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.

Network Tomography of Binary Network Performance Characteristics

Abstract: In network performance tomography, characteristics of the network interior, such as link loss and packet latency, are inferred from correlated end-to-end measurements. Most work to date is based on exploiting packet level correlations, e.g., of multicast packets or unicast emulations of them. However, these methods are often limited in scope-multicast is not widely deployed-or require deployment of additional hardware or software infrastructure. Some recent work has been successful in reaching a less detailed goal: identifying the lossiest network links using only uncorrelated end-to-end measurements. In this paper, we abstract the properties of network performance that allow this to be done and exploit them with a quick and simple inference algorithm that, with high likelihood, identifies the worst performing links. We give several examples of real network performance measures that exhibit the required properties. Moreover, the algorithm is sufficiently simple that we can analyze its performance explicitly

Data integration using similarity joins and a word-based information representation language

Abstract: The integration of distributed, heterogeneous databases, such as those available on the World Wide Web, poses many problems. Herer we consider the problem of integrating data from sources that lack common object identifiers. A solution to this problem is proposed for databases that contain informal, natural-language “names” for objects; most Web-based databases satisfy this requirement, since they usually present their information to the end-user through a veneer of text. We describe WHIRL, a “soft” database management system which supports “similarity joins,” based on certain robust, general-purpose similarity metrics for text. This enables fragments of text (e.g., informal names of objects) to be used as keys. WHIRL includes textual objects as a built-in type, similarity reasoning as a built-in predicate, and answers every query with a list of answer substitutions that are ranked according to an overall score. Experiments show that WHIRL is much faster than naive inference methods, even for short queries, and efficient on typical queries to real-world databases with tens of thousands of tuples. Inferences made by WHIRL are also surprisingly accurate, equaling the accuracy of hand-coded normalization routines on one benchmark problem, and outerperforming exact matching with a plausible global domain on a second.

Analysis of hybrid selection/maximal-ratio combining in Rayleigh fading

Abstract: We use a novel virtual branch technique to succinctly derive the mean and variance of the combiner output signal-to-noise ratio for hybrid selection/maximal-ratio combining in a multipath-fading environment.

Big data integration

Abstract: The Big Data era is upon us: data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Since the value of data explodes when it can be linked and fused with other data, addressing the big data integration (BDI) challenge is critical to realizing the promise of Big Data. BDI differs from traditional data integration in many dimensions: (i) the number of data sources, even for a single domain, has grown to be in the tens of thousands, (ii) many of the data sources are very dynamic, as a huge amount of newly collected data are continuously made available, (iii) the data sources are extremely heterogeneous in their structure, with considerable variety even for substantially similar entities, and (iv) the data sources are of widely differing qualities, with significant differences in the coverage, accuracy and timeliness of data provided. This seminar explores the progress that has been made by the data integration community on the topics of schema mapping, record linkage and data fusion in addressing these novel challenges faced by big data integration, and identifies a range of open problems for the community.

Labeling dynamic XML trees

Abstract: We present algorithms to label the nodes of an XML tree which is subject to insertions and deletions of nodes. The labeling is done such that (1) we label each node immediately when it is inserted and this label remains unchanged, and (2) from a pair of labels alone, we can decide whether one node is an ancestor of the other. This problem arises in the context of XML databases that support queries on the structure of the documents as well us on the changes made to the documents over time. We prove that our algorithms assign the shortest possible labels (up to a constant factor) which satisfy these requirements.We also consider the same problem when "clues" that provide guarantees on possible future insertions are given together with newly inserted nodes. Such clues can be derived from the DTD or from statistics on similar XML trees. We present algorithms that use the clues to assign shorter labels. We also prove that the length of our labels is close to the minimum possible.