Joung-Joon Kim

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.

TMOM: a moving object main memory-based DBMS for telematics services

Recently with the growth of the Internet and the activation of wireless communication, telematics services are emerging as promising next-generation business in the IT area. In order to provide telematics services, technologies in various areas are required but particularly DBMS technology for efficient data processing and management is an essential key technology for all types of telematics services. This paper designed and implemented TMOM (Telematics Moving Object Main Memory DBMS) to meet efficiently the requirements that telematics services should be provided in real-time. TMOM follows the spatial data model of OpenGIS Simple Features Specification for SQL, and provides spatial, spatio-temporal and temporal types and corresponding operators for processing the moving object data. In addition, it supports optimized spatial, spatio-temporal and trajectory indexes in the main memory for fast search of large moving object data and provides a recovery function that can minimize disk input-output to maximize system performance. Also, for high transmission efficiency in data import/export between telematics applications and a back-end DBMS, it supports a compression function and a data caching function suitable for the characteristics of spatial data.

Development of an embedded spatial MMDBMS for spatial mobile devices

Recently, with the development of wireless communications and mobile computing, interest in mobile computing is rising. Mobile computing can be regarded as an environment where a user carries mobile devices and shares resources with a server via wireless communications. A mobile database, which can be used in the various fields, refers to a database used in these mobile devices. Especially, LBS which utilizes location information of users becomes an essential field of mobile computing. In order to support LBS in the mobile environment, there must be an Embedded Spatial MMDBMS that can efficiently manage large spatial data in spatial mobile devices. Therefore, in this paper, we developed an Embedded Spatial MMDBMS, extended from the HSQLDB which is an existing MMDBMS for PC, to manage spatial data efficiently in spatial mobile devices. The Embedded Spatial MMDBMS adopted the spatial data model proposed by ISO, provided the arithmetic coding method suitable for spatial data, and supported the efficient spatial index suitable for spatial mobile devices. In addition, the system offered the spatial data display capability and supported the data caching and synchronization capability between the Embedded Spatial MMDBMS and the GIS server.

An Extended R-Tree Indexing Method Using Selective Prefetching in Main Memory

Recently, researches have been performed on a general method that can improve the cache performance of the R-Tree in the main memory to reduce the size of an entry so that a node can store more entries. However, this method generally requires additional processes to reduce information of entries. In addition, the cache miss always occurs on moving between a parent node and a child node. To solve these problems, this paper proposes the SPR-Tree (Selective Prefetching R-Tree), which is an extended R-Tree indexing method using selective prefetching according to node size in the main memory. The SPR-Tree can produce wider nodes to optimize prefetching without additional modifications on the R-Tree. Moreover, the SPR-Tree can reduce the cache miss that can occur in the R-Tree. In our simulation, the search, insert, and delete performance of the SPR-Tree improved up to 40%, 10%, 30% respectively, compared with the R-Tree.

Handling Frequent Updates of Moving Objects Using the Dynamic Non-uniform Grid

For services related with the u-LBS and u-GIS, most previous works had try to solve frequent updates as a lot of moving objects by extending traditional R-tree. In related works, however, processing for a situation occurring partial denseness of many objects is so hard because these haven’t considering the non-uniform distribution. Thus, we proposed new scheme to solve problems above by using the dynamic non-uniform grid. Due to its result of split isn’t equal, our proposed scheme can allow distributed processing locally for dense moving objects. Also it has several in-memory buffers to handle frequent updates of massively moving objects lazily.