Eunice Ponce de León

Data Mining in Web Applications

The World Wide Web is rapidly emerging as an important medium for commerce as well as for the dissemination of information related to a wide range of topics (e.g., business and government). According to most predictions, the majority of human information will be available on the Web. These huge amounts of data raise a grand challenge, namely, how to turn the Web into a more useful information utility (Garofalakis et al., 1999) . At the moment with the popularity of Internet, people are exhibited to a lot of information that is available for study. Nowadays there is also a great amount of applications and services that are available through Internet as they are seeking, chats, sales, etc., nevertheless much of that information is not useful for many people, but in the area of Data Mining, all the information available in the Internet represents a work opportunity and it is possible to do a lot of analysis on the basis of these with specific purposes. Knowledge Discovery and Data Mining are powerful data analysis tools. The rapid dissemination of these technologies calls for an urgent examination of their social impact. We show an overview of these technologies. The terms “Knowledge Discovery” and “Data Mining” are used to describe the ‘non-trivial extraction of implicit, previously unknown and potentially useful information from data (Wahlstrom & Roddick, 2000). Knowledge discovery is a concept that describes the process of searching on large volumes of data for patterns that can be considered knowledge about the data. The most well-known branch of knowledge discovery is data mining.

Planning and Allocation Tasks in a Multicomputer System as a Multi-objective Problem

In this article we address the task planning and assignment problem in a multicomputer system using architectural 2D mesh. The problem of planning and allocation of tasks to a group of computers consists of several sub-problems that can be made to correspond to functions to optimize.The proposed solution to this problem is; first: establish the identification of distinct parts that are involved, such as; maximizing processor usage, minimize task wait time in the queue and avoid indefinite task delay (starvation). Second: a planning algorithm and an allocation algorithm are implemented through the search engine within the queue, the first algorithm makes a previous planning to the allocation to identify the task lists that fit in the mesh, and the second is a sole variant distribution algorithm to identify the best allocations in the processor mesh through a dynamic quadratic allocation. Finally, our final results are presented; they allow us to see that a previous allocation in the queue and a search engine allocation of the tasks best positions in the available (free) sub meshes, are determining factors for bettering the longevity of the processors and optimize answer time in a multicomputer system.

Evolutionary Multi-Objective Algorithms

The versatility that genetic algorithm (GA) has proved to have for solving different problems, has make it the first choice of researchers to deal with new challenges. Currently, GAs are the most well known evolutionary algorithms, because their intuitive principle of operation and their relatively simple implementation; besides they have the ability to reflect the philosophy of evolutionary computation in an easy and quick way.

Automated Analog Synthesis with an Estimation of the Distribution Algorithm

This paper presents a set of evolutionary mechanisms embedded on an estimation of distribution algorithm (MITEDA-AC) that performs the synthesis of an analog low pass filter. Analog circuits are modeled with linked lists in order to represent and evolve both, topology and sizing. The developed representation mechanism ensures that generated circuits be feasible, and in order to reduce the gap between real circuits and those evolvable, the concept of preferred values was included on representation and generation mechanisms. The algorithm interacts with SPICE to performance evaluation of each individual in the population. MITEDA-AC was inspired by the COMIT because like this, it uses bivariate probability distributions to generate the optimal dependency tree, but without local optimizers. Features integrated in the learning mechanism of this evolvable algorithm, were the number of capacitors, resistors and inductors included in each circuit of the population. This paper describes the algorithm and discusses its results.