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In this paper, we propose that various keyword-based queries be processed over XML streams in a multi-query processing way. Our algorithms rely on parsing stacks designed for simultaneously matching terms from several distinct queries and use new query indexes to speed up search operations when processing a large number of queries. Besides defining a new problem and novel solutions, we perform experiments in which aspects related to performance and scalability are examined.

Recommender systems have been applied in several areas, including e-Health systems, which refers to information and health services enhanced through technology. However, most studies aim at imposing rules to improve lifestyle, rather than recommending nutrition and physical activities. In this context, this study aims to develop a system for recommending physical activities for hypertensive patients to create opportunities for the patients so they can search for and create a healthy lifestyle. To achieve this goal, we elaborated on a hypertensive user profile model, called HyperModel2PAR, and a physical activity recommender system for hypertensive patients, called HyperRecSysPA. The model resulting from this study is composed of 32 elements divided into three groups, which were used in the modeling of user profiles within the system for generating HyperRecSysPA recommendations. The developed system was validated by physicians who answered a specific questionnaire. As a result, ∼ 75% of the recommendations generated were approved. Therefore, this study has prospective contributions to the literature, since both models obtained conclusive results in the assessments performed.

The user generated content available in online communities is easy to create and consume. Lately, it also became strategically important to companies interested in obtaining population feedback on products, merchandising, etc. One of the most important online communities is Twitter: recent statistics report 65 million new tweets each day. However, processing this amount of data is very costly and a big portion of the content is simply not useful for strategic analysis. Thus, in order to filter the data to be analyzed, we propose a new method for ranking the most influential users in Twitter. Our approach is based on a combination of the user position in networks that emerge from Twitter relations, the polarity of her opinions and the textual quality of her tweets. Our experimental evaluation shows that our approach can successfully identify some of the most influential users and that interactions between users provide the best evidence to determine user influence.