MLPQ: A LINEAR CONSTRAINT DATABASE SYSTEM WITH AGGREGATE OPERATORS

In this project report, I will discuss a Multiple Linear Programming Query (MLPQ) system and the theoretical background of this system.The MPLQ system is developed to solve some realistic problems involving both linear programming (UP) techniques and linear constraint databases (LCDBs) theory. The MLPQ system is aimed at providing a mechanism of bridging these two important areas. system basically consists of three parts which are a linear constraint database, an LP solver, and an interface between the LCDB and the LP solver. The LCDB of the MLPQ system contains multiple linear programming problems. The LP solver used in the MPLQ is an implementation Of the SIMPLEX method. An important feature of the MLPQ system is that it can handle the SQL aggregate Operators, such as minimum Min, maximum Max, summation Sum, and average Avg. The MLPQ system provides an efficient way of evaluation of aggregate operators for linear constraint databases

Constraint Databases and Geographic Information Systems

Constraint databases and geographic information systems share many applications. However, constraint databases can go beyond geographic information systems in efficient spatial and spatiotemporal data handling methods and in advanced applications. This survey mainly describes ways that constraint databases go beyond geographic information systems. However, the survey points out that in some areas constraint databases can learn also from geographic information systems

07212 Abstracts Collection -- Constraint Databases, Geometric Elimination ang Geographic Information Systems

From 20.05. to 25.05., the Dagstuhl Seminar 07212 ``Constraint Databases, Geometric Elimination and Geographic Information Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

User Interface Improvement for MLPQ System

This thesis describes the experience of migrating the MLPQ constraint database system, a complex standalone Multiple Document Interface (MDI) application, to a server-based remote accessible application. Centralized, standalone MDI application is a common style for personal software products in Windows. For a database management system, server-based, thin-client computing is a more popular infrastructure. Migrating an existing standalone constraint database application to be a web accessible constraint database server is the main goal of this thesis. This migration process provides a method for the constraint database system to collaborate with other specific applications. We rebuild the desktop MLPQ constraint database system to be a web constraint database server. The analysis method, design model and sample codes in this thesis can be considered as a successful development sample for other development of migrating a desktop MDI system to a remote accessible web service application. We also design and implement a function that finds the complement of a 2-D geometric constraint relation. Advisor: Peter Reves

Efficient traffic congestion estimation using multiple spatio-temporal properties

Traffic estimation is an important issue to analyze the traffic congestion in large-scale urban traffic situations. Recently, many researchers have used GPS data to estimate traffic congestion. However, how to fuse the multiple data reasonably and guarantee the accuracy and efficiency of these methods are still challenging problems. In this paper, we propose a novel method Multiple Data Estimation (MDE) to estimate the congestion status in urban environment with GPS trajectory data efficiently, where we estimate the congestion status of the area through utilizing multiple properties, including density, velocity, inflow and previous status. Among them, traffic inflow and previous status (combination of time and space factors) are not both used in other existing methods. In order to ensure the accuracy and efficiency, we apply dynamic weights of data and parameters in MDE method. To evaluate our methods, we apply it on large-scale taxi GPS data of Beijing and Shanghai. Extensive experiments on these two real-world datasets demonstrate the significant improvements of our method over several state-of-the-art methods

Strategic directions in constraint programming

An abstract is not available

ANÁLISIS DE DESEMPEÑO DE ALGORITMOS DE REGRESIÓN USANDO SCIKIT-LEARN

Machine learning and deep learning are currently áreas of extremely active research, since they allow projections and estimates of variables based on historical data and the execution of mathematical algorithms; These algorithms are specialized to performregressions, classification, prediction or other applications where they are used large volumes of data and decisions with a high margin of precision are required [1]. The large amount of data available today provides great opportunities and the transformative potential of different sectors such as medicine, banking, engineering, sports, among others; but they also present great challenges in the use information, since a poor or erroneous analysis of the data leads to to wrong decision making. As data grows, learning deep takes a leading role in the analysis and solution of problems with a high degree of complexity that in a natural environment are not easy to solve because they present nuances that only with the use of deep learning algorithms can be observed. [two]. Improvements in computational power, large volumes of information, fast data storage and parallelization have contributed to the analysis and prediction of Big Data in areas such as price prediction, análisis of medical images, traffic control and even the study of the performance of the football team, among many others. With all of the above, an idea of ​​the current importance of this area of ​​science is given and how pertinent it is to work on this topicEl aprendizaje automático y el aprendizaje profundo son actualmente áreasde investigación extremadamente activas, ya que permiten proyecciones yestimaciones de variables basadas en datos históricos y la ejecución de algoritmosmatemáticos; estos algoritmos están especializados para realizarregresiones, clasificación, predicción u otras aplicaciones donde se utilizangrandes volúmenes de datos y se requieren decisiones con un alto margen de precisión[1]. La gran cantidad de datos disponibles en la actualidad brinda grandes oportunidadesy el potencial transformador de diferentes sectores como medicina, banca,ingeniería, deportes, entre otros; pero también presentan grandes desafíos en el usoefectivo de la información, ya que un análisis deficiente o erróneo de los datos conducea una toma de decisiones equivocada. A medida que los datos crecen, el aprendizajeprofundo toma un papel protagonista en el análisis y solución de problemas con unalto grado de complejidad que en un entorno natural no son fáciles de resolver porquepresentan matices que solo con el uso de algoritmos de aprendizaje profundo se puedenobservar. [2]. Las mejoras en el poder computacional, los grandes volúmenes deinformación, el almacenamiento rápido de datos y la paralelización han contribuidoal análisis y la predicción de Big Data en áreas como la predicción de precios, el análisisde imágenes médicas, el control del tráfico e incluso el estudio del rendimiento delequipo de fútbol, entre muchas otras. Con todo lo anterior, se da una idea de la importanciaactual de esta área de la ciencia y lo pertinente que es trabajar en este tema [3]

Tractability Frontiers in Probabilistic Team Semantics and Existential Second-Order Logic over the Reals

Peer reviewe

Dagstuhl News January - December 2007

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic