Profiling Attitudes for Personalized Information Provision

PAROS is a generic system under design whose goal is to offer personalization, recommendation, and other adaptation services to information providing systems. In its heart lies a rich user model able to capture several diverse aspects of user behavior, interests, preferences, and other attitudes. The user model is instantiated with profiles of users, which are obtained by analyzing and appropriately interpreting potentially arbitrary pieces of user-relevant information coming from diverse sources. These profiles are maintained by the system, updated incrementally as additional data on users becomes available, and used by a variety of information systems to adapt the functionality to the users’ characteristics

Heuristic algorithms for similar configuration retrieval in spatial databases

Abstract. The search for similar configurations is an important research topic for content-based image retrieval in G.I.S. and spatial databases. Due to the complexity of the problem, finding the fittest solution in a large database is computationally intractable. Our work is focused on designing, implementing and experimentally evaluating two heuristic algorithms, an evolutionary and a hill-climbing one, that provide an approximate solution. With the use of spatial indexes we manage to efficiently deal with considerably large queries. We utilize a similarity framework that addresses topological, directional and distance relations. In this framework the problem of retrieving similar configurations is defined as a binary constraint satisfaction problem. Our work complements the existing work on similarity retrieval with two efficient, stochastic, algorithms.

Adaptive Compression for Fast Scans on String Columns

State-of-the-art OLAP systems tend to use columnar data representations, as these are both suitable for analytics and amenable to compression. Local dictionary value encoding has been shown to achieve high compression rates for string columns while still allowing fast filtered scans. In this paper, we argue that the effectiveness and efficiency of local dictionary compression is limited by data repetition across file blocks and by dictionary look-ups inside each block during filtered scan execution. To address this problem, we introduce an adaptive compression technique that is based on differential dictionaries and targets both storage efficiency and query performance. The proposed scheme reduces dramatically the need to store repeated values across different file blocks and significantly accelerates read operations by reducing the time needed for dictionary look-ups. A preliminary set of experiments has given very promising results, showing that, in many cases, the proposed new dictionary compression scheme is much more efficient than existing techniques, occasionally up to an order of magnitude