Filippo Furfaro

RFID-based systems for object tracking and supply chain management have been emerging since the RFID technology proved effective in monitoring movements of objects. The monitoring activity typically results in huge numbers of readings, thus making the problem of efficiently retrieving aggregate information from the collected data a challenging issue. In fact, tackling this problem is of crucial importance, as fast answers to aggregate queries are often mandatory to support the decision making process. In this regard, a compression technique for RFID data is proposed, and used as the core of a system supporting the efficient estimation of aggregate queries. Specifically, this technique aims at constructing a lossy synopsis of the data over which aggregate queries can be estimated, without accessing the original data. Owing to the lossy nature of the compression, query estimates are approximate, and are returned along with intervals that are guaranteed to contain the exact query answers. The effectiveness of the proposed approach has been experimentally validated, showing a remarkable trade-off between the efficiency and the accuracy of the query estimation.

We address the issue of incorporating a particular yet expressive form of integrity constraints (namely, denial constraints) into probabilistic databases. To this aim, we move away from the common way of giving semantics to probabilistic databases, which relies on considering a unique interpretation of the data, and address two fundamental problems: consistency checking and query evaluation. The former consists in verifying whether there is an interpretation which conforms to both the marginal probabilities of the tuples and the integrity constraints. The latter is the problem of answering queries under a "cautious" paradigm, taking into account all interpretations of the data in accordance with the constraints. In this setting, we investigate the complexity of the above-mentioned problems, and identify several tractable cases of practical relevance.