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.

In search of the elusive ground truth: the internet's as-level connectivity structure

Abstract: Despite significant efforts to obtain an accurate picture of the Internet's actual connectivity structure at the level of individual autonomous systems (ASes), much has remained unknown in terms of the quality of the inferred AS maps that have been widely used by the research community. In this paper we assess the quality of the inferred Internet maps through case studies of a set of ASes. These case studies allow us to establish the ground truth of AS-level Internet connectivity between the set of ASes and their directly connected neighbors. They also enable a direct comparison between the ground truth and inferred topology maps and yield new insights into questions such as which parts of the actual topology are adequately captured by the inferred maps, and which parts are missing and why. This information is critical in assessing for what kinds of real-world networking problems the use of currently inferred AS maps or proposed AS topology models are, or are not, appropriate. More importantly, our newly gained insights also point to new directions towards building realistic and economically viable Internet topology maps.

A social reinforcement learning agent

Abstract: We report on our reinforcement learning work on Cobot, a software agent that resides in the well-known online chat community LambdaMOO. Our initial work on Cobot~\cite{cobotaaai} provided him with the ability to collect {\em social statistics\/} and report them to users in a reactive manner. Here we describe our application of reinforcement learning to allow Cobot to proactively take actions in this complex social environment, and adapt his behavior from multiple sources of human reward. After 5 months of training, Cobot received 3171 reward and punishment events from 254 different Lambda\-MOO users, and learned nontrivial preferences for a number of users. Cobot modifies his behavior based on his current state in an attempt to maximize reward. Here we describe LambdaMOO and the state and action spaces of Cobot, and report the statistical results of the learning experiment.

Query optimization in compressed database systems

Abstract: Over the last decades, improvements in CPU speed have outpaced improvements in main memory and disk access rates by orders of magnitude, enabling the use of data compression techniques to improve the performance of database systems. Previous work describes the benefits of compression for numerical attributes, where data is stored in compressed format on disk. Despite the abundance of string-valued attributes in relational schemas there is little work on compression for string attributes in a database context. Moreover, none of the previous work suitably addresses the role of the query optimizer: During query execution, data is either eagerly decompressed when it is read into main memory, or data lazily stays compressed in main memory and is decompressed on demand onlyIn this paper, we present an effective approach for database compression based on lightweight, attribute-level compression techniques. We propose a IIierarchical Dictionary Encoding strategy that intelligently selects the most effective compression method for string-valued attributes. We show that eager and lazy decompression strategies produce sub-optimal plans for queries involving compressed string attributes. We then formalize the problem of compression-aware query optimization and propose one provably optimal and two fast heuristic algorithms for selecting a query plan for relational schemas with compressed attributes; our algorithms can easily be integrated into existing cost-based query optimizers. Experiments using TPC-H data demonstrate the impact of our string compression methods and show the importance of compression-aware query optimization. Our approach results in up to an order speed up over existing approaches.

Generalized Algorithms for Constructing Statistical Language Models

Abstract: Recent text and speech processing applications such as speech mining raise new and more general problems related to the construction of language models. We present and describe in detail several new and efficient algorithms to address these more general problems and report experimental results demonstrating their usefulness. We give an algorithm for computing efficiently the expected counts of any sequence in a word lattice output by a speech recognizer or any arbitrary weighted automaton; describe a new technique for creating exact representations of n-gram language models by weighted automata whose size is practical for offline use even for a vocabulary size of about 500,000 words and an n-gram order n = 6; and present a simple and more general technique for constructing class-based language models that allows each class to represent an arbitrary weighted automaton. An efficient implementation of our algorithms and techniques has been incorporated in a general software library for language modeling, the GRM Library, that includes many other text and grammar processing functionalities.

Let's stop pushing the envelope and start addressing it: a reference task agenda for HCI

Abstract: We identify a problem with the process of research in the human-computer interaction (HCI) community-an overemphasis on "radical invention" at the price of achieving a common research focus. Without such a focus, it is difficult to build on previous work, to compare different interaction techniques objectively, and to make progress in developing theory. These problems at the research level have implications for practice, too; as researchers we often are unable to give principled design advice to builders of new systems. We propose that the HCI community try to achieve a common focus around the notion of reference tasks. We offer arguments for the advantages of this approach as well as consider potential difficulties. We explain how reference tasks have been highly effective in focusing research into information retrieval and speech recognition. We discuss what factors have to be considered in selecting HCI reference tasks and present an example reference task (for searching speech archives). This example illustrates the nature of reference tasks and points to the issues and problems involved in constructing and using them. We conclude with recommendations about what steps need to be taken to execute the reference task research agenda. This involves recommendations about both the technical research that needs to be done and changes in the way that the HCI research community operates. The technical research involves identification of important user tasks by systematic requirements gathering, definition and operationalization of reference tasks and evaluation metrics, and execution of task-based evaluation, along with judicious use of field trials. Perhaps more important, we have also suggested changes in community practice that HCI must adopt to make the reference tasks idea work. We must create forums for discussion of common tasks and methods by which people can compare systems and techniques. Only by doing this can the notion of reference tasks be integrated into the process of research and development, enabling the field to achieve the focus it desperately needs.