# Choudur Lakshminarayan, Austin US

## Choudur Lakshminarayan, Austin, TX US

Patent application number | Description | Published |
---|---|---|

20100114624 | SYSTEM AND METHOD FOR OPTIMIZING FINANCIAL PERFORMANCE GENERATED BY MARKETING INVESTMENTS UNDER BUDGET CONSTRAINTS - Methods, systems, and computer program products are provided for optimizing financial performance. Time series data describing the financial performance generated by corresponding marketing investments is provided to configure an econometric model. Linear coefficients of the econometric model are adjusted in accordance with the qualitative factors received as inputs from experts. The econometric model is transformed into an aggregated non-linear econometric model that includes non-linear factors that cause the financial performance to change at a varying rate as a function of the marketing investments. An allocation of the marketing investments generated by the aggregated non-linear econometric model is adjusted by applying an optimization algorithm to provide an optimized financial performance. | 05-06-2010 |

20100114648 | SYSTEM AND METHOD FOR INCORPORATING QUALITATIVE INPUTS INTO ECONOMETRIC MODELS - Methods, systems, and computer program products are provided for incorporating qualitative factors into an econometric model. Time series data describing the financial performance generated by corresponding marketing investments that are made as a function of time is provided to configure an econometric model. The econometric model includes linear coefficients that define a linear relationship between the financial performance and the corresponding marketing investments. The linear coefficients are adjusted in accordance with the qualitative factors received as inputs from experts, thereby enabling the qualitative factors to be quantified into the econometric model. | 05-06-2010 |

20100114794 | PREDICTION OF FINANCIAL PERFORMANCE FOR A GIVEN PORTFOLIO OF MARKETING INVESTMENTS - Methods, systems, and computer program products are provided for quantifying financial impact of marketing investments. Time series data describing the financial performance generated by corresponding marketing investments that are made as a function of time is provided to configure an econometric model. The econometric model, which describes a linear relationship between the financial performance and the corresponding marketing investments, is transformed into an aggregated non-linear econometric model that includes non-linear factors causing the financial performance to change at a varying rate as a function of the marketing investments. | 05-06-2010 |

20100114869 | GENERATING A QUERY PLAN FOR ESTIMATING A NUMBER OF UNIQUE ATTRIBUTES IN A DATABASE - In a method for generating a query plan for estimating a number of unique entry counts of an attribute in a database, a sample of entries in the database is identified, at least one of a sampling percent and a coefficient of variance of the entries in the sample is identified, and a skewness of the entries in the sample is calculated. In addition, at least one of a plurality of estimators is selected based upon the skewness of the entries and at least one of the sampling percent and the coefficient of variance of the entries in the sample. Moreover, a query plan is generated from the selected at least one of the plurality of estimators. A query optimizer for performing the method is provided. | 05-06-2010 |

20110010405 | Compression of non-dyadic sensor data organized within a non-dyadic hierarchy - Sensor data is received from one or more sensors. The sensor data is organized within a hierarchy. The sensor data is organized within a hierarchy that is non-dyadic. A processor of a computing device generates a discrete wavelet transform, based on the sensor data and based on the hierarchy of the sensor data, to compress the sensor data. The sensor data, as has been compressed via generation of the discrete wavelet transform, is processed. | 01-13-2011 |

20110184934 | WAVELET COMPRESSION WITH BOOTSTRAP SAMPLING - A method for compressing an initial dataset may be implemented on a data processing system. The method may include generating a group of bootstrap samples of wavelet coefficients from the initial dataset using a wavelet basis function. An average quantile of the group of bootstrap samples of wavelet coefficients may be determined. The group of wavelet coefficients may be compressed by deleting initial wavelet coefficients having magnitudes less than the coefficient cutoff value equal to the average quantile. The compressed group of wavelet coefficients and the wavelet basis function may be used to approximate the initial dataset. | 07-28-2011 |

20120084287 | ESTIMATION OF UNIQUE DATABASE VALUES - Estimation of unique values in a database can be performed where a data field having multiple information values is provided in the database. The data field can be partitioned into multiple intervals such that each interval includes a range of information values. An interval specific Bloom filter can be calculated for each of the multiple intervals. A binary Bloom filter value can be calculated for an information value within an interval specific Bloom filter. The binary Bloom filter value can represent whether the information value is unique. A number of unique values in the database can be determined based on calculated binary Bloom filter values. | 04-05-2012 |

20120089357 | METHOD AND APPARATUS FOR IDENTIFYING ANOMALIES OF A SIGNAL - A method and apparatus are disclosed for identifying anomalies of a signal, by analyzing a signal using a frequency-based technique, analyzing results of the frequency-based analysis using a statistical analysis technique, determining one or more limits based on the statistical analysis, and comparing a frequency domain representation of the signal to the limits to identify anomalies of the signal. | 04-12-2012 |

20120095989 | Estimating a Number of Unique Values in a List - A method determines a number of unique values in a sample of a list of values and estimates a number of the unique values for an unsampled portion of the list of values. The method estimates a number of the unique values in the list by adding the number of unique values in the sample to the number of the unique values in the unsampled portion. | 04-19-2012 |

20120317061 | TIME ENCODING USING INTEGRATE AND FIRE SAMPLER - Systems and methods of time encoding using an integrate and fire (IF) sampler are disclosed. In an example, a method includes receiving input signals for separate classes. The method also includes generating a pulse train based on the input signals. The method also includes binning the pulse train to generate a feature vector. | 12-13-2012 |

20130030761 | STATISTICALLY-BASED ANOMALY DETECTION IN UTILITY CLOUDS - Systems and methods for detecting anomalies in a large scale and cloud datacenter are disclosed. Anomaly detection is performed in an automated, statistical-based manner by using a parametric Gini coefficient technique or a non-parametric Tukey technique. In the parametric Gini coefficient technique, sample data is collected within a look-back window. The sample data is normalized to generate normalized data, which is binned into a plurality of bins defined by bin indices. A Gini coefficient and a threshold are calculated for the look-back window and the Gini coefficient is compared to the threshold to detect an anomaly in the sample data. In the non-parametric Tukey technique, collected sample data is divided into quartiles and compared to adjustable Tukey thresholds to detect anomalies in the sample data. | 01-31-2013 |

20130067106 | MULTI-REGIME DETECTION IN STREAMING DATA - Systems and methods for multi-regime detection in streaming data are disclosed. An example method includes generating vectors for each sample of the streaming data. The method also includes inducing mean independence of the vectors to find an embedded data trajectory. The method also includes comparing the embedded data trajectory with known data trajectories. The method also includes issuing an alert if the embedded data trajectory corresponds to a known data trajectory indicating an anomaly in the streaming data. | 03-14-2013 |

20130080375 | ANOMALY DETECTION IN DATA CENTERS - Systems and methods of anomaly detection in data centers. An example method may include analyzing time series data for the data center by testing statistical hypotheses. The method may also include constructing upper and lower bounds based on the statistical hypotheses. The method may also include flagging anomalies in the time series data falling outside of the upper and lower bounds. | 03-28-2013 |

20130085715 | ANOMALY DETECTION IN STREAMING DATA - Systems and methods for anomaly detection in streaming data are disclosed. An example method includes applying statistical analysis to streaming data in a sliding window. The method also includes extracting a feature. The method also includes determining class assignment for the feature using class conditional probability densities and a threshold. | 04-04-2013 |

20130110761 | SYSTEM AND METHOD FOR RANKING ANOMALIES | 05-02-2013 |

20130191309 | Dataset Compression - Compression of an initial dataset is implemented on a data processing system. The initial dataset can be transformed ( | 07-25-2013 |

20130226941 | SYSTEM AND METHOD FOR CLASSIFYING SIGNALS USING THE BLOOM FILTER - The present disclosure generally relates to data processing. Bloom filters are used to process data at high speed. A Bloom filter that is initialized based on a source string can be used to quickly determine the similarity between the source string and a query string. | 08-29-2013 |

20130226972 | METHODS AND SYSTEMS FOR PROCESSING DATA ARRAYS USING BLOOM FILTERS - The present disclosure relates to computing techniques. Data arrays are processed using Bloom filters to determine aggregate count, maximum, and minimum. These methods can be used on different types of data, including data groups, partial groups, data cubes, hypercubes, and others. | 08-29-2013 |

20140032450 | CLASSIFYING UNCLASSIFIED SAMPLES - A system and method for classifying unclassified samples. The method includes detecting a number of classes including training samples in training data sets. The method includes, for each class, determining a vector for each training sample based on a specified number of nearest neighbor distances between the training sample and neighbor training samples, and determining a class distribution based on the vectors. The method also includes detecting an unclassified sample in a data set and, for each class, determining a vector for the unclassified sample based on the specified number of nearest neighbor distances between the unclassified sample and nearest neighbor training samples within the class, and determining a probability that the unclassified sample is a member of the class based on the vector and the class distribution. The method further includes classifying the unclassified sample based on the probabilities. | 01-30-2014 |

20140149433 | Estimating Unique Entry Counts Using a Counting Bloom Filter - A method of estimating a number of unique entry counts of an attribute in a database comprises, with a processor: identifying a sample of entries from an attribute database, determining frequencies of a number of input observations of the sample of entries, determining a number of high frequency values of the sample of entries, and estimating a number of unique entry counts of an attribute within the attribute database using a counting Bloom filter and based on the frequencies of the input observations and the high frequency values. | 05-29-2014 |

20140210632 | AMPLITUDE AND FREQUENCY-BASED DETERMINATION - A method includes computing, by an amplitude feature computation engine, an amplitude feature of a frame of time-series data. The method further includes computing, by a frequency feature computation engine, a frequency feature of the frame of time-series data. | 07-31-2014 |

20140324743 | AUTOREGRESSIVE MODEL FOR TIME-SERIES DATA - A technique includes fitting an autoregressive integrated moving average (ARIMA) model to time-series data. The technique further includes the computation of autoregression coefficients from the ARIMA model applied to the time-series data. The autoregression coefficients may be usable for data classification purposes. | 10-30-2014 |

20140330768 | INCREMENTALLY UPDATED SAMPLE TABLES - An example apparatus may include a processor and a memory device including computer program code. The memory device and the computer program code may be for, with the processor, causing the apparatus to delete, in a sample table, rows corresponding to a predicate, wherein rows in the sample table are representative of a random sample of rows in a base table of a database; generate sample rows representative of a random sample of rows in the base table corresponding to the predicate; and add the sample rows to the sample table to generate an incrementally updated sample table. | 11-06-2014 |

20150025908 | CLUSTERING AND ANALYSIS OF ELECTRONIC MEDICAL RECORDS - A technique includes clustering a plurality of electronic patient records (PRs) based on related diagnostic codes into a plurality of clusters, and analyzing one of the plurality of clusters to determine variations in resource usage within the cluster. | 01-22-2015 |