Business Objects Software Limited

Paul Pallath

Rouzbeh Razavi

Ying Wu

Paul O'Hara

Malte Christian Kaufmann

Satinder Singh

Orla Cullen

Jayanta Roy

Ronan O'Connell

Robbie O'Brien

Girish Kalasa Ganesh Pai

The present disclosure describes methods, systems, and computer program products for enabling advanced analytics with large datasets. One computer-implemented method includes receiving, by operation of a computer system, a dataset of multiple data records, each of the plurality of data records comprising one or more features and a target variable; selecting key features among the one or more features based at least on relevance measures of the one or more features with respect to the target variable; dividing the dataset into multiple subsets; for each of the multiple subsets, identifying a number of clusters and respective centroids of the number of clusters based on the key features; identifying a number of final centroids based on the respective centroids of the number of clusters for the each of the number of subsets, the number of final centroids being respective centroids of a number of final clusters; and for each data record in the multiple subsets, assigning the data record to one of the number of final clusters based on distances between the data record and the number of final centroids.

Enabling advanced analytics with large data sets

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.

Time series analysis using a clustering based symbolic representation

The disclosure generally describes computer-implemented methods, software, and systems, including a method for generating executable components. One method includes identifying a user request to create a new function based pre-existing algorithms, the new function to be used in an application used by a user; providing a set of available algorithms from an algorithm library; receiving a selection by a user of an algorithm from the available algorithms; providing a set of available parameters associated with the selected algorithm; receiving an election by the user of one or more parameters from the set of available parameters; generating an executable component in response to receiving the selection of the algorithm and the election of the one or more parameters, the executable component performing the selected algorithm using at least the elected one or more parameters; and storing the executable component for subsequent execution in response to the requested new function.

Automated approach for integrating automated function library functions and algorithms in predictive analytics

The present disclosure describes methods, systems, and computer program products for detecting anomalies in an Internet-of-Things (IoT) network. One computer-implemented method includes receiving, by operation of a computer system, a dataset of a plurality of data records, each of the plurality of data records comprising a plurality of features and a target variable, the plurality of features and target variable including information of a manufacturing environment; identifying a set of normal data records from the dataset based on the target variable; identifying inter-feature correlations by performing correlation analysis on the set of normal data records; and detecting anomaly based on the inter-feature correlations for predictive maintenance.

Growing community of inventors

Naas, Ireland

Paul Pallath