• Journal of KIISE:Databases
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• v.36 no.2
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• pp.112-122
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• 2009

The EPCglobal leading the development in RFID standards proposed Event Cycle Specification (ECSpec) and Event Cycle Reports (ECReports) for the standard about RFID middleware interface. ECSpec is a specification for filtering and collecting RFID tag data and is treated as a Continuous Query (CQ) processed during fixed time intervals repeatedly. ECReport is a specification for describing the results after ECSpec is processed. Thus, it is efficient to apply Query Indexing technique designed for the continuous query processing. This query index processes ECSpecs as data and tag events as queries for efficiency. In logistics environment, the similar or same products are transferred together. Also, when RFID tags attached to the products are acquired, the acquisition events occur massively for the short period. For these properties, it is inefficient to process the massive events one by one. In this paper, we propose a technique reducing similar search process by considering tag events which are collected by the report period in ECSpec, as a range query. For this group processing, we suggest a queuing method for collecting tag events efficiently and a structure for generating range queries in the queues. The experiments show that performance is enhanced by the proposed methods.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.547-558
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• 2003

From the viewpoint of that music melody has the similar features to time-series data, music melody is transformed to a time-series data with normalization and corrections and the similarity between melodies is defined as the Euclidean distance between the transformed time-series data. Then, based the similarity between melodies of a music object, melodies are clustered and the representative of each cluster is extracted as one of theme melodies for the music. To construct the theme melody index, a theme melody is represented as a point of the multidimensional metric space of M-tree. For retrieval of user's query melody, the query melody is also transformed into a time-series data by the same way of indexing phase. To retrieve the similar melodies to the query melody given by user from the theme melody index the range query search algorithm is used. By the implementation of the prototype system using the proposed theme melody index we show the effectiveness of the proposed methods.

• The KIPS Transactions:PartD
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• v.11D no.2
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• pp.269-280
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• 2004

The Pyramid-Technique is based on mapping n-dimensional space data into one-dimensional data and expresses it as a B+-tree. By solving the problem of search time complexity the pyramid technique also prevents the effect of "phenomenon of dimensional curse" which is caused by treatment of hypercube range query in n-dimensional data space. The SPY-TEC applies the space division strategy in pyramid method and uses spherical range query suitable for similarity search so that Improves the search performance. However, nearest neighbor query is more efficient than range query because it is difficult to specify range in similarity search. Previously proposed index methods perform well only in the specific distribution of data. In this paper, we propose an efficient searching technique for nearest neighbor object using PdR-Tree suggested to improve the search performance for high dimensional data such as multimedia data. Test results, which uses simulation data with various distribution as well as real data, demonstrate that PdR-Tree surpasses both the Pyramid-Technique and SPY-TEC in views of search performance.rformance.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.9
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• pp.243-250
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• 2021

Recently, with the development of cloud computing, interest in database outsourcing is increasing. However, when the database is outsourced, there is a problem in that the information of the data owner is exposed to internal and external attackers. Therefore, in this paper, we propose a parallel range query processing algorithm that supports privacy protection. The proposed algorithm uses the Paillier encryption system to support data protection, query protection, and access pattern protection. To reduce the operation cost of a checking protocol (SRO) for overlapping regions in the existing algorithm, the efficiency of the SRO protocol is improved through a garbled circuit. The proposed parallel range query processing algorithm is largely composed of two steps. It consists of a parallel kd-tree search step that searches the kd-tree in parallel and safely extracts the data of the leaf node including the query, and a parallel data search step through multiple threads for retrieving the data included in the query area. On the other hand, the proposed algorithm provides high query processing performance through parallelization of secure protocols and index search. We show that the performance of the proposed parallel range query processing algorithm increases in proportion to the number of threads and the proposed algorithm shows performance improvement by about 5 times compared with the existing algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.336-339
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• 2013

Location-Based services that collect location information for moving object and utilize in real life are being used in many aspects by the development of wireless network technology. Accordingly, new index structures are required to efficiently retrieve the consecutive location of moving objects. This paper addresses algorithms that make index structure by using Douglas-Peucker Algorithm and process range query efficiently on moving objects trajectories. Our algorithms are going to make smaller size of index structure and process more efficiently.

• Journal of KIISE:Databases
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• v.32 no.3
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• pp.254-262
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• 2005

This paper presents a new sequential indexing method called segment-page indexing (SP-indexing) for multidimensional range queries. The design objectives of SP-indexing are twofold:(1) improving the range query performance of multidimensional indexing methods (MIMs) and (2) providing a compromise between optimal index clustering and the full index reorganization overhead. Although more than ten years of database research has resulted in a great variety of MIMs, most efforts have focused on data-level clustering and there has been less attempt to cluster indexes. As a result, most relevant index nodes are widely scattered on a disk and many random disk accesses are required during the search. SP-indexing avoids such scattering by storing the relevant nodes contiguously in a segment that contains a sequence of contiguous disk pages and improves performance by offering sequential access within a segment. Experimental results demonstrate that SP-indexing improves query performance up to several times compared with traditional MIMs using small disk pages with respect to total elapsed time and it reduces waste of disk bandwidth due to the use of simple large pages.

• Journal of the Korea Convergence Society
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• v.7 no.6
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• pp.43-48
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• 2016

Wireless sensor networks (WSN) can be more reliable and easier to program and use with the help of sensor database management systems (SDMS). SDMS establish a user-friendly SQL-based interface to process declarative user-defined queries over sensor readings from WSN. Typical queries in SDMS are ad-hoc snapshot queries and long-running, continuous queries. In SDMSs queries are flooded to all nodes in the sensor net, and query results are sent back from nodes that have qualified results to a base station. For query flooding to all nodes, and result flooding to the base station, a lot of communication energy consuming is required. This paper suggests an efficient in-network index solution, named Distributed Information Gathering (DIG) to process range queries in a sensor net environment that can save energy by reducing query and result flooding.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.1-18
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• 2008

Because we can usually get many information through analyzing trajectories of moving objects on spatial networks, efficient trajectory index structures are required to achieve good retrieval performance on their trajectories. However, there has been little research on trajectory index structures for spatial networks such as FNR-tree and MON-tree. Also, because FNR-tree and MON-tree store the segment unit of moving objects, they can't support the trajectory of whole moving objects. In this paper, we propose an efficient trajectory index structures based on signatures on a spatial network, named SigMO-Tree. For this, we divide moving object data into spatial and temporal attributes, and design an index structure which supports not only range query but trajectory query by preserving the whole trajectory of moving objects. In addition, we divide user queries into trajectory query based on spatio-temporal area and similar-tralectory query, and propose query processing algorithms to support them. The algorithm uses a signature file in order to retrieve candidate trajectories efficiently Finally, we show from our performance analysis that our trajectory index structure outperforms the existing index structures like FNR-Tree and MON-Tree.

• Journal of KIISE
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• v.42 no.4
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• pp.522-529
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• 2015

Ever since the R-tree was proposed to index multi-dimensional data, many efforts have been made to improve its query performances. One common trend to improve query performance is to parallelize query processing with the use of multi-core architectures. To this end, a GPU-base R-tree has been recently proposed. However, even though a GPU-based R-tree can exhibit an improvement in query performance, it is limited in its ability to handle large volumes of data because GPUs have limited physical memory. To address this problem, we propose MGR-tree (Multi-GPU R-tree), which can manage large volumes of data by dividing nodes into multiple GPUs. Our experiments show that MGR-tree is up to 9.1 times faster than a sequential search on a GPU and up to 1.6 times faster than a conventional GPU-based R-tree.

• Journal of KIISE:Databases
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• v.32 no.2
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• pp.129-141
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• 2005

Although spatio-temporal databases have received considerable attention recently, there has been little work on processing range sum queries on the historical records of moving objects despite their importance. Since to answer range sum queries, the direct access to a huge amount of data incurs prohibitive computation cost, materialization techniques based on existing index structures are recently suggested. A simple but effective solution is to apply the materialization technique to the MVR-tree known as the most efficient structure for window queries with spatio-temporal conditions. However, the MVR-tree has a difficulty in maintaining pre-aggregated results inside its internal nodes due to cyclic paths between nodes. Aggregate structures based on other index structures such as the HR-tree and the 3DR-tree do not provide satisfactory query performance. In this paper, we propose a new indexing technique called the Adaptive Partitioned Aggregate R-Tree (APART) and query processing algorithms to efficiently process range sum queries in many situations. Experimental results show that the performance of the APART is typically above 2 times better than existing aggregate structures in a wide range of scenarios.