Showing papers by "Irina Rish published in 2000"

Resolution versus Search: Two Strategies for SAT

Abstract: The paper compares two popular strategies for solving propositional satisfiability, i>backtracking search and i>resolution, and analyzes the complexity of a directional resolution algorithm (DR) as a function of the “width” (i>wa) of the problem"s graph. Our empirical evaluation confirms theoretical prediction, showing that on low-i>wa problems DR is very efficient, greatly outperforming the backtracking-based Davis–Putnam–Logemann–Loveland procedure (DP). We also emphasize the knowledge-compilation properties of DR and extend it to a tree-clustering algorithm that facilitates query answering. Finally, we propose two hybrid algorithms that combine the advantages of both DR and DP. These algorithms use control parameters that bound the complexity of resolution and allow time/space trade-offs that can be adjusted to the problem structure and to the user"s computational resources. Empirical studies demonstrate the advantages of such hybrid schemes.

Recognizing End-User Transactions in Performance Management

Abstract: Providing good quality of service (e.g., low response times) in distributed computer systems requires measuring enduser perceptions of performance. Unfortunately, such measures are often expensive or impossible to obtain. Herein, we propose a machine-learning approach to recognizing enduser transactions consisting of sequences of remote procedure calls (RPCs) received at a server. Two problems are addressed. The first problem is labeling an RPC sequence that corresponds to one transaction instance with the correct transaction type. This is akin to text classification. The second problem is transaction recognition, a more comprehensive task that involves segmenting RPC sequences into transaction instances and labeling those instances with transaction types. This problem is similar to segmenting sounds into words as in speech understanding. Using Naive Bayes approach, we tackle the labeling problem with four combinations of feature vectors and probability distributions: RPC occurrences with the Bernoulli distribution and RPC counts with the multinomial, geometric, and shifted geometric distributions. Our approach to transaction recognition uses a dynamic-programming Viterbi algorithm that searches for a most likely segmentation of an RPC sequence into a sequence of transactions, assuming transaction independence and using our classifiers to select a most likely transaction label for a given RPC sequence. For both problems, good accuracies are obtained, although the labeling problem achieves higher accuracies (up to 87%) than does transaction recognition (64%).

Methods and apparatus for selecting a data classification model using meta-learning

Abstract: A data classification method and apparatus are disclosed for labeling unknown objects. The disclosed data classification system employs a model selection technique that characterizes domains and identifies the degree of match between the domain meta-features and the learning bias of the algorithm under analysis. An improved concept variation meta-feature or an average weighted distance meta-feature, or both, are used to fully discriminate learning performance, as well as conventional meta-features. The “concept variation” meta-feature measures the amount of concept variation or the degree of lack of structure of a concept. The present invention extends conventional notions of concept variation to allow for numeric and categorical features, and estimates the variation of the whole example population through a training sample. The “average weighted distance” meta-feature of the present invention measures the density of the distribution in the training set. While the concept variation meta-feature is high for a training set comprised of only two examples having different class labels, the average weighted distance can distinguish between examples that are too far apart or too close to one other.

Method and system for recognizing end-user transactions

Abstract: A method and system are described for end-user transaction recognition based on server data such as sequences of remote procedure calls (RPCs). The method may comprise machine-learning techniques for pattern recognition such as Bayesian classification, feature extraction mechanisms, and a dynamic-programming approach to segmentation of RPC sequences. The method preferably combines information-theoretic and machine-learning approaches. The system preferably includes a learning engine and an operation engine. A learning engine may comprise a data preparation subsystem (feature extraction) and a Bayes Net learning subsystem (model construction). The operation engine may comprise transaction segmentation and transaction classification subsystems.