Un método de acceso espacio-temporal restringido a redes fijas

Con el rápido avance tecnológico en computación móvil, realizar el seguimiento de los cambios de las posiciones y/o formas de los objetos, se hace cada vez más necesario. Actualmente, un gran rango de aplicaciones utiliza bases de datos que representan información de objetos en movimiento. El principal objetivo de las mismas es almacenar y recuperar de manera eficiente estas posiciones, para lo cual se necesitan índices. Los índices existentes tratan objetos en movimiento que se mueven en un escenario sin restricciones. El escenario restringido a redes es un caso especial e importante, donde el interés reside en las posiciones de los objetos respecto a la red y no a su referencia bidimensional. Por ejemplo, algunas aplicaciones son autos que se mueven en rutas, trenes en vías férreas, entre otras. En nuestra investigación nos abocamos al estudio métodos de acceso para objetos cuyo movimiento está restringido a redes, con el objetivo de diseñar e implementar un método de acceso espacio-temporal. Nuestra propuesta cuenta con la habilidad de almacenar información histórica y actual acerca de las posiciones de los objetos que se mueven sobre redes fijas. Además, potencia el conjunto original de consultas y brinda nuevos lineamientos de investigación en el tema.Eje: Ingeniería de software y base de datosRed de Universidades con Carreras en Informática (RedUNCI

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature

I+MON-TREE: índice espacio-temporal para objetos en movimiento

Con la proliferación de la computación móvil, la habilidad de indexar eficientemente los movimientos de los objetos móviles es cada vez más importante. Actualmente, variadas aplicaciones utilizan bases de datos que mantienen información de objetos en movimiento. El principal objetivo de las mismas es almacenar y recuperar de manera eficiente los movimientos realizados, para lo cual se necesita contar con índices. Los objetos en movimiento pueden desplazarse en escenarios diversos, con y sin restricciones. El escenario restringido a redes fijas es un caso especial, donde el interés reside particularmente en las posiciones de los objetos en la red y no en una referencia bidimensional. Por ejemplo, algunas aplicaciones son autos que se mueven en rutas, trenes en vías férreas, entre otras. En este artículo presentamos nuestra propuesta de un método de acceso espacio-temporal restringido a redes fijas, llamado I+MON-Tree. El mismo cuenta con la habilidad de mantener información histórica y actual acerca de las posiciones de los objetos que se mueven sobre las redes fijas. Además, se pueden resolver los tipos de consultas más requeridos en estos casos, tales como Time Slice, Rango, Ventana y Trayectoria, con un buen desempeño en el uso de los recursos, y fundamentalmente, se puede sintetizar todo en un métodoWith the proliferation of the mobile computing, the ability to index efficiently the movements of mobile objects becomes important. Actually, varied applications to use databases that to maintain information of objects in movement. The main objective of the same is to store and to recover of efficient way the movements realized, for which it is need to count with the indexes. The objects in movement can to move in divers scenes, with or without restrictions. The restrict stage to fixed networks is a special case, where the interest to reside particularly in the positions of the objects in the network and not in a two-dimensional reference. For example, some applications are cars that the move in routs, trains in railway amongst. In this article, we present our proposal of spatiotemporal access method restrict to fixed networks, called I+MON-Tree. The same account with the ability of to maintain the historic and present information about the positions of the objects that move on the fixed networks. Besides, it is can to solve the types of queries more required in this cases, just as Time Slice, Range, Window and Trajectory, with a good perform in the use of the resources and fundamentally can synthesize all in a method.III Workshop de Ingeniería de Software y Bases de Datos (WISBD)Red de Universidades con Carreras en Informática (RedUNCI

I+MON-TREE: índice espacio-temporal para objetos en movimiento

Con la proliferación de la computación móvil, la habilidad de indexar eficientemente los movimientos de los objetos móviles es cada vez más importante. Actualmente, variadas aplicaciones utilizan bases de datos que mantienen información de objetos en movimiento. El principal objetivo de las mismas es almacenar y recuperar de manera eficiente los movimientos realizados, para lo cual se necesita contar con índices. Los objetos en movimiento pueden desplazarse en escenarios diversos, con y sin restricciones. El escenario restringido a redes fijas es un caso especial, donde el interés reside particularmente en las posiciones de los objetos en la red y no en una referencia bidimensional. Por ejemplo, algunas aplicaciones son autos que se mueven en rutas, trenes en vías férreas, entre otras. En este artículo presentamos nuestra propuesta de un método de acceso espacio-temporal restringido a redes fijas, llamado I+MON-Tree. El mismo cuenta con la habilidad de mantener información histórica y actual acerca de las posiciones de los objetos que se mueven sobre las redes fijas. Además, se pueden resolver los tipos de consultas más requeridos en estos casos, tales como Time Slice, Rango, Ventana y Trayectoria, con un buen desempeño en el uso de los recursos, y fundamentalmente, se puede sintetizar todo en un métodoWith the proliferation of the mobile computing, the ability to index efficiently the movements of mobile objects becomes important. Actually, varied applications to use databases that to maintain information of objects in movement. The main objective of the same is to store and to recover of efficient way the movements realized, for which it is need to count with the indexes. The objects in movement can to move in divers scenes, with or without restrictions. The restrict stage to fixed networks is a special case, where the interest to reside particularly in the positions of the objects in the network and not in a two-dimensional reference. For example, some applications are cars that the move in routs, trains in railway amongst. In this article, we present our proposal of spatiotemporal access method restrict to fixed networks, called I+MON-Tree. The same account with the ability of to maintain the historic and present information about the positions of the objects that move on the fixed networks. Besides, it is can to solve the types of queries more required in this cases, just as Time Slice, Range, Window and Trajectory, with a good perform in the use of the resources and fundamentally can synthesize all in a method.III Workshop de Ingeniería de Software y Bases de Datos (WISBD)Red de Universidades con Carreras en Informática (RedUNCI

Improving Performance of Spatial Network Queries

Spatial network queries, for example KNN or range, operate on systems where objects are constrained to locations on a network. Current spatial network query algorithms rely on forms of network traversal which have a high complexity proportional to the size of the network making, them poor for large real-world networks. In this thesis, an alternative method of approximating the results of spatial network queries with a high level of accuracy is introduced. Distances between network points are stored in an M-Tree index, a balanced tree index where metric distance determines data ordering. The M-Tree uses the chessboard metric on network points embedded in a higher dimensional space using tRNE. Using the M-Tree both KNN and range queries are computed more efficiently than network traversal. Error rates of the M-Tree are low, with accuracies of 97% possible on KNN queries and perfect accuracy with 2% extra results on range queries

Dynamic-parinet (D-parinet) : indexing present and future trajectories in networks

While indexing historical trajectories is a hot topic in the field of moving objects (MO) databases for many years, only a few of them consider that the objects movements are constrained. DYNAMIC-PARINET (D-PATINET) is designed for capturing of trajectory data flow in multiple discrete small time interval efficiently and to predict a MO’s movement or the underlying network state at a future time. The cornerstone of D-PARINET is PARINET, an efficient index for historical trajectory data. The structure of PARINET is based on a combination of graph partitioning and a set of composite B+-tree local indexes tuned for a given query load and a given data distribution in the network space. D-PARINET studies continuous update of trajectory data and use interpolation to predict future MO movement in the network. PARINET and D-PARINET can easily be integrated into any RDBMS, which is an essential asset particularly for industrial or commercial applications. The experimental evaluation under an off-the-shelf DBMS using simulated traffic data shows that DPARINET is robust and significantly outperforms the R-tree based access methods

Linking Moving Object Databases with Ontologies

This work investigates the supporting role of ontologies for supplementing the information contained in moving object databases. Details of the spatial representation as well as the sensed location of moving objects are frequently stored within a database schema. However, this knowledge lacks the semantic detail necessary for reasoning about characteristics that are specific to each object. Ontologies contribute semantic descriptions for moving objects and provide the foundation for discovering similarities between object types. These similarities can be drawn upon to extract additional details about the objects around us. The primary focus of the research is a framework for linking ontologies with databases. A major benefit gained from this kind of linking is the augmentation of database knowledge and multi-granular perspectives that are provided by ontologies through the process of generalization. Methods are presented for linking based on a military transportation scenario where data on vehicle position is collected from a sensor network and stored in a geosensor database. An ontology linking tool, implemented as a stand alone application, is introduced. This application associates individual values from the geosensor database with classes from a military transportation device ontology and returns linked value-class pairs to the user as a set of equivalence relations (i.e., matches). This research also formalizes a set of motion relations between two moving objects on a road network. It is demonstrated that the positional data collected from a geosensor network and stored in a spatio-temporal database, can provide a foundation for computing relations between moving objects. Configurations of moving objects, based on their spatial position, are described by motion relations that include isBehind and inFrontOf. These relations supply a user context about binary vehicle positions relative to a reference object. For example, the driver of a military supply truck may be interested in knowing what types of vehicles are in front of the truck. The types of objects that participate in these motion relations correspond to particular classes within the military transportation device ontology. This research reveals that linking a geosensor database to the military transportation device ontology will facilitate more abstract or higher-level perspectives of these moving objects, supporting inferences about moving objects over multiple levels of granularity. The details supplied by the generalization of geosensor data via linking, helps to interpret semantics and respond to user questions by extending the preliminary knowledge about the moving objects within these relations

Moving Objects Indexing

Tato práce se zaměřuje na navržení vhodného indexování pohybujících se objektů. S rozšířením mobilních zařízení je potřeba spravovat rozsáhlé množiny časoprostorových dat. Práce zahrnuje problematiku časoprostorových dat a základní obecné přístupy indexování těchto dat. Dále ukazuje podporu prostorových dat systému Oracle. Pohyb je typicky reprezentován trajektorií ve dvou dimenzionálním prostoru se složkou času ve třetí dimenzi. Součástí práce jsou experimenty v databázovém systému Oracle na uměle vygenerovaných datech.This work is aimed for proposing acceptable indexing of moving objects. With the enlargement of mobile computing it is needed to manage large sets of spatiotemporal data. We introduce the problem of spatiotemporal data and basic general approaches of indexing these data. Further, we show support of spatial data in Oracle. The movement is typically represented as trajectory in two dimensional space with temporal component in third dimension. The thesis contains experiments performed in database Oracle on artificially generate data.

Enabling near-term prediction of status for intelligent transportation systems: Management techniques for data on mobile objects

Location Dependent Queries (LDQs) benefit from the rapid advances in communication and Global Positioning System (GPS) technologies to track moving objects\u27 locations, and improve the quality-of-life by providing location relevant services and information to end users. The enormity of the underlying data maintained by LDQ applications - a large quantity of mobile objects and their frequent mobility - is, however, a major obstacle in providing effective and efficient services. Motivated by this obstacle, this thesis sets out in the quest to find improved methods to efficiently index, access, retrieve, and update volatile LDQ related mobile object data and information. Challenges and research issues are discussed in detail, and solutions are presented and examined. --Abstract, page iii