• IEIE Transactions on Smart Processing and Computing
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• v.5 no.3
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• pp.222-229
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• 2016

Linked Data is a powerful technology for storing and publishing the structures of data. It is helpful for web applications because of its usefulness through semantic query data. However, using Linked Data is not easy for ordinary users who lack knowledge about the structure of data or the query syntax of Linked Data. For that problem, we propose a translator component that is used for translating RESTful/JSON request messages into SPARQL commands based on ontology - a metadata that describes the structure of data. Clients do not need to worry about the structure of stored data or SPARQL, a kind of query language used for querying linked data that not many people know, when they insert a new instance or query for all instances of any specific class with those complex structure data. In addition, the translator component has the search function that can find a set of data from multiple classes based on finding the shortest paths between the target classes - the original set that user provide, and target classes- the users want to get. This translator component will be applied for any dynamic ontological structure as well as automatically generate a SPARQL command based on users' request message.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.75-84
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• 2015

A data allocation technique is essential to improve the performance of data broadcast systems. This paper explores the issues for allocating data items on broadcast channels to process multiple-data queries in the environment where query profiles and query request rates are given, and proposes a new data allocation scheme named IDAS. The proposed scheme employs the strategy that the broadcast frequency of each data is determined by the square root value of its relative access probability. IDAS could enhance the performance of query response time since it can process queries of high request rate fast and show a resonable degree of query data adjacency. Simulation is performed to evaluate the performance of the proposed scheme. The simulation results show that IDAS outperforms other schemes in terms of the average response time.

• 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 the Korean Society for Library and Information Science
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• v.45 no.2
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• pp.209-228
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• 2011

Web search behavior has evolved. People now search using many diverse information devices in various situations. To monitor these scattered and shifting search patterns, an improved way of learning and analysis are needed. Traditional web search studies relied on the server transaction logs and single query instance analysis. Since people use multiple smart devices and their searching occurs intermittently through a day, a bundled query research could look at the whole context as well as penetrating search needs. To observe and analyze bundled queries, we developed a proprietary research software set including a log catcher, query bundling tool, and bundle monitoring tool. In this system, users' daily search logs are sent to our analytic server, every night the users need to log on our bundling tool to package his/her queries, a built in web survey collects additional data, and our researcher performs deep interviews on a weekly basis. Out of 90 participants in the study, it was found that a normal user generates on average 4.75 query bundles a day, and each bundle contains 2.75 queries. Query bundles were categorized by; Query refinement vs. Topic refinement and 9 different sub-categories.

• 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.

• Journal of KIISE:Databases
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• v.36 no.3
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• pp.226-234
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• 2009

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Due to the characteristics of a data stream, it is impossible to save all the data elements of a data stream. Therefore it is necessary to define a new synopsis structure to store the summary information of a data stream. For this purpose, this paper proposes a cuboid prefix tree that can be effectively employed in evaluating an iceberg query over data streams. A cuboid prefix tree only stores those itemsets that consist of grouping attributes used in GROUP BY query. In addition, a cuboid prefix tree can compute multiple iceberg queries simultaneously by sharing their common sub-expressions. A cuboid prefix tree evaluates an iceberg query over an infinitely generated data stream while efficiently reducing memory usage and processing time, which is verified by a series of experiments.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.271-281
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• 2006

Due to the development of Internet, number of online documents and the amount of web services are increasing dramatically. However, there are several procedures required, before you actually find what you were looking for. These procedures are necessary to Internet users, but it takes time to search. As a method to systematize and simplify this repetitive job, this paper suggests a DotQuery based interactive search agent. This agent enables a user to search, from his computer, a plenty of information through the DotQuery service. which includes natural languages. and it executes several procedures required instead. This agent also functions as a plug-in service within general web browsers such as Internet Explorer and decodes the DotQuery service. Then it analyzes the DotQuery from a user through its own program and acquires service results through multiple browsers of its own.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.356-360
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• 2003

There have been many considerations to develop new content services that integrate a variety of contents databases being already constructed and then produce new content services which are more valuable than existing services in many applications such as Internet portal, EC, and CRM. By doing the above thing, the burden of searching databases to access interesting databases and service applications can be reduced and the database availability of users is also enhanced through a single view integrating multiple contents database. This paper presents implementation details of the query processing system that is a core component of the database integration system, which can construct a virtual database that integrates databases being managed by multiple heterogeneous database systems using XML data model and support a quay facility on the integrated database.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.35-43
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• 2014

A data allocation technique is essential to improve the performance of data broadcast systems. This paper explores the issues for allocating data items on broadcast channels in the environment where multiple-data queries are submitted, and proposes a new data allocation scheme named DAMA. The proposed scheme employs the strategy that the broadcast frequency of each data is determined by the moving average of its access probability. DAMA could enhance the performance of query response time since it is capable of controlling the influence of access probability properly according to the query size. Simulation is performed to evaluate the performance of the proposed scheme. The simulation results show that the performance of DAMA is superior to other schemes in terms of the average response time.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.5
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• pp.285-293
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• 2013

The procedure of the arbitration which is the tag collision is essential because the multiple tags response simultaneously in the same frequency to the request of the Reader. This procedure is known as Anti-collision and it is a key technology in the RFID system. In this paper, we propose the Bit Pattern Prediction Algorithm(BPPA) for the efficient identification of the multiple tags. The BPPA is based on the tree algorithm using the time slot and identify the tag quickly and efficiently using accurate bit pattern prediction method. Through mathematical performance analysis, We proved that the BPPA is an O(n) algorithm by analyzing the worst-case time complexity and the BPPA's performance is improved compared to existing algorithms. Through MATLAB simulation experiments, we verified that the BPPA require the average 1.2 times query per one tag identification and the BPPA ensure stable performance regardless of the number of the tags.