• Journal of KIISE:Databases
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• v.29 no.1
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• pp.44-57
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• 2002

Multiple spatial queries are defined as two or more spatial range queries to be executed at the same time. The primary processing of internet-based map services is to simultaneously execute multiple spatial queries. To improve the throughput of multiple queries, the time of disk I/O in processing spatial queries significantly should be reduced. The declustering scheme of a spatial dataset of the MIMD architecture cannot decrease the disk I/O time because of random seeks for processing multiple queries. This thesis presents query scheduling strategies to ease the problem of inter-query random seeks. Query scheduling is achieved by dynamically re-ordering the priority of the queued spatial queries. The re-ordering of multiple queries is based on the inter-query spatial relationship and the latency of query processing. The performance test shows that the time of multiple query processing with query scheduling can be significantly reduced by easing inter-query random seeks as a consequence of enhanced hit ratio of disk cache.

• Journal of the Korean Society for Library and Information Science
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• v.31 no.3
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• pp.135-146
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• 1997

Different users or the same user using controlled versus free-text vocabularies could generate different queries for the same information need. It has been known in the information retrieval literature that different query representations may retrieve different sets of documents. In this paper, we first generate multiple query vectors from a given information problem by using different relevance feedback methods. Then, we combine the multiple query vectors into a single query vector. We also show through experiments that significant improvements can be achieved by the combination of the multiple query vectors.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.867-878
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• 2006

In the past few years, the research of sensor networks is forced dramatically. Specially, while the research for maintaining the power of a sensor is focused, we are also concerned nth query processing related with the optimization of multiple continuous queries for decreasing in unnecessary energy consumption of sensor networks. We present the fragmentation algorithm to solve the redundancy problem in multiple continuous queries that increases in the count or the amount of transmitting data in sensor networks. The fragmentation algorithm splits one query into more than two queries using the query index (QR-4ree) in order to reduce the redundant query region between a newly created query and the existing queries. The R*-tree should be reorganized to the QR-tree right to the structure suggested. In the result, we preserve 20 percentage of the total energy in the sensor networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1286-1306
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• 2022

Searchable symmetric encryption (SSE) provides a safe and effective solution for retrieving encrypted data on cloud servers. However, the existing SSE schemes mainly focus on single keyword search in single client, which is inefficient for multiple keywords and cannot meet the needs for multiple clients. Considering the above drawbacks, we propose a scheme enabling dynamic multi-client and Boolean query in searchable symmetric encryption for cloud storage system (DMC-SSE). DMC-SSE realizes the fine-grained access control of multi-client in SSE by attribute-based encryption (ABE) and novel access control list (ACL), and supports Boolean query of multiple keywords. In addition, DMC-SSE realizes the full dynamic update of client and file. Compared with the existing multi-client schemes, our scheme has the following advantages: 1) Dynamic. DMC-SSE not only supports the dynamic addition or deletion of multiple clients, but also realizes the dynamic update of files. 2) Non-interactivity. After being authorized, the client can query keywords without the help of the data owner and the data owner can dynamically update client's permissions without requiring the client to stay online. At last, the security analysis and experiments results demonstrate that our scheme is safe and efficient.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.597-618
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• 2019

Categorical range aggregation, which is conceptually equivalent to running a range aggregation query separately on multiple datasets, returns the query result on each dataset. The challenge is when the number of dataset is as large as hundreds or thousands, it takes a lot of computation time and I/O. In previous work, only a single dimension of the range restriction has been solved, and in practice, more applications are being used to calculate multiple range restriction statistics. We proposed MCRI-Tree, an index structure designed to solve multi-dimensional categorical range aggregation queries, which can utilize main memory to maximize the efficiency of CRA queries. Specifically, the MCRI-Tree answers any query in $O(nk^{n-1})$ I/Os (where n is the number of dimensions, and k denotes the maximum number of pages covered in one dimension among all the n dimensions during a query). The practical efficiency of our technique is demonstrated with extensive experiments.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.101-104
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• 2007

Efficient Query processing and optimization are critical for reducing network traffic and decreasing latency of query when accessing and manipulating sensor data of large-scale sensor networks. Currently it has been studied in sensor database projects. These works have mainly focused on in-network query processing for sensor networks and assumes homogeneous sensor networks, where each sensor network has same hardware and software configuration. In this paper, we present a framework for efficient query processing over heterogeneous sensor networks. Our proposed framework introduces query processing paradigm considering two heterogeneous characteristics of sensor networks: (1) data dissemination approach such as push, pull, and hybrid; (2) query processing capability of sensor networks if they may support in-network aggregation, spatial, periodic and conditional operators. Additionally, we propose multi-query optimization strategies supporting cross-translation between data acquisition query and data stream query to minimize total cost of multiple queries. It has been implemented in WSN middleware, COSMOS, developed by ETRI.

• Journal of Digital Contents Society
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• v.8 no.4
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• pp.541-546
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• 2007

Traditional query processing method is not efficient for continuous queries with large continuous stream data. This paper proposes a data-driven query processing method for stream data. The structure of query plan and query execution method are presented. With the proposed method, multiple query processing and sharing among queries can be achieved. Also query execution time can be reduced by storing partial results of query execution. This paper showed an example of query processing with XML data and XQuery query.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.449-457
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• 2001

As XML has become an emerging standard for information exchange on the World Wide Web, it has gained attention in database communities to extract information from XML seen as a database model. XML queries are based on regular path queries, which find objects reachable by given regular expressions. To answer many kinds of user queries, it is necessary to evaluate queries that have multiple regular path expressions. However, previous work such as query rewriting and query optimization in the frame work of semistructured data has dealt with a single regular expression. For queries that have multiple regular expressions we suggest a two phase optimizing technique: 1. query rewriting using views by finding the mappings from the view's body to the query's body and 2. for rewritten queries, evaluating each query conjunct and combining them. We show that our rewriting algorithm is sound and our query evaluation technique is more efficient than the previous work on optimizing semistructured queries.

• Journal of KIISE:Databases
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• v.36 no.1
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• pp.8-21
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• 2009

A wireless sensor network is a computer network which consists of spatially distributed devices, called sensor nodes. In wireless sensor networks, energy efficiency is a key issue since sensor nodes must resides upon limited energy. To retrieve sensor information without dealing with the network issues, a sensor network is treated as conceptual database on which query can be requested. When multiple queries are requested for processing in a wireless sensor network, energy consumption can be significantly reduced if common partial results among similar queries can be effectively shared. In this paper, we propose an energy efficient multi-query processing technique based on the coverage relationship between multiple queries. When a new query is requested, our proposed technique derives an equivalent query from queries running at the moment, if it is derivable. Our technique first computes the set of running queries that may derive a partial result of the new query and then test if this set covers all the result of the new query attribute-wise and tuple-wise. If the result of the new query can be derived from the results of executing queries, the new query derives its result at the base station instead of being executed in the sensor network.

• Spatial Information Research
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• v.21 no.2
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• pp.23-33
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• 2013

W ith the recent advancement of IoT (Internet of Things) technology, there has been much interest in the spatial query processing which energy-efficiently acquires sensor readings from sensor nodes inside specified geographical area of interests. Therefore, various kinds of spatial query processing algorithms and distributed spatial indexing methods have been proposed. They can minimize energy consumption of sensor nodes by reducing wireless communication among them using in-network spatial filtering technology. However, they cannot optimize multiple spatial queries which w ill be w idely used in IoT, because most of them have focused on a single spatial query optimization. Therefore, we propose a new multiple spatial query optimization algorithm which can energy-efficiently process multiple spatial queries in a sensor network. The algorithm uses a concept of 'query merging' that performs the merged set after merging multiple spatial queries located at adjacent area. Here, our algorithm makes a decision on which is better between the merged and the separate execution of queries. For such the decision making, we additionally propose the cost estimation method on the spatial query execution. Finally, we analyze and clarify our algorithm's distinguished features using the spatial indexing methods of GR-tree, SPIX, CPS.