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.

On multi-column foreign key discovery

Abstract: A foreign/primary key relationship between relational tables is one of the most important constraints in a database. From a data analysis perspective, discovering foreign keys is a crucial step in understanding and working with the data. Nevertheless, more often than not, foreign key constraints are not specified in the data, for various reasons; e.g., some associations are not known to designers but are inherent in the data, while others become invalid due to data inconsistencies. This work proposes a robust algorithm for discovering single-column and multi-column foreign keys. Previous work concentrated mostly on discovering single-column foreign keys using a variety of rules, like inclusion dependencies, column names, and minimum/maximum values. We first propose a general rule, termed Randomness, that subsumes a variety of other rules. We then develop efficient approximation algorithms for evaluating randomness, using only two passes over the data. Finally, we validate our approach via extensive experiments using real and synthetic datasets.

Declarative automated cloud resource orchestration

Abstract: As cloud computing becomes widely deployed, one of the challenges faced involves the ability to orchestrate a highly complex set of subsystems (compute, storage, network resources) that span large geographic areas serving diverse clients. To ease this process, we present COPE (Cloud Orchestration Policy Engine), a distributed platform that allows cloud providers to perform declarative automated cloud resource orchestration. In COPE, cloud providers specify system-wide constraints and goals using COPElog, a declarative policy language geared towards specifying distributed constraint optimizations. COPE takes policy specifications and cloud system states as input and then optimizes compute, storage and network resource allocations within the cloud such that provider operational objectives and customer SLAs can be better met. We describe our proposed integration with a cloud orchestration platform, and present initial evaluation results that demonstrate the viability of COPE using production traces from a large hosting company in the US. We further discuss an orchestration scenario that involves geographically distributed data centers, and conclude with an ongoing status of our work.

The design of a class mechanism for Moby

Abstract: Typical class-based languages, such as C++ and JAVA, provide complex class mechanisms but only weak module systems. In fact, classes in these languages incorporate many of the features found in richer module mechanisms. In this paper, we describe an alternative approach to designing a language that has both classes and modules. In our design, we rely on a rich ML-style module system to provide features such as visibility control and parameterization, while providing a minimal class mechanism that includes only those features needed to support inheritance. Programmers can then use the combination of modules and classes to implement the full range of class-based features and idioms. Our approach has the advantage that it provides a full-featured module system (useful in its own right), while keeping the class mechanism quite simple.We have incorporated this design in MOBY, which is an ML-style language that supports class-based object-oriented programming. In this paper, we describe our design via a series of simple examples, show how various class-based features and idioms are realized in MOBY, compare our design with others, and sketch its formal semantics.

Approximate NN queries on streams with guaranteed error/performance bounds

Abstract: In data stream applications, data arrive continuously and can only be scanned once as the query processor has very limited memory (relative to the size of the stream) to work with. Hence, queries on data streams do not have access to the entire data set and query answers are typically approximate. While there have been many studies on the k Nearest Neighbors (kNN) problem in conventional multi-dimensional databases, the solutions cannot be directly applied to data streams for the above reasons. In this paper, we investigate the kNN problem over data streams. We first introduce the e-approximate kNN (ekNN) problem that finds the approximate kNN answers of a query point Q such that the absolute error of the k-th nearest neighbor distance is bounded by e. To support ekNN queries over streams, we propose a technique called DISC (aDaptive Indexing on Streams by space-filling Curves). DISC can adapt to different data distributions to either (a) optimize memory utilization to answer ekNN queries under certain accuracy requirements or (b) achieve the best accuracy under a given memory constraint. At the same time, DISC provide efficient updates and query processing which are important requirements in data stream applications. Extensive experiments were conducted using both synthetic and real data sets and the results confirm the effectiveness and efficiency of DISC.

Rounding algorithms for a geometric embedding of minimum multiway cut

Abstract: Given an undirected graph with edge costs and a subset of k ≥ 3 nodes called terminals, a multiway, or k-way, cut is a subset of the edges whose removal disconnects each terminal from the others. The multiway cut problem is to find a minimum-cost multiway cut. T his problem is Max-SNP hard. Recently Calinescu, Karloff, and Rabani (STOC’98) gave a novel geometric relaxation of the problem and a rounding scheme that produced a (3/2 − 1/k)-approximation algorithm. In this paper, we study their geometric relaxation. In parti cular, we study the worst-case ratio between the value of the relaxation and the value of the minimum multicut (the so-called integrality gap of the relaxation). For k = 3, we show the integrality gap is 12/11, giving tight upper and lower bounds. That is, we exhibit a family of graphs with integrality gaps arbit rarily close to 12/11 and give an algorithm that finds a cut of value 12/11 times the relaxation value. Our lower bound shows that this is the best