• The Transactions of the Korea Information Processing Society
• /
• v.6 no.5
• /
• pp.1166-1176
• /
• 1999

The spationtemporal databases support a historical informations as well as spatial managements for various kinds of objects in the real world, and can be efficiently used in many applications such as geographic information system, urban plan system, car navigation system. However it is difficult to represent efficiently historical operations with conventional database query language for spatial objects. In terms of cost for query processing, it also degenerates performance of query processing because of syntactic limitations which is innate in conventional query representation. So in this paper, we introduce a new query language, entitled as STQL, which has been extended on the basis of the most popular relational database query language SQL. And we implement as well as evaluate a spationtemporal query processing system that get a query written by STQL and then process it in a main memory.

• Journal of KIISE:Computing Practices and Letters
• /
• v.6 no.2
• /
• pp.113-119
• /
• 2000

In this paper, we design and implement a query processor of real-time main memory database systems, which reflect the characteristics of main memory database systems and satisfy timing constraints. The proposed query processor manages real-time data that has timing constraint by exploiting meta database. It supports CLI in order to make application programs. It also supports extended CLI and stored CLI. The former can be expressed the Information on real-time transaction. The latter is designed to support frequently processed transaction. The proposed query processor is implemented as query processor of real-time database management systems. We Present performance evaluation results that illustrate ratio of transaction, which satisfy deadline are increased by the query processing ability of system and the efficient management of real-time data.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.766-768
• /
• 2003

Selectivity estimation for spatial query is curial in Spatial Database Management Systems(SDBMS). Many works have been performed to estimate accurate selectivity. Although they deal with some problems such as false-count, multi-count arising from properties of spatial dataset, they can not get such effects in little memory space.* Therefore, we need to compress spatial dataset into little memory. In this paper, we propose a new technique called MW Histogram which is able to compress summary data and get reasonable results. Our method is based on two techniques:(a)MinSkew partitioning algorithm which deal with skewed spatial datasets. efficiently (b) Wavelet transformation which compression effect is proven. We evaluate our method via real datasets. The experimental result shows that the MW Histogram has the ability of providing estimates with low relative error and retaining the similar estimates even if memory space is small.

• Journal of the Korea Society of Computer and Information
• /
• v.8 no.3
• /
• pp.40-46
• /
• 2003

The high-performance processors have recently employed sophisticated techniques to overlap and simultaneously execute multiple computation and memory operations. For the query processing of database management systems, those hardware characteristics are the important research issue. The latest works show that the cache miss penalty between main memory and CPU becomes new bottlenecks and the branch misprediction causes serious resource-waste. An effcient algorithm for query processing of aggregate functions considering these hardware characteristics was proposed in this dissertation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.459-462
• /
• 2003

Selectivity estimation of queries not only provides useful information to the query processing optimization but also may give users with a preview of processing results. In this paper, we investigate the problem of selectivity estimation in the context of a spatial dataset. Although several techniques have been proposed in the literature to estimate spatial query result sizes, most of those techniques still have some drawback in the case that a large amount of memory is required to retain accurate selectivity. To eliminate the drawback of estimation techniques in previous works, we propose a new method called MW Histogram. Our method is based on two techniques: (a) MinSkew partitioning algorithm that processes skewed spatial datasets efficiently (b) Wavelet transformation which compression effect is proven. We evaluate our method via real datasets. With the experimental result, we prove that the MW Histogram has the ability of providing estimates with low relative error and retaining the similar estimates even if memory space is small.

• The KIPS Transactions:PartD
• /
• v.11D no.1
• /
• pp.11-22
• /
• 2004

As random access memory chip gets cheaper, it becomes affordable to realize main memory-based database systems. Consequently, reducing cache misses emerges as the most important issue in current main memory databases, in which CPU speeds have been increasing at 60% per year, compared to the memory speeds at 10% per you. In this paper, we design and implement a main-memory database system for real-time mobile GIS. Our system is composed of 5 modules： the interface manager provides the interface for PDA users； the memory data manager controls spatial and non-spatial data in main-memory using virtual memory techniques； the query manager processes spatial and non-spatial query : the index manager manages the MR-tree index for spatial data and the T-tree index for non-spatial index : the GIS server interface provides the interface with disk-based GIS. The MR-tree proposed propagates node splits upward only if one of the internal nodes on the insertion path has empty space. Thus, the internal nodes of the MR-tree are almost 100% full. Our experimental study shows that the two-dimensional MR-tree performs search up to 2.4 times faster than the ordinary R-tree. To use virtual memory techniques, the memory data manager uses page tables for spatial data, non- spatial data, T-tree and MR-tree. And, it uses indirect addressing techniques for fast reloading from disk.

• The KIPS Transactions:PartD
• /
• v.16D no.6
• /
• pp.835-844
• /
• 2009

Today, most of all the query processors in the world generally use the 'Pipelining' method to acquire fast response time (first record latency) and less memory usage. Each of the operator nodes in the Query Execution Plan (QEP) provides Open(), Next(), and Close() functions for their interface to facilitate the iterator mechanism. However, the embedded database systems for the mobile devices, based on the FLASH memory, usually require a function like Previous(), which returns the previous records from current position. It is because that, in the embedded environment, the mobile devices cannot fully provide it main memory to store all the query results. So, whenever needed the previously read records the user (program) should re-fetch the previous records using the Previous() function: the BACKWARD data fetch. In this paper, I introduce the 'Direction Switching Problem' caused by the Previous() function and suggest 'Block-wise Skipping' method to fully utilize the benefits of the block-based data transfer mechanism, which is widely accepted by most of the today's relational database management systems.

• Journal of KIISE:Databases
• /
• v.33 no.2
• /
• pp.224-238
• /
• 2006

As XML became the standard of data exchange in the internet, the needs for effective query processing for XML data in streaming environment is increasing. Applying the existing database technique which processes data with the unit of tuple to the streaming XML data causes the out-of-memory problem due to limited memory volume. Likewise the cost for searching query path and accessing specific data may be remarkably increased because of special structure of XML. In a word it is unreasonable to apply the existing database system to the streaming environment that processes query for partial data, not the whole one. Thus, it should be able to search partial streaming data that rapidly satisfies join predicate through using low-capacity memory, based on a store technique suitable to streaming XML data. In this thesis, in order to study the store technique for low-capacity memory, the PCDATA and the CDATA-related parts, which can be used as predicate on join query, were fetched and saved. In addition, in an attempt to compare rapid join predicates, the range window of streaming XML data was set with the object of selectively joining windows that satisfies the query, based on Cardinality * and + among the structure information of DTD.

• Journal of Information Processing Systems
• /
• v.4 no.1
• /
• pp.9-16
• /
• 2008

This paper describes the design and implementation of a two-tier DBMS for handling massive data and providing faster response time. In the present day, the main requirements of DBMS are figured out using two aspects. The first is handling large amounts of data. And the second is providing fast response time. But in fact, Traditional DBMS cannot fulfill both the requirements. The disk-oriented DBMS can handle massive data but the response time is relatively slower than the memory-resident DBMS. On the other hand, the memory-resident DBMS can provide fast response time but they have original restrictions of database size. In this paper, to meet the requirements of handling large volumes of data and providing fast response time, a two-tier DBMS is proposed. The cold-data which does not require fast response times are managed by disk storage manager, and the hot-data which require fast response time among the large volumes of data are handled by memory storage manager as snapshots. As a result, the proposed system performs significantly better than disk-oriented DBMS with an added advantage to manage massive data at the same time.

• Journal of Korea Multimedia Society
• /
• v.14 no.5
• /
• pp.669-680
• /
• 2011

R-trees are widely used in various areas such as geographical information systems, CAD systems and spatial databases in order to efficiently index multi-dimensional data. As data sets used in these areas grow in size and complexity, however, range query operations on R-tree are needed to be further faster to meet the area-specific constraints. To address this problem, there have been various research efforts to develop strategies for acceleration query processing on R-tree by using the buffer mechanism or parallelizing the query processing on R-tree through multiple disks and processors. As a part of the strategies, approaches which parallelize query processing on R-tree through Graphics Processor Units(GPUs) have been explored. The use of GPUs may guarantee improved performances resulting from faster calculations and reduced disk accesses but may cause additional overhead costs caused by high memory access latencies and low data exchange rate between GPUs and the CPU. In this paper, to address the overhead problems and to adapt GPUs efficiently, we propose a novel approach which uses a GPU as a buffer to parallelize query processing on R-tree. The use of buffer algorithm can give improved performance by reducing the number of disk access and maximizing coalesced memory access resulting in minimizing GPU memory access latencies. Through the extensive performance studies, we observed that the proposed approach achieved up to 5 times higher query performance than the original CPU-based R-trees.