Personalization systems based upon the analysis of users' surfing behavior imply three phases: data collection, pattern discovery and recommendation. Due to the dimension of log files and high processing time, the first two phases are achieved offline, in a batch process. In this article, we propose Wise Recommender System (WRS), an architecture for adaptive web applications. Within this framework, usage data is implicitly obtained by the data collection submodule. This allows for the extraction of usage data, online and in real time, by using a proactive approach. For the pattern discovery, we efficiently used association rule mining among both frequent and infrequent items. This is due to the fact that the pattern discovery module transactionally processes users' sessions and uses incremental storage of rules. Finally, we will show that WRS can be easily implemented within any web application, thanks to the efficient integration of the three phases into an online transactional process.

Adopting public cloud services implies a loose of control in the management process of the outsourced infrastructure. This raises legal and trust concerns among executives and decision factors regarding confidentiality of data being moved in cloud. We propose a protocol based on a secret sharing scheme in which data is split in optimal chunks, each chunk carrying a minimum informational content relative to the entire informational content of the data set. The file chunks are stored in multiple cloud storage volumes in a way that minimizes the probability for an insider or an attacker to reconstruct the original data set. The splitting heuristic is based on Kullback-Leibler as a metric of chunk optimality while the chunk distribution strategy uses a probabilistic model.

As the cloud technologies are largely studied and mobile technologies are evolving, new directions for development of mobile learning tools deployed on cloud are proposed.. M-Learning is treated as part of the ubiquitous learning paradigm and is a pervasive extension of E-Learning technologies. Development of such learning tools requires specific development strategies for an effective abstracting of pedagogical principles at the software design and implementation level. Current paper explores an interdisciplinary approach for designing and development of cloud based M-Learning tools by mapping a specific development strategy used for educational programs to software prototyping strategy. In order for such instruments to be user effective from the learning outcome point of view, the evaluation process must be rigorous as we propose a metric model for expressing the trainee's overall learning experience with evaluated levels of interactivity, content presentation and graphical user interface usability.

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