How to Upload Your First Dataset

Introduction

Once you are logged in to the application, you will be prompted with a welcome screen. This welcome screen will guide you through the basic steps to get familiar with the application. The first step is to upload your own data into the system, allowing you to visualize your process and begin your process improvement journey.

Overview

The Upload New Data screen allows you to upload your datasets into ProcessMind quickly and easily. You can upload various file formats such as CSV, TSV, TXT, XLS, XLSX, and XLSB. The process is simple, and this guide will walk you through the steps to successfully upload your data. For more details on the supported file formats and how to structure the data please read the Data section.

Step-by-Step Instructions

Step 1: Preparing Your Data

• Ensure that your dataset is saved in one of the supported file formats (CSV, TSV, TXT, XLS, XLSX, or XLSB).
• Check that your file is properly structured and that the data is clean for better processing in ProcessMind.

Step 2: Uploading Your Dataset

• Drag and Drop Method:
  • Simply drag your dataset file from your device and drop it into the large “Upload Dataset” box.
• Manual File Selection:
  • Click inside the “Upload Dataset” box or on the upload icon. A file dialog will open, allowing you to browse and select the dataset file from your computer.

Step 3: Confirmation and Processing

• Once you’ve uploaded the file, ProcessMind will automatically start processing the data. The system will give you feedback how the data is being processed. You do not need to wait until the processing is finished, you can continue with other tasks in the application and will receive a notification once the dataset is processed.
• If your data is successfully uploaded, you will be directed to the next screen for further configuration or analysis.

Step 4: Verify the data

Once the data is loaded you can start fine tune the data where ProcessMind did not match the proper field automatically. For more details on how to fine tune the data please read the Data section.