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公开(公告)号：US20250013819A1
公开(公告)日：2025-01-09
申请号：US18348172
申请日：2023-07-06
Applicant: Business Objects Software Ltd.
Inventor： Ying Wu , Malte Christian Kaufmann
IPC: G06F40/18 , G06F3/0482
Abstract: A data analyzer highlighter highlights elements of a user interface to enable a user to better understand and analyze the data presented. To do this, a first visualization is generated in a user interface. A configuration panel including elements for selecting statistical techniques is also generated in the user interface. Selections are obtained via the user interface of one or more statistical techniques. Then statistics are determined from the dataset using each of the one or more selected statistical techniques. Rows of data or the columns of data are then sorted based on a number of extreme values in the particular row or column, wherein the extreme value is a minimum value, a maximum value, or an outlier value. A second visualization sorted based on the number of extreme values in the particular row or column is then generated in the user interface.
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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：US20210357417A1
公开(公告)日：2021-11-18
申请号：US16876441
申请日：2020-05-18
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Ying Wu , Paul O'Connor , Esther Rodrigo Ortiz , Artur Stulka , Mateusz Lewandowski , Paul Sheedy , Mairtin Keane , Paul O'Hara , Malte Christian Kaufmann , Robert McGrath
IPC: G06F16/2457 , G06F16/2458 , G06F16/215 , G06F16/22
Abstract: The present disclosure involves systems, software, and computer implemented methods for ranking time dimensions. One example method includes receiving a request for an insight analysis for a dataset that includes a value dimension and a set of multiple date dimensions. Each date dimension is converted into a time series and a value quality factor is determined for each time series that represents a level of data quality for the time series. A time series informative factor is determined for each time series that represents how informative the time series is within a specified time window. An insight score is determined, for each time dimension, based on the determined value quality factors and the determined time series informative factors. The insight score for the time dimension is provided, for at least some of the time dimensions.
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公开(公告)号：US20210349911A1
公开(公告)日：2021-11-11
申请号：US16867036
申请日：2020-05-05
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Ben Murphy , Ying Wu , Paul O'Hara , Emmet Norton , Malte Christian Kaufmann , Orla Cullen
Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically detecting hot areas in heat map visualizations. One example method includes identifying a two-dimensional heat map. The identified two-dimensional heat map is converted to a one-dimensional heat map. Cells of the one-dimensional heat map are clustered using a density-based clustering algorithm to generate at least one dense region of cells. A mean value of cells in each dense region is calculated and the dense regions are sorted by mean value in descending order. An approach for identifying hot areas is selected and the selected approach is used to identify at least one dense region as a hot area of the one-dimensional heat map.
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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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Patent Agency Ranking
公开(公告)号：US11693879B2
公开(公告)日：2023-07-04
申请号：US17324667
申请日：2021-05-19
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul O'Hara , Ying Wu , Jiazheng Li , Cathal McGovern , Malte Christian Kaufmann , Esther Rodrigo Ortiz , Kerry O'Connor , Michael Golden , Satinder Singh , Vlad Zat
IPC: G06F16/26 , G06F16/2458
CPC classification number: G06F16/26 , G06F16/2465
Abstract: Systems and methods include reception of a set of data including continuous features and a discrete feature, each continuous feature associated with a plurality of values and the discrete feature associated with a plurality of discrete values, determine, for each continuous feature, a relationship factor representing a relationship between the discrete feature and the continuous feature based on the plurality of values associated with the continuous feature and the plurality of discrete values, identify one of the continuous features associated with a largest one of the determined relationship factors, generate, for each of the other features, a correlation factor representing a correlation between the continuous feature and the identified continuous feature, determine, for each of the continuous features other than the identified continuous feature, a composite relationship score based on the relationship factor and the correlation factor associated with the feature, and present a visualization associated with the discrete feature, the identified continuous feature, and a continuous feature associated with a largest composite relationship score.
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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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公开(公告)号：US20250013668A1
公开(公告)日：2025-01-09
申请号：US18754570
申请日：2024-06-26
Applicant: Business Objects Software Ltd.
Inventor： Paul O'Hara , Ying Wu , Malte Christian Kaufmann
Abstract: Anomalies may be detected using a multiple machine learning model anomaly detection framework. A clustering model is trained using an unsupervised machine learning algorithm on a historical anomaly dataset. A plurality of clusters of records are determined by applying the historical anomaly dataset to the clustering model. Then it is determined whether each cluster of the plurality of clusters is an anomaly-type cluster or a normal-type cluster. The plurality of labels for the plurality of records are updated based on the particular record's cluster classification. Non-pure clusters are determined from among the plurality of clusters based on a purity threshold. A supervised machine learning model is trained for each of the non-pure clusters using the records in the given cluster and the labels for each of those records. Then, predictions of an anomaly are made using the clustering model and the supervised machine learning models.
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公开(公告)号：US11321332B2
公开(公告)日：2022-05-03
申请号：US16876463
申请日：2020-05-18
Applicant: BUSINESS OBJECTS SOFTWARE LTD.
Inventor： Paul O'Hara , Ying Wu , Esther Rodrigo Ortiz , Paul O'Connor , Gabor Szabo , Artur Stulka
IPC: G06F16/00 , G06F16/2458 , G06F17/18 , G06F16/23
Abstract: The present disclosure involves systems, software, and computer implemented methods for automatically recommending one or more frequencies for time series data. One example method includes receiving a request for an insight analysis for an input time series included in a dataset. For each of multiple frequencies to analyze, the input time series is transformed into a frequency time series. An absolute percentage change impact factor and an absolute trend impact factor are determined for each frequency time series. A frequency interest score is determined based on the determined absolute percentage change factors and the determined absolute trend impact factors, for each time frequency time series. The frequency interest score is provided for at least some of the frequency time series.
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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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