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.

Integer priority queues with decrease key in constant time and the single source shortest paths problem

Abstract: We consider Fibonacci heap style integer priority queues supporting insert and decrease key operations in constant time. We present a deterministic linear space solution that with n integer keys support delete in O(log log n) time. If the integers are in the range [0,N), we can also support delete in O(log log N) time.Even for the special case of monotone priority queues, where the minimum has to be non-decreasing, the best previous bounds on delete were O((log n)1/(3-e)) and O((log N)1/(4-e)). These previous bounds used both randomization and amortization. Our new bounds a deterministic, worst-case, with no restriction to monotonicity, and exponentially faster.As a classical application, for a directed graph with n nodes and m edges with non-negative integer weights, we get single source shortest paths in O(m+n log log n) time, or O(m+n log log C) if C is the maximal edge weight. The later solves an open problem of Ahuja, Mehlhorn, Orlin, and Tarjan from 1990.

Merging the results of approximate match operations

Abstract: Data Cleaning is an important process that has been at the center of research interest in recent years. An important end goal of effective data cleaning is to identify the relational tuple or tuples that are "most related" to a given query tuple. Various techniques have been proposed in the literature for efficiently identifying approximate matches to a query string against a single attribute of a relation. In addition to constructing a ranking (i.e., ordering) of these matches, the techniques often associate, with each match, scores that quantify the extent of the match. Since multiple attributes could exist in the query tuple, issuing approximate match operations for each of them separately will effectively create a number of ranked lists of the relation tuples. Merging these lists to identify a final ranking and scoring, and returning the top-K tuples, is a challenging task. In this paper, we adapt the well-known footrule distance (for merging ranked lists) to effectively deal with scores. We study efficient algorithms to merge rankings, and produce the top-K tuples, in a declarative way. Since techniques for approximately matching a query string against a single attribute in a relation are typically best deployed in a database, we introduce and describe two novel algorithms for this problem and we provide SQL specifications for them. Our experimental case study, using real application data along with a realization of our proposed techniques on a commercial data base system, highlights the benefits of the proposed algorithms and attests to the overall effectiveness and practicality of our approach.

Performance of WDM transport networks

Abstract: Wavelength division multiplexed point-to-point transport is becoming commonplace in wide area networks. With the expectation that the next step is end-to-end networking of wavelengths (in the optical domain without conversion to electronics), there is a need for new design techniques, a new understanding of the performance issues, and a new performance evaluation methodology in such networks. This paper describes approaches to that end, summarizes research results, and points to open problems.

Simulation results for an interference-limited multiple input multiple output cellular system

Abstract: We describe a simulation study of a cellular system using multiple-input multiple-output (MIMO) antenna techniques along with adaptive modulation and aggressive frequency reuse. We show, for the case of 3 transmit and 3 receive antennas, how much MIMO systems outperform systems with receive-diversity-only when noise dominates. When co-channel interference from surrounding cells dominates, the differences shrink, as do the absolute numbers. We quantify these reductions for the specific cases studied, and discuss further areas of research.

Analyzing and predicting question quality in community question answering services

Abstract: Users tend to ask and answer questions in community question answering (CQA) services to seek information and share knowledge. A corollary is that myriad of questions and answers appear in CQA service. Accordingly, volumes of studies have been taken to explore the answer quality so as to provide a preliminary screening for better answers. However, to our knowledge, less attention has so far been paid to question quality in CQA. Knowing question quality provides us with finding and recommending good questions together with identifying bad ones which hinder the CQA service. In this paper, we are conducting two studies to investigate the question quality issue. The first study analyzes the factors of question quality and finds that the interaction between askers and topics results in the differences of question quality. Based on this finding, in the second study we propose a Mutual Reinforcement-based Label Propagation (MRLP) algorithm to predict question quality. We experiment with Yahoo!~Answers data and the results demonstrate the effectiveness of our algorithm in distinguishing high-quality questions from low-quality ones.