Ki-Joon Han

A grid-based node split algorithm for managing current location data of moving objects

Abstract There is rapidly increasing interest in Location Based Service (LBS) which utilizes location data of moving objects. To efficiently manage the huge amounts of location data in LBS, the GALIS (Gracefully Aging Location Information System) architecture, a cluster-based distributed computing architecture, is proposed. The GALIS using the non-uniform 2-level grid algorithm performs load balancing and indexing for nodes. However, the non-uniform 2-level grid algorithm has a problem creating unnecessary nodes when moving objects are crowded in a certain region. Therefore, a new node split algorithm, which is more efficient for various distribution of moving objects, is proposed in this paper. Because the algorithm proposed in this paper considers spatial distribution for the current location of moving objects, it can perform efficient load balancing without creating unnecessary nodes even when moving objects are congested in a certain region. Besides, the various data distribution configuration for moving objects has been experimented by implementing node split simulators and it’s been verified that the proposed algorithm can split nodes more efficiently than the existing algorithm.

A real-time mobile GIS based on the HBR-tree for location based services

Recently, as the growth of the wireless Internet, PDA, and HPC, the focus of research and development has been changed to LBS (location based service). To offer LBS efficiently, there must be a real-time GIS platform that can deal with dynamic status of moving objects and a location index which can deal with the characteristics of location data. Therefore, in this paper, we studied the real-time mobile GIS based on the HBR-tree to manage mass of location data efficiently. The real-time mobile GIS mainly consists of the HBR (Hash Based R)-tree and the real-time GIS platform. The HBR-tree is a combined index type of the R-tree and the spatial hash index. Although location data are updated frequently, update operations are done within the same hash table in the HBR-tree, so it costs less than other tree-based indexes. The real-time GIS platform consists of a real-time GIS engine, a middleware, and a mobile client.

Distributed scalable location data management system based on the GALIS architecture

A challenging task in the LBS system engineering is to implement a highly scalable system architecture which can manage moderate-size configurations handling thousands of moving items as well as upper-end configurations handling millions of moving items. The architecture named the GALIS is a cluster-based distributed computing system architecture that consists of multiple data processors, each dedicated to keeping records relevant to a different geographical zone and a different time zone. In this paper, we explain a prototype location data management system structuring major components of GALIS by employing the TMO programming scheme, including the execution engine middleware developed to support real-time distributed object programming and real-time distributed computing system design. We present how to generate realistic location sensing reports and how to process such location reports and location-related queries. Some experimental results showing performance factors regarding distributed query processing are also explained.

STMPE: an efficient movement pattern extraction algorithm for spatio-temporal data mining

With the recent development of LBS(Location Based Service) and Telematics, the use of spatio-temporal data mining which extracts useful knowledge such as movement patterns of moving objects gets increasing. However, the existing movement pattern extraction methods including STPMine1 and STPMine2 create lots of candidate movement patterns when the minimum support is low. As a result of that, the performance of time and space is sharply increased as a weak point. Therefore, in this paper, we suggest the STMPE (Spatio-Temporal Movement Pattern Extraction) algorithm in order to efficiently extract movement patterns of moving objects from the large capacity of spatio-temporal data. The STMPE algorithm generalizes spatio-temporal data and minimizes the use of memory. Because it produces and maintains short-term movement patterns, the frequency of database scan can be minimized. Actually, the STMPE algorithm was improved twice to 10 times better than STPMine1 and STPMine2 from the result of performance evaluation.

Aggregate Queries in Wireless Sensor Networks

Recently as efficient processing of aggregate queries for fetching desired data from sensors has been recognized as a crucial part, in-network aggregate query processing techniques are studied intensively in wireless sensor networks. Existing representative in-network aggregate query processing techniques propose routing algorithms and data structures for processing aggregate queries. However, these aggregate query processing techniques have problems such as high energy consumption in sensor nodes, low accuracy of query processing results, and long query processing time. In order to solve these problems and to enhance the efficiency of aggregate query processing in wireless sensor networks, this paper proposes Bucket-based Parallel Aggregation (BPA). BPA divides a query region into several cells according to the distribution of sensor nodes and builds a quadtree, and then processes aggregate queries in parallel for each cell region according to routing. It sends data in duplicate by removing redundant data, which, in turn, enhances the accuracy of query processing results. Also, BPA uses a bucket-based data structure in aggregate query processing, and divides and conquers the bucket data structure adaptively according to the number of data in the bucket. In addition, BPA compresses data in order to reduce the size of data in the bucket and performs data transmission filtering when each sensor node sends data. Finally, in this paper, we prove its superiority through various experiments using sensor data.

TMO-structured distributed location information system prototype

To realize location-based services, it is essential to handle the extreme situation that must cope with a very large volume, at least millions, of moving items. In this paper, we describe a location information system prototype of GALIS, which is a cluster-based scalable distributed computing system architecture which consists of multiple data processors, each dedicated to keeping records relevant to a different geographical zone and a different time zone. The proposed system contains all of the major computing nodes of GALIS architecture and is developed as a middleware on top of commercial main-memory database engines and spatial database engines, for short-term data and long-term data processing, respectively. To show the usefulness of our system, some experimental results showing clear advantages of distributed computing are also explained.

UR-tree: an efficient index for uncertain data in ubiquitous sensor networks

With the rapid development of technologies related to Ubiquitous Sensor Network (USN), sensors are being utilized in various application areas. In general, a sensor has a low computing capacity and power and keeps sending data to the central server. In this environment, uncertain data can be stored in the central server due to delayed transmission or other reasons and make query processing produce wrong results. Thus, this paper examines how to process uncertain data in ubiquitous sensor networks and suggests an efficient index, called UR-tree, for uncertain data. The index reduces the cost of update by delaying update in uncertainty areas. In addition, it solves the problem of low accuracy in search resulting from update delay by delaying update only for specific update areas. Lastly, we analyze the performance of UR-tree and prove the superiority of its performance by comparing its performance with that of R-Tree and PTI using various datasets.

An efficient compression technique for a multi-dimensional index in main memory

Recently, in order to retrieve data objects efficiently according to spatial locations in the spatial main memory DBMS, various multi-dimensional index structures for the main memory have been proposed, which minimize failures in cache access by reducing the entry size. However, because the reduction of entry size requires compression based on the MBR (Minimum Bounding Rectangle) of the parent node or the removal of redundant MBR, the cost of MBR reconstruction increases and the efficiency of search is lowered in index update and search. Thus, to reduce the cost of MBR reconstruction, this paper proposed a RSMBR (Relative-Sized MBR) compression technique, which applies the base point of compression differently in case of broad distribution and narrow distribution. In case of broad distribution, compression is made based on the left-bottom point of the extended MBR of the parent node, and in case of narrow distribution, the whole MBR is divided into cells of the same size and compression is made based on the left-bottom point of each cell. In addition, MBR was compressed using a relative coordinate and the MBR size to reduce the cost of search in index search. Lastly, we evaluated the performance of the proposed RSMBR compression technique using real data, and proved its superiority.

Spatial TinyDB: A Spatial Sensor Database System for the USN Environment

For the Ubiquitous Sensor Network (USN) environment, which generally uses spatial as well as aspatial sensor data, a sensor database system to manage these data is essential. For this reason, sensor database systems such as TinyDB and Cougar are being developed by researchers. However, as most of these systems do not support spatial data types and spatial operators for managing spatial sensor data, they are not suitable for the USN environment. Therefore, in this paper, we design and implement Spatial TinyDB which is a spatial sensor database system that extends TinyDB to support spatial data types and spatial operators for the efficient management of spatial sensor data. In particular, Spatial TinyDB provides memory management and filtering functions to reduce system overload caused by sensor data streams. Finally, we prove that Spatial TinyDB is superior by comparing its actual performance, in terms of execution time, accuracy, and memory usage, with that of TinyDB.

An Efficient Indexing Technique for Location Prediction of Moving Objects

The necessity of the future index is increasing to predict the future location of moving objects promptly for various location-based services. However, the prediction performance of most future indexes is lowered by the heavy load of extensive future trajectory search in long-range future queries, and their index maintenance cost is high due to the frequent update of future trajectories. Thus, this paper proposes the Probability Cell Trajectory-Tree (PCT-Tree), a cell-based future indexing technique for efficient long-range future location prediction. The PCT-Tree reduces the size of index by building the probability of extensive past trajectories in the unit of cell, and predicts reliable future trajectories using information on past trajectories. Therefore, the PCT-Tree can minimize the cost of communication in future trajectory prediction and the cost of index rebuilding for updating future trajectories. Through experiment, we proved the superiority of the PCT-Tree over existing indexing techniques in the performance of long-range future queries.