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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
On Static And Dynamic Partitioning Behavior Of Large-Scale Networks, Derek Leonard, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov
On Static And Dynamic Partitioning Behavior Of Large-Scale Networks, Derek Leonard, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov
Computer Science Faculty Publications
In this paper, we analyze the problem of network disconnection in the context of large-scale P2P networks and understand how both static and dynamic patterns of node failure affect the resilience of such graphs. We start by applying classical results from random graph theory to show that a large variety of deterministic and random P2P graphs almost surely (i.e., with probability 1-o(1)) remain connected under random failure if and only if they have no isolated nodes. This simple, yet powerful, result subsequently allows us to derive in closed-form the probability that a P2P network develops isolated nodes, and therefore partitions, …
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Computer Science Faculty Publications
Clustering is well-suited for Web mining by automatically organizing Web pages into categories, each of which contains Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain in particular, currently there is no such method suitable for Web page clustering. In an attempt to address this problem, we discover a constant factor that characterizes the Web domain, based on which we propose a new method for automatically determining the number of clusters in Web page data sets. We discover that the …
Personalization By Program Slicing, Saverio Perugini, Naren Ramakrishnan
Personalization By Program Slicing, Saverio Perugini, Naren Ramakrishnan
Computer Science Faculty Publications
Personalization involves customizing information access to the end-user. As any new area of computer science research it lacks formal models to guide the design of systems. In this paper, we present a modeling methodology, based on generative programming, for personalizing interactions with hierarchical websites. The methodology entails modeling a user’s interaction with a site in a program and applying program slicing to personalize the interaction. While preserving interactivity, this approach does not require the designer to anticipate all possible user interactions a priori and provide interfaces for each. Moreover, it provides a theoretical, systematic, and implementation-neutral way to design systems …
Recommender Systems Research, Saverio Perugini
Recommender Systems Research, Saverio Perugini
Computer Science Faculty Publications
We outline the history of recommender systems from their roots in information retrieval and filtering to their role in today’s Internet economy. Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. Research in recommender systems lies at the intersection of several areas of computer science, such as artificial intelligence and human-computer interaction, and has progressed to an important research area of its own. It is important to note that recommendations are not delivered within a vacuum, but rather cast within an informal community of users …
The Good, Bad And The Indifferent: Explorations In Recommender System Health, Benjamin J. Keller, Sun-Mi Kim, N. Srinivas Vemuri, Naren Ramakrishnan, Saverio Perugini
The Good, Bad And The Indifferent: Explorations In Recommender System Health, Benjamin J. Keller, Sun-Mi Kim, N. Srinivas Vemuri, Naren Ramakrishnan, Saverio Perugini
Computer Science Faculty Publications
Our work is based on the premise that analysis of the connections exploited by a recommender algorithm can provide insight into the algorithm that could be useful to predict its performance in a fielded system. We use the jumping connections model defined by Mirza et al. [6], which describes the recommendation process in terms of graphs. Here we discuss our work that has come out of trying to understand algorithm behavior in terms of these graphs. We start by describing a natural extension of the jumping connections model of Mirza et al., and then discuss observations that have come from …
A Generative Programming Approach To Interactive Information Retrieval: Insights And Experiences, Saverio Perugini, Naren Ramakrishnan
A Generative Programming Approach To Interactive Information Retrieval: Insights And Experiences, Saverio Perugini, Naren Ramakrishnan
Computer Science Faculty Publications
We describe the application of generative programming to a problem in interactive information retrieval. The particular interactive information retrieval problem we study is the support for "out-of-turn interaction" with a website – how a user can communicate input to a website when the site is not soliciting such information on the current page, but will do so on a subsequent page. Our solution approach makes generous use of program transformations (partial evaluation, currying, and slicing) to delay the site’s current solicitation for input until after the user’s out-of-turn input is processed. We illustrate how studying out-of-turn interaction through a generative …
Introduction: Data Communication And Topology Algorithms For Sensor Networks, Stephan Olariu, David Simplot-Ryl, Ivan Stojmenovic
Introduction: Data Communication And Topology Algorithms For Sensor Networks, Stephan Olariu, David Simplot-Ryl, Ivan Stojmenovic
Computer Science Faculty Publications
(First paragraph) We are very proud and honored to have been entrusted to be Guest Editors for this special issue. Papers were sought to comprehensively cover the algorithmic issues in the “hot” area of sensor networking. The concentration was on network layer problems, which can be divided into two groups: data communication problems and topology control problems. We wish to briefly introduce the five papers appearing in this special issue. They cover specific problems such as time division for reduced collision, fault tolerant clustering, self-stabilizing graph optimization algorithms, key pre-distribution for secure communication, and distributed storage based on spanning trees …