Abstract
Multimedia retrieval brings new challenges, mainly derived from the mismatch between the level of the user interaction-high-level concepts, and that of the automatically processed descriptors-low-level features. The effective use of the low-level descriptors is therefore mandatory. Many data structures have been proposed for managing the representation of multidimensional descriptors, each geared toward efficiency in some set of basic operations. The paper introduces a highly parametrizable structure called the BitMatrix, along with its search algorithms. The BitMatrix is compared with existing methods, all implemented in a common framework. The tests have been performed on two datasets, with parameters covering significant ranges of values. The BitMatrix has proved to be a robust and flexible structure that can compete with other methods for multidimensional descriptor indexing.

Abstract
Usage analysis of a Web Information System is a valuable help to predict user needs, to assess system's impact and to guide to its improvement. This is usually done analysing clickstreams, a low-level approach, with huge amounts of data that calls for data warehouse techniques. This paper presents a dimensional model to monitor user behaviour in Higher Education Web Information Systems and an architecture for the extraction, transformation and load process. These have been applied in the development of a data warehouse to monitor the use of SIGARRA, the University of Porto's Higher Education Web Information System. The efficiency and effectiveness of this monitorization method were confirmed by the knowledge extracted from a 3 month period analysis. A brief description of the main results and recommendations are also described.

Abstract
In real-world information retrieval systems, the underlying document collection is rarely stable or definitive. This work is focused on the study of signals extracted from the content of documents at different points in time for the purpose of weighting individual terms in a document. The basic idea behind our proposals is that terms that have existed for a longer time in a document should have a greater weight. We propose 4 term weighting functions that use each document's history to estimate a current term score. To evaluate this thesis, we conduct 3 independent experiments using a collection of documents sampled from Wikipedia. In the first experiment, we use data from Wikipedia to judge each set of terms. In a second experiment, we use an external collection of tags from a popular social bookmarking service as a gold standard. In the third experiment, we crowdsource user judgments to collect feedback on term preference. Across all experiments results consistently support our thesis. We show that temporally aware measures, specifically the proposed revision term frequency and revision term frequency span, outperform a term-weighting measure based on raw term frequency alone.

Abstract

Abstract

Abstract