Abstract: Methods and apparatuses for creating data analysis (DA) systems for generating insights (also known as results) from a plurality of data sources without requiring the designer/implementer of the DA system or the user to understand complicated technology details such as for example coding, big data technology or high-end business analytics algorithms.

Abstract: The present invention relates to systems and methods for cloud based consumer sentiment analysis with social insights. Data is integrated from a plurality of data sources, including a structured data source, an unstructured data source, a social data source, and a syndicated data source. Key attributes are selected from the integrated data, and may be name value pair requests. From these key attributes, consumer segments, sentiments and attribute correlations may be generated. The segments are generated from the social data. The correlation is generated using clustering algorithms. In some embodiments, generating sentiment and generating correlations dynamically utilizes models according to attributes of the integrated data. Polarity, emotion and topicality may be calculated for the generation of visualizations.

Abstract: Methods and apparatuses for organizations to monitor, analyze and respond to unstructured and structured data that is related to their Governance, Risk, and Compliance (GRC) programs. Embodiments of the invention generated mapped Risk Control Matrices (RCMs) and/or insights for improving the GRC process from unstructured and structured data. Natural language processing is employed to process the aggregated data from various data sources to create attributes and contributors. The attributes and weighted contributors are processed to form mapped RCMs and/or GRC-related insights.

Abstract: A method and system for displaying predictions on a spatial map includes using a data analyzer for analyzing heterogeneous data having a spatial component to find utilizable data and using machine learning to automatically extract relationships from the utilizable data. The extracted relationships are used to make a prediction about at least one location on the spatial map and present that prediction in an oblique or perspective view. An interface presents the prediction on the spatial map in the form of a heat map overlying a 3-D topographical map. Although the 3-D map can be shown as any form of graphical projection including an oblique projection or orthographic projection, preferably a perspective view is used. It is also preferred that the graphical projection be interactive. The heat map may be 2-D or 3-D and be selectively displayed depending on the preference of a user.

Abstract: A method and system including software makes predictions about potential business locations. The method includes the steps of providing heterogeneous data including a spatial component, extracting entities from the heterogeneous data, clustering the entities, and making a prediction about the business location using the clustered entities. Entities preferably include stores, people, or other physical objects and each has attributes. Preferably the entities include at least one of the following attributes: Value Ratio, Focal Values, Impact, Revenue Difference, Support, and Baseline Value. In this way, predictions made about the business locations will account for the nature of the business and enable comparison of one business location to another.

Abstract: A method and system for making predictions about business locations. The method includes providing a spatial map and analyzing heterogeneous data having a spatial component to find utilizable data. Relationships are automatically extracted from the utilizable data. The step of automatically extracting relationships includes generating a composite indicator, which correlates spatial data with unstructured data. The extracted relationships are presented on a spatial map to make a prediction about at least one business location. Preferably, the predictions are presented as a rank-ordered list on the spatial map and a heat map overlays the spatial map to indicate predictions about particular regions.

Abstract: A method and system that utilizes OLAP and supporting data structures for making predictions about business locations. The method includes providing a spatial map and analyzing heterogeneous data having a spatial component to find utilizable data. Relationships are automatically extracted from the utilizable data by employing machine learning. The step of automatically extracting relationships includes generating a composite indicator, which correlates spatial data with unstructured data. The extracted relationships are presented on a spatial map to make a prediction about at least one business location. Preferably, the predictions are presented as a rank-ordered list on the spatial map and a heat map overlays the spatial map to indicate predictions about particular regions.