O setor de turismo e os arranjos produtivos locais no estado de São Paulo: especificidades e interdependências

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Best after rain : waterfall discharges and the tourist experience

Waterfalls attract tourists because they are aesthetically appealing landscape features that are not part of everyday experience. It is generally understood that falls are usually seen at their best when there is a copious flow of water, especially after heavy rain. Guidebooks often contain this observation when referring to waterfalls, sometimes warning readers that the flow may be severely reduced during dry periods. Indeed, many visitors are disappointed when they see falls at such times. Some are saddened when the discharge of a waterfall has been depleted by the abstraction of water upstream for power generation or other purposes. While, for those in search of the Sublime or merely the superlative, size is often important, small waterfalls can give great pleasure to lovers of landscape beauty. According to guidebooks, however, even these falls are usually best seen after rain. Drawing on tourist and travel literature and personal journals from the eighteenth century to the present, and with reference to examples from different parts of the world, this paper discusses the importance of discharge in the tourist experience of waterfalls.

Personalised ontology learning and mining for web information gathering

Over the last decade, the rapid growth and adoption of the World Wide Web has further exacerbated user needs for e±cient mechanisms for information and knowledge location, selection, and retrieval. How to gather useful and meaningful information from the Web becomes challenging to users. The capture of user information needs is key to delivering users' desired information, and user pro¯les can help to capture information needs. However, e®ectively acquiring user pro¯les is di±cult. It is argued that if user background knowledge can be speci¯ed by ontolo- gies, more accurate user pro¯les can be acquired and thus information needs can be captured e®ectively. Web users implicitly possess concept models that are obtained from their experience and education, and use the concept models in information gathering. Prior to this work, much research has attempted to use ontologies to specify user background knowledge and user concept models. However, these works have a drawback in that they cannot move beyond the subsumption of super - and sub-class structure to emphasising the speci¯c se- mantic relations in a single computational model. This has also been a challenge for years in the knowledge engineering community. Thus, using ontologies to represent user concept models and to acquire user pro¯les remains an unsolved problem in personalised Web information gathering and knowledge engineering. In this thesis, an ontology learning and mining model is proposed to acquire user pro¯les for personalised Web information gathering. The proposed compu- tational model emphasises the speci¯c is-a and part-of semantic relations in one computational model. The world knowledge and users' Local Instance Reposito- ries are used to attempt to discover and specify user background knowledge. From a world knowledge base, personalised ontologies are constructed by adopting au- tomatic or semi-automatic techniques to extract user interest concepts, focusing on user information needs. A multidimensional ontology mining method, Speci- ¯city and Exhaustivity, is also introduced in this thesis for analysing the user background knowledge discovered and speci¯ed in user personalised ontologies. The ontology learning and mining model is evaluated by comparing with human- based and state-of-the-art computational models in experiments, using a large, standard data set. The experimental results are promising for evaluation. The proposed ontology learning and mining model in this thesis helps to develop a better understanding of user pro¯le acquisition, thus providing better design of personalised Web information gathering systems. The contributions are increasingly signi¯cant, given both the rapid explosion of Web information in recent years and today's accessibility to the Internet and the full text world.

A Personalized Ontology Model for Web Information Gathering

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.

Automated gathering of Web information: An in-depth examination of agents interacting with search engines

The Web has become a worldwide repository of information which individuals, companies, and organizations utilize to solve or address various information problems. Many of these Web users utilize automated agents to gather this information for them. Some assume that this approach represents a more sophisticated method of searching. However, there is little research investigating how Web agents search for online information. In this research, we first provide a classification for information agent using stages of information gathering, gathering approaches, and agent architecture. We then examine an implementation of one of the resulting classifications in detail, investigating how agents search for information on Web search engines, including the session, query, term, duration and frequency of interactions. For this temporal study, we analyzed three data sets of queries and page views from agents interacting with the Excite and AltaVista search engines from 1997 to 2002, examining approximately 900,000 queries submitted by over 3,000 agents. Findings include: (1) agent sessions are extremely interactive, with sometimes hundreds of interactions per second (2) agent queries are comparable to human searchers, with little use of query operators, (3) Web agents are searching for a relatively limited variety of information, wherein only 18% of the terms used are unique, and (4) the duration of agent-Web search engine interaction typically spans several hours. We discuss the implications for Web information agents and search engines.

Tourist use of mopeds in Queensland

Tourist use of mopeds in Queensland is encouraged by licensing regulations permitting moped riding for car licence holders, who may lack prior knowledge or experience of moped or motorcycle use. Using official crash and registration data, this research examines moped use by tourists, identified as crash-involved riders holding an interstate or overseas licence. Tourists were more likely to be younger, female, in single vehicle crashes, and deemed at fault than Queensland licence holders. Potential safety issues include poor riding skills, inexperience, inattention and lack of protective clothing. Moped rental companies play an important role in managing client crash and injury risks. These risks could also be reduced through introduction of more stringent licensing requirements, though this may be detrimental to moped rental companies as well as to tourist mobility and enjoyment. The discussion considers the relevance of adventure tourism perspectives and theory to the use of mopeds by tourists.