Processing Moving kNN Queries Using Influential Neighbor Sets

نویسندگان

  • Chuanwen Li
  • Yu Gu
  • Jianzhong Qi
  • Ge Yu
  • Rui Zhang
  • Wang Yi
چکیده

The moving k nearest neighbor query, which computes one’s k nearest neighbor set and maintains it while at move, is gaining importance due to the prevalent use of smart mobile devices such as smart phones. Safe region is a popular technique in processing the moving k nearest neighbor query. It is a region where the movement of the query object does not cause the current k nearest neighbor set to change. Processing a moving k nearest neighbor query is a continuing process of checking the validity of the safe region and recomputing it if invalidated. The size of the safe region largely decides the frequency of safe region recomputation and hence query processing efficiency. Existing moving k nearest neighbor algorithms lack efficiency due to either computing small safe regions and have to recompute frequently or computing large safe regions (i.e., an order-k Voronoi cell) with a high cost. In this paper, we take a third approach. Instead of safe regions, we use a small set of safe guarding objects. We prove that, as long as the the current k nearest neighbors are closer to the query object than the safe guarding objects, the current k nearest neighbors stay valid and no recomputation is required. This way, we avoid the high cost of safe region recomputation. We also prove that, the region defined by the safe guarding objects is the largest possible safe region. This means that the recomputation frequency of our method is also minimized. We conduct extensive experiments comparing our method with the state-of-the-art method on both real and synthetic data sets. The results confirm the superiority of our method.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Safe Exit Algorithm for Moving k Nearest Neighbor Queries in Directed and Dynamic Spatial Networks

The process of moving k nearest neighbor (MkNN) queries has been studied extensively in spatial network databases. Existing state-of-the-art algorithms have focused primarily on handling MkNN queries in undirected and static spatial networks where every edge is undirected and its weight does not change over time. However, little attention has been paid to MkNN queries in directed and dynamic sp...

متن کامل

Approximate Continuous K Nearest Neighbor Queries for Continuous Moving Objects with Pre-defined Paths

Continuous K nearest neighbor queries (C-KNN) on moving objects retrieve the K nearest neighbors of all points along a query trajectory. In existing methods, the cost of retrieving the exact C-KNN data set is expensive, particularly in highly dynamic spatio-temporal applications. The cost includes the location updates of the moving objects when the velocities change over time and the number of ...

متن کامل

Processing All k-Nearest Neighbor Queries in Hadoop

A k-nearest neighbor (kNN) query, which retrieves nearest k points from a database is one of the fundamental query types in spatial databases. An all k-nearest neighbor query (AkNN query), a variation of a kNN query, determines the k-nearest neighbors for each point in the dataset in a query process. In this paper, we propose a method for processing AkNN queries in Hadoop. We decompose the give...

متن کامل

High-dimensional kNN joins with incremental updates

The k Nearest Neighbor (kNN) join operation associates each data object in one data set with its k nearest neighbors from the same or a different data set. The kNN join on high-dimensional data (high-dimensional kNN join) is an especially expensive operation. Existing high-dimensional kNN join algorithms were designed for static data sets and therefore cannot handle updates efficiently. In this...

متن کامل

Indexing land surface for efficient kNN query

The class of k Nearest Neighbor (kNN) queries is frequently used in geospatial applications. Many studies focus on processing kNN in Euclidean and road network spaces. Meanwhile, with the recent advances in remote sensory devices that can acquire detailed elevation data, the new geospatial applications heavily operate on this third dimension, i.e., land surface. Hence, for the field of database...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • PVLDB

دوره 8  شماره 

صفحات  -

تاریخ انتشار 2014