BJR-Tree: Fast Skyline Computation Algorithm for Serendipitous Searching Problems

High-throughput label-free single cell screening technology has been studied for noninvasive analysis of various kinds of cells. Selecting the prominent cells with extreme features from a large number of cells is an important and interesting problem. We call this problem the serendipitous searching problem (SSP), where it is important to find entries located near the rind of the population in multi-dimensional feature space. We tackle the SSP as a continuous skyline computation. The skyline computation is originally used to extract interesting entries from a database with multi-attributions. The skyline points are continuously updated at the time existing entries disappear and new entries arrive. In this paper, we propose a balanced jointed rooted tree (BJR-tree) algorithm and a nondominated relation cache (ND-cache) for continuous skyline computation. The BJR-tree expresses the dominance relation as an arc and stores the "dominated" relations. The ND-cache complements the BJR-tree by reducing the recalculation of the dominance relations. The execution times of the BJR-tree and existing continuous skyline computation algorithms are compared on randomly constructed synthetic datasets with multiple temporal and spatial features. The BJR-tree is then evaluated on actual measured information of blood cells. On the two and eight-dimensional synthetic datasets, the BJR-tree computed the continuous skylines approximately 3x and 70x faster than the LookOut, respectively. On actual datasets, the BJR-tree is approximately 2.4x faster than the LookOut.