Ramya Priya Updated by Ramya Priya

To help you navigate the platform and understand its features better, we have compiled a glossary of all the Tellius-specific terms and their definitions.

Search Query: A question or request relating to the data of interest.

Search Guide: A user guide containing tips on how to ask different types of questions, which also includes a set of pre-defined and customizable search examples for the selected Business View.

Search Inspector: The configuration of the output that is shown to a user after executing a search, and can be hidden on the screen or reopened at any time.

Vizpad: An interactive dashboard with multiple chart types and various filter options. It also integrates with the rest of the platform - with unlimited drilling through dimensions, anomaly detection, one-click insights, and predictive modeling.

Viz: An individual chart within a Vizpad.

Insight: Insights refer to the outcomes derived from machine learning or statistical inference techniques to extract a better understanding of your data.

Key Driver: These are the critical factors that significantly impact a business or event. For example, identifying the key characteristics of returning customers can help businesses to understand their customer base.

Trend: Trend analysis helps you identify the factors driving changes in your business KPIs over time. For example, it can help you investigate the reasons behind a sudden surge in revenue in March 2021 as compared to February 2021.

Comparison: Understand the differences between two or more groups or segments based on your business KPIs. For example, you can compare the revenue generated by millennials for cell phones versus headphones to identify which product category is more profitable for this group.

Anomalies/Correlations: While exploring search or visualization, anomalies help identify key relationships or significant changes in dimensions.

Regression: A type of supervised machine learning that involves predicting a continuous variable based on other input variables. For example, predicting the price of a house based on its size, location, and other relevant factors.

Classification: A type of supervised machine learning that involves predicting a discrete (binary or multi-class) label or category for a given input. For example, predicting customer churn.

Time Series Regression: a statistical method for predicting future values of a time series based on its historical trend. For example, predicting the temperature for the next hour based on its past values.

Time Series Regression differs from Regression in that it only uses the historical trend of the time series itself for prediction and is only applicable to time-based data.

Clustering: An unsupervised machine learning technique that involves identifying natural groupings or patterns in data.

Target (dependent variable): The output variable that you are trying to predict. It is stored as an individual field in your data. For example, in a model that predicts house prices, the target variable would be the price.

Feature (independent variable): An input variable representing a property or characteristic to help predict the target variable. It is stored as an individual field in your data. For example, in a model that predicts house prices, the features could include the size, location, number of bedrooms etc. Machine learning models often have many features as inputs.

Connector: A source system or application from which data can be imported into Tellius.

Datasource: A unique combination of a Connector and the credentials used to authorize the connection to that source. For example, you may have different data warehouses or schemas that different users have access to, with the same Connector.

Datasets: Single table-like structures of data with rows and columns.

Business View: A Business View is a logical layer that defines how datasets relate to one another (similar to a data model). It helps users create and build logical data models to start their analysis, enabling them to identify their logical schema and connect their datasets accurately from various sources together into one view of data that can be easily accessed and analyzed.

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