Efficient Identification of Common Subsequences from Big Data Streams Using Sliding Window Technique

We propose an efficient Frequent Sequence Stream algorithm for identifying the top k most frequent subsequences over big data streams. Our Sequence Stream algorithm gains its efficiency by its time complexity of linear time and very limited space complexity. With a pre-specified subsequence window size S and the k value, in very high probabilities, the Sequence Stream algorithm retrieve the top k most frequent subsequences of size S. The Stream Sequence algorithm also provides a high accuracy of the estimation of the number of occurrences of each promoted subsequence. Our experiments indicate several factors that influence the result accuracy of the Sequence Stream algorithm: stream size, subsequence size S and frequency of the subsequence.

@article{Alhudhaif2014,
  title     = {Efficient Identification of Common Subsequences from Big Data Streams Using Sliding Window Technique},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {5},
  number    = {11},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {Adi Alhudhaif},
  doi       = {10.14569/IJACSA.2014.051106},
  url       = {https://doi.org/10.14569/IJACSA.2014.051106}
}