Urban dengue is common in most countries of the Americas, but has been rare in the United States for more than half a century. In 1999 we investigated an outbreak of the disease that affected Nuevo Laredo, Tamaulipas, Mexico, and Laredo, Texas, United States, contiguous cities that straddle the international border. The incidence of recent cases, indicated by immunoglobulin M antibody serosurvey, was higher in Nuevo Laredo, although the vector, Aedes aegypti, was more abundant in Laredo. Environmental factors that affect contact with mosquitoes, such as air-conditioning and human behavior, appear to account for this paradox. We conclude that the low prevalence of dengue in the United States is primarily due to economic, rather than climatic, factors.

Abstract. Data analysis is needed in connection with query processing, to produce data summary information in the form of rules or assertions that allow semantic query optimisation or direct query answering without consulting the data itself. The goal of an intelligent analyser in this context is to produce robust rules, stable in the presence of data changes, which allow easy rule maintenance as data changes, and provide rapid query reformulation, refutation or answering. It must also limit the rule set to rules useful for query processing.

Abstract. Semantic Query Optimisation makes use of the semantic knowledge of a database (rules) to perform query transformation. Rules are normally learned from former queries fired by the user. Over time, however, this can result in the rule set becoming very large thereby degrading the efficiency of the system as a whole. Such a problem is known as the utility problem. This paper seeks to provide a solution to the utility problem through the use of statistical techniques in selecting and maintaining an optimal rule set. Statistical methods have, in fact, been used widely in the field of Knowledge Discovery to identify and measure relationships between attributes. Here we extend the approach to Semantic Query Optimisation using the Chi-square statistical method which is integrated into a prototype query optimiser developed by the authors. We also present a new technique for calculating Chi-square, which is faster and more efficient than the traditional method in this situation.

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This paper examines the properties of metadata in the form of IF THEN rules which contain two predicates on attributes of a relational database table. For example: a(15 .. 30) ⇒ d(243 .. 271), which means "if the value of attribute 'a' in a tuple is in the range 15 to 30 then the value of attribute 'd' will be in the range 243 to 271." Metadata of this kind is useful in Semantic Query Optimisation and Remote Cache Management. The two predicates (antecedent and consequent) in each rule are Selection Conditions or constraints of the type found in database queries. Each condition therefore denotes a subset of a database table. Rules can be cascaded, using subrange containment as the link between successive rules. The set of rules can therefore be regarded as a set of edges in a Condition Dependency Graph, and using the rule-set is path discovery in the graph. The purpose of the current paper is to introduce some of the properties of attribute-pair range rules.