Luigi Grimaudo

Learn More
Network visibility is a critical part of traffic engineering, network management, and security. The most popular current solutions - Deep Packet Inspection (DPI) and statistical classification, deeply rely on the availability of a training set. Besides the cumbersome need to regularly update the signatures, their visibility is limited to classes the(More)
Large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. The automatic analysis of such huge data volume is a challenging task since a large amount of interesting knowledge can be extracted. Association rule mining is an exploratory data(More)
Network visibility is a critical part of traffic engineering, network management, and security. Recently, unsupervised algorithms have been envisioned as a viable alternative to automatically identify classes of traffic. However, the accuracy achieved so far does not allow to use them for traffic classification in practical scenario. In this paper, we(More)
Traffic classification is still today a challenging problem given the ever evolving nature of the Internet in which new protocols and applications arise at a constant pace. In the past, so called behavioral approaches have been successfully proposed as valid alternatives to traditional DPI based tools to properly classify traffic into few and coarse(More)
Frequent generalized itemset mining is a data mining technique utilized to discover a high-level view of interesting knowledge hidden in the analyzed data. By exploiting a taxonomy, patterns are usually extracted at any level of abstraction. However, some misleading high-level patterns could be included in the mined set. This paper proposes a novel(More)
In the era of smart cities huge data volumes are continuously generated and collected, thus prompting the need for efficient and distributed data mining approaches. Generalized itemset mining is an established data mining technique, which entails the discovery of multiple-level patterns hidden in the analyzed data by exploiting analyst-provided taxonomies.(More)
Twitter data has recently been considered to perform a large variety of advanced analysis. Analysis of Twitter data imposes new challenges because the data distribution is intrinsically sparse, due to a large number of messages post every day by using a wide vocabulary. Aimed at addressing this issue, generalized itemsets sets of items at different(More)
YouTube relies on a massively distributed Content Delivery Network (CDN) to stream the billions of videos in its catalogue. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet Service Providers (ISPs), which are compelled to optimize end-users'(More)
Twitter, currently the leading microblogging social network, has attracted a great body of research works. This paper proposes a data analysis framework to discover groups of similar twitter messages posted on a given event. By analyzing these groups, user emotions or thoughts that seem to be associated with specific events can be extracted, as well as(More)
YouTube relies on a massively distributed content delivery network (CDN) to stream the billions of videos in its catalog. Unfortunately, very little information about the design of such CDN is available. This, combined with the pervasiveness of YouTube, poses a big challenge for Internet service providers (ISPs), which are compelled to optimize end-users'(More)