Sampling Techniques in Big Data Streams

Introduction to Sampling

• Sampling: Process of collecting a representative subset from streaming data.
• Importance: Reduces computational resources and time.
• Goal: Ensure sampled data retains significant characteristics of the entire stream.
• Applications: Used to find crucial aggregates without processing entire data.

Types of Sampling Techniques

1. Fixed Proportion Sampling

   • Definition: Sampling data with a fixed percentage (proportion) of the known or approximate count of the data stream.
   • Advantages: Usually ensures a representative sample; good when the data size is very large and computational resources are high.
   • Challenges: Can lead to under-representation or over-representation; requires high computational power for large data volumes.
   • Example: Analyzing user sentiments on social media by sampling 1% of tweets.

2. Fixed Size Sampling

   • Definition: Samples a fixed number of records from the entire data stream.
   • Advantages: Useful for reducing data volume; simpler to implement.
   • Disadvantages: Does not guarantee a representative sample; can be biased if the data distribution is not random.
   • Example: Online store analyzing 1000 out of 10,000 customer orders every hour.

3. Biased Reservoir Sampling

   • Definition: Selects a subset of data streams based on a non-uniform, predetermined probability distribution.
   • Advantages: Suitable when resources are constrained (limited memory or computational power).
   • Disadvantages: May introduce significant biases; requires careful adjustment of analysis parameters.
   • Example: Selecting product ratings from users who tend to give more accurate ratings, based on user history.

4. Concise Sampling

   • Definition: Maintains a small, fixed-size reservoir while achieving a representative sample using unique attributes.
   • Advantages: Retains characteristics of the entire stream; allows adjustments based on main memory size.
   • Challenges: Limited by memory size; needs parameter adjustment for best results.
   • Example: A bank analyzing customer spending habits by selecting distinct customer IDs from transaction streams.

Conclusion

• All four techniques have their specific use cases and advantages/disadvantages.
• Choice of technique depends on the data characteristics and available resources.

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