Your company has a huge repository of data – market segment statistics, products, sales history, client and prospect profiles, client service reports, financial reports – and much of it is disparate and on incompatible databases. This asset is often unrecognised for its value to YOUR business. These data structures are complex, unstructured multi-modal. Data is an untapped resource; an undervalued company asset. Computational DataNotes is the key to unlocking the value of YOUR data.
Computational DataNotes computes and uncovers the nascent structures of the data that are hidden. Our novel algorithms extract data pattern structures that have meaningful value for the business. Patterns that have usefulness for bespoke business planning and strategy. (This next sentence will be used to demonstrate each structure – – Data structures that are similar – have same structure. Anomaly, Normalcy, Dissimilarity, Similarity, Outliers )
Data incomplete? Missing Values?
We resolve this data barrier for your business. Our algorithms learn from your data and Synthesize Missing Values by Learning Distribution from existing data.
Example: Boston Housing appraisal Excel datasheets missing values for some rows. Learn Distribution learns the distribution for particular Column(s) and compute and replace the missing data values
A single independent unit of computation, which renders the intricate and hidden aspects of your data with most advanced Scientific Visualization.These Datanotes structure computational structures deployed as easily accessible web objects.
Example: A utility company’s turbine generating electricity, ye the most worrisome parameter of the gigantic turbine is the Gearbox temperatures to avoid permanent damage to the machine.The DataNote incorporates Adaptive Learning Prediction function as the core of its Alarm to warn the management of Temperature anomalies.
Example: Interactive algorithms e.g. K-NN which empowers the management to review the Similarity of their current data with past data to look for past reference points to understand the present behavior of their systems by learning from the past behavior. (Note: Few seconds of delay for the interactive to load)
DataNotes are periodically computed according to the management’s need.
Unlock the Wealth in Your Data
Machine Learning (ML): is a method of data analysis that automates analytical model-building. It is a branch of artificial intelligence based on the construct that systems can learn from data, identify patterns and make decisions with minimal human supervision.
Machine Learning ML: Adaptive learning from examples e.g. we do not know how a dynamical system works and want to determine the specifics of this dynamical system. ML learns from samples and provides insights and inferences. Systems can learn from data, identify patterns and make decisions with minimal human intervention.
Example: 2D images of damaged eroded or corroded surfaces are transformed into 3D landscapes endued with peaks and valleys by means of an advanced Neural Network learning algorithm that learns from landscape images most familiar to human visual processing.
This allows management of a refinery to have more natural view of pipes and welding surfaces and better language for technical discourse.
Artificial Intelligence AI: Mimics some behaviour of the human being. For example, an element or component of AI is Natural Language Processing that writes analysis as if it was written by a human.