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.

Understanding internet topology: principles, models, and validation

Abstract: Building on a recent effort that combines a first-principles approach to modeling router-level connectivity with a more pragmatic use of statistics and graph theory, we show in this paper that for the Internet, an improved understanding of its physical infrastructure is possible by viewing the physical connectivity as an annotated graph that delivers raw connectivity and bandwidth to the upper layers in the TCP/IP protocol stack, subject to practical constraints (e.g., router technology) and economic considerations (e.g., link costs). More importantly, by relying on data from Abilene, a Tier-1 ISP, and the Rocketfuel project, we provide empirical evidence in support of the proposed approach and its consistency with networking reality. To illustrate its utility, we: 1) show that our approach provides insight into the origin of high variability in measured or inferred router-level maps; 2) demonstrate that it easily accommodates the incorporation of additional objectives of network design (e.g., robustness to router failure); and 3) discuss how it complements ongoing community efforts to reverse-engineer the Internet.

Text joins in an RDBMS for web data integration

Abstract: The integration of data produced and collected across autonomous, heterogeneous web services is an increasingly important and challenging problem. Due to the lack of global identifiers, the same entity (e.g., a product) might have different textual representations across databases. Textual data is also often noisy because of transcription errors, incomplete information, and lack of standard formats. A fundamental task during data integration is matching of strings that refer to the same entity. In this paper, we adopt the widely used and established cosine similarity metric from the information retrieval field in order to identify potential string matches across web sources. We then use this similarity metric to characterize this key aspect of data integration as a join between relations on textual attributes, where the similarity of matches exceeds a specified threshold. Computing an exact answer to the text join can be expensive. For query processing efficiency, we propose a sampling-based join approximation strategy for execution in a standard, unmodified relational database management system (RDBMS), since more and more web sites are powered by RDBMSs with a web-based front end. We implement the join inside an RDBMS, using SQL queries, for scalability and robustness reasons. Finally, we present a detailed performance evaluation of an implementation of our algorithm within a commercial RDBMS, using real-life data sets. Our experimental results demonstrate the efficiency and accuracy of our techniques.

Efficient Generation of Shared RSA Keys (Extended Abstract)

Abstract: We describe efficient techniques for three (or more) parties to jointly generate an RSA key. At the end of the protocol an RSA modulus N = pq is publicly known. None of the parties know the factorization of N. In addition a public encryption exponent is publicly known and each party holds a share of the private exponent that enables threshold decryption. Our protocols are efficient in computation and communication.

Multi-Level, Multi-Dimensional Coding for High-Speed and High-Spectral-Efficiency Optical Transmission

Abstract: We review and study several single carrier based multi-level and multi-dimensional coding (ML-MDC) technologies recently demonstrated for spectrally-efficient 100-Gb/s transmission. These include 16-ary PDM-QPSK, 64-ary PDM-8PSK, 64-ary PDM-8QAM as well as 256-ary PDM-16 QAM. We show that high-speed QPSK, 8PSK, 8QAM, and 16QAM can all be generated using commercially available optical modulators using only binary electrical drive signals through novel synthesis methods, and that all of these modulation formats can be detected using a universal receiver front-end and digital coherent detection. We show that the constant modulus algorithm (CMA), which is highly effective for blind polarization recovery of PDM-QPSK and PDM-8PSK signals, is much less effective for PDM-8QAM and PDM-16 QAM. We then present a recently proposed, cascaded multi-modulus algorithm for these cases. In addition to the DSP algorithms used for constellation recovery, we also describe a DSP algorithm to improve the performance of a coherent receiver using single-ended photo-detection. The system impact of ASE noise, laser phase noise, narrowband optical filtering and fiber nonlinear effects has been investigated. For high-level modulation formats using full receiver-side digital compensation, it is shown that the requirement on LO phase noise is more stringent than the signal laser. We also show that RZ pulse shaping significantly improves filter- and fiber-nonlinear tolerance. Finally we present three high-spectral-efficiency and high-speed DWDM transmission experiments implementing these ML-MDC technologies.

Support Vector Method for Multivariate Density Estimation

Abstract: A new method for multivariate density estimation is developed based on the Support Vector Method (SVM) solution of inverse ill-posed problems. The solution has the form of a mixture of densities. This method with Gaussian kernels compared favorably to both Parzen's method and the Gaussian Mixture Model method. For synthetic data we achieve more accurate estimates for densities of 2, 6, 12, and 40 dimensions.