@article {Mic2013, title = {Basic Network Data Analysis}, number = {D3.1}, year = {2013}, month = {05/2013}, institution = {mPlane Consortium}, type = {Public Deliverable}, address = {Torino}, abstract = {

This document describes the requirements, input, output for the algorithms needed to perform analytic tasks on a large amount of data, in the context of WP3. Starting from the use cases defined in WP1, we identify the algorithms needed to address the various scenario requirements. Operating on a large amount of data, these algorithms strive for parallel and scalable approaches; the designing and implementation of the algorithm itself can be a challenging research task since today very little is known concerning how to develop efficient and scalable algorithms that runs on parallel processing frameworks.
The algorithm in the storage layer are characterized by the fact that they operate on a large amount of data, and produce a concise representation of it, extracting features and aggregating it, so that the produced output is easier to handle and understand. Depending on the amount of data produced, on the scenario characteristics and on the time constraints, algorithms can require a real time (or near real time) or a batch processing.
For each algorithm and use case, the input data and the initial state, the computation to run and the output produced are described.

}, keywords = {algorithms, big data, storage}, issn = {D3.1}, author = {Pietro Michiardi and Antonio Barbuzzi and Alessandro Finamore and Stefano Traverso and Daniele Apiletti and Elena Baralis and Tania Cerquitelli and Silvia Chiusano and Luigi Grimaudo and A. Rufini and Francesco Matera and A. Valentii and Maurizio Dusi and Mohamed Ahmed and Tivadar Szemethy and L. N{\'e}meth and R. Szalay and Ilias Leontiadis and Yan Grunenberger and P. Casas and Alessandro D{\textquoteright}Alconzo and A B{\"a}r and D Rossi and YiXi Gong} } @inproceedings {Val2012, title = {Breaking for commercials: characterizing mobile advertising}, booktitle = {Proceedings of the 2012 ACM conference on Internet measurement conference}, year = {2012}, publisher = {ACM}, organization = {ACM}, address = {Boston, MA}, abstract = {

Mobile phones and tablets can be considered as the first incarnation of the post-PC era. Their explosive adoption rate has been driven by a number of factors, with the most signifcant influence being applications (apps) and app markets. Individuals and organizations are able to develop and publish apps, and the most popular form of monetization is mobile advertising.

The mobile advertisement (ad) ecosystem has been the target of prior research, but these works typically focused on a small set of apps or are from a user privacy perspective. In this work we make use of a unique, anonymized data set corresponding to one day of traffic for a major European mobile carrier with more than 3 million subscribers. We further take a principled approach to characterize mobile ad traffic along a number of dimensions, such as overall traffic, frequency, as well as possible implications in terms of en- ergy on a mobile device.

Our analysis demonstrates a number of inefficiencies in today{\textquoteright}s ad delivery. We discuss the benefits of well-known techniques, such as pre-fetching and caching, to limit the energy and network signalling overhead caused by current systems. A prototype im- plementation on Android devices demonstrates an improvement of 50\% in terms of energy consumption for offline ad-sponsored apps while limiting the amount of ad related traffic.\ 

}, keywords = {advertisement, caching, cellular, energy, smartphones, traffic}, isbn = {978-1-4503-1705-4}, doi = {10.1145/2398776.2398812}, url = {http://doi.acm.org/10.1145/2398776.2398812}, author = {Narseo Vallina-Rodriguez and Jay Shah and Alessandro Finamore and Yan Grunenberger and Konstantina Papagiannaki and Hamed Haddadi and Jon Crowcroft} }