Abstract: In an example embodiment, machine learning techniques are utilized to create virtual tables that connect to actual tables in a user's own system. The virtual table predicts how the user's data can be used to populate fields in newer versions of software that the user already runs, even when those fields are not present in the version that the user already runs. These tables may then be used in a specialized tool, which displays in one area of the display a screen of the version of the software that the user is currently running (“the existing version”) and displays in another area of the display a screen of the version of the software that the user is comparing to the existing version. Both display areas display the same screen, as rendered by their respective different versions of the software, using the same underlying base data.

Abstract: In an example embodiment, a solution is provided for encrypting and decrypting data in which the solution itself creates unique symbols, reducing or eliminating the possibility that a hacker or other malicious actor can understand what the symbols mean, let alone decrypt them. More particularly, for encryption, the characters of an original text is split into individual vector-based characters, and each of these vector-based characters are split into subcharacters at intersection points. Each of the split characters are then rotated, and the rotated characters are organized one on top of each other. The characters are then connected to each other, and the intersection points of the subcharacters within the characters that were used to split the characters into subcharacters are passed into a machine learning model that is trained to add lines between intersection points that have no lines, as noise to further obfuscate the original text.

Abstract: In an example embodiment, machine learning techniques are applied to allow data analysis tools to automatically analyze data, come to conclusions about the data, make decisions based on those conclusions, and then execute those decisions, all without requiring human intervention. A complete end-to-end heavyweight engine is provided using these machine learning techniques.

Abstract: In an example embodiment, machine learning is utilized to automatically present and explain analysis of multidimensional objects categorized based on context in an enterprise data warehouse. The system is capable of handling dependencies to provide clear insights demonstrated in a native language of an end user. The system is multilingual and capable of framing explanations based on natural language processing (NLP), artificial intelligence (AI), and machine learning. It converts the textual predictions into speech synthesis in the user-understandable native format and then dictates the analysis using the speech synthesis.

Abstract: In an example embodiment, machine learning is utilized to automatically present and explain analysis of multidimensional objects categorized based on context in an enterprise data warehouse. The system is capable of handling dependencies to provide clear insights demonstrated in a native language of an end user. The system is multilingual and capable of framing explanations based on natural language processing (NLP), artificial intelligence (AI), and machine learning. It converts the textual predictions into speech synthesis in the user-understandable native format and then dictates the analysis using the speech synthesis.

Abstract: Methods, systems, and computer-readable storage media for receiving, by a summarized view platform, a request for a summarized view, the request including one or more attributes, identifying, by the summarized view platform, a MDO within an EDW system based on the one or more attributes, receiving, by the summarized view platform, a query result including a set of values, and displaying, by the summarized view platform, the summarized view of the MDO, the summarized view including attributes of the MDO, at least one attribute being populated with a value of the set of values.