Applications of Count-min sketch

  • Compressed Sensing
  • Networking
  • NLP
  • Stream Processing
  • Frequency tracking
  • Extension: Heavy-hitters
  • Extension: Range-query

Count-Min Sketch Data Structure with Implementation

The Count-Min Sketch is a probabilistic data structure and is defined as a simple technique to summarize large amounts of frequency data. Count-min sketch algorithm talks about keeping track of the count of things. i.e, How many times an element is present in the set.

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What is Count-Min Sketch?

Count-min sketch approach was proposed by Graham Cormode and S. Muthukrishnan. in the paper approximating data with the count-min sketch published in 2011/12. Count-min sketch is used to count the frequency of the events on the streaming data. Like the Bloom filter, Count-min sketch algorithm also works with hash codes. It uses multiple hash functions to map these frequencies on to the matrix (Consider sketch here a two dimensional array or matrix)....

Need for Count-Min Sketch

Since Count-Min Sketch is used to find the frequency of an element, one might think if there is actually a need for such data structure! The answer is Yes. Let us see with the help of an example....

How does Count-Min Sketch work?

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Implementation of Count-min sketch using Guava library in Java:

Let’s look at the below example step by step....

Time and Space Complexity of Count-Min Sketch Data Structure

We can implement the Count-min sketch using Java library provided by Guava. Below is the step by step implementation:...

Applications of Count-min sketch:

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Issue with Count-min sketch and its solution:

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Conclusion:

Now let’s look at the time and space consumed with this type of approach (wrt to above Java-Guava Implementation)...

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