Mobile Location Indexing Based On Synthetic Moving Objects

Today, the number of researches based on the data they move known as mobile objects indexing came out from the traditional static one. There are some indexing approaches to handle the complicated moving positions. One of the suitable ideas is pre-ordering these objects before building index structure. In this paper, a structure, a presorted-nearest index tree algorithm is proposed that allowed maintaining, updating, and range querying mobile objects within the desired period. Besides, it gives the advantage of an index structure to easy data access and fast query along with the retrieving nearest locations from a location point in the index structure. A synthetic mobile position dataset is also proposed for performance evaluation so that it is free from location privacy and confidentiality. The detail experimental results are discussed together with the performance evaluation of KDtree-based index structure. Both approaches are similarly efficient in range searching. However, the proposed approach is especially much more save time for the nearest neighbor search within a range than KD tree-based calculation

Efficient Methods for Aggregate Reverse Rank Queries

Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users. ARR queries are designed to focus on product bundling in marketing. Manufacturers are mostly willing to bundle several products together for the purpose of maximizing benefits or inventory liquidation. This naturally leads to an increase in data on users and products. Thus, the problem of efficiently processing ARR queries become a big issue. In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It has to process a large portion user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance

多次元データに対するランキング問合せ処理に関する研究

筑波大学 (University of Tsukuba)201

筑波大学計算科学研究センター 平成28年度 年次報告書

まえがき　……　21 センター組織と構成員　……　32 平成 28 年度の活動状況　……　73 各研究部門の報告　……　10I. 素粒子物理研究部門　……　10II. 宇宙物理研究部門　……　36III. 原子核物理研究部門　……　64IV. 量子物性研究部門　……　88V. 生命科学研究部門　……　106 V-1. 生命機能情報分野　……　106 V-2. 分子進化分野　……　122VI. 地球環境研究部門　……　140VII. 高性能計算システム研究部門　……　154VIII. 計算情報学研究部門　……　205 Ⅷ-1. データ基盤分野　……　205 Ⅷ-2. 計算メディア分野　……　22