International Business Machines Corporation

Marc S Altshuller

Jing-Yun Shyr

Damir Spisic

Margaret J Vais

Neil Whitney

Graham John Wills

Daniel Jay Rope

Yea Jane Chu

Michael D Woods

Devendra G Tasgaonkar

A method, system, and/or computer program product analyses event transactional related data to generate insights and predictions, which are pre-created to efficiently respond to requests for prediction/forecasting information, in order to improve the operation of the prediction-generating computer. One or more processors receive a series of structured data, where each entry (E) from the series of structured data has one or more time fields Tand one or more attributes A. In response to determining that the series of structured data is transactional, one or more processors select a time field Tthat meets an aggregation criterion, and then aggregate the transactional data from the time field Tinto a time series data format. One or more processors consolidate results from a time series analysis and a regression analysis of the transformed transactional data to create a consolidated result, which is used to respond to a request for prediction/forecasting information.

Transforming a transactional data set to generate forecasting and prediction insights

A data portion of a data set utilized in a computerized visualization is analyzed to identify one or more areas of interest each including data values representing distinguishable features relative to the data set. An explanation for the data values of each of the one or more areas of interest is determined. Each explanation is based on other data portions of the data set contributing to the distinguishable features. At least one display layer including labels describing the one or more areas of interest is generated. The labels include the explanation for each of the one or more areas of interest. The at least one display layer is disposed over the computerized visualization to produce an annotated visualization with the labels positioned proximate the one or more areas of interest.

Generation of annotated computerized visualizations with explanations for areas of interest

A method for comparing predictive data models based on a predictive model search is provided. The method may include receiving a first and second portion of a set of data. The method may also include identifying a first and second variation of the second portion, wherein the first variation is different from the second variation. The method may further include generating first predictive data models based on the first variation, and second predictive data models based on the second variation. Additionally, the method may include applying a criteria to rank the first and second predictive data models based on predictive strength. The method may also include presenting a display of the ranked criteria, comprising the first portion, and a portion of the first and second predictive data models, wherein the portion of the first and second predictive data models are collectively ranked and presented according to the predictive strength.

Predictive model search by communicating comparative strength

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Toronto, Canada

Marc S Altshuller