Home
Partners
Research interests
Projects
People
Opportunities (tesi di laurea IT)
Publications
Software
Events
Publications by year:
Publications by type: Conference papers - Journals - Technical Reports - Books - Book Chapters - Phd Thesis - Master Thesis (Tesi di Laurea Magistrale) - Bachelor Thesis (Tesi di Laurea)
Publications by author:
Publications by project:
Free search: Search


2015

R. Baldoni, S. Bonomi, M. Platania, L. Querzoni
Efficient Notification Ordering for Geo-Distributed Pub/Sub Systems

IEEE Transactions on Computers (accepted for publication), 2015

Abstract [+]

"A distributed event notification service (ENS) is at the core of modern messaging infrastructures providing applications with scalable and robust publish/subscribe communication primitives. Such ENSs can route events toward subscribers using multiple paths with different lengths and latencies. As a consequence, subscribers can receive events out of order. In this paper, we propose a novel solution for ordered notifications on top of an existing distributed topic-based ENS. Our solutions guarantees that each pair of events published in the system will be notified in the same order to all their target subscribers independently from the topics they are published in. It endows a distributed timestamping mechanism based on a multistage sequencer that produces timestamps whose size is dynamically adjusted to accommodate changing subscriptions in the system. An extensive experimental evaluation based on a prototype implementation shows that the timestamping mechanism is able to scale from several points of view (i.e., number of publisher and subscribers, event rate). Furthermore, it shows how the deployment flexibility of our solution makes it perform better in terms of timestamp size and timestamp generation latency when the system load exhibits geographic topic popularity, that is, matching subscriptions and publications are geographically clustered. This makes our solution particularly well suited to be deployed in geo-distributed infrastructures."

Downloads:pdf - Paper (accepted version) - Main
pdf - Paper (accepted version) - Supplemental Material
bib - BibTeX reference



F. Petroni, L. Querzoni, R. Beraldi, M. Paolucci
LCBM: a fast and lightweight collaborative filtering algorithm for binary ratings

Submitted to international journal, 2015

Abstract [+]

"In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet industry. Collaborative Filtering (CF) represents today’s a widely adopted strategy to build recommendation engines. The most advanced CF techniques (i.e. those based on matrix factorization) provide high quality results, but may incur prohibitive computational costs when applied to very large data sets. In this paper we present Linear Classifier of Beta distributions Means (LCBM), a novel collaborative filtering algorithm for binary ratings that is (i) inherently parallelizable and (ii) provides results whose quality is on-par with state-of-the-art solutions (iii) at a fraction of the computational cost."

Downloads:bib - BibTeX reference



F. Petroni, L. Querzoni, G. Iacoboni, K. Daudjee
HDRF: Efficient Stream-Based Partitioning for Power-Law Graphs

Submitted to international conference, 2015

Abstract [+]

"Balanced graph partitioning is a fundamental problem that is receiving growing attention with the emergence of large-scale graphs in a variety of real-world applications such as social networks, machine learning and the Web. Several recent distributed graph-computing (DGC) frameworks have emerged that provide tailored programming abstractions for these applications. In DGC frameworks, the partitioning strategy plays an important role since it drives the communication cost and the workload balance among computing nodes, thereby affecting system performance. However, existing partitioning algorithms either do not exploit or exploit only partially a key characteristic of natural graphs commonly found in the real-world: their highly skewed power-law degree distributions. In this paper, we propose High-Degree (are) Replicated First (HDRF), a novel streaming vertex-cut balanced graph partitioning algorithm. HDRF effectively exploits skewed degree distributions by explicitly taking into account vertex degree in the placement decision. We experimentally evaluate and compare HDRF with existing solutions on both synthetic and real-world graphs. We show that HDRF outperforms all existing algorithms in partitioning quality while guaranteeing load balance."

Downloads:bib - BibTeX reference



A. Anagnostopoulos, F. Petroni, M. Sorella
COLITA: Collaborative Interest-Driven Targeted Advertising

Submitted to international conference, 2015

Abstract [+]

"Compared with online advertising, offline advertising commonly fails to achieve a satisfactory level of targeting, because of the lack of specific information on customers interests and mobility patterns, being forced to rely on imprecise and aggregate demographic estimates. In this work we use the Twitter social network to gather information about users' degree of interest in given advertising categories and about the common routes that they follow for recommending physical locations for advertising. Given an advertisement belonging to one of the examined categories, we estimate the most promising areas to be selected for the placement of an ad that can maximize its targeted effectiveness. To the best of our knowledge this is the first work on offline advertising in urban areas making use of (publicly available) data from social networks."

Downloads:bib - BibTeX reference



A. Bessi et al.
Viral Misinformation: The Role of Homophily and Polarization

Submitted to international conference, 2015

Abstract [+]

"The World Economic Forum listed the diffusion of false information on online social networks as one of the main risks for our society. Indeed, the spreading of unsubstantiated rumors either intentionally or unintentionally misleading can have serious consequences such as in the case of rumors about Ebola causing disruption to health-care workers. Targeting Facebook, we characterize consumption patterns of 1.2M Italian users with respect to verified (science news) and unverified (conspiracy news) rumors. We show that users’ engagement on verified (or unverified) content correlates with the number of friends having similar consumption patterns (homophily). Finally, we measure how this social system responded to the injection of 4,709 false information. We find that the frequent exposure to unsubstantiated rumors (polarization) is a good measure to detect homophile clusters where false rumors are more likely to spread."

Downloads:bib - BibTeX reference



A. Bessi et al.
Everyday the Same Picture: Popularity and Content Diversity

Submitted to international conference, 2015

Abstract [+]

"Facebook is flooded by diverse and heterogeneous content, from kittens up to music and news, passing through satirical and funny stories. Each piece of that corpus reflects the heterogeneity of the underlying social background. In the Italian Facebook we have found an interesting case: a page having more than 40K followers that every day posts the same picture of a popular Italian singer. In this work, we use such a page as a control to study and model the relationship between content heterogeneity on popularity. In particular, we use that page for a comparative analysis of information consumption patterns with respect to pages posting science and conspiracy news. In total, we analyze about 2M likes and 190K comments, made by approximately 340K and 65K users, respectively. We conclude the paper by introducing a model mimicking users selection preferences accounting for the heterogeneity of contents."

Downloads:bib - BibTeX reference



L. Aniello, L. Querzoni, R. Baldoni
High Frequency Batch-oriented Computations over Large Sliding Time Windows

Future Generation Computer Systems, volume 43, pages 1-11, Elsevier, 2015

Abstract [+]

"Today’s business workflows are very likely to include batch computations that periodically analyze subsets of data within specific time ranges to provide strategic information for stakeholders and other interested parties. The frequency of these batch computations provides an effective measure of data analytics freshness available to decision makers. Nevertheless, the typical amounts of data to elaborate in a batch are so large that a computation can take very long. Considering that usually a new batch starts when the previous one has completed, the frequency of such batches can thus be very low. In this paper we propose a model for batch processing based on overlapping sliding time windows that allows to increase the frequency of batches. The model is well suited to scenarios (e.g., financial, security etc) characterized by large data volumes, observation windows in the order of hours (or days) and frequent updates (order of seconds). The model introduces multiple metrics whose aim is reducing the latency between the end of a computation time window and the availability of results, increasing thus the frequency of the batches. These metrics specifically take into account the organization of input data to minimize its impact on such latency. The model is then instantiated on the well-known Hadoop platform, a batch processing engine based on the MapReduce paradigm, and a set of strategies for efficiently arranging input data is described and evaluated."

Downloads:pdf - Paper (accepted version)
bib - BibTeX reference



S. Bonomi, A. Del Pozzo, R. Baldoni
Building Regular Registers with Rational Malicious Servers and Anonymous Clients

Technical report 1 - MIDLAB - 2015

Abstract [+]

"The paper addresses the problem of emulating a regular register in a synchronous distributed system where clients invoking ${\sf read}()$ and ${\sf write}()$ operations are anonymous while server processes maintaining the state of the register may be compromised by rational adversaries (i.e., a server might behave as \emph{rational malicious byzantine} process). We first model our problem as a Bayesian game between a client and a rational malicious server where the equilibrium depends on the decisions of the malicious server (behave correctly and not be detected by clients vs returning a wrong register value to clients with the risk of being detected and then excluded by the computation). We prove such equilibrium exists and finally we design a protocol implementing the regular register that forces the rational malicious server to behave correctly."

Downloads:pdf - Technical Report Netys
bib - BibTeX reference



C. Esposito, M. Platania, R. Beraldi
Reliable and Timely Event Notification for Publish/Subscribe Services Over the Internet

IEEE/ACM Transactions on Networking, 2015
Downloads:bib - BibTeX reference

Top -


DISCLAIMER - The reports contained in this page are included by the contributing authors as a mechanism to ensure timely dissemination of scholarly/technical information on a non-commerical basis. Copyright and all rights therein are maintained by the authors, despite the fact they have offered this information electronically. It is understood that all individuals copying this information will adhere to the terms/ constraints invoked by each author's copyright. Reports may not be copied for commercial redistribution, republication, or dissemination without the explicit permission of the authors. Sections of some of these reports have been published by IEEE, Springer-Verlag, Kluwer etc. and have Copyright. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the publisher.