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Your search keyword '"Kavé Salamatian"' showing total 3 results
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3 results on '"Kavé Salamatian"'

1. Faving Reciprocity in Content Sharing Communities: A Comparative Analysis of Flickr and Twitter

Author

Jong Gun Lee, Kavé Salamatian, Panayotis Antoniadis, Networks and Performance Analysis (NPA), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), and Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])

Subjects
Computer science, business.industry, Process (engineering), Social software, Internet privacy, 02 engineering and technology, computer.software_genre, Popularity, World Wide Web, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Upload, Reciprocity (social psychology), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Peer to peer computing, 020201 artificial intelligence & image processing, Content sharing, Content (Freudian dream analysis), business, computer
Abstract

International audience; In the Web 2.0 era, users share and discover interesting content via a network of relationships created in various social networking or content sharing sites. They can become for example contacts, followers or friends and express their appreciation of specific content uploaded by their peers by faving, retweeting or liking them depending on whether they are in Flickr, Twitter or Facebook respectively. Then they can discover additional content of interest through the lists of favorites of their contacts and so on. This faving (or favoring) functionality becomes thus a central part of content sharing communities for two purposes: (a) it helps the propagation of content amongst users and (b) it stimulates users' participation and activity. In this paper, we make a first step to understand users' faving behavior in content sharing communities in terms of reciprocity using publicly available datasets from Flickr and Twitter. Do users favor content only when they really appreciate it or they often feel the need to reciprocate when their content is appreciated by one of their contacts or even by a stranger? Do people take advantage of this process to gain popularity? What is the impact of the design, the social software, of a specific community and the type of content shared? These are some of the questions that our first results help to answer.

Published
2010
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2. A Multi-partitioning Approach to Building Fast and Accurate Counting Bloom Filters

Author

Institute of Computing Technology [Biejing] (ICT) ; Chinese Academy of Science (CAS), Computer and Communication School ; Hunan University, Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC) ; Université de Savoie, Computer Science and Engineering Dept. (MSU CS) ; Michigan State University, Huang, Kun, Zhang, Dafang, Xie, Gaogang, Kavé, Salamatian, Liu, Alex, Li, Wei, Institute of Computing Technology [Biejing] (ICT) ; Chinese Academy of Science (CAS), Computer and Communication School ; Hunan University, Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC) ; Université de Savoie, Computer Science and Engineering Dept. (MSU CS) ; Michigan State University, Huang, Kun, Zhang, Dafang, Xie, Gaogang, Kavé, Salamatian, Liu, Alex, and Li, Wei

Abstract

International audience, Bloom filters are space-efficient data structures for fast set membership queries. Counting Bloom Filters (CBFs) extend Bloom filters by allowing insertions and deletions to support dynamic sets. The performance of CBFs is critical for various applications and systems. This paper presents a novel approach to building a fast and accurate data structure called Multiple-Partitioned Counting Bloom Filter (MPCBF) that addresses large-scale data processing challenges. MPCBF is based on two ideas: reducing the number of memory accesses from k (for k hash functions) in the standard CBF to only one memory access in the basic MPCBF-1 case, and a hierarchical structure to improve the false positive rate. We also generalize MPCBF-1 to MPCBF-g to accommodate up to g memory accesses. Our simulation and implementation in MapReduce show that MPCBF outperforms the standard CBF in terms of speed and accuracy. Compared to CBF, at the same memory consumption, MPCBF significantly reduces the false positive rate by an order of magnitude, with a reduction of processing overhead by up to 85.9%.

3. Scalable TCAM-based regular expression matching with compressed finite automata

Author

Institute of Computing Technology [Biejing] (ICT) ; Chinese Academy of Science (CAS), Computer and Communication School ; Hunan University, Computer Science and Engineering Dept. (MSU CS) ; Michigan State University, Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC) ; Université de Savoie, Huang, Kun, Ding, Linxuan, Xie, Gaogang, Zhang, Dafang, Liu, Alex, Kavé, Salamatian, Institute of Computing Technology [Biejing] (ICT) ; Chinese Academy of Science (CAS), Computer and Communication School ; Hunan University, Computer Science and Engineering Dept. (MSU CS) ; Michigan State University, Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC) ; Université de Savoie, Huang, Kun, Ding, Linxuan, Xie, Gaogang, Zhang, Dafang, Liu, Alex, and Kavé, Salamatian

Abstract

International audience, Regular expression (RegEx) matching is a core function of deep packet inspection in modern network devices. Previous TCAM-based RegEx matching algorithms a priori assume that a deterministic finite automaton (DFA) can be built for a given set of RegEx patterns. However, practical RegEx patterns contain complex terms like wildcard closure and repeat character, and it may be impossible to build a DFA with a reasonable number of states. This results in prior work to being infeasible in practice. Moreover, TCAM-based RegEx matching is required to scale to a large-scale set of RegEx patterns. In this paper, we propose a compressed finite automaton implementation called (CFA) for scalable TCAM-based RegEx matching. CFA is designed to reduce TCAM space by using three compression techniques: transition, character, and state compressions. Experiments on realistic RegEx pattern sets show CFA highly outperforms previous solutions in terms of TCAM space, matching throughput, and TCAM power consumption.

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