公开(公告)号：US20210365471A1

公开(公告)日：2021-11-25

申请号：US16877909

申请日：2020-05-19

Applicant: BUSINESS OBJECTS SOFTWARE LTD.

Inventor： Paul O'Hara ,  Robert McGrath ,  Ying Wu ,  Shekhar Chhabra ,  Eoin Goslin ,  Pat Connaughton ,  John Bowden ,  Alan Maher ,  David Hutchinson ,  Leanne Long ,  Malte Christian Kaufmann ,  Pukhraj Saxena ,  Priti Mulchandani ,  Anirban Banerjee

IPC: G06F16/26 ,  G06F16/28 ,  G06F16/2458

Abstract: The present disclosure involves systems, software, and computer implemented methods for generating insights based on numeric and categorical data. One example method includes receiving a request for an insight analysis for a dataset that includes at least one continuous feature and at least one categorical feature. Continuous features can have any value within a range of numerical values and categorical features are enumerated features that can have a value from a predefined set of values. A selection of a first continuous feature for analysis is received, and at least one categorical feature is identified for analysis. A deviation factor and a relationship factor are determined for each identified categorical feature. An insight score is determined for each identified categorical feature that combines the deviation factor and the relationship factor for the categorical feature. The insight score is provided for at least some of the identified categorical features.

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.