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.

Increasing Internet Capacity Using Local Search

Abstract: Open Shortest Path First (OSPF) is one of the most commonly used intra-domain internet routing protocol. Traffic flow is routed along shortest paths, splitting flow evenly at nodes where several outgoing links are on shortest paths to the destination. The weights of the links, and thereby the shortest path routes, can be changed by the network operator. The weights could be set proportional to the physical lengths of the links, but often the main goal is to avoid congestion, i.e. overloading of links, and the standard heuristic recommended by Cisco (a major router vendor) is to make the weight of a link inversely proportional to its capacity. We study the problem of optimizing OSPF weights for a given a set of projected demands so as to avoid congestion. We show this problem is NP-hard, even for approximation, and propose a local search heuristic to solve it. We also provide worst-case results about the performance of OSPF routing vs. an optimal multi-commodity flow routing. Our numerical experiments compare the results obtained with our local search heuristic to the optimal multi-commodity flow routing, as well as simple and commonly used heuristics for setting the weights. Experiments were done with a proposed next-generation AT&T WorldNet backbone as well as synthetic internetworks.

Building a Turkish Treebank

Abstract: We present the issues that we have encountered in designing a treebank architecture for Turkish along with rationale for the choices we have made for various representation schemes. In the resulting representation, the information encoded in the complex agglutinative word structures are represented as a sequence of inflectional groups separated by derivational boundaries. The syntactic relations are encoded as labeled dependency relations among segments of lexical items marked by derivation boundaries. Our current work involves refining a set of treebank annotation guidelines and developing a sophisticated annotation tool with an extendable plug-in architecture for morphological analysis, morphological disambiguation and syntactic annotation disambiguation.

Distributed feature composition: a virtual architecture for telecommunications services

Abstract: Distributed Feature Composition (DFC) is a new technology for feature specification and composition, based on a virtual architecture offering benefits analogous to those of a pipe-and-filter architecture. In the DFC architecture, customer calls are processed by dynamically assembled configurations of filter-like components: each component implements an applicable feature, and communicates with its neighbors by featureless internal calls that are connected by the underlying architectural substrate.

Integrating communication and information through ContactMap

Abstract: Visualizing personal social networks, the system allows users to model and arrange their own in maps of individual contacts and groups, along with the relationships among them.

Truth discovery and copying detection in a dynamic world

Abstract: Modern information management applications often require integrating data from a variety of data sources, some of which may copy or buy data from other sources. When these data sources model a dynamically changing world (e.g., people's contact information changes over time, restaurants open and go out of business), sources often provide out-of-date data. Errors can also creep into data when sources are updated often. Given out-of-date and erroneous data provided by different, possibly dependent, sources, it is challenging for data integration systems to provide the true values. Straightforward ways to resolve such inconsistencies (e.g., voting) may lead to noisy results, often with detrimental consequences.In this paper, we study the problem of finding true values and determining the copying relationship between sources, when the update history of the sources is known. We model the quality of sources over time by their coverage, exactness and freshness. Based on these measures, we conduct a probabilistic analysis. First, we develop a Hidden Markov Model that decides whether a source is a copier of another source and identifies the specific moments at which it copies. Second, we develop a Bayesian model that aggregates information from the sources to decide the true value for a data item, and the evolution of the true values over time. Experimental results on both real-world and synthetic data show high accuracy and scalability of our techniques.