The Hybrid-Layer Index: A synergic approach to answering top-k queries in arbitrary subspaces

Abstract

Abstract-In this paper, we propose the Hybrid-Layer Index (simply, the HL-index) that is designed to answer top-k queries efficiently when the queries are expressed on any arbitrary subset of attributes in the database. Compared to existing approaches, the HL-index significantly reduces the number of tuples accessed during query processing by pruning unnecessary tuples based on two criteria, i.e., it filters out tuples both (1) globally based on the combination of all attribute values of the tuples like in the layer-based approach (simply, layer-level filtering) and (2) based on individual attribute values specifically used for ranking the tuples like in the list-based approach (simply, list-level filtering). Specifically, the HL-index exploits the synergic effect of integrating the layer-level filtering method and the list-level filtering method. Through an in-depth analysis of the interaction of the two filtering methods, we derive a tight bound that reduces the number of tuples retrieved during query processing while guaranteeing the correct query results. We propose the HLindex construction and retrieval algorithms and formally prove their correctness. Finally, we present the experimental results on synthetic and real datasets comparing the performance of the HL-index to other state-of-the-art indexes. Our experiments demonstrate that the HL-index shows the best (or close to best) performance in most scenarios regardless of the size of the dataset, the number of attributes in the tuples, and the number of attributes used in the queries.

Adaptive convex skyline: a threshold-based project partitioned layer-based index for efficient-processing top-k queries in entrepreneurship applications

“…We perform experiments using synthetic and real dataset. For the synthetic dataset, we generate uniform dataset using the generator mentioned in Borzsonyi et al [4]. The dataset consists of from two-to eight-dimensional dataset of 10K objects.…”

“…To compute the convex hulls for the experiments, we used Qhull library [3]. To compute skylines, we used the SFS algorithm [4]. We conducted all the experiments on an Intel i5-760 quad core processor running at 2.80 GHz Linux PC with 16GB of main memory.…”

“…On the other hand, we can get a few top results immediately after the query was issued using the top-k query technique. A top-k query returns k tuples with the highest or the lowest scores from a relation [4,5,8,9]. The following is an example of top-k query in healthcare application.…”

“…This iteration continues until no tuples remain. The hybrid-layer index (HL-index) [4,5] is hybrid method of the layerbased and list-based [7] approaches. It first constructs layers with convex hull vertices as ONION does, and then constructs d sorted lists within each layer.…”

“…These approaches find the top-k answers by accessing just the first few layers. ONION [3], HL-index [4,5], AppRI [14], DG [15], PL-index [6] and AppCSE [12] are well-known methods of this approach. ONION and HL-index use convex hull [1] to construct layers.…”