Improvements in or relating to inhibition of corrosion by natural waters in cooling systems

This paper presents an approach for ranking semantic Web service advertisements with respect to a service request. The use of recall and precision is proposed as suitable measures for determining the degree of match between the request and the advertisement. Ranking is based on the use of the domain ontology to infer the semantic similarity between the parameters of the request and the advertisement. The proposed approach is applicable to several types of ontologies, ranging from simple taxonomies to highly expressive ontologies, such as OWL ontologies

Australian swimmer Miss Kitty Mackay, New South Wales, 7 March 1932 [picture].

Title devised from accompanying information where available.; Part of the: Fairfax archive of glass plate negatives.; Fairfax number: 3415.; Also available online at: http://nla.gov.au/nla.pic-vn6219999; Acquired from Fairfax Media, 2012

Analyzing and predicting spatial crime distribution using crowdsourced and open data

Data analytics has an ever increasing impact on tackling various societal challenges. In this article, we investigate how data from several heterogeneous online sources can be used to discover insights and make predictions about the spatial distribution of crime in large urban environments. A series of important research questions is addressed, following a purely data-driven approach and methodology. First, we examine how useful different types of data are for the task of crime levels prediction, focusing especially on how prediction accuracy can be improved by combining data from multiple information sources. To that end, we not only investigate prediction accuracy across all individual areas studied, but also examine how these predictions affect the accuracy of identified crime hotspots. Then, we look into individual features, aiming to identify and quantify the most important factors. Finally, we drill down to different crime types, elaborating on how the prediction accuracy and the importance of individual features vary across them. Our analysis involves six different datasets, from which more than 3,000 features are extracted, filtered, and used to learn models for predicting crime rates across 14 different crime categories. Our results indicate that combining data from multiple information sources can significantly improve prediction accuracy. They also highlight which features affect prediction accuracy the most, as well as for which particular crime categories the predictions are more accurate. © 2018 ACM

Graph-based matching of composite OWL-S services

International audienceExisting techniques for Web service discovery focus mainly on matching functional parameters of atomic services, such as inputs and outputs. However, one of the main advantages of Web services is that they are often composed into more complex processes to achieve a given goal. Applying such techniques in these cases, ignores the workflow structure of the composite process, and therefore may produce matches that are not very accurate. To overcome this limitation, we propose in this paper a graph-based method for matching composite services, that are semantically described as OWL-S processes. We propose a graph representation of composite OWL-S processes and we introduce a matching algorithm that performs comparisons not only at the level of individual components but also at the structural level, taking into consideration the control flow among the atomic components. We also report our preliminary results of our experimental evaluation