Durable Top-K Instant-Stamped Temporal Records with User-Specified Scoring Functions
Junyang Gao, Stavros Sintos, Pankaj K. Agarwal, Jun Yang

TL;DR
This paper introduces a new approach for identifying durable top-$k$ records in instant-stamped temporal data using user-defined scoring functions, with algorithms that significantly outperform existing methods.
Contribution
The paper formulates the durable top-$k$ query problem with user-specified scoring and window parameters, and proposes efficient algorithms with theoretical analysis.
Findings
Algorithms outperform baselines by up to 100x
Effective in real and synthetic datasets
Provides comprehensive complexity analysis
Abstract
A way of finding interesting or exceptional records from instant-stamped temporal data is to consider their "durability," or, intuitively speaking, how well they compare with other records that arrived earlier or later, and how long they retain their supremacy. For example, people are naturally fascinated by claims with long durability, such as: "On January 22, 2006, Kobe Bryant dropped 81 points against Toronto Raptors. Since then, this scoring record has yet to be broken." In general, given a sequence of instant-stamped records, suppose that we can rank them by a user-specified scoring function , which may consider multiple attributes of a record to compute a single score for ranking. This paper studies "durable top- queries", which find records whose scores were within top- among those records within a "durability window" of given length, e.g., a 10-year window…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Management and Algorithms · Advanced Database Systems and Queries · Data Mining Algorithms and Applications
