This article discusses the ICT literacy of secondary education teachers in Greece. Regardless of their specialty, a representable sample of 700 in-service teachers from 283 secondary education schools throughout Greece, participated in our questionnaire survey that was conducted from December 2017 until June 2018. The teachers were questioned about their familiarity with personal computer and smartphone use, whether they use ICT devices in the classroom, while also reporting any obstacles they face in order to implement ICT in the educational process. The data collected enabled us to review the teachers’ ICT literacy. The ICT literacy definition we endorse is the one offered by ETS (Educational Testing Service) (2007); “ICT literacy is using digital technology, communications tools, and/or networks to access, manage, integrate, evaluate, and create information in order to function in a knowledge society”. The findings of the research showed that the majority of Greek teachers are skilled ICT users, embracing their implementation in their teaching interventions, although ICT related Greek policies are contradictory.

A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters.

We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries, nearest neighbors, spatial skylines, and reverse nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.

A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, consider a real estate agency office that holds a database with available flats for lease. A customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within a distance range from them. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Our methods are experimentally evaluated for a wide range of problem settings.