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The paper presents a web based system that aims to improve Web exploration by enabling users to discover knowledge associated to his need. Indeed, in exploratory search, the user is willing not only to find documents relevant with respect to his query but he is also interested in learning, discovering and understanding knowledge on complex and sometimes unknown topics. We rely on DBpedia to explore the semantics of keywords thus suggesting potentially interesting related topics or keywords to the user and enabling user-friendly and intelligent content discovery.

The paper presents an ontological approach for enabling personalized searching framework facilitating the user access to desired contents. Through the ontologies the system will express key entities and relationships describing resources in a formal machine-processable representation. An ontology-based knowledge representation could be used for content analysis and concept recognition, for reasoning processes and for enabling user-friendly and intelligent content retrieval.

The paper presents the guidelines of a project of three Italian Universities (Bologna, Siena, Trento) which aim is to investigate the use of mobile computing technologies to support the learning processes in a University context. The project covers three main areas. The first area is concerned with finding effective models for mobile learning. The second regards the evaluation of learning processes in mobile learning environments. The third focuses on the technological aspects of mobile learning, and on their integration with e-Learning systems, and more generally, with the information systems of the academic institutions. The project has its foundations in the availability of significant experience on e-learning real processes, and on the availability of the source code of an e-learning system developed in previous projects and currently used by different faculties, and of the newer platform that gathers the experience obtained in the past.

The rising auto usage deriving from growth in jobs and residential population is making traffic congestion less tolerable in urban and suburban areas. This results in air pollution, energy waste and unproductive and unpleasant consumption of people’s time. Public transport cannot be the only answer to this increasing transport demand. Car pooling has emerged to be a viable possibility for reducing private car usage in congested areas. Its actual practice requires a suitable information system support and, most important, the capability of effectively solving the underlying combinatorial optimization problem. This paper presents an application of the ANTS approach, one of the approaches which follow the Ant Colony Optimization (ACO) paradigm, to the car pooling optimization problem. Computational results are presented both on datasets derived from the literature about problems similar to car pooling and on real-world car pooling instances.

Ant Colony Optimization is a paradigm for designing combinatorial optimization metaheuristic algorithms, which construct a solution on the basis of information provided both by some standard constructive heuristic and by previously obtained solutions. In this chapter, we present current results obtained by ACO algorithms on several hard combinatorial optimization problems. Furthermore, we describe in more detail a particular ACO algorithm, the ANTS metaheuristic, presenting its general structure and reporting results obtained on the quadratic and on the frequency assignment problems.

The materialization of fragmented views in data warehouses has the objective of improving the system response time for a given workload. It represents a combinatorial optimization problem arising in the logical design of data warehouses which has so far received little attention from the optimization community. This paper describes the application of a metaheuristic approach, namely the ANTS approach, to this problem. In particular, we propose an integer programming formulation of the problem, derive an efficient lower bound and embed it in an ANTS algorithm. Preliminary computational results, obtained on the well-known TPC-D benchmark, are presented.

We describe an approach to image segmentation based on a two-layer module that is executed until a good segmentation is achieved, providing an evolution of previous segmentation results at each execution. The first layer performs a global segmentation of an image of decreasing area at each evolution by adopting a genetic algorithm learning technique to select segmentation parameters that give better results. The second layer provides the input to the next evolution by selecting the segmented regions that need further optimisation. A main goal of our system is to perform the segmentation without using neither ground-truth information nor human judgement. Thus, edge detection is performed to assess the performance of region segmentation and to guide the evolution of segmentation. Experimental results are consistent with what is observed visually.