Continuous Nearest Neighbor Queries over Sliding Windows

Abstract—This paper studies continuous monitoring of nearest neighbor (NN) queries over sliding window streams. According to this model, data points continuously stream in the system, and they are considered valid only while they belong to a sliding window that contains 1) the W most recent arrivals (count-based) or 2) the arrivals within a fixed interval W covering the most recent time stamps (time-based). The task of the query processor is to constantly maintain the result of long-running NN queries among the valid data. We present two processing techniques that apply to both count-based and time-based windows. The first one adapts conceptual partitioning, the best existing method for continuous NN monitoring over update streams, to the sliding window model. The second technique reduces the problem to skyline maintenance in the distance-time space and precomputes the future changes in the NN set. We analyze the performance of both algorithms and extend them to variations of NN search. Finally, we compare their efficiency through a comprehensive experimental evaluation. The skyline-based algorithm achieves lower CPU cost, at the expense of slightly larger space overhead. Index Terms—Location-dependent and sensitive, spatial databases, query processing, nearest neighbors, data streams, sliding windows.

Maintenance of Spatial Queries on Continuously Moving Points

Cars, aircraft, mobile cell phones, ships, tanks, and mobile robots all have the common property that they are moving objects. A kinematic representation can be used to describe the location of these objects as a function of time. For example, a moving point can be represented by the linear function p(t) = x_0 + (t - t_0)v, where x_0 is the start location, t_0 is the start time, and v is its velocity vector. Instead of storing the location of the object at a given time in a database, the coefficients of the function are stored. When an object's behavior changes enough that the function describing its location is no longer accurate, the function coefficients for the object are updated.Because the objects are represented as a function of time, spatial query results can change even when no transactions update the database. Our hypothesis is that algorithms for the maintenance of spatial queries on kinematic point data types can be developed to support updates to base relations as time advances that are more efficient than straight forward adaptations of previous work. We present algorithms to maintain k-nearest neighbor, spatial join, and spatial semijoin queries in this domain. We compare by experimentation these new algorithms to more straight forward adaptations of previous work to support updates. Experiments are conducted using synthetic uniformly distributed data, and real aircraft flight data. The primary metric of comparison is the number of I/O disk accesses needed to maintain the query results and supporting data structures. A system to query and visualize results on moving object data, in a client-server environment, is also presented. The work presented here is built upon a culmination of our previously published work, including work on continuously moving point queries [35, 36], and client-server systems [31, 33, 34]

Continuous Spatial Query Processing:A Survey of Safe Region Based Techniques

In the past decade, positioning system-enabled devices such as smartphones have become most prevalent. This functionality brings the increasing popularity of location-based services in business as well as daily applications such as navigation, targeted advertising, and location-based social networking. Continuous spatial queries serve as a building block for location-based services. As an example, an Uber driver may want to be kept aware of the nearest customers or service stations. Continuous spatial queries require updates to the query result as the query or data objects are moving. This poses challenges to the query efficiency, which is crucial to the user experience of a service. A large number of approaches address this efficiency issue using the concept of safe region . A safe region is a region within which arbitrary movement of an object leaves the query result unchanged. Such a region helps reduce the frequency of query result update and hence improves query efficiency. As a result, safe region-based approaches have been popular for processing various types of continuous spatial queries. Safe regions have interesting theoretical properties and are worth in-depth analysis. We provide a comparative study of safe region-based approaches. We describe how safe regions are computed for different types of continuous spatial queries, showing how they improve query efficiency. We compare the different safe region-based approaches and discuss possible further improvements

Continuous Probabilistic Nearest-Neighbor Queries for Uncertain Trajectories

This work addresses the problem of processing continuous nearest neighbor (NN) queries for moving objects trajectories when the exact position of a given object at a particular time instant is not known, but is bounded by an uncertainty region. As has already been observed in the literature, the answers to continuous NN-queries in spatio-temporal settings are time parameterized in the sense that the objects in the answer vary over time. Incorporating uncertainty in the model yields additional attributes that affect the semantics of the answer to this type of queries. In this work, we formalize the impact of uncertainty on the answers to the continuous probabilistic NN-queries, provide a compact structure for their representation and efficient algorithms for constructing that structure. We also identify syntactic constructs for several qualitative variants of continuous probabilistic NN-queries for uncertain trajectories and present efficient algorithms for their processing. 1

Continuous Monitoring of Top-K Queries over Sliding Windows

Given a dataset P and a preference function f, a top-k query retrieves the k tuples in P with the highest scores according to f. Even though the problem is well-studied in conventional databases, the existing methods are inapplicable to highly dynamic environments involving numerous long-running queries. This paper studies continuous monitoring of top-k queries over a fixed-size window W of the most recent data. The window size can be expressed either in terms of the number of active tuples or time units. We propose a general methodology for top-k monitoring that restricts processing to the sub-domains of the workspace that influence the result of some query. To cope with high stream rates and provide fast answers in an on-line fashion, the data in W reside in main memory. The valid records are indexed by a grid structure, which also maintains book-keeping information. We present two processing techniques: the first one computes the new answer of a query whenever some of the current top-k points expire; the second one partially pre-computes the future changes in the result, achieving better running time at the expense of slightly higher space requirements. We analyze the performance of both algorithms and evaluate their efficiency through extensive experiments. Finally, we extend the proposed framework to other query types and a different data stream model. Copyright 2006 ACM

Query Processing in Spatial Databases Containing Obstacles

Despite the existence of obstacles in many database applications, traditional spatial query processing assumes that points in space are directly reachable and utilizes the Euclidean distance metric. In this paper, we study spatial queries in the presence of obstacles, where the obstructed distance between two points is defined as the length of the shortest path that connects them without crossing any obstacles. We propose efficient algorithms for the most important query types, namely, range search, nearest neighbours, e-distance joins, closest pairs and distance semi-joins, assuming that both data objects and obstacles are indexed by R-trees. The effectiveness of the proposed solutions is verified through extensive experiments

Skyline queries in dynamic environments

Ph.DDOCTOR OF PHILOSOPH