Sketching the order of events
This work addresses the challenge of efficiently processing and learning from large-scale streaming data, representing an incremental advancement in stream sketching techniques.
The paper tackles the problem of analyzing massive data streams by introducing 'ordered moments' as features that generalize existing stream sketches to arbitrary order, providing theoretical guarantees like universality important for learning algorithms.
We introduce features for massive data streams. These stream features can be thought of as "ordered moments" and generalize stream sketches from "moments of order one" to "ordered moments of arbitrary order". In analogy to classic moments, they have theoretical guarantees such as universality that are important for learning algorithms.