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Aggregation in relational databases is accomplished through hashing andsorting interval data, which is computationally expensive and scales poorly asthe data volumes grow.  In this paper, we show how quantitative interval and time-series data inrelational attributes can be represented using delta summary values rather thanabsolute values. The need for sorting to determine the row corresponding tosome maximum timestamp is negated, reducing the time complexity from at leastO(n log(n)) towards O(n) and improving query execution times. We illustratethis new method in the relational algebra, present the implementationalgorithmically, and test an implementation in two leading RDBMS productsagainst the use of normal equivalents.  We found this delta summation technique to be most effective for use caseswith additive, numerical data upon which it is necessary to frequently obtainthe latest values, and where the row cardinalities are in the order of 10^5.Our experiments found the proposed new delta summation technique could executefaster than the equivalent standard selection method by up to 22.4%, reducingthe overall query cost in some circumstances by up to 24.0%, reducing I/O loadby up to 60.6%, but with increased query costs for write operations, anincrease in CPU time and memory allocation, uncertain performance with very lowor very high cardinalities and inconsistent results across different RDBMSplatforms.

An improved method of delta summation for faster current value selection across filtered subsets of interval and temporal relational data