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.

PREFER: a system for the efficient execution of multi-parametric ranked queries

Abstract: Users often need to optimize the selection of objects by appropriately weighting the importance of multiple object attributes. Such optimization problems appear often in operations' research and applied mathematics as well as everyday life; e.g., a buyer may select a home as a weighted function of a number of attributes like its distance from office, its price, its area, etc.We capture such queries in our definition of preference queries that use a weight function over a relation's attributes to derive a score for each tuple. Database systems cannot efficiently produce the top results of a preference query because they need to evaluate the weight function over all tuples of the relation. PREFER answers preference queries efficiently by using materialized views that have been pre-processed and stored.We first show how the result of a preference query can be produced in a pipelined fashion using a materialized view. Then we show that excellent performance can be delivered given a reasonable number of materialized views and we provide an algorithm that selects a number of views to precompute and materialize given space constraints.We have implemented the algorithms proposed in this paper in a prototype system called PREFER, which operates on top of a commercial database management system. We present the results of a performance comparison, comparing our algorithms with prior approaches using synthetic datasets. Our results indicate that the proposed algorithms are superior in performance compared to other approaches, both in preprocessing (preparation of materialized views) as well as execution time.

A semidefinite program for distillable entanglement

Abstract: We show that the maximum fidelity obtained by a positive partial transpose (p.p.t.) distillation protocol is given by the solution to a certain semidefinite program. This gives a number of new lower and upper bounds on p.p.t. distillable entanglement (and thus new upper bounds on 2-locally distillable entanglement). In the presence of symmetry, the semidefinite program simplifies considerably, becoming a linear program in the case of isotropic and Werner states. Using these techniques, we determine the p.p.t. distillable entanglement of asymmetric Werner states and "maximally correlated" states. We conclude with a discussion of possible applications of semidefinite programming to quantum codes and 1-local distillation.

Personalized multimedia services using a mobile service platform

Abstract: In this paper we address the research issues in providing personalized multimedia services, which enable a mobile user to remotely record video programs, control cameras, and request the delivery of pre-recorded or live video content to his or her own mobile device. We describe a mobile service platform that authenticates users who send service requests from various mobile devices, transcodes video content based on user and device profiles, and authorizes the delivery of content from a media server to the proper client device. The media server adapts automatically to the fluctuations of the wireless channel conditions for reasonable viewing on the client device. The mobile service platform essentially manages the control path, while the media server handles the actual content delivery. We discuss various aspects of the integration and report our successful experiments conducted on wireless LAN and CDPD networks.

Profiling resource usage for mobile applications: a cross-layer approach

Abstract: Despite the popularity of mobile applications, their performance and energy bottlenecks remain hidden due to a lack of visibility into the resource-constrained mobile execution environment with potentially complex interaction with the application behavior. We design and implement ARO, the mobile Application Resource Optimizer, the first tool that efficiently and accurately exposes the cross-layer interaction among various layers including radio resource channel state, transport layer, application layer, and the user interaction layer to enable the discovery of inefficient resource usage for smartphone applications. To realize this, ARO provides three key novel analyses: (i) accurate inference of lower-layer radio resource control states, (ii) quantification of the resource impact of application traffic patterns, and (iii) detection of energy and radio resource bottlenecks by jointly analyzing cross-layer information. We have implemented ARO and demonstrated its benefit on several essential categories of popular Android applications to detect radio resource and energy inefficiencies, such as unacceptably high (46%) energy overhead of periodic audience measurements and inefficient content prefetching behavior.