Martín G. Marchetta

A reference framework following a proactive approach for Product Lifecycle Management

Product Lifecycle Management (PLM) has been identified as a key concept within manufacturing industries for improving product quality, time-to-market and costs. Previous works on this field are focused on processes, functions and information models, and those aimed at putting more intelligence on products are related to specific parts of the product lifecycle (e.g. supply chain management, shop floor control). Therefore, there is a lack of a holistic approach to PLM, putting more intelligence on products through the complete lifecycle. In this paper, a PLM framework supported by a proactive approach based on intelligent agents is proposed. The developed model aims at being a first step toward a reference framework for PLM, and complements past works on both product information and business process models (BPM), by putting proactivity on products behavior. An example of an instantiation of the reference framework is presented as a case study.

An artificial intelligence planning approach to manufacturing feature recognition

Within manufacturing, features have been widely accepted as useful concepts, and in particular they are used as an interface between CAD and CAPP systems. Previous research on feature recognition focus on the issues of intersecting features and multiple interpretations, but do not address the problem of custom features representation. Representation of features is an important aspect for making feature recognition more applicable in practice. In this paper a hybrid procedural and knowledge-based approach based on artificial intelligence planning is presented, which addresses both classic feature interpretation and also feature representation problems. STEP designs are presented as case studies in order to demonstrate the effectiveness of the model.

Method to Reduce Complexity and Response Time in a Web Search

Living in times of technological changes that alter our daily activities, involving tasks such as reading the newspaper, following the weather, scheduling a trip, are usually executed after perusal of the gigantic repository of information, commonly known as the World Wide Web. However some problems are still associated with the information found in such a vast amount of information: heterogeneity, availability, distribution, quality and quantity of irrelevant information. Recent work has suggested different ways of grouping similar information sources, trying to give solutions to these problems. However, some domains are more complex than others. For example, a person looking for tourist information, is generally overwhelmed by visiting various websites. This paper proposes the implementation of a method to retrieve and group web information sources, depending on the services they offer; thereby allowing the user to get accurate answers; thus reducing the time and complexity in the search.

FiPaWeb: A method for filtering web pages based on user needs

The rapid evolution of technology and the widespread use of Internet have provided a wealth of information sources available on the Web. These sources present problems: heterogeneity of content, availability of information, lots of irrelevant information. Recent work has suggested different ways to group similar sources of information, trying to solve the problems. However, there are more complex than other domains, where to find the solution is even more complex problem. In this paper, we propose a method to retrieve and group web information sources based on the information provided to allow users to get accurate answers, reducing time and complexity in the search. To validate the proposed model, a case study is presented in the domain of tourism.