AT&T Labs

About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.

Drawing graphs by eigenvectors: theory and practice

Abstract: The spectral approach for graph visualization computes the layout of a graph using certain eigenvectors of related matrices. Two important advantages of this approach are an ability to compute optimal layouts (according to specific requirements) and a very rapid computation time. In this paper, we explore spectral visualization techniques and study their properties from different points of view. We also suggest a novel algorithm for calculating spectral layouts resulting in an extremely fast computation by optimizing the layout within a small vector space.

Network-aware forward caching

Abstract: This paper proposes and evaluates a Network Aware Forward Caching approach for determining the optimal deployment strategy of forward caches to a network. A key advantage of this approach is that we can reduce the network costs associated with forward caching to maximize the benefit obtained from their deployment. We show in our simulation that a 37% increase to net benefits could be achieved over the standard method of full cache deployment to cache all POPs traffic. In addition, we show that this maximal point occurs when only 68% of the total traffic is cached.Another contribution of this paper is the analysis we use to motivate and evaluate this problem. We characterize the Internet traffic of 100K subscribers of a US residential broadband provider. We use both layer 4 and layer 7 analysis to investigate the traffic volumes of the flows as well as study the general characteristics of the applications used. We show that HTTP is a dominant protocol and account for 68% of the total downstream traffic and that 34% of that traffic is multimedia. In addition, we show that multimedia content using HTTP exhibits a 83% annualized growth rate and other HTTP traffic has a 53% growth rate versus the 26% over all annual growth rate of broadband traffic. This shows that HTTP traffic will become ever more dominent and increase the potential caching opportunities. Furthermore, we characterize the core backbone traffic of this broadband provider to measure the distance travelled by content and traffic. We find that CDN traffic is much more efficient than P2P content and that there is large skew in the Air Miles between POP in a typical network. Our findings show that there are many opportunties in broadband provider networks to optimize how traffic is delivered and cached.

SPoT: a trainable sentence planner

Abstract: Sentence planning is a set of inter-related but distinct tasks, one of which is sentence scoping, i.e. the choice of syntactic structure for elementary speech acts and the decision of how to combine them into one or more sentences. In this paper, we present SPoT, a sentence planner, and a new methodology for automatically training SPoT on the basis of feedback provided by human judges. We reconceptualize the task into two distinct phases. First, a very simple, randomized sentence-plan-generator (SPG) generates a potentially large list of possible sentence plans for a given text-plan input. Second, the sentence-plan-ranker (SPR) ranks the list of output sentence plans, and then selects the top-ranked plan. The SPR uses ranking rules automatically learned from training data. We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan.

'I didn't buy it for myself' privacy and ecommerce personalization

Abstract: Ecommerce personalization can help web sites build and retain relationships with customers, but it also raises a number of privacy concerns. This paper outlines the privacy risks associated with personalization and describes a number of approaches to personalization system design that can reduce these risks. This paper also provides an overview of the fair information practice principles and discusses how they may be applied to the design of personalization systems, and introduces privacy laws and self-regulatory guidelines relevant to personalization. Privacy risks can be reduced when personalization system designs allow for pseudonymous interactions, client-side data stores, and task-based personalization. In addition, interfaces that allow users to control the collection and use of their profile information can further ease privacy concerns.