• The Journal of Information Systems
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• v.18 no.3
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• pp.327-349
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• 2009

Traditional keyword-based searching methods suffer from low accuracy and high complexity due to the rapid growth in the amount of information. Accordingly, many researchers attempt to implement a so-called semantic search which is based on the semantics of the user's query. Semantic information can be described using a semantic modeling language, such as Topic Map. In this paper, we propose a new method to map a topic map to a traditional Relational Database (RDB) without any information loss. Although there have been a few attempts to map topic maps to RDB, they have paid scant attention to handling multi-role topics. In this paper, we propose a new storage structure to map multi-role topics to traditional RDB. The proposed structure consists of a mapping table, role tables, and content tables. Additionally, we devise a query translator to convert a user's query to one appropriate to the proposed structure.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.413-416
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• 2008

In the presence of multiple continuous queries, multi-query optimizing is a new challenge to process multiple stream data in real-time. So, in this paper, we proposed an approach to optimize multi-query of sliding window on network traffic data streams and do some comparisons to traditional queries without optimizing. We also detail some method of scheduling on different data streams, while different scheduling made different results. We test the results on variety of multi-query processing schedule, and proofed the proposed method is effectively optimized the data stream similar multi-queries.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1259-1276
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• 2017

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of Internet of Things (IoT) technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatialtemporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.

• 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 the Korea Society of Computer and Information
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• v.14 no.2
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• pp.69-77
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• 2009

Stream service environment demands real-time query processing for voluminous data which are ceaselessly delivered from tremendous sources. Typical R-tree based query processing technologies cannot efficiently handle such situations, which require repetitive and inefficient exploration from the tree root on every data event. However, many stream data including sensor readings show high locality, which we exploit to reduce the search space of queries to explore. In this paper, we propose a query processing scheme exploiting the locality of stream data. From the simulation, we conclude that the proposed scheme performs much better than the traditional ones in terms of scalability and exploration efficiency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.2
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• pp.154-173
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• 2010

Database outsourcing is unavoidable in the near future. In the scenario of data stream outsourcing, the data owner continuously publishes the latest data and associated authentication information through a service provider. Clients may register queries to the service provider and verify the result's correctness, utilizing the additional authentication information. Research on On-line Stream Analytics (OLSA) is motivated by extending the data cube technology for higher multi-level abstraction on the low-level-abstracted data streams. Existing work on OLSA fails to consider the issue of database outsourcing, while previous work on stream authentication does not support OLSA. To close this gap and solve the problem of OLSA query authentication while outsourcing data streams, we propose MDAHRB and MDAHB, two multi-dimensional authentication approaches. They are based on the general data model for OLSA, the stream cube. First, we improve the data structure of the H-tree, which is used to store the stream cube. Then, we design and implement two authentication schemes based on the improved H-trees, the HRB- and HB-trees, in accordance with the main stream query authentication framework for database outsourcing. Along with a cost models analysis, consistent with state-of-the-art cost metrics, an experimental evaluation is performed on a real data set. It exhibits that both MDAHRB and MDAHB are feasible for authenticating OLSA queries, while MDAHRB is more scalable.

• Journal of Internet Computing and Services
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• v.13 no.3
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• pp.93-105
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• 2012

A data stream is a series of tuples which are generated in real-time, incessant, immense, and volatile manner. As new information technologies are actively emerging, stream processing methods are being needed to efficiently handle data streams. Especially, finding out an efficient evaluation for a multi-way join would make outstanding contributions toward improving the performance of a data stream management system because a join operation is one of the most resource-consuming operators for evaluating queries. In this paper, in order to evaluate efficiently a multi-way join continuous query, we propose a novel method to decrease the cost of a query by eliminating unsuccessful intermediate results. For this, we propose a matrix-based structure for monitoring data streams and estimate the number of final result tuples of the query and find out unsuccessful tuples by matrix multiplication operations. And then using these information, we process efficiently a multi-way join continuous query by filtering out the unsuccessful tuples in advance before actual evaluation of the query.

• 한국경영정보학회:학술대회논문집
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• 2008.06a
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• pp.620-627
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• 2008

In this paper, recognizing the importance of the database query optimization design methods, we implemented mobile database with mobile program (J2ME) which is a useful database procedures. In doing so, we emphasize the logical query optimization which brings mobile database to performance improvement. The research implies that the suggested mobile program (J2ME) would contribute to the realization of the efficient mobile database as the related technology develops in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.691-694
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• 2010

Since the data collected at a sensor node is the stream data, for processing efficiently user queries, the query index should be constructed at each node. To construct the minimized query index at the node, it is required to reduce the number of query constraints inserted into the query index. In this paper, we propose the scheme of the query constraints distribution using the multi-dimensional data index in order to diminish the number of the inserted query constraints.