Josilene Aires Moreira

MovieMatcher: A Heuristic to Match Movies Based on Metadata from Different Data Sources

With the advent of Web 2.0 and the behavior change which it brought, there are millions of users worldwide contributing to different databases with various forms of data, such as movie ratings, for example. Moreover, the same real-world object (a song, a band or a movie) can be modeled using different ontologies or represented in different ways within the same ontology. Thus, the same film is often described by different attributes in different databases, making it difficult to perform an automatic mapping between those databases. We propose MovieMatcher, which is a heuristic that matches films across different databases using their metadata. After performing 2 experiments with the attempt to match 500 films to IMDb and Rotten Tomatoes databases, MovieMatcher had a success rate of 97.4% and 94.1%, in contrast to an alternative, simpler approach (title exact matching), which had a success rate of 80.8% and 81.9%, respectively.

Vulnerability in the Information Age: What Browsers Reveal

The exponential growth in the use of web services and applications has increased the amount of personal information registered on websites and databases worldwide. Consequently, users are more exposed to vulnerability flaws and more subject to the impact of leaking this information. This article investigates how personal information is handled by three popular browsers: Internet Explorer, Mozilla Firefox and Google Chrome, by analyzing data collected in shared research laboratories. Through the analysis of data stored by browsers on a shared computer laboratory, we found a large number of cookies with important personal information, showing that these browsers can reveal more than the users would like.