An unbalanced approach to metric space searching

Proximity queries (the searching problem generalized beyond exact match) is mostly modeled as metric space. A metric space consists of a collection of objects and a distance function defined among them. The goal is to preprocess the data set (a slow procedure) to quickly answer proximity queries. This problem have received a lot of attention recently, specially in the pattern recognition community. The Excluded Middle Vantage Point Forest (VP–forest) is a data structure designed to search in high dimensional vector spaces. A VP–forest is built as a collection of balanced Vantage Point Trees (VP–trees). In this work we propose a novel two-fold approach for searching. Firstly we extend the VP– forest to search in metric spaces, and more importantly we test a counterintuitive modification to the VP–tree, namely to unbalance it. In exact searching an unbalanced data structure perform poorly, and most of the algorithmic effort is directed to obtain a balanced data structure. The unbalancing approach is motivated by a recent data structure (the List of Clusters ) specialized in high dimensional metric space searches, which is an extremely unbalanced data structure (a linked list) outperforming other approaches.Eje: AlgoritmosRed de Universidades con Carreras en Informática (RedUNCI

Fully dynamic and memory-adaptative spatial approximation trees

Hybrid dynamic spatial approximation trees are recently proposed data structures for searching in metric spaces, based on combining the concepts of spatial approximation and pivot based algorithms. These data structures are hybrid schemes, with the full features of dynamic spatial approximation trees and able of using the available memory to improve the query time. It has been shown that they compare favorably against alternative data structures in spaces of medium difficulty. In this paper we complete and improve hybrid dynamic spatial approximation trees, by presenting a new search alternative, an algorithm to remove objects from the tree, and an improved way of managing the available memory. The result is a fully dynamic and optimized data structure for similarity searching in metric spaces.Eje: Teoría (TEOR)Red de Universidades con Carreras en Informática (RedUNCI

Solving All-k-Nearest Neighbor Problem without an Index

Among the similarity queries in metric spaces, there are one that obtains the k-nearest neighbors of all the elements in the database (All-k-NN). One way to solve it is the naïve one: comparing each object in the database with all the other ones and returning the k elements nearest to it (k-NN). Another way to do this is by preprocessing the database to build an index, and then searching on this index for the k-NN of each element of the dataset. Answering to the All-k-NN problem allows to build the k-Nearest Neighbor graph (kNNG). Given an object collection of a metric space, the Nearest Neighbor Graph (NNG) associates each node with its closest neighbor under the given metric. If we link each object to their k nearest neighbors, we obtain the k Nearest Neighbor Graph (kNNG).The kNNG can be considered an index for a database, which is quite efficient and can allow improvements. In this work, we propose a new technique to solve the All-k-NN problem which do not use any index to obtain the k-NN of each element. This approach solves the problem avoiding as many comparisons as possible, only comparing some database elements and taking advantage of the distance function properties. Its total cost is significantly lower than that of the naïve solution.XVI Workshop Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

A hybrid data structure for searching in metric spaces

The concept of “approximate” searching has applications in a vast number of fields. Some examples are non-traditional databases (e. g. storing images, fingerprints or audio clips, where the concept of exact search is of no use and we search instead for similar objects), text searching, information retrieval, machine learning and classification, image quantization and compression, computational biology, and function prediction.Eje: Base de datosRed de Universidades con Carreras en Informática (RedUNCI

Optimizing the spatial approximation tree from the root

Many computational applications need to look for information in a database. Nowadays, the predominance of nonconventional databases makes the similarity search (i.e., searching elements of the database that are "similar" to a given query) becomes a preponderant concept. The Spatial Approximation Tree has been shown that it compares favorably against alternative data structures for similarity searching in metric spaces of medium to high dimensionality ("difficult" spaces) or queries with low selectivity. However, for the construction process the tree root has been randomly selected and the tree ,in its shape and performance, is completely determined by this selection. Therefore, we are interested in improve mainly the searches in this data structure trying to select the tree root so to reflect some of the own characteristics of the metric space to be indexed. We regard that selecting the root in this way it allows a better adaption of the data structure to the intrinsic dimensionality of the metric space considered, so also it achieves more efficient similarity searches.Facultad de Informátic

Optimizing the spatial approximation tree from the root

Many computational applications need to look for information in a database. Nowadays, the predominance of nonconventional databases makes the similarity search (i.e., searching elements of the database that are "similar" to a given query) becomes a preponderant concept. The Spatial Approximation Tree has been shown that it compares favorably against alternative data structures for similarity searching in metric spaces of medium to high dimensionality ("difficult" spaces) or queries with low selectivity. However, for the construction process the tree root has been randomly selected and the tree ,in its shape and performance, is completely determined by this selection. Therefore, we are interested in improve mainly the searches in this data structure trying to select the tree root so to reflect some of the own characteristics of the metric space to be indexed. We regard that selecting the root in this way it allows a better adaption of the data structure to the intrinsic dimensionality of the metric space considered, so also it achieves more efficient similarity searches.Facultad de Informátic

Efecto de los determinantes sociales de la salud sobre los resultados del tratamiento antituberculosis en el Callao 2017

Analiza los determinantes sociales de la salud que influyen en el inicio temprano del tratamiento, el egreso con “éxito” y el termino en el tiempo estipulado en las personas afectadas por tuberculosis. Se realizo un estudio cuantitativo, descriptivo y transversal, en 1 593 personas afectadas por tuberculosis que ingresaron a tratamiento de enero a diciembre del año 2017. Las variables de estudio se operacionalizaron en factores individuales de las 1025 personas tratadas por tuberculosis sensible y en los factores contextuales de los 45 servicio de salud de Callao y se realizó un análisis de regresión logística multinivel. Encuentra que los determinantes sociales individuales influyen en el inicio temprano en 85.4%, en el egreso con “éxito” en 92.7% y el término del tratamiento en el tiempo estipulado en 88.7%, mientras que el 14.6%, 7.3% y 13% de esta variabilidad en cada caso es explicada por los servicios de salud, determinante social de la salud per se. Los factores individuales relevantes fueron: edad, sexo masculino, frotis positivo, irregularidad en el tratamiento; mientras que los factores contextuales más importantes de acuerdo a la “mediana de los odds ratio (MOR)” de los servicios fueron: la atención de 12 horas, que tengan puerta de acceso independiente y la presencia de agentes comunitarios para TB. Concluye que el curso del tratamiento de la tuberculosis sensible es influido no sólo por los factores individuales sino de manera importante por los contextuales en la atención primaria

La Alfabetización Informacional (ALFIN) aplicada a las tecnologías de la información en la Biblioteca Central "Salomón de la Selva" de la UNAN-Managua^aPropuesta del programa de formción de usuarios

En esta investigación dentro del marco de Seminario de Graduación presentamos el proceso llevado a cabo en el Diseño de una Propuesta del Programa de Formación de Usuarios en la Biblioteca Central “Salomón de la Selva” de la UNAN-Managua, ya que no existe un Programa de Alfabetización Informacional, para cumplir con los objetivos del mismo y la inexistencia de un Programa formal, provoca en los estudiantes el desconocimiento del funcionamiento y los servicios que ofrece la unidad d e información. Para recopilar la información nos apoyamos en las técnicas de instrumentos tales como guía de entrevista, cuestionario y la observación, las mismas nos brindaron información para conocer los factores que inciden en la problemática que se presenta dentro de la Biblioteca en cuanto a la búsqueda de la información. Como parte del marco referencial que acompaña la investigación se presentan algunos elementos conceptuales que permiten identificar el desarrollo de la Formación de Usuarios y su aplicación en la biblioteca universitaria “Salomón de la Selva” de la UNAN-Managua, estos nos ayudaron a profundizar más sobre el tema de la Formación de Usuarios. Finalmente se diseñó una propuesta de un Programa de Formación de Usuarios para brindar a los estudiantes de nuevo ingreso los elementos necesarios para fomentar una actitud crítica, analítica e interpretativa en el manejo integral de la información, que les permita obtener un buen rendimiento académico. Presentamos nuestras conclusiones sobre el tema abordado, y hacemos algunas recomendaciones que contribuirán para la implementación del Programa

Approximate Nearest Neighbor Graph via Index Construction

Given a collection of objects in a metric space, the Nearest Neighbor Graph (NNG) associate each node with its closest neighbor under the given metric. It can be obtained trivially by computing the nearest neighbor of every object. To avoid computing every distance pair an index could be constructed. Unfortunately, due to the curse of dimensionality the indexed and the brute force methods are almost equally inefficient. This bring the attention to algorithms computing approximate versions of NNG. The DiSAT is a proximity searching tree. It is hierarchical. The root computes the distances to all objects, and each child node of the root computes the distance to all its subtree recursively. Top levels will have accurate computation of the nearest neighbor, and as we descend the tree this information would be less accurate. If we perform a few rebuilds of the index, taking deep nodes in each iteration, keeping score of the closest known neighbor, it is possible to compute an Approximate NNG (ANNG). Accordingly, in this work we propose to obtain de ANNG by this approach, without performing any search, and we tested this proposal in both synthetic and real world databases with good results both in costs and response quality.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

All Near Neighbor GraphWithout Searching

Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Graph (NNG) consists in finding the nearest neighbor of each object in the collection. Without an index the total cost of NNG is quadratic. Using an index the cost would be sub-quadratic if the search for individual items is sublinear. Unfortunately, due to the so called curse of dimensionality the indexed and the brute force methods are almost equally inefficient. In this paper we present an efficient algorithm to build the Near Neighbor Graph (nNG), that is an approximation of NNG, using only the index construction, without actually searching for objects.Facultad de Informátic