Anomaly detection mechanisms to find social events using cellular traffic data

The design of new tools to detect on-the-fly traffic anomaly without scalability problems is a key point to exploit the cellular system for monitoring social activities. To this goal, the paper proposes two methods based on the wavelet analysis of the cumulative cellular traffic. The utilisation of the wavelets permits to easily filter “normal” traffic anomalies such as the periodic trends present in the cellular traffic. The two presented approaches, denoted as Spatial Analysis (SA) and Time Analysis (TA), differ on how they consider the spatial information of the traffic data. We examine the performance of the considered algorithms using cellular traffic data acquired from one the most important Italian Mobile Network Operator in the city of Milan throughout December 2013. The results highlight the weak points of TA and some important features of SA. Both approaches overcome the performance of one reference algorithm present in literature. The strategy used in the SA emerges as the most suitable for exploiting the spatial correlation when we aim at the detection of the traffic anomaly focused on the localisation of social events

Temporal Locality in Today's Content Caching: Why it Matters and How to Model it

The dimensioning of caching systems represents a difficult task in the design of infrastructures for content distribution in the current Internet. This paper addresses the problem of defining a realistic arrival process for the content requests generated by users, due its critical importance for both analytical and simulative evaluations of the performance of caching systems. First, with the aid of YouTube traces collected inside operational residential networks, we identify the characteristics of real traffic that need to be considered or can be safely neglected in order to accurately predict the performance of a cache. Second, we propose a new parsimonious traffic model, named the Shot Noise Model (SNM), that enables users to natively capture the dynamics of content popularity, whilst still being sufficiently simple to be employed effectively for both analytical and scalable simulative studies of caching systems. Finally, our results show that the SNM presents a much better solution to account for the temporal locality observed in real traffic compared to existing approaches.Comment: 7 pages, 7 figures, Accepted for publication in ACM Computer Communication Revie

Unravelling the Impact of Temporal and Geographical Locality in Content Caching Systems

To assess the performance of caching systems, the definition of a proper process describing the content requests generated by users is required. Starting from the analysis of traces of YouTube video requests collected inside operational networks, we identify the characteristics of real traffic that need to be represented and those that instead can be safely neglected. Based on our observations, we introduce a simple, parsimonious traffic model, named Shot Noise Model (SNM), that allows us to capture temporal and geographical locality of content popularity. The SNM is sufficiently simple to be effectively employed in both analytical and scalable simulative studies of caching systems. We demonstrate this by analytically characterizing the performance of the LRU caching policy under the SNM, for both a single cache and a network of caches. With respect to the standard Independent Reference Model (IRM), some paradigmatic shifts, concerning the impact of various traffic characteristics on cache performance, clearly emerge from our results.Comment: 14 pages, 11 Figures, 2 Appendice

A Vocabulary for Growth: Topic Modeling of Content Popularity Evolution

In this paper, we present a novel method to predict the long-term popularity of user-generated content (UGC). At first, the method clusters the dynamics of UGC popularity into a vocabulary of growth in popularity (sequence) by using a mixture model. Eventually, the method assigns to each sequence a topic model to describe the dynamics of the sequence in a compact way. We then use this topic model to identify similar patterns of growth in popularity of newly observed UGC. The proposed method has two key features: First, it considers the historical dynamics of the UGC popularity, and second it provides long-term popularity prediction. Results on the real dataset of UGC show that the proposed method is flexible, and able to accurately forecast the complete growth in popularity of a given UGC

Definizione e sviluppo di strumenti di supporto ai processi di pianificazione strategica e gestione prodotto: il caso Azimut-Benetti di Viareggio

Questa tesi nasce dalla mia esperienza presso i cantieri dell’Azimut Benetti di Viareggio. L’obiettivo perseguito dall’organizzazione era quello di migliorare la gestione della documentazione all’interno del cantiere. Individuate le maggiori inefficienze nella documentazione della pianificazione aziendale e individuate le cause delle inefficienze nella scarsa coerenza di contenuto tra i diversi documenti e nella difficoltà del loro aggiornamento, è stato deciso di utilizzare un unico supporto nel quale razionalizzare e uniformare le informazioni e i dati della pianificazione aziendale. Altre inefficienze nella gestione delle informazioni sono state riscontrate nella specifica commerciale nella quale vengono valutati e preventivati gli extra richiesti dal cliente. L’organizzazione desidera che il processo di valutazione e preventivazione dell’extra sia razionalizzato e semplificato; inoltre per eliminare le inefficienze prettamente operative desidera ristrutturare il file di supporto, per ridurre i tempi di processazione

On-demand Time-decaying Bloom Filters for Telemarketer Detection

Several traffic monitoring applications may benefit from the availability of efficient mechanisms for approximately tracking smoothed time averages rather than raw counts. This paper provides two contributions in this direction. First, our analysis of Time-decaying Bloom filters, formerly proposed data structures devised to perform approximate Exponentially Weighted Moving Averages on streaming data, reveals two major shortcomings: biased estimation when measurements are read in arbitrary time instants, and slow operation resulting from the need to periodically update all the filter’s counters at once. We thus propose a new construction, called On-demand Time-decaying Bloom filter, which relies on a continuous-time operation to overcome the accuracy/performance limitations of the original window-based approach. Second, we show how this new technique can be exploited in the design of high performance stream-based monitoring applications, by developing VoIPSTREAM, a proof-of-concept real-time analysis version of a formerly proposed system for telemarketing call detection. Our validation results, carried out over real telephony data, show how VoIPSTREAM closely mimics the feature extraction process and traffic analysis techniques implemented in the offline system, at a significantly higher processing speed, and without requiring any storage of per-user call detail records

Principles, Systems and Applications of IP Telecommunications. Services and Security for Next Generation Networks / Second International Conference, IPTComm 2008 Heidelberg, Germany, July 1-2, 2008 Revised Selected Papers

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