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.

公开(公告)号：US20180165599A1

公开(公告)日：2018-06-14

申请号：US15376271

申请日：2016-12-12

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Balazs Pete ,  Declan Kearney ,  Cathal McGovern ,  Simon Dornan ,  Jennifer Keane ,  Michael Golden ,  Orla Cullen ,  Robert McGrath ,  Shekhar Chhabra ,  Kerry O'Connor ,  Malte Christian Kaufmann ,  John Julian

IPC: G06N99/00 ,  G06F17/50

CPC classification number: G06N20/00 ,  G06F17/5009

Abstract: Techniques are described for integrating predictive models into applications, to enable the applications to provide predictive functionality. Using the framework according to implementations, predictive models and their supporting libraries may be incorporated into applications without requiring application developers to be knowledgeable regarding the particular features of the predictive models and/or libraries. The framework exposes a common and consistent application programming interface (API) on top of the predictive libraries. Applications can use the API to interact with the predictive models, thus enabling the applications to leverage predictive functionality. Implementations also provide an API which may be used by applications to request the retraining of predictive models.