EdenEngine

• EdenEngine is an analytics engine for MagicEden NFT Collections data, obtained through the MagicEden API.
• The engine is implemented as a KNIME workflow, using the KNIME Analytics Platform.
• The current implementation of the engine focuses on the analysis of the text data in the description field of the data on Collections, which can be accessed through the Collections API call.
• In other words, EdenEngine is initially/currently implemented as a text analytics engine that analyzes text content of MagicEden NFT Collections.

Start Here:

• YouTube Video is the demo video for the MagicEden API Hackathon hackathon.

EdenEngine Team

• Gurdal Ertek (System design, Algorithm design, Analytics modeling, System documentation)
• Al Ain, United Arab Emirates

How to Use EdenEngine

Step 1: Create Local Folder and Download Files

• Download and paste all the files in the root folder of the EdenEngine github repository to C:\MagicEden.
• The folder name is important, as all the results files generated by the engine will be saved in this folder.
• Also create a sub-folder under that folder, as C:\MagicEden\Topics, to serve as the folder where the topic analysis word clouds will be saved.

Step 2: Access MagicEden API

• Double-click on MagicEden API Call for 500 Collections.URL shortlink, or enter into browser's URL address field the following:
• https://api-mainnet.magiceden.dev/v2/collections?offset=0&limit=500.

Step 3: Query with MagicEden API

• If the number of collections to be analyzed is different than 500, change the last three digits accordingly (ex: change 500 to 150 for analyzing only 150 collections).
• By default, MagicEden API allows retrieval of data for a maximum of 500 collections.
• Save the resulting page as a json file under C:\MagicEden, with the default name of collections.json

Step 4: Install KNIME

• If you do not currently have KNIME installed, download and install from KNIME official website.

Step 5: Query with MagicEden API

• Under C:\MagicEden, double-click on the workflow file Collections - KNIME Analysis Engine.knwf, which will open the workflow in KNIME.

Step 6: Install Missing KNIME Extensions

• The EdenEngine workflow is using multiple KNIME extensions that are not installed as default with KNIME.
• So, just follow the simple steps on the screen and install all the missing extensions.
• The progress of installation will be displayed on the lower right corner of KNIME.
• When the installation of extensions is finished, the EdenEngine analytics engine is ready to run.

Step 7: Run EdenEngine

• In KNIME, click the green Execute all executable nodes (Shift+F7) play button on the ribbon to run the EdenEngine workflow.
• The default json file to be read is specified in the light-red JSON Reader node as `C:\MagicEden\collections.json```. Since you downloaded the file with the same name and moved to the default folder, to overwrite the old file, KNIME workflow should run smoothly.
• The running of EdenEngine may be stuck within the last module (bottom yellow frame), titled Multidimensional Scaling for Terms. If this happens, double-click on the green MDS block, click Apply, save with Ctrl+S and then click the green play button again (alternatively, press on your keyboard Shirt+F7).
• The module that consumes the most time is the Topic Extraction module (right side of the workflow) and the module that comes after that, titled Generating word clouds for each topic.
• The results are now generated.
• Interactive plots are also generated within the worflow.

Step 8: Analyze the Results

• To analyze the results within KNIME, right-click on the dark blue nodes in the workflow, which are the Bar Chart and Tag Cloud nodes, and select Interactive View in the pop-up menu.
• To analyze the results within KNIME using Python, open the Node Repository and search for Python to use the Python-related KNIME nodes. You can also search for sample workflows under the KNIME Hub.
• To analyze the generated Excel files externally, open the Excel files in your favorite analytics suite, and analyze with the appropriate tools. The generated Excel files are ready for visual analytics. For example, the file Collections - Orange Model.ows in this repository can be open in Orange data analytics platform and used to analyze the Excel data externally in Orange.

EdenEngine Screenshot

EdenEngine Generated Results Files

Future Plans

Future work on EdenEngine is envisioned as follows:

1. Deploying the app on the web, through KNIME Server.
2. Developing an intuitive user interface, where the user can make simple selections and specify simple parameters, which feed into the analytics engine.
3. Creating many more modules within the engine, so that different types of data from the API are analyzed and result files are generated for them. For example, there can be analysis of NFT listings within the collections and the activities for them; there can be analysis of tokens, wallets, and much more.
4. Going beyond descriptive analytics (term frequencies, MDS, topic analysis) and implementing predictive and even prescriptive analytics.
5. Using Python-related nodes within KNIME to conduct many of the visual analytics, as well as further analyses, within KNIME.
6. Developing analytics tools/dashboards for posterior analysis of the generated results.
7. Using image processing techniques to derive features from the contents of listed NFTs, and analyze those features to come up with insights.