Business Objects Software Limited

Ying Wu

Paul O'Hara

Malte Christian Kaufmann

Paul Pallath

Esther Rodrigo Ortiz

Orla Cullen

Artur Stulka

Paul O'Connor

Alan McShane

Paul Sheedy

Anirban Banerjee

Cathal McGovern

Michael Golden

Vlad Zat

Satinder Singh

Mairtin Keane

Gabor Szabo

Jiazheng Li

Kerry O'Connor

Ben Murphy

Emmet Norton

Mateusz Lewandowski

Robert McGrath

Gareth Maguire

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.

Multiple machine learning model anomaly detection framework

A system and method including receiving numeric data of a first dataset including a plurality of columns having numeric values with one of the plurality of columns specified as a target column; generating a trained generative model based on numeric values in non-target columns of the plurality of columns; generating a trained predictive model based on numeric values in non-target columns of the plurality of columns being input variables and the target column being a target variable; generating, by the trained generative model, a new set of numeric data for the non-target columns; generating predicted target values for the non-target columns by the trained predictive model using the new set of numeric data as an input to the predictive model; and generating anonymized numeric data for the first dataset by combining the new set of numeric data and the target column populated with the generated predicted target values.

Data anonymization for cloud analytics

A system and method include input of data records to a first trained predictive model to obtain a predicted value associated with each input data record. A model region is then associated with each of the input data records based on the first trained predictive model, the input data records and the predicted values. Enhanced input data records are generated by, for each model region, adding derived values of engineered features associated with the model region to input data records associated with the model region and default values of the engineered features associated with the model region to input training records not associated with the model region. The enhanced input data records are input to a second trained predictive model to obtain an enhanced predicted value associated with each input data record.

Framework for providing improved predictive model

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.

Automatic hot area detection in heat map visualizations

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.

Composite relationship discovery framework

A system and method including determining, for a specified target measure column of a first dataset including a plurality of records, the metadata of the first dataset, including a probability distribution for the specified target column and dimension scores for the dimensions for the first dataset conditioned on the specified target measure column, where the first dataset comprises a plurality of columns including the at least one target measure column and a plurality of non-numeric, dimension columns for the records of the first dataset; determining, for a subset of data of the first dataset based on one or more specified variables, dimension scores for the dimensions of the subset of data approximately derived from the determined metadata of the first dataset; and providing recommendations of top contributors based on the approximated dimension scores of dimensions of the subset of data.

Top contributor recommendation for cloud analytics

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.

Flexible algorithm for time dimension ranking

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.

Automatic frequency recommendation for time series data

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to predict future Day Sales Outstanding (DSO) forecasts for a number of future time periods. In one aspect, a method includes receiving open receivables financial line item data and revenue financial line item data, providing the open receivables financial line item data to a DSO predictor engine to generate a predicted open receivables that includes a multi-step time series forecasting regression generated from the open receivables financial line item data, providing the revenue financial line item data to the DSO predictor engine to generate a predicted revenue comprising the multi-step time series forecasting regression generated from the revenue financial line item data; generating a predicted DSO with the predicted open receivables and predicted revenue, and providing the predicted DSO to a client device.

Multi-step day sales outstanding forecasting

A method includes receiving a plurality of items, grouping the plurality of items into a plurality of clusters, where each of the plurality of clusters comprises items having similar features to one another, applying a classification model to each cluster to predict whether each item of a cluster will be delivered on time or delivered late, applying a regression model that determines an expected measure of tardiness of each item predicted to be delivered late, and outputting a delivery date prediction for each item predicted to be delivered late based on the expected measure of tardiness of the item.

Delivery prediction with degree of delivery reliability

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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Maynooth, Ireland

Ying Wu