Reasoning about river basins: WaWO+ revisited

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper characterizes part of an interdisciplinary research effort on Artificial Intelligence (AI) techniques and tools applied to Environmental Decision-Support Systems (EDSS). WaWO+ the ontology we present here, provides a set of concepts that are queried, advertised and used to support reasoning about and the management of urban water resources in complex scenarios as a River Basin. The goal of this research is to increase efficiency in Data and Knowledge interoperability and data integration among heterogeneous environmental data sources (e.g., software agents) using an explicit, machine understandable ontology to facilitate urban water resources management within a River Basin.Peer ReviewedPostprint (author's final draft

tweetStimuli : discovering social structure of influence

Social influence has become a field of study about how people might induce effect on others. Diffusion of information in large networks has been studied to analyze how the information flows over the network producing cascades as a main proxy of influence. For instance, microblogs such as Twitter has allowed to identify and rank influencers based on message propagation (retweets). Different factors of influence on Twitter have been identified such as: audience, interaction, users’ actions and message content. In this paper, a new web application is presented. It allows to study these factors in a temporal order based on the perspective of local influence: given a target user, who influences the user as well as who has been influenced by the user. This application is able to retrieve all retweets and favorites to filter and rank them from different perspectives based on the type of tweets and attributes such as mentions or hashtags, as well as two kind of visualizations: clusters and networks which are the outcome of user behavior by retweeting and marking as favorites

Discovering social structures of local influence by using tweetStimuli

Information diffusion in large-scale networks has been studied to identify the users influence. The influence has been targeted as a key feature either to reach large populations or influencing public opinion. Through the use of micro-blogs, such as Twitter, global influencers have been identified and ranked based on message propagation (retweets). In this paper, a new application is presented, which allows to find first and classify then the local influence on Twitter: who have influenced you and who have been influenced by you. Until now, social structures of tweets’ original authors that have been either retweeted or marked as favourites are unobservable. Throughout this application, these structures can be discovered and they reveal the existence of communities formed by users of similar profile (that are connected among them) interrelated with other similar profile users’ communitiesPeer Reviewe

Gestió i tractament d’aigües residuals

Peer reviewe