Efficient Updates for Continuous Skyline Computations

We address the problem of maintaining

continuous skyline queries

efficiently over dynamic objects with

d

dimensions. Skyline queries are an important new search capability for multi-dimensional databases. In contrast to most of the prior work, we focus on the unresolved issue of frequent data object updates. In this paper we propose the

ESC

algorithm, an

E

fficient update approach for

S

kyline

C

omputations, which creates a pre-computed

second skyline

set that facilitates an efficient and incremental skyline update strategy and results in a quicker response time. With the knowledge of the

second skyline

set,

ESC

enables (1) to efficiently find the substitute skyline points from the

second skyline

set only when removing or updating a skyline point (which we call a first skyline point) and (2) to delegate the most time-consuming skyline update computation to another independent procedure, which is executed after the complete updated query result is reported. We leverage the basic idea of the traditional

BBS

skyline algorithm for our novel design of a two-threaded approach. The first skyline can be replenished quickly from a small set of second skylines - hence enabling a fast query response time - while de-coupling the computationally complex maintenance of the second skyline. Furthermore, we propose the

Approximate Exclusive Data Region

algorithm (

AEDR

) to reduce the computational complexity of determining a candidate set for second skyline updates. In this paper, we evaluate the

ESC

algorithm through rigorous simulations and compare it with existing techniques. We present experimental results to demonstrate the performance and utility of our novel approach.