiency achieved by the TDIDT family´s algorithms when are generating valid models of the data in classification problems with the Gain of Entropy through Maximum Entropy Principle.Fil: Perichinsky, Gregorio. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Jiménez Rey, Elizabeth Miriam. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Grossi, María Delia. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Vallejos, Félix Anibal. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Servetto, Arturo Carlos. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Orellana, Rosa Beatriz. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Plastino, Ángel Luis. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Física; Argentin

Taxonomic evidence applying algorithms of intelligent data mining : Asteroids families

Numerical Taxonomy aims to group in clusters, using so-called structure analysis of operational taxonomic units (OTUs or taxons or taxa) through numerical methods. Clusters that consitute families was the purpose of this series of last projects. Structural analysis, based on their phenotypic characteristics, exhibits the relationships, in terms of degrees of similarity, between two or more OTUs. Entities formed by dynamic domains of attributes, change according to taxonomical requirements: Classification of objects to form families. Taxonomic objects are represented by semantics application of Dynamic Relational Database Model. Families of OTUs are obtained employing as tools i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix. The main contribution up until now is to introduce the concept of spectrum of the OTUs, based in the states of their characters. The concept of families’ spectra emerges, if the superposition principle is applied to the spectra of the OTUs, and the groups are delimited through the maximum of the Bienaymé-Tchebycheff relation, that determines Invariants (centroid, variance and radius). A new taxonomic criterion is thereby formulated. An astronomic application is worked out. The result is a new criterion for the classification of asteroids in the hyperspace of orbital proper elements. Thus, a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining. This paper analyses the application of Machine Learning techniques to Data Mining. We focused our interest on the TDIDT (Top Down Induction Trees) induction family from pre-classified data, and in particular to the ID3 and the C4.5 algorithms, created by Quinlan. We tried to determine the degree of efficiency achieved by the TDIDT family’s algorithms when applied in data mining to generate valid models of the data in classification problems with the Gain of Entropy. The Informatics (Data Mining and Computational Taxonomy), is always the original objective of our researches.Eje: Bases de datosRed de Universidades con Carreras en Informática (RedUNCI

Taxonomic evidence and robustness of the classification applying intelligent data mining.

Numerical Taxonomy aims to group in families, using so-called structure analysis of operational taxonomic units (OTUs or taxons or taxa). Clusters that constitute families with a new criterion, is the purpose of this series of papers. Structural analysis, based on phenotypic characteristics, exhibits the relationships, in terms of degrees of similarity, through the computation of the Matrix of Similarity, applying the technique of integration dynamic of independent domains, of the semantics of the Dynamic Relational Database Model. The main contribution is to introduce the concept of spectrum of the OTUs, based in the states of their characters. The concept of families' spectra emerges, if the principles of superposition and interference, and the Invariants (centroid, variance and radius) determined by the maximum of the Bienaymé-Tchebycheff relation, are applied to the spectra of the OTUs. Using in successive form an updated database through the increase of the cardinal of the tuples, and as the resulting families are the same, we ascertain the robustness of the method. Through Intelligent Data Mining, we focused our interest on the Quinlan algorithms, applied in classification problems with the Gain of Entropy, we contrast the Computational Taxonomy, obtaining a new criterion of the robustness of the method.Eje: Aplicaciones (APLI)Red de Universidades con Carreras en Informática (RedUNCI

Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families

The Numeric Taxonomy aims to group operational taxonomic units in clusters (OTUs or taxons or taxa), using the denominated structure analysis by means of numeric methods. These clusters that constitute families are the purpose of this series of projects and they emerge of the structural analysis, of their phenotypical characteristic, exhibiting the relationships in terms of grades of similarity of the OTUs, employing tools such as i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix and in this way the significant concept of spectrum of the OTUs is introduced, being based the same one on the state of their characters. A new taxonomic criterion is thereby formulated and a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining, when apply of Machine Learning techniques, in particular to the C4.5 algorithms, created by Quinlan, the degree of efficiency achieved by the TDIDT family´s algorithms when are generating valid models of the data in classification problems with the Gain of Entropy through Maximum Entropy Principle.Facultad de Ciencias Astronómicas y GeofísicasFacultad de Ciencias Exacta

Taxonomic evidence applying intelligent information algorithm and the principle of maximum entropy: the case of asteroids families

The Numeric Taxonomy aims to group operational taxonomic units in clusters (OTUs or taxons or taxa), using the denominated structure analysis by means of numeric methods. These clusters that constitute families are the purpose of this series of projects and they emerge of the structural analysis, of their phenotypical characteristic, exhibiting the relationships in terms of grades of similarity of the OTUs, employing tools such as i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix and in this way the significant concept of spectrum of the OTUs is introduced, being based the same one on the state of their characters. A new taxonomic criterion is thereby formulated and a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining, when apply of Machine Learning techniques, in particular to the C4.5 algorithms, created by Quinlan, the degree of efficiency achieved by the TDIDT family´s algorithms when are generating valid models of the data in classification problems with the Gain of Entropy through Maximum Entropy Principle.Facultad de Ciencias Astronómicas y GeofísicasFacultad de Ciencias Exacta

Spectra in taxonomic evidence in databases III : Application in celestial bodies. Asteroids families

Numerical Taxonomy aims to group in clusters, using so-called structure analysis of operational taxonomic units (OTUs or taxons or taxa) through numerical methods. These clusters constitute families. Structural analysis, based on their phenotypic characteristics, exhibits the relationships, in terms of degrees of similarity, between two or more OTUs. Entities formed by dynamic domains of attributes, change according to taxonomical requirements: Classification of objects to form families or clusters. Taxonomic objects are here represented by application of the semantics of the Dynamic Relational Database Model. Families of OTUs are obtained employing as tools i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix. The main contribution of the present work is to introduce the concept of spectrum of the OTUs, based in the states of their characters. The concept of families’ spectra emerges, if the superposition principle is applied to the spectra of the OTUs, and the groups are delimited through the maximum of the Bienaymé-Tchebycheff relation, that determines Invariants (centroid, variance and radius). Applying the integrated, independent domain technique dynamically to compute the Matrix of Similarity, and, by recourse to an iterative algorithm, families or clusters are obtained. A new taxonomic criterion is thereby formulated. An astronomic application is worked out. The result is a new criterion for the classification of asteroids in the hyperspace of orbital proper elements (the well-known Families of Hirayama). Using an updated database of asteroids we ascertain the robustness of the method. Thus, a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining. The Informatics (Data Mining and Computational Taxonomy), is always the original objective of our researches.Eje: Ingeniería de Software y Base de DatosRed de Universidades con Carreras en Informática (RedUNCI

Taxonomic evidence applying algorithms of intelligent data mining : Asteroids families

Numerical Taxonomy aims to group in clusters, using so-called structure analysis of operational taxonomic units (OTUs or taxons or taxa) through numerical methods. Clusters that consitute families was the purpose of this series of last projects. Structural analysis, based on their phenotypic characteristics, exhibits the relationships, in terms of degrees of similarity, between two or more OTUs. Entities formed by dynamic domains of attributes, change according to taxonomical requirements: Classification of objects to form families. Taxonomic objects are represented by semantics application of Dynamic Relational Database Model. Families of OTUs are obtained employing as tools i) the Euclidean distance and ii) nearest neighbor techniques. Thus taxonomic evidence is gathered so as to quantify the similarity for each pair of OTUs (pair-group method) obtained from the basic data matrix. The main contribution up until now is to introduce the concept of spectrum of the OTUs, based in the states of their characters. The concept of families’ spectra emerges, if the superposition principle is applied to the spectra of the OTUs, and the groups are delimited through the maximum of the Bienaymé-Tchebycheff relation, that determines Invariants (centroid, variance and radius). A new taxonomic criterion is thereby formulated. An astronomic application is worked out. The result is a new criterion for the classification of asteroids in the hyperspace of orbital proper elements. Thus, a new approach to Computational Taxonomy is presented, that has been already employed with reference to Data Mining. This paper analyses the application of Machine Learning techniques to Data Mining. We focused our interest on the TDIDT (Top Down Induction Trees) induction family from pre-classified data, and in particular to the ID3 and the C4.5 algorithms, created by Quinlan. We tried to determine the degree of efficiency achieved by the TDIDT family’s algorithms when applied in data mining to generate valid models of the data in classification problems with the Gain of Entropy. The Informatics (Data Mining and Computational Taxonomy), is always the original objective of our researches.Eje: Bases de datosRed de Universidades con Carreras en Informática (RedUNCI

Taxonomic evidence and robustness of the classification applying intelligent data mining.

Numerical Taxonomy aims to group in families, using so-called structure analysis of operational taxonomic units (OTUs or taxons or taxa). Clusters that constitute families with a new criterion, is the purpose of this series of papers. Structural analysis, based on phenotypic characteristics, exhibits the relationships, in terms of degrees of similarity, through the computation of the Matrix of Similarity, applying the technique of integration dynamic of independent domains, of the semantics of the Dynamic Relational Database Model. The main contribution is to introduce the concept of spectrum of the OTUs, based in the states of their characters. The concept of families' spectra emerges, if the principles of superposition and interference, and the Invariants (centroid, variance and radius) determined by the maximum of the Bienaymé-Tchebycheff relation, are applied to the spectra of the OTUs. Using in successive form an updated database through the increase of the cardinal of the tuples, and as the resulting families are the same, we ascertain the robustness of the method. Through Intelligent Data Mining, we focused our interest on the Quinlan algorithms, applied in classification problems with the Gain of Entropy, we contrast the Computational Taxonomy, obtaining a new criterion of the robustness of the method.Eje: Aplicaciones (APLI)Red de Universidades con Carreras en Informática (RedUNCI

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware