Data prefetching is an important technique for overcoming the increasing gap between the processor and memory speeds. Traditonal metrics for prefetching have been shown to be ineffective and misleading. Prefetch taxonomies provide interesting insights into the performance of prefetch algorithms. In this report, we present an analysis of two prefetch algorithms using a prefetch classification ta...

This paper explores the automatic construction of a multilingual Lexical Knowledge Base from pre-existing lexical resources. We present a new and robust approach for linking already existing lexical/semantic hierarchies. We used a constraint satisfaction algorithm (relaxation labeling) to select -among all the candidate translations proposed by a bilingual dictionarythe right English WordNet sy...

This paper presents a combination of algorithms for automatic ontology building based mainly on lexical cooccurrence statistics. We populate an ontology with hypernymy links, thus we refer more specifically to a taxonomy of lexical units (nouns organized by hypernymy relations) rather than an ontology of formally defined concepts. A set of combined statistical procedures produce fragments of ta...

We investigate machine learning methods for automatically integrating objects from different taxonomies into a master taxonomy. This problem is not only currently pervasive on the Web, but is also important to the emerging Semantic Web. A straightforward approach to automating this process would be to build classifiers through machine learning and then use these classifiers to classify objects ...

-A Distributed denial of service (DDoS) attack uses multiple machines operating in concern to attack a network or site. It is the most important security problem for IT managers. These attacks are very simple organized for intruders and hence so disruptive. The detection and defense of this attack has specific importance among network specialists. In this paper a new and smart taxonomy of DDoS ...

Partially specified data are commonplace in many practical applications of machine learning where different instances are described at different levels of precision relative to an attribute value taxonomy (AVT). This paper describes AVT-NBL – a variant of the Naïve Bayes Learning algorithm that effectively exploits user-supplied attribute value taxonomies to construct compact and accurate Naïve...

The objective of this work is the realization of an algorithm to provide a query suggestion feature in order to support the search engine of a commercial web site. Starting from web server logs, our solution creates a model analyzing the queries submitted by the users. Given a submitted query, the system searches the most adequate queries to suggest. Our method implements an already known sessi...

The design of educational software must be based on clear pedagogical objectives. In this article we introduce two interactive assistants, each one intended to assist in a specific greedy algorithm: AMO for the knapsack problem and SEDA for the activity selection problem. We describe their educational objectives by means of the levels in Bloom’s taxonomy and the functions these assistants provi...

Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions is evolving into an important research area. In the past, researchers usually assumed databases were static and items were on a single level to simplify data mining problems. Thus, most of algorithms proposed focused on a single level, and did not utilize prev...