FiVO/QStorMan Semantic Toolkit for Supporting Data-Intensive Applications in Distributed Environments

In this paper we present a semantic-based approach for supporting data-intensive applications in distributed environments. The approach is characterized by usage of explicit definition of non-functional quality parameters regarding storage systems, semantic descriptions of the available storage infrastructre and monitoring data concering the infrastructure workload and users operation, along with an implementation of the approach in the form of a toolkit called FiVO/QStorMan. In particular, we describe semantic descriptions, which are exploited in the storage resource provisioning process. In addition, the paper describes results of the performed experimental evaluation of the toolkit, which confirm the effectiveness of the proposed approach for the storage resource provisioning

Learning Agent for a Service-Oriented Context-Aware Recommender System in Heterogeneous Environment

Traditional recommender systems provide users with customized recommendations of products or services. They employ various technologies and algorithms in order to search and select the best options available while taking into account the user's context. Increasingly often, such systems run on devices in heterogeneous environments (including mobile devices) making use of their functionalities: various sensors (e.g. movement, light), wireless data transmission technologies and positioning systems (e.g. GPS) among others. In this paper, we propose an innovative recommender system that determines the best service (including photo and movie conversion) and simultaneously accommodates the context of the device in a heterogeneous environment. The system allows the choice between various service providers that make their resources available using cloud computing as well as having the services performed locally. In order to determine the best possible recommendation for users, we employ the concept of learning agents, which has not been thoroughly researched in connection with recommender systems so far