International Business Machines Corporation

Jayram S Thathachar

Maxim I Sviridenko

Tracy Jay Kimbrel

Robert Krauthgamer

Baruch M Schieber

Joseph L Hellerstein

Mark Steven Squillante

Maria Minkoff

Joseph Phillip Bigus

David Allan Pease

Cheryl Ann Kieliszewski

Laura Challman Anderson

Sridevi Koushik Kanagal Ramesh

Lior Horesh

Shivkumar Kalyanaraman

John A Gunnels

Irina Rish

Shivakumar Vaithyanathan

Vijay Arya

Kenneth Lee Clarkson

Huaiyu Zhu

Devasenapathi Periagraharam Seetharamakrishnan

Sujay Sunil Parekh

Tal Kachman

Donald Allen Schlosnagle

Ashutosh Garg

Jeffrey Robert Pilgrim

Ismail Yunus Akhalwaya

Parikshit Gopalan

Robert Krauthgarner

A method for performing sparse quantum Fourier transform computation includes defining a set of quantum circuits, each quantum circuit comprising a Hadamard gate and a single frequency rotation operator, said set of quantum circuits being equivalent to a quantum Fourier transform circuit. The method includes constructing a subset of said quantum circuits in a quantum processor, said quantum processor having a quantum representation of a classical distribution loaded into a quantum state of said quantum processor. The method includes executing said subset of said quantum circuits on said quantum state, and performing a measurement in a frequency basis to obtain a frequency distribution corresponding to said quantum state.

Quantum sparse Fourier transform

One embodiment provides a method including: collecting system log files comprising a plurality of log messages representing activity within the system; generating a plurality of message templates by (i) clustering the plurality of messages into groups having similar activity patterns and (ii) generating a message template for each of the groups; identifying message templates of interest and determining the message templates representing actual end-user events as opposed to system events, wherein the determining comprises (i) identifying a seed set of event types that represent known end-user events based upon identifying the event as being known to be initiated by a user and (ii) correlating the event to the known end-user event based upon a time correlation between the known end-user event and the event within the message template of interest.

Generation of end-user sessions from end-user events identified from computer system logs

One embodiment provides a method including: collecting, from a plurality of collaborative computing systems, system log files comprising a plurality of log messages representing activity within the system, wherein the activity within the system corresponds to system activity and end-user activity; generating a plurality of message templates, each of the plurality of message templates representing an event type by clustering the plurality of messages into groups having similar activity patterns; identifying, from the plurality of message templates, a subset of message templates representing potential end-user events; differentiating end-user events from system events, in the subset of message templates representing potential end-user events, comprising identifying a seed set of event types that represent known end-user events and correlating potential end-user events remaining after identifying the seed set of event types with the known end-user events; and grouping, based upon the differentiating, the event types representing end-user events into user sessions.

Methods and arrangements for determining a network connectivity model. Meters are interfaced with, and periodic synchronous power-related measurements are collected from different meters. Measurement messages are sent to a predetermined location for being consolidated and processed, and a network connectivity model is thereupon determined.

Determining a connectivity model in smart grids

Disclosed is a method of scanning a data stream in a single pass to obtain uniform data samples from selected intervals. The method comprises randomly selecting elements from the stream for storage in one or more data buckets and, then, randomly selecting multiple samples from the bucket(s). Each sample is associated with a specified interval immediately prior to a selected point in time. There is a balance of probabilities between the selection of elements stored in the bucket and the selection of elements included in the samples so that elements scanned during the specified interval are included in the sample with equal probability. Samples can then be used to estimate the degree of sortedness of the stream, based on counting how many elements in the sequence are the rightmost point of an interval such that majority of the interval's elements are inverted with respect to the interval's rightmost element.

Method of obtaining data samples from a data stream and of estimating the sortedness of the data stream based on the samples

A benefit task system implements a policy for allocating resources to yield some benefit. The method implemented may be applied to a variety of problems, and the benefit may be either tangible (e.g., profit) or intangible (e.g., customer satisfaction). In one example, the method is applied to server allocation in a Web site server 'farm' given full information regarding future loads to maximize profits for the Web hosting service provider. In another example, the method is applied to the allocation of telephone help in a way to improve customer satisfaction. In yet another example, the method is applied to distributed computing problem where the resources to be allocated are general purpose computers connected in a network and used to solve computationally intensive problems. Solution of the Web server 'farm' problem is based on information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach which reduces the Web server farm problem to a minimum-cost network flow problem, which can be solved in polynomial time. Similar solutions are applicable to other resource allocation problems.

Dynamic resource allocation using known future benefits

A method for server allocation in a Web server 'farm' is based on limited information regarding future loads to achieve close to the greatest possible revenue based on the assumption that revenue is proportional to the utilization of servers and differentiated by customer class. The method of server allocation uses an approach of 'discounting the future'. Specifically, when the policy faces the choice between a guaranteed benefit immediately and a potential benefit in the future, the decision is made by comparing the guaranteed benefit value with a discounted value of the potential future benefit. This discount factor is exponential in the number of time units that it would take a potential benefit to be materialized. The future benefits are discounted because by the time a benefit will be materialized, things might change and the algorithm might decide to make another choice for a potential (even greater) benefit.

Dynamic resource allocation using projected future benefits

Disclosed is a system, method, and program storage device of aggregating opinions comprising consolidating a plurality of expressed opinions on various dimensions of topics as discrete probability distributions, generating an aggregate opinion as a single point probability distribution by minimizing a sum of weighted divergences between a plurality of the discrete probability distributions, and presenting the aggregate opinion as a Bayesian network, wherein the divergences comprise Kullback-Liebler distance divergences, and wherein the expressed opinions are generated by experts and comprise opinions on sentiments of products and services. Moreover, the aggregate opinion predicts success of the products and services. Furthermore, the experts are arranged in a hierarchy of knowledge, wherein the knowledge comprises the various dimensions of topics for which opinions may be expressed upon.

Method to hierarchical pooling of opinions from multiple sources

A system and method are described for constructing and implementing generic software agents for automated tuning of computer systems and applications. The framework defines the modules and interfaces to allow agents to be created in a modular fashion. The specifics of the target system are captured by adaptors that provide a uniform interface to the target system. Data in the agent is managed by a metric manager, and controller modules implement the desired control algorithms. The modular structure and common interfaces allow for the construction of generic agents that are applicable to a wide variety of target systems, and can use a wide variety of control algorithms.

Object-oriented framework for generic adaptive control

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.

Method and system for recognizing end-user transactions

A system and method is described for generic automated tuning for performance management. The system comprises a target to be controlled and a generic automated tuning agent (GATA) that performs this control. The controlled target provides interfaces to metrics relating to workload, service levels, and configuration information, as well as a means to adjust tuning controls that determine resource allocations within the target. The GATA inputs the metrics, estimates new tuning control settings based on service objectives specified by administrators, and outputs the tuning control settings.

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San Jose, CA, United States of America

Jayram S Thathachar