Haojun Wang

Processing of Continuous Location-Based Range Queries on Moving Objects in Road Networks

With the proliferation of mobile devices, an increasing number of urban users subscribe to location-based services. This trend has led to significant research interest in techniques that address two fundamental requirements: road network-based distance computation and the capability to process moving objects as points of interests. However, there exist few techniques that support both requirements simultaneously. To address these challenges, we propose a novel approach to process continuous range queries. We build on our previous work of an infrastructure that supports location-based snapshot queries on MOVing objects in road Networks (MOVNet). We introduce several significant features to enable continuous queries. The dual index structure that we proposed for MOVNet has been appropriately modified. We further appoint a number of connecting vertices in each cell and precompute the distances among them to expedite query processing. Most importantly, to alleviate the effects of frequent object updates, we introduce a Shortest-Distance-based Tree (SD-Tree). We illustrate that the network connectivity and distance information can be preserved and reused by the SD-Tree when the query point location is updated; hence, reducing the continuous query update cost. Our experimental results demonstrate that our method yields excellent performance with a very large number of moving objects.

Snapshot location-based query processing on moving objects in road networks

Location-based services are increasingly popular and it is a key challenge to efficiently support query processing. We present a novel design to process large numbers of location-based snapshot queries on MOVing objects in road Networks (MOVNet, for short). MOVNets dual-index design utilizes an on-disk R-tree to store the network connectivities and an in-memory grid structure to maintain moving object position updates. A method to speedily compute the overlapping grid cells in the network relates these two indices. Based on the above features we propose algorithms to support mobile network distance range queries. We demonstrate via experimental results that MOVNet yields excellent performance while scaling to a very large number of moving objects.

Adaptive nearest neighbor queries in travel time networks

Nearest neighbor (NN) searches represent an important class of queries in geographic information systems (GIS). Most nearest neighbor algorithms rely on static distance information to compute NN queries (e.g., Euclidean distance or spatial network distance). However, the final goal of a user when performing an NN search is often to travel to one of the search results. Based on this observation, finding the nearest neighbors in terms of travel time is more realistic than the actual distance. In the existing NN algorithms dynamic real-time events (e.g., traffic congestions, detours, etc.) are usually not considered and hence the pre-computed nearest neighbor objects may not accurately reflect the shortest travel time. In this demonstration we present ANNATTO, a novel adaptive nearest neighbor query model for travel time networks which integrates both spatial networks and real-time traffic event information. The ANNATTO system includes the implementation of a globalbased adaptive nearest neighbor algorithm and a localbased greedy nearest neighbor algorithm that both utilize real-time traffic information to provide adaptive nearest neighbor search results.

ASPEN: an adaptive spatial peer-to-peer network

Geographic Information Systems (GIS) are increasingly managing very large sets of data and hence a centralized data repository may not always provide the most scalable solution. Here we introduce a novel approach to manage spatial data by leveraging structured Peer-to-Peer (P2P) systems based on Distributed Hash Tables (DHTs). DHT algorithms provide efficient exact-match object search capabilities without requiring global indexing and as a result they are extremely scalable. Furthermore, the adoption of uniform hash functions ensures excellent load balancing. However, range queries -- which are very common with spatial data -- cannot be executed efficiently because the hash functions unfortunately destroy any existing data locality. Here we report on the design of an Adaptive Spatial Peer-to-pEer Network (ASPEN) that extends Content Addressable Networks (CAN) to preserve spatial locality information while also retaining many of the load balancing properties of DHT systems. We introduce the concept of scatter regions, which are spatial data distribution units that optimize both load balancing and spatial range query processing at the same time. We present a data object key generation function and algorithms for spatial range queries. We rigorously evaluate our technique with both synthetic and real world data sets and the results demonstrate the efficient execution of spatial range queries in the ASPEN system.

Spatial data query support in peer-to-peer systems

The distributed hash table (DHT) mechanisms have been proposed to manage data in very large, structured peer-to-peer (P2P) systems. DHT algorithms provide efficient exact-match object search capabilities without requiring global indexing and are hence extremely scalable. However, range queries - which are very common with spatial data - cannot be executed efficiently in these systems because the adoption of uniform hash functions to ensure excellent load balancing unfortunately destroys any existing data locality. In this report we propose a novel technique to preserve spatial locality information while also keeping some of the load balancing properties of DHT based systems. We describe our design as an extension of content-addressable networks (CAN) and illustrate the feasibility of supporting spatial range queries.

MAPLE: A Mobile Scalable P2P Nearest Neighbor Query System for Location-based Services

In this demonstration we present MAPLE, a scalable peer-to-peer nearest neighbor (NN) query system for mobile environments. MAPLE is designed for the efficient sharing of query results cached in the local storage of mobile peers. The MAPLE system is innovative in its ability to either fully or partially compute location-dependent nearest neighbor objects on each host. The demonstration illustrates how cooperative data sharing and distributed processing among mobile peers results in a considerable reduction of the load on remote spatial databases.

Partition-based lazy updates for continuous queries over moving objects

Continuous spatial queries posted within an environment of moving objects produce as their results a time-varying set of objects. In the most ambitious case both queries and data objects are dynamic, making it very challenging to find an efficient query evaluation strategy. The significant overhead related to frequent location updates from moving objects often results in poor performance. The most advanced existing techniques use the concept of simple geometric safe regions to delay or avoid location updates. We introduce a Partition-based Lazy Update (PLU) algorithm that elevates this idea further by adopting Location Information Tables (LIT) which (a) allow each moving object to estimate possible query movements and issue a location update only when it may affect any query results and (b) enable smart server probing that results in fewer messages. Among the significant advantages, our technique performs well even in very highly dynamic environments (with up to 100% mobility) where many other techniques deteriorate. PLU can be efficiently implemented and we demonstrate its query performance improvement of up to 28% over the current state-of-the-art.

A Novel Dual-Index Design to Efficiently Support Snapshot Location-Based Query Processing in Mobile Environments

Location-based services are increasingly popular recently. Many applications aim to support a large number of users in metro area (i.e., dense networks). To cope with this challenge, we present a framework that supports location-based services on MOVing objects in road Networks (MOVNet, for short) [26]. MOVNets dual-index design utilizes an on-disk R-tree to store the network connectivities and an in-memory grid structure to maintain moving object position updates. In this paper, we extend the functionality of MOVNet to support snapshot range queries as well as snapshot k nearest neighbor queries. Given an arbitrary edge in the space, we analyze the minimum and maximum number of grid cells that are possibly affected. We show that the maximum bound can be used in snapshot range query processing to prune the search space. We demonstrate via theoretical analysis and experimental results that MOVNet yields excellent performance with various networks while scaling to a very large number of moving objects.

A Distributed Geotechnical Information Management and Exchange Architecture

Web services can help facilitate the exchange and utilization of geotechnical information. Such data is of critical interest to a growing number of municipal, state, and federal agencies as well as private enterprises. However, the lack of service infrastructures among heterogeneous data sources operating under different administrative organizations or agencies hampers the full use of geotechnical information. This article describes a Web-services-based way to manage geotechnical data via XML

ANNATTO: Adaptive Nearest Neighbor Queries in Travel Time Networks

Nearest neighbor (NN) searches represent an important class of queries in geographic information systems (GIS). Most nearest neighbor algorithms rely on static distance information to compute NN queries (e.g., Euclidean distance or spatial network distance). However, the final goal of a user when performing an NN search is often to travel to one of the search results. Based on this observation, finding the nearest neighbors in terms of travel time is more realistic than the actual distance. In the existing NN algorithms dynamic real-time events (e.g., traffic congestions, detours, etc.) are usually not considered and hence the pre-computed nearest neighbor objects may not accurately reflect the shortest travel time. In this demonstration we present ANNATTO, a novel adaptive nearest neighbor query model for travel time networks which integrates both spatial networks and real-time traffic event information. The ANNATTO system includes the implementation of a globalbased adaptive nearest neighbor algorithm and a localbased greedy nearest neighbor algorithm that both utilize real-time traffic information to provide adaptive nearest neighbor search results.