Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Synopsis data structures for massive data sets
External memory algorithms
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
New directions in traffic measurement and accounting
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Frequency Estimation of Internet Packet Streams with Limited Space
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
What's hot and what's not: tracking most frequent items dynamically
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Approximate fairness through differential dropping
ACM SIGCOMM Computer Communication Review
Optimal approximations of the frequency moments of data streams
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
CAM conscious integrated answering of frequent elements and top-k queries over data streams
Proceedings of the 4th international workshop on Data management on new hardware
Hi-index | 0.01 |
We present an algorithm for finding frequent elements in a stream where the arrivals are not bursty. Depending on the amount of burstiness in the stream our algorithm detects elements with frequency at least t with space between Õ(F1/t2) and Õ(F2/t2) where F1 and F2 are the first and the second frequency moments of the stream respectively. The latter space complexity is achieved when the stream is completely bursty; i.e., most elements arrive in contiguous groups, and the former is attained when the arrival order is random. Our space complexity is Õ(αF1/t2) where a is a parameter that captures the burstiness of a stream and lies between 1 and F2/F1. A major advantage of our algorithm is that even if the relative frequencies of the different elements is fixed, the space complexity decreases with the length of the stream if the stream is not bursty.