公开(公告)号：US12229130B2

公开(公告)日：2025-02-18

申请号：US17696726

申请日：2022-03-16

Applicant: Business Objects Software Ltd.

Inventor： Justin Wong

IPC: G06F16/2453 ,  G06F16/242 ,  G06F16/26

Abstract: Some embodiments provide a program that receives a first selection of a first set of attributes in a first data model, a second selection of a second set of attribute in a second data model, a third selection of a first dimension in the first data model, and a fourth selection of a second dimension in second data model. The program further receives a request for data for a visualization. The program also generates a blend definition based on the first set of attributes, the second set of attributes, the first dimension in the first data model, and the second dimension in the second data model. The program further generates a blended query model based on the blend definition. The program also executes a query based on the blended query model to generate a result set of data. The program provides a visualization that includes the result set of data.

公开(公告)号：US12205203B2

公开(公告)日：2025-01-21

申请号：US18351288

申请日：2023-07-12

Applicant: Business Objects Software Ltd

Inventor： Paul O'Hara ,  Malte Christian Kaufmann ,  Esther Rodrigo Ortiz ,  Conor White

IPC: G06T11/20 ,  G06F18/2431

Abstract: Using approximated bin intervals to label the histograms provides clarity and allows for the histogram to be more intuitively understood. A dataset may comprise a plurality of records having a plurality of features including one or more continuous features. A selection of a continuous feature may be obtained. A bin width based on a number of bins and feature statistics of the continuous feature may be determined. An approximated bin interval range is determined by applying a bin mask based on the bin width to the feature statistics. An approximated bin width is determined based on the number of bins and the approximated bin interval range. Approximated bin intervals for the histogram are determined based on the approximated bin width. A histogram is generated having bins with intervals based the approximated bin intervals.

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

IPC: G06F16/28 ,  G06F16/23

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.

公开(公告)号：US20240184794A1

公开(公告)日：2024-06-06

申请号：US18074704

申请日：2022-12-05

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Mairtin KEANE ,  Vlad ZAT ,  Shane Conroy ,  Michael ZAKHARENKOV ,  Cathal McGOVERN ,  Raffaele SANGIOVANNI ,  Michael GOLDEN ,  Esther Rodrigo ORTIZ

IPC: G06F16/2458 ,  G06F16/248

CPC classification number: G06F16/2477 ,  G06F16/248

Abstract: Systems and methods include reception of an object instance representing a date dimension member and comprising a key value, a user interface representation of the date dimension member, key values of ancestor date dimension members, and user interface representations of the one or more ancestor date dimension members, display of the user interface representation and the user interface representations of the ancestor date dimension members, reception of a request to cast the date dimension member to a higher granularity, and, in response to the request, generation of a second object instance representing a second date dimension member comprising key values of a second one or more of the ancestor date dimension members and user interface representations of the second one or more of the ancestor date dimension members, and display of the user interface representations of the second one or more of the ancestor date dimension members.

公开(公告)号：US20240184753A1

公开(公告)日：2024-06-06

申请号：US18072944

申请日：2022-12-01

Applicant: BUSINESS OBJECTS SOFTWARE LTD

Inventor： George Kyriacou

IPC: G06F16/21 ,  G06F11/14 ,  G06F11/34

CPC classification number: G06F16/211 ,  G06F11/1433 ,  G06F11/3419 ,  G06F2201/80

Abstract: A method, a system, and computer program product for updating various database artifacts using design definitions. A design time definition is received for a database artifact stored in a database. Design time metadata is extracted from the design time definition. Runtime metadata is retrieved from the database, the runtime metadata corresponding to the database artifact. Differences are determined between the design time metadata and the runtime metadata. An update query is generated based on the differences between the design time metadata and the runtime metadata. The update query is executed to update the database artifact.

公开(公告)号：US20240184705A1

公开(公告)日：2024-06-06

申请号：US18061907

申请日：2022-12-05

Applicant: Business Objects Software LTD

Inventor： Raffaele Sangiovanni

IPC: G06F12/0871

CPC classification number: G06F12/0871

Abstract: Computer-readable media, methods, and systems are disclosed for an in-memory cache in a memory of a client device. The system may send a first request for a first data from the client device to the in-memory cache and may receive a null response. The system may send a second request from the client device for the first data to a server and may receive a response from the server with the first data. The system may then send the first data to the in-memory cache and store the first data in the in-memory cache, thereby eliminating an additional request for the first data from the server.

公开(公告)号：US20240062101A1

公开(公告)日：2024-02-22

申请号：US17890073

申请日：2022-08-17

Applicant: BUSINESS OBJECTS SOFTWARE LTD

Inventor： Paul O'Hara

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A historical feature contribution score dataset comprising a number of sets of scores generated by machine learning model may be obtained. Additional feature contribution score sets may be materialized such that the size of each additional feature contribution score set is based on a corresponding randomly selected values within a set-size range. A training dataset may be produced that includes feature contribution scores and corresponding classification labels extracted from the historical feature contribution score dataset and the additional feature contribution score sets. The classification labels may indicate an amount that the corresponding feature contribution scores contribute to a prediction of a target feature. A machine learning model may be trained to predict the classification labels using the training dataset. An input feature contribution score set may be applied to the machine learning model to obtain predicted classification labels.

公开(公告)号：US20240020896A1

公开(公告)日：2024-01-18

申请号：US18351288

申请日：2023-07-12

Applicant: Business Objects Software Ltd

Inventor： Paul O'Hara ,  Malte Christian Kaufmann ,  Esther Rodrigo Ortiz ,  Conor White

IPC: G06T11/20 ,  G06F18/2431

CPC classification number: G06T11/206 ,  G06F18/2431

Abstract: Using approximated bin intervals to label the histograms provides clarity and allows for the histogram to be more intuitively understood. A dataset may comprise a plurality of records having a plurality of features including one or more continuous features. A selection of a continuous feature may be obtained. A bin width based on a number of bins and feature statistics of the continuous feature may be determined. An approximated bin interval range is determined by applying a bin mask based on the bin width to the feature statistics. An approximated bin width is determined based on the number of bins and the approximated bin interval range. Approximated bin intervals for the histogram are determined based on the approximated bin width. A histogram is generated having bins with intervals based the approximated bin intervals.

公开(公告)号：US11809444B2

公开(公告)日：2023-11-07

申请号：US16711273

申请日：2019-12-11

Applicant: Business Objects Software Ltd.

Inventor： Xin Qiu ,  Fangzhou Yin ,  Ivan Cheng ,  Yang Jin ,  James Lloyd

IPC: G06F16/26 ,  G06F3/04847 ,  G06F16/9538 ,  G06F16/9535 ,  G06F3/0482

CPC classification number: G06F16/26 ,  G06F3/0482 ,  G06F3/04847 ,  G06F16/9535 ,  G06F16/9538

Abstract: The present disclosure pertains to linked filter groups for linking a driver element of a user interface to a receiver element of the user interface such that data filters applied to driver element are propagated to the receiver element. A first linked group may include a first set of filters for a first visualization of the driver element and a first set of element identifiers of other elements in the user interface, including a first identifier of a first receiver element. When updating visualizations of the receiver element, the first set of filters of the first linked group may be aggregated with a second set of filters of the first receiver element based on the receiver element being a member of the first linked group. As such, the updates to the receiver element are based on both sets of filters.

公开(公告)号：US11727284B2

公开(公告)日：2023-08-15

申请号：US16712792

申请日：2019-12-12

Applicant: Business Objects Software Ltd

Inventor： Yann Le Biannic

IPC: G06N5/04 ,  G06N20/00

CPC classification number: G06N5/04 ,  G06N20/00

Abstract: Techniques and solutions are described for analyzing results of a machine learning model. A result is obtained for a data set that includes a first plurality of features. A plurality of feature groups are defined. At least one feature group contains a second plurality of features of the first plurality of features. The second plurality of features is less than all of the first plurality of features. Feature groups can be defined based on determining dependencies between features of the first plurality of features, including using contextual contribution values. Group contextual contribution values can be determined for feature groups by aggregating contextual contribution values of the constituent features of the feature groups.