We apply a more realistic cost model than has often been used in the past to the problems of: (1) implementing join algorithms, (2) implementing relational operations which make use of multi-attribute hash les, and (3) creating better multi-attribute hash le organisations using knowledge of the queries which will be asked. This results in a substantial improvement in performance. For some probl...

In this paper we propose the double auction allocation models for computational grids, and two double auction algorithms for resource allocation: Single Price Double Auction algorithm (SPDA), and Multiple Attribute Double Auction algorithm (MADA). The fairness and comprehensive performance are introduced into the traditional double auction models to evaluate impact of price, speed and storage o...

One of the most important factors in quality improvement of transportation, communication, accommodation, utilities, etc is urban infrastructures development. Certainly, with the intention of achieving the goal, approaches to urban infrastructures management must be ranked by Scientific and logical evaluation and calculation. This paper presents evaluation of governmental, private, and public–p...

Knowledge transfer between object classes has been identified as an important tool for scalable recognition. However, determining which knowledge to transfer where remains a key challenge. While most approaches employ varying levels of human supervision, we follow the idea of mining linguistic knowledge bases to automatically infer transferable knowledge. In contrast to previous work, we explic...

BACKGROUND The entity-attribute-value representation with classes and relationships (EAV/CR) provides a flexible and simple database schema to store heterogeneous biomedical data. In certain circumstances, however, the EAV/CR model is known to retrieve data less efficiently than conventionally based database schemas. OBJECTIVE To perform a pilot study that systematically quantifies performanc...

Rough set is a valid mathematical tool that deals with uncertain, vague and incomplete information of decision systems. Attribute reduction is one of the issues in rough set theory, and core attributes are indispensable in the process of attribute reduction. Recently, some researchers proposed the method of binary discernibility matrix to compute the results of attribute reduction, which can no...

Attribute reduction is an important inductive learning issue addressed by the Rough Sets society. Most existing works on this issue use the minimal attribute bias, i.e., searching for reducts with the minimal number of attributes. But this bias does not work well for datasets where different attributes have different sizes of domains. In this paper, we propose a more reasonable strategy called ...

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the per...