Density Functions subject to a Co-Matroid Constraint

In this paper we consider the problem of finding the {\em densest} subset subject to {\em co-matroid constraints}. We are given a {\em monotone supermodular} set function $f$ defined over a universe $U$, and the density of a subset $S$ is defined to be f(S)/\crd{S}. This generalizes the concept of graph density. Co-matroid constraints are the following: given matroid \calM a set $S$ is feasible, iff the complement of $S$ is {\em independent} in the matroid. Under such constraints, the problem becomes \np-hard. The specific case of graph density has been considered in literature under specific co-matroid constraints, for example, the cardinality matroid and the partition matroid. We show a 2-approximation for finding the densest subset subject to co-matroid constraints. Thus, for instance, we improve the approximation guarantees for the result for partition matroids in the literature

Measurement Based Optimal Source Shaping with a Shaping+Multiplexing Delay Constraint

Most on-line (i.e., not stored) Variable Bit Rate sources would find it difficult to a priori declare the traffic parameters required by a connection admission control strategy. There is thus the problem of measurement based on-line estimation of source parameters. In this paper we address the problem of selection of source parameters based on minimising a bufferbandwidth cost function in the network, for a specified delay QoS Violation Probability. We consider the shaping delay plus first hop multiplexing delay; this is adequate, for example, for n statistically identical packet voice sources being multiplexed at a PBX, or in approaches where the end-to-end delay bound is broken into per hop delay bounds. Our approach yields a leaky bucket rate parameter ae , and the sum of the shaper buffer and leaky bucket depth (Bs + oe). We show that, for a fluid source model, for a linear buffer-bandwidth cost function, and for lossless multiplexing, a sustainable rate parameter of ae and burst..

Automatically identifying known software problems

Re-occurrence of the same problem is very common in many large software products. By matching the symptoms of a new problem to those in a database of known problems, automated diagnosis and even selfhealing for re-occurrences can be (partially) realized. This paper exploits function call stacks as highly structured symptoms of a certain class of problems, including crashes, hangs, and traps. We propose and evaluate algorithms for efficiently and accurately matching call stacks by a weighted metric of the similarity of their function names, after first removing redundant recursion and uninformative (poor discriminator) functions from those stacks. We also describe a new indexing scheme to speed queries to the repository of known problems, without compromising the quality of matches returned. Experiments conducted using call stacks from actual product problem reports demonstrate the improved accuracy (both precision and recall) resulting from our new stack-matching algorithms and removal of uninformative or redundant function names, as well as the performance and scalability improvements realized by indexing call stacks. We also discuss how call-stack matching can be used in both self-managing (or autonomic systems) and human “help desk” applications. 1

Exploratory Navigation and Selective Reading

Navigating a collection of documents can be facilitated by obtaining a human-understandable concept hierarchy with links to the content. This is a non-trivial task for two reasons. First, defining concepts that are understandable by an average consumer and yet meaningful for a large variety of corpora is hard. Second, creating semantically meaningful yet intuitive hierarchical representation is hard, and can be task dependent. We present out system Navigation.ai which automatically processes a document collection, induces a concept hierarchy using Wikipedia and presents an interactive interface that helps user navigate to individual paragraphs using concepts