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公开(公告)号：US20190155824A1
公开(公告)日：2019-05-23
申请号：US16256519
申请日：2019-01-24
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul Pallath , Rouzbeh Razavi
IPC: G06F16/28 , G06F16/2458
Abstract: The present disclosure describes methods, systems, and computer program products for enabling advanced analytics with large datasets. One computer-implemented method includes receiving, by operation of a computer system, a dataset of multiple data records, each of the plurality of data records comprising one or more features and a target variable; selecting key features among the one or more features based at least on relevance measures of the one or more features with respect to the target variable; dividing the dataset into multiple subsets; for each of the multiple subsets, identifying a number of clusters and respective centroids of the number of clusters based on the key features; identifying a number of final centroids based on the respective centroids of the number of clusters for the each of the number of subsets, the number of final centroids being respective centroids of a number of final clusters; and for each data record in the multiple subsets, assigning the data record to one of the number of final clusters based on distances between the data record and the number of final centroids.
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公开(公告)号：US20220172130A1
公开(公告)日：2022-06-02
申请号：US17673307
申请日：2022-02-16
Applicant: BUSINESS OBJECTS SOFTWARE LTD
Inventor： Ying Wu , Paul Pallath , Paul O'hara
Abstract: A method includes receiving training data including sequential data, determining a plurality of future time points, generating a first prediction by applying a first forecasting algorithm to the training data, generating a second prediction by applying a second forecasting algorithm to the training data, extracting predicted values from the first prediction and the second prediction that corresponds to a future time point of the plurality of future time points, applying a regression model in sequence on each of the plurality of future time points to generate a final predicted value of each of the plurality of future time points, and outputting the final predicted values of the plurality of future time points.
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公开(公告)号：US20190179835A1
公开(公告)日：2019-06-13
申请号：US16277725
申请日：2019-02-15
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul Pallath , Ying Wu
IPC: G06F16/28 , G06N20/00 , G06F16/2458
Abstract: Techniques are described for performing a time series analysis using a clustering based symbolic representation. Implementations employ a clustering based symbolic representation applied to time series data. In some implementations, the time series data is discretized into subsequences with regular time intervals, and symbols encoding the time intervals may be derived by performing clustering algorithms on the subsequences. In the new representation, a time series is transformed into a sequence of categorical values. The symbolic representation is suitable to perform time series classification and forecast with higher accuracy and greater efficiency compared to previously used techniques. Through use of the symbolic representation, a dimension reduction is applied to transform the time sequences to a feature space with lower dimensions. As output of such transformation, a new representation is obtained based on the original time series. This new reduced-dimension representation improves the efficiency of time series data mining and forecasting.
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公开(公告)号：US10013303B2
公开(公告)日：2018-07-03
申请号：US15496348
申请日：2017-04-25
Applicant: Business Objects Software Ltd.
Inventor： Paul Pallath , Rouzbeh Razavi
CPC classification number: G06F11/079 , G05B23/02 , G05B23/0254 , G06F11/0709 , G06F11/0751 , G06F11/0787 , H04L67/12
Abstract: The present disclosure describes methods, systems, and computer program products for detecting anomalies in an Internet-of-Things (IoT) network. One computer-implemented method includes receiving, by operation of a computer system, a dataset of a plurality of data records, each of the plurality of data records comprising a plurality of features and a target variable, the plurality of features and target variable including information of a manufacturing environment; identifying a set of normal data records from the dataset based on the target variable; identifying inter-feature correlations by performing correlation analysis on the set of normal data records; and detecting anomaly based on the inter-feature correlations for predictive maintenance.
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公开(公告)号：US11036766B2
公开(公告)日：2021-06-15
申请号：US16277725
申请日：2019-02-15
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul Pallath , Ying Wu
IPC: G06F16/2458 , G06F16/28 , G06N20/00 , G06N20/20
Abstract: Techniques are described for performing a time series analysis using a clustering based symbolic representation. Implementations employ a clustering based symbolic representation applied to time series data. In some implementations, the time series data is discretized into subsequences with regular time intervals, and symbols encoding the time intervals may be derived by performing clustering algorithms on the subsequences. In the new representation, a time series is transformed into a sequence of categorical values. The symbolic representation is suitable to perform time series classification and forecast with higher accuracy and greater efficiency compared to previously used techniques. Through use of the symbolic representation, a dimension reduction is applied to transform the time sequences to a feature space with lower dimensions. As output of such transformation, a new representation is obtained based on the original time series. This new reduced-dimension representation improves the efficiency of time series data mining and forecasting.
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Patent Agency Ranking
公开(公告)号：US10037025B2
公开(公告)日：2018-07-31
申请号：US14877764
申请日：2015-10-07
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul Pallath , Rouzbeh Razavi
CPC classification number: G06F11/079 , G05B23/02 , G05B23/0254 , G06F11/0709 , G06F11/0751 , G06F11/0787 , H04L67/12
Abstract: The present disclosure describes methods, systems, and computer program products for detecting anomalies in an Internet-of-Things (IoT) network. One computer-implemented method includes receiving, by operation of a computer system, a dataset of a plurality of data records, each of the plurality of data records comprising a plurality of features and a target variable, the plurality of features and target variable including information of a manufacturing environment; identifying a set of normal data records from the dataset based on the target variable; identifying inter-feature correlations by performing correlation analysis on the set of normal data records; and detecting anomaly based on the inter-feature correlations for predictive maintenance.
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公开(公告)号：US20170102978A1
公开(公告)日：2017-04-13
申请号：US14877764
申请日：2015-10-07
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul Pallath , Rouzbeh Razavi
IPC: G06F11/07
CPC classification number: G05B23/02 , G05B23/0254 , G06F11/0709 , G06F11/0751 , G06F11/0787 , G06F11/079 , H04L67/12
Abstract: The present disclosure describes methods, systems, and computer program products for detecting anomalies in an Internet-of-Things (IoT) network. One computer-implemented method includes receiving, by operation of a computer system, a dataset of a plurality of data records, each of the plurality of data records comprising a plurality of features and a target variable, the plurality of features and target variable including information of a manufacturing environment; identifying a set of normal data records from the dataset based on the target variable; identifying inter-feature correlations by performing correlation analysis on the set of normal data records; and detecting anomaly based on the inter-feature correlations for predictive maintenance.
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公开(公告)号：US11562002B2
公开(公告)日：2023-01-24
申请号：US16256519
申请日：2019-01-24
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul Pallath , Rouzbeh Razavi
IPC: G06F16/28 , G06F16/2458
Abstract: The present disclosure describes methods, systems, and computer program products for enabling advanced analytics with large datasets. One computer-implemented method includes receiving, by operation of a computer system, a dataset of multiple data records, each of the plurality of data records comprising one or more features and a target variable; selecting key features among the one or more features based at least on relevance measures of the one or more features with respect to the target variable; dividing the dataset into multiple subsets; for each of the multiple subsets, identifying a number of clusters and respective centroids of the number of clusters based on the key features; identifying a number of final centroids based on the respective centroids of the number of clusters for the each of the number of subsets, the number of final centroids being respective centroids of a number of final clusters; and for each data record in the multiple subsets, assigning the data record to one of the number of final clusters based on distances between the data record and the number of final centroids.
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公开(公告)号：US20180285769A1
公开(公告)日：2018-10-04
申请号：US15475482
申请日：2017-03-31
Applicant: Business Objects Software Ltd.
Inventor： Ying Wu , Paul Pallath
Abstract: The present disclosure involves systems, software, and computer implemented methods for learning relationships between concepts using an artificial immune system. A method includes identifying a set of concepts; determining a state value for each concept at each of a set of time points; generating an initial state and a system response; designating the system response as an antigen a clonal selection algorithm; generating a set of candidate weight matrices to be used as a population of antibodies in the clonal selection algorithm; determining a system response for each antibody; determining an affinity value for each antibody, using the system response for the antibody, the affinity value for a respective antibody representing how closely the respective antibody fits the antigen; cloning a set of antibodies based on the affinity values; repeating the cloning until a stopping point is reached; and selecting a candidate weight matrix with a highest affinity value.
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公开(公告)号：US20180211270A1
公开(公告)日：2018-07-26
申请号：US15415534
申请日：2017-01-25
Applicant: Business Objects Software Ltd.
Inventor： Ying Wu , Paul Pallath , Achim Becker
IPC: G06Q30/02
CPC classification number: G06Q30/0204 , G06Q30/0269
Abstract: Systems and methods for machine-trained adaptive content targeting are provided. The system generates a recommendation model, which includes creating a plurality of offer clusters. Each offer cluster comprises offers having similar features. The system assigns a new offer to one of the plurality of offer clusters. The assigning of the new offer occurs without having to retrain the recommendation model. The system also generates a plurality of user clusters, whereby users within each of the plurality of user clusters share similar behavior. A classification model for predicting an offer cluster from the plurality of offer clusters is created for each of the plurality of user clusters. The system then performs a recommendation process for a new user that includes selecting one or more relevant offers from a predicted offer cluster based on the classification model.
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