Continuous k-dominant skyline computation on multidimensional data streams

Abstract

Skyline queries are important due to their usefulness in many application domains. However, by increasing the number of attributes, the probability that a tuple dominates another one is reduced significantly. To attack this problem, k-dominant skylines have been proposed, relaxing the definition of domination. In this paper, we study the problem of continuous monitoring of k-dominant skylines, where multiple queries are running concurrently. The proposed method divides the space in pairs of attributes. For each pair, we compute skyline tuples and we exploit them to eliminate candidates tuples of the queries and we combine the partial results. The proposed scheme uses only simple domination checks and it is applicable to the streaming case as well as to ad-hoc insertions and deletions. Experiments, based on different data distributions, show the efficiency of the proposed scheme in comparison to existing methods.

Continuous Probabilistic Skyline Queries for Uncertain Moving Objects in Road Network

“…(1) initialization(); (2) // compute events; (3) for any point in (4) for each point in (5) compute begin time s time and end time e time of each event; (6) if s time ≤ && e time ≥ (7) push it into ; //valid event; (8) //handle events; (9) time = , = (10) while (time ≤ ) (11) while ( != NULL) (12) = ⋅ erase(); // pop queue's head; (13) while ( ⋅ begin ≤ time) (14) handle ⋅ push back( ); // push into queue; (15) = ⋅ erase(); (16) handle ( In second step, the worst cost of comparison for static dominant relationship between objects is ( ( − 1)/2). The interaction time for arbitrary two moving objects' distance function will cost ( 1 ), where 1 is constant.…”

“…The R-tree based I-Eager and I-Lazy algorithms were brought forth by Tao and Papadias [13] who firstly studied how to update and maintain the skyline results in dynamic datasets.. Tian et al [14] introduced GICSC updating skyline query sets dynamically which better fits for low dimension metrics. Kontaki et al [15] exerted efforts to maintain -dominant skyline objects with maximum user's preference. Huang et al [4] probed into the continuous skyline query for certain moving objects.…”

On finding skylines in external memory

“…At this point, we are facing a (h+1, d)-merge problem on Σ and be trivially solved by BNL in O(N 2 /(MB)) I/Os. The existing k-dominant-skyline algorithms [8,17,25] are heuristic, and have the same complexity as BNL in the worst case. …”

Continuous Processing of Preference Queries in Data Streams

“…In [21] the continuous k-dominant skyline evaluation over sliding windows is studied. The proposed algorithm CoSM uQ handles multiple continuous queries.…”