Jorge Horacio Doorn

Elicitation and conversion of hidden objects and restrictions in a database schema

Mapping a database schema from one model into another, with a higher semantic capacity, is a current research subject with application in several development fields, such as schema integration and translation, migration from legacy systems and reengineering of poor quality or no-longer accurate data models. Inclusion dependencies are one of the most important concepts in relational databases and they are the key to perform some reengineering of database schemas. Referential integrity restrictions (rir), a particular case of an inclusion constraint, requires that the set of distinct values occurring in some specified column, simple or composite (foreign key), must be a subset of the set of distinct primary key values drawn from the same domain. Pure inclusion dependencies (id), however, may apply between other pairs of attributes also (alternate keys or non-keys). Database schemas containing ids frequently reveal the presence of hidden objects and misrepresented relationships and, as a consequence, increase the effort to develop program applications and maintain the integrity. This work presents a heuristics for the conversion of schemas with ids into equivalent schemas with only rirs. In case some irreducible ids remain, a semantic interpretation of their necessity and maintenance is given.

Static detection of sources of dynamic anomalies in a network of referential integrity restrictions

Under certain circumstances, basic operations over tables in a relational database, where integrity reslxictions such as referential and null restrictions have been specified, may produce unpredictable results, not detectable by means of a static analysis of the schema. When the design includes redundancies or when the set of restrictions is contradictory it is easy to detect and prevent future exrors, but there are situations that require a dynamic analysis. In this paper, the properties of networks of referencial integrity reslfictions that contain irregularities are analyzed, and the mtomalies that may appear when data act~mlization in such environment is done are studied in order to define criteria and develop an algorithm to generate rules for proper handling of inconsistencies.

Low-coupled parallel strategy for Monte Carlo radiation dose calculation

In this work a parallelization technique for Monte Carlo calculation of the radiation absorbed dose is presented. The proposed method reduces the overall time of calculation exploiting the probabilistic nature of the problem. Reducing dose calculation times to meet practical application of use of results is a topic of great interest in the Monte Carlo simulation, dosimetry evaluation and treatment planning. The general problem of dose calculation parallelization was analyzed. The optimal number of calculation units for a given context was also determined.

Time improvement of photoelectric effect calculation for absorbed dose estimation

Ionizing radiation therapy is a very useful tool in cancer treatment. It is very important to determine absorbed dose in human tissue to accomplish an effective treatment. A mathematical model based on affected areas is the most suitable tool to estimate the absorbed dose. Lately, Monte Carlo based techniques have become the most reliable, but they are time expensive. Absorbed dose calculating programs using different strategies have to choose between estimation quality and calculating time. This paper describes an optimized method for the photoelectron polar angle calculation in photoelectric effect, which is significant to estimate deposited energy in human tissue. In the case studies, time cost reduction nearly reached 86%, meaning that the time needed to do the calculation is approximately 1/7 th of the non optimized approach. This has been done keeping precision invariant.