Ranking Large Temporal Data
Jeffrey Jestes, Jeff M. Phillips, Feifei Li, Mingwang Tang

TL;DR
This paper introduces new methods for ranking objects in large temporal datasets based on aggregated scores over time intervals, addressing limitations of instant top-k queries with efficient exact and approximate solutions.
Contribution
It presents the first comprehensive study on aggregate top-k queries on temporal data, including novel exact and approximate algorithms with theoretical analysis.
Findings
Methods are efficient and scalable on large datasets.
Approximate algorithms provide quality guarantees.
Experimental results outperform baseline approaches.
Abstract
Ranking temporal data has not been studied until recently, even though ranking is an important operator (being promoted as a firstclass citizen) in database systems. However, only the instant top-k queries on temporal data were studied in, where objects with the k highest scores at a query time instance t are to be retrieved. The instant top-k definition clearly comes with limitations (sensitive to outliers, difficult to choose a meaningful query time t). A more flexible and general ranking operation is to rank objects based on the aggregation of their scores in a query interval, which we dub the aggregate top-k query on temporal data. For example, return the top-10 weather stations having the highest average temperature from 10/01/2010 to 10/07/2010; find the top-20 stocks having the largest total transaction volumes from 02/05/2011 to 02/07/2011. This work presents a comprehensive…
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Taxonomy
TopicsData Management and Algorithms · Advanced Database Systems and Queries · Time Series Analysis and Forecasting
