Performance Analysis of Data Management for High Velocity Streams in Edge Computing
Implementation plan:
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Step 1: Initially, We collect the data from real-time stock exchange using APIs.
Step 2: Then, We preprocess the data using the Z-score normalization technique.
Step 3: Next, we Cluster the data using k-means clustering approaches to identify patterns and group similar data points.
Step 4: Next, We implement the Hidden Markov Model with Wavelet transform (HMM-WT) for Data Transformation.
Step 5: Then, we implement Data Streaming analysis to check the quality of data. (Example checking whether the spread for analysis is proper etc). If quality is not as per the standard then again preprocessing is to be done.
Step 6: Next, we Store the data on the edge using LSTM (Long Short Term Memory) and Time series database.
Step 7: Next, we Analyze the data stored in the edge to make a faster trend prediction. We optimize the data using Spatio Temporal Fusion Network with Bald Eagle Search Optimizer (STFN-BESO).
Step 8: Finally, we plot graph for the following metrics:
8.1: Data volume vs Latency (ms)
8.2: Data volume vs Encryption Time (ms)
8.3: Data volume vs Decryption Time (ms)
8.4: Time vs Trend Prediction Accuracy (%)
8.5: Number of Clusters vs Response Time (ms)
8.6 Number of Iteration vs Computational Cost (%)
Software Requirements:
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1. Development Tool: Python 3.11.9
2. Operating System: Windows-11 (64-bit)
Note :-
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) If the plan satisfies your requirement, Please confirm with us.
4) Project based on Simulation only, not a real time project.
5) Please understand that any modifications made to the confirmed implementation plan will not be made before or after the project development.
