Hewlett-Packard Development Company, L.p.

Future Wei Technologies, Inc.

Entit Software LLC

Jerry Zhaolin Shan

Fereydoon Safai

Evan Randy Kirshenbaum

Henri Jacques Suermondt

Dirk Manfred Beyer

Xin Zhang

Shailendra K Jain

Jose Luis Beltran Guerrero

Umeshwar Dayal

Meichun Hsu

Ming C Hao

Qiming Chen

Ming-Chien Shan

Rajan Mathew Lukose

Alex Xin Zhang

Cipriano A Santos

Mehmet Oguz Sayal

Julie Ward Drew

Yunhong Zhou

Kay-Yut Chen

Maria Teresa Gonzalez Diaz

Hsiu-Khuem Tang

Peter J Wright

Ivan Adrian Lopez Sanchez

Ming Hu

Chao Chen

Richard Stormo

Lauren E Turner

Luyi Wang

An apparatus and method are provided for ranking content categories. Multiple sessions of usage data is collected for usage activities of a plurality of users (associated with a group) on a plurality of categories of content. The usage data is analyzed for an average group usage of each category across the sessions and a variation of the average group usage of each category across the sessions. Individual usage data is also collected for usage activities of at least one user on the categories of content. The individual usage data reflects an aggregated usage of each category for the at least one user. Category usage is estimated on each category for the at least one user based on a weighted combination of the aggregated usage and the average group usage according to the variation. An amount of weight of the aggregated usage in the weighted combination is inversely related to the variation. The categories of content are ranked, via at least one processor, utilizing the estimated category usage on each category. Further, a presentation including the ranking is sent to a user device of the at least one user for being output via an interface.

Apparatus and method for ranking content categories

In-database parallel analytics is disclosed. An example method includes receiving a function in a functional programming environment. The example method includes pushing operations for the function to a database layer for executing the operations. The example method also includes returning results of the operations to the functional programming environment.

In-database parallel analytics

A report indicating a user-reported probability of a successful outcome is received. A behavior and information model is estimated based on the report. The behavior and information model includes a behavior model component having a bias parameter and a consistency parameter. The behavior and information model includes an information model component having a first user-believed probability of a successful outcome and a second user-believed probability of a successful outcome. The behavior and information model is used to yield a model-determined probability of a successful outcome that more accurately reflects a probability of a successful outcome than the user-reported probability of a successful outcome does.

Behavior and information model to yield more accurate probability of successful outcome

Embodiments include methods, apparatus, and systems for forecasting using a time domain analysis. One embodiment is a computer implemented method that receives plural cycle lengths identified in time series data and builds a model using a time domain analysis of the time series data. The model is used to predict future events or future data points.

Forecasting through time domain analysis

A method for determining call center resource allocation can include modeling call center performance over an operations time period using a computer. A number of replicas of the modeled call center performance are simulated, using the computer, over a planning time period, each replica having random contact arrivals and contact service times following a stochastic arrival and service process according to a probability distributions of inter-arrival time and service time. Multiple iterations of each simulation are run on the computer to optimize call center resource allocation. A particular simulation iteration is tested against a criterion of convergence, and call center resource is allocated based on the particular simulation iteration with a successful criterion of convergence.

Call center resource allocation

Methods, machine readable media, and systems for market forecasting are provided. An example of a method for market forecasting includes modeling market characteristics of market participants for a type of product and deriving variability of an attribute corresponding to a market characteristic coefficient of the type of product for each of the market participants. The method includes resampling from a distribution of the variability of the attribute for each of the market participants and remodeling the market characteristics of the market participants for the type of product using the resampled attribute. The method includes forecasting future market characteristics of the market participants for the type of product according to the remodeled market characteristics.

Market forecasting

Embodiments of the present invention include a computational forecasting system that includes an identity of a dependent variable of interest and identities of a plurality of candidate indicators along with historical data or stored references to historical data, forecast-problem parameters stored in an electronic memory of the one or more electronic computers, an independent-variable selection component that generates correlations to the dependent variable of interest and lag times for the candidate indicators, and uses the generated correlations and lag times to select a number of the candidate indicators as a set of independent variables, and a model-generation component that, using a regression method, generates forecast models for the dependent variable of interest until a model that meets an acceptance criterion or criteria is obtained.

System and method for constructing forecast models

There is provided a system and method for estimating a parameter that represents data describing a physical system. An exemplary method comprises randomizing data representative of a population of items for which the parameter is known. The method may additionally comprise generating data representative of a pseudo population of items using a known perturbation, the data representative of the pseudo population of items being included with the data representative of the population of items for which the parameter is known to form a revised population and selecting a bootstrap sample of a minimum sample size of the revised population. A sensitivity study is performed on the parameters of the items comprising the bootstrap sample to determine a level of change of a predicted parameter value relative to a parameter value of the sample. At least one of a range, a probability distribution or the minimum sample size is revised based on the parameter for items comprising the bootstrap sample to produce at least one of a revised range, a revised probability distribution or a revised minimum sample size, taking into account an effect of the known perturbation applied to the pseudo population. The steps of selecting, performing and revising are repeated until the sensitivity study indicates that the level of change of the parameter is acceptably small. A value of the parameter is estimated for the population based on a parameter corresponding to the acceptably small level of change.

System and method for estimating a parameter that represents data describing a physical system

Systems and methods for detecting an interesting event in a data stream. More specifically, a monitoring system is configured to monitor a data stream and establish a sensitivity parameter based on sequences generated from values in a first portion of the data stream. A detector may be trained using the sensitivity parameter to detect the occurrence of an interesting event in the data stream.

Techniques for monitoring a data stream

Disclosed herein are systems and methods for forecasting with model-based PDF (probability density function) estimates. Some method embodiments may comprise: estimating model parameters for a time series, calculating a PDF for the time series, and generating a forecast from the PDF. The model parameters may comprise a variance for a hidden noise source, and the PDF for the time series may be based at least in part on an estimated variance for the hidden noise source.

Methods and systems for forecasting with model-based PDF estimates

A trend of attributes associated with plural market participants is determined. A representation of the trend is computed, and market models for the market participants are built according to the representation of the trend.

Building market models for plural market participants

To analyze a time series of data that exhibits seasonal effects, the time series is processed to obtain a representation in the frequency domain. According to the representation, plural cycle lengths are identified as representing different seasonal effects of the data in the time series, where a first of the plural cycle lengths is greater than a second of the plural cycle lengths.

Analyzing time series data that exhibits seasonal effects

To detect a change in data produced by a system, predicted data values for plural time points are computed. Actual data values for the plural time points are received, and residual values are derived from differences between the predicted data values and actual data values. Based on the computed residual values, a time point at which the change in data occurred is determined.

Detecting change in data

User input regarding a target system on which a software application is to be deployed is received. A benchmark system from plural candidate benchmark systems is matched to the target system. An estimated performance of the software application on the target system or an estimated utilization of resources of the target system by the software application is computed based on information relating to the matched benchmark system.

Computing estimated performance of a software application in a target system

Aggregation of data values in a data set is computed to produce aggregate values. The aggregate values are partitioned into plural aggregate value sets. Fitting with respect to the plural aggregate value sets is performed. Based on the fitting, at least one time point corresponding to occurrence of a change in the data values is determined.

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Palo Alto, CA, United States of America

Jerry Zhaolin Shan