Sung-Hyun Shin

K-Nearest Neighbor query processing methods in road network space: Performance evaluation

The road network now opens a new application area for the classic k-NN queries, which retrieve k objects closest to a given query point. There are many previous methods for k-NN query processing. However, all of them are not sufficient in experimental evaluation of performance. In this paper, we present the way that extensively evaluates prior methods for processing of k-NN queries. To achieve this goal, we first describe well-known previous methods for retrieving k objects closest to a given query point. Next, we establish the criteria against which previous methods have been evaluated for k-NN query processing. Last, we build the experiment environment using the real network data and then perform extensive performance evaluation.

Efficient shortest path finding of k-nearest neighbor objects in road network databases

This paper addresses an efficient path finding scheme that complements classic k-NN (Nearest Neighbor) queries for the road network. Aiming at finding both k objects and the shortest paths to them at the same time, this paper first selects candidate objects by the k-NN search scheme based on the underlying index structure and then finds the path to each of them by the modified A* algorithm. The path finding step stores the intermediary paths from the query point to all of the scanned nodes and then attempts to match the common segment with a path to a new node, instead of repeatedly running the A* algorithm for all k points. Additionally, the cost to the each object calculated in this step makes it possible to finalize the k objects from the candidate set as well as to order them by the path cost. Judging from the result, the proposed scheme can eliminate redundant node scans and provide one of the most fundamental building blocks for location-based services in the real-life road network.

Efficient Shortest Path Search in Large Road Network Environment: A Heuristic Approach

Abstract The road network now opens a new application area for the classic k-nearestneighbors (k-NN) queries, which retrieve k objects closest to a given query point. However, since most existing schemes are built on top of the Euclidean distance, they just And the k objects, failing in discovering the shortest paths to them and thus possibly bringing the so-called false dismissal problem. Aiming at finding both k objects and the shortest paths at the same time, this paper first selects candidate objects by the k-NN search scheme according to the underlying index structure and then finds the path to each of them by the modified A* algorithm. The path finding step stores the intermediary paths from the query point to all of the scanned nodes and then attempts to match the path segment common between the stored paths and the path to a new scan node instead of repeatedly running A* algorithm for each k point. Experiment results show that, for the road network data of Oldenburg Road Network and California Road Network, the proposed scheme improves the search speed by 1.3–3.0 times, compared with incremental network expansion, post-Dijkstra, and naïve method, also reducing the number of scan nodes by 11.8–66.8%.

Incremental Maintenance of Horizontal Views Using a PIVOT Operation and a Differential File in Relational DBMSs

ABSTRACT To analyze multidimensional data conveniently and efficiently, OLAP (On-Line Analytical Processing) systems or e-business are widely using views in a horizontal form to represent measurement values over multiple dimensions. These views can be stored as materialized views derived from several sources in order to support accesses to the integrated data. The horizontal views can provide effective accesses to complex queries of OLAP or e-business. However, we have a problem of occurring maintenance of the horizontal views since data sources are distributed over remote sites. We need a method that propagates the changes of source tables to the corresponding horizontal views. In this paper, we address incremental maintenance of horizontal views that makes it possible to reflect the changes of source tables efficiently. We first propose an overall framework that processes queries over horizontal views transformed from source tables in a vertical form. Under the proposed framework, we propagate the change of vertical tables to the corresponding horizontal views. In order to execute this view maintenance process efficiently, we keep every change of vertical tables in a differential file and then modify the horizontal views with the differential file. Because the differential file is represented as a vertical form, its tuples should be converted to those in a horizontal form to apply them to the out-of-date horizontal view. With this mechanism, horizontal views can be efficiently refreshed with the changes in a differential file without accessing source tables. Experimental results show that the proposed method improves average performance by 1.2～5.0 times over the existing methods.Keywords:OLAP, Data Warehouse, Multidimensional Data, PIVOT, Differential File

A New Multidimensional Point Access Method for Efficient Sequential Processing of Region Queries

The B + -tree was proposed to support sequential processing in the B-tree. To the extent of authors’ knowledge, however, there have been no studies supporting sequential processing in multidimensional point access methods(PAMs). To do this, the cells in a multilevel and multidimensional space managed by a multidimensional PAM must be linearly ordered systematically. In this paper, we discuss an approach that linearly orders cells in a multilevel and multidimensional space and propose a novel sequential processing algorithm for region queries using this approach. We then propose the MLGF-Plus applying this approach to the MLGF.

Incremental maintenance of horizontal view using PIVOT operation and differential files in relational DBMSs

To analyze multidimensional data conveniently and efficiently, OLAP(online analytical processing) systems are widely using views with horizontal form to represent measurement values over multiple dimensions, while conventional RDBMSs usually manage tables with vertical form. In this paper, we propose an incremental maintenance of these horizontal views which updates efficiently them according to the changes of base source tables. We first propose an overall framework processing queries over horizontal views transformed from source tables with vertical form. In order to execute this view maintenance process efficiently, we keep every changes of source tables into differential files then we modify the horizontal views with the differential files. Because differential files have vertical forms, their tuples are converted to horizontal forms which are identical to horizontal views then they are applied to the maintenance of these views. Experimental results show that the proposed method is more effective than existing methods.

Efficient Storage and Querying of Horizontal Tables Using a PIVOT Operation in Commercial Relational DBMSs

In recent years, a horizontal table with a large number of attributes is widely used in OLAP or e-business applications to analyze multidimensional data efficiently. For efficient storing and querying of horizontal tables, recent works have tried to transform a horizontal table to a traditional vertical table. Existing works, however, have the drawback of not considering an optimized PIVOT operation provided (or to be provided) in recent commercial RDBMSs. In this paper we propose a formal approach that exploits the optimized PIVOT operation of commercial RDBMSs for storing and querying of horizontal tables. To achieve this goal, we first provide an overall framework that stores and queries a horizontal table using an equivalent vertical table. Under the proposed framework, we then formally define 1) a method that stores a horizontal table in an equivalent vertical table and 2) a PIVOT operation that converts a stored vertical table to an equivalent horizontal view. Next, we propose a novel method that transforms a user-specified query on horizontal tables to an equivalent PIVOT-included query on vertical tables. In particular, by providing transformation rules for all five elementary operations in relational algebra as theorems, we prove our method is theoretically applicable to commercial RDBMSs. Experimental results show that, compared with the earlier work, our method reduces storage space significantly and also improves average performance by several orders of magnitude. These results indicate that our method provides an excellent framework to maximize performance in handling horizontal tables by exploiting the optimized PIVOT operation in commercial RDBMSs.

An efficient sheet partition technique for very large relational tables in OLAP

Spreadsheets such as Microsoft Excel are OLAP(On-Line Analytical Processing) [2] applications to easily analyze complex multidimensional data. In general, spreadsheets provide grid-like graphical interfaces together with various chart tools [4,5]. However, previous work on OLAP spreadsheets adopts a naive approach that directly retrieves, transmits, and presents all the resulting data at once. Thus, it is difficult to use the previous work for very large relational tables with millions of rows or columns due to the communication and space overhead.

A PIVOT based Query Optimization Technique for Horizontal View Tables in Relational Databases

For effective analyses in various business applications, OLAP(On-Line Analytical Processing) systems represent the multidimensional data as the horizontal format of tables whose columns are corresponding to values of dimension attributes. Because the traditional RDBMSs have the limitation on the maximum number of attributes in table columns(MS SQLServer and Oracle permit each table to have up to 1,024 columns), horizontal tables cannot be directly stored into relational database systems. In this paper, we propose various efficient optimization strategies in transforming horizontal queries to equivalent vertical queries. To achieve this goral, we first store a horizontal table using an equivalent vertical table, and then develop various query transformation rules for horizontal table queries using the PIVOT operator. In particular, we propose various alternative query transformation rules for the basic relational operators, selection, projection, and join. Here, we note that the transformed queries can be executed in several ways, and their execution times will differ from each other. Thus, we propose various optimization strategies that transform the horizontal queries to the equivalent vertical queries when using the PIVOT operator. Finally, we evaluate these methods through extensive experiments and identify the optimal transformation strategy when using the PIVOT operator.

Generating trajectories on road networks

Recently, researches are being in progress using the trajectories of moving objects. Most researches usually used data generated by trajectory generators since it is difficult to obtain a trajectory data set of moving objects in real world. Most previous trajectory generators created trajectories of objects moving over Euclidean space, and therefore they can not be directly applied to road network environment. In this paper, we propose a method for generating trajectories of objects moving over road networks. To generate trajectories, we consider the most important characteristic of network-based moving objects that in real world most objects move on given networks with the shortest path from a starting point to a destination. The trajectory data set of moving objects which is generated by the proposed method can be used in various applications such as location-based services since it reflects the users driving preference on real network environments.