We propose a SAO index to approximately answer arbitrary linear optimization queries in a sliding window of a data stream. It uses limited memory to maintain the most "important" tuples. At any time, for any linear optimization query, we can retrieve the approximate top-K tuples in the sliding window almost instantly. The larger the amount of available memory, the better the quality of the answers is. More importantly, for a given amount of memory, the quality of the answers can be further improved by dynamically allocating a larger portion of the memory to the outer layers of the SAO index.
Authors in REF proposed the SAO (Stream Approximate Onion-like structure) index for linear optimization queries against data streams.
Answering linear optimization queries with an approximate stream index
Knowledge and Information Systems
Knowledge and Information Systems
Computer Science