• Journal of KIISE:Databases
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• v.31 no.3
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• pp.219-231
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• 2004

Recently, together with increasing use of multimedia data, many works on moving objects in video databases have been made. Moving objects change visual features and spatial positions with the lapse of time in video data. And they arc related to the other objects or events. In this paper, we propose a new modeling and various query types of moving objects for content based retrieval in video databases. The proposed modeling represents visual features, moving trajectories and semantic contents related to objects. Therefore, it allows to process various query types. And we propose various query operators for the retrieval types. To show the superiority of our modoling, we implement the retrieval systems and compare it with the existing methods in terms of the supporting query types. The proposed method supports various query types and improves the efficiency of the query processing over the existing methods.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.185-196
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• 2010

Existing methods to compute reverse skyline queries are not correct to process the queries in dataset with region objects which have conditions like a price is 5~7 dollars and a distance to beach is 1km~2km, since they consider datasets with only point objects. To solve the problem, we propose a novel method to process reverse skyline queries for region objects in this paper. It has advantages. First, it is expected to get a good performance, because it is extended from efficient reverse skyline (ERSL) algorithm which is a best algorithm to computing reverse skyline queries in datasets with point objects. Second, it can give a right of choice unlike the others to a person requesting the query. That is because results of reverse skyline have a difference preference according to proposed pruning methods and overlap relations. This algorithm is a first for supporting region objects. Therefore there are not any other algorithms to compare their performance. For that reason, our experiment to prove the efficiency of proposed algorithm is focused what conditions give an effect to its performance and result and how much time it needs to process the query.

• Spatial Information Research
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• v.19 no.3
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• pp.95-106
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• 2011

Recently as efficient processing of aggregate queries for fetching desired data from sensors has been recognized as a crucial part, in-network aggregate query processing techniques are studied intensively in wireless sensor networks. Existing representative in-network aggregate query processing techniques propose routing algorithms and data structures for processing aggregate queries. However, these aggregate query processing techniques have problems such as high energy consumption in sensor nodes, low accuracy of query processing results, and long query processing time. In order to solve these problems and to enhance the efficiency of aggregate query processing in wireless sensor networks, this paper proposes Bucket-based Parallel Aggregation(BPA). BPA divides a query region into several cells according to the distribution of sensor nodes and builds a Quad-tree, and then processes aggregate queries in parallel for each cell region according to routing. And it sends data in duplicate by removing redundant data, which, in turn, enhances the accuracy of query processing results. Also, BPA uses a bucket-based data structure in aggregate query processing, and divides and conquers the bucket data structure adaptively according to the number of data in the bucket. In addition, BPA compresses data in order to reduce the size of data in the bucket and performs data transmission filtering when each sensor node sends data. Finally, in this paper, we prove its superiority through various experiments using sensor data.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.67-76
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• 2018

This paper addresses the problem of efficient processing of region monitoring queries. The centralized methods used for existing region monitoring query processing assumes that the mobile object periodically sends location-updates to the server and the server continues to update the query results. However, a large amount of location updates seriously degrade the system performance. Recently, some distributed methods have been proposed for region monitoring query processing. In the distributed methods, the server allocates to all objects i) a resident domain that is a subspace of the workspace, and ii) a number of nearby query regions. All moving objects send location updates to the server only when they leave the resident domain or cross the boundary of the query region. In order to allocate the resident domain to the moving object along with the nearby query region, we use a query index structure that is constructed by splitting the workspace recursively into equal halves. However, However, the above index structure causes unnecessary division, resulting in deterioration of system performance. In this paper, we propose an adaptive split method to reduce unnecessary splitting. The workspace splitting is dynamically allocated i) considering the spatial relationship between the query region and the resultant subspace, and ii) the distribution of the query region. We proposed an enhanced QR-tree with a new splitting method. Through a set of simulations, we verify the efficiency of the proposed split methods.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.27-42
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• 2009

In realizing ubiquitous computing, techniques of efficiently using the limited resource at client such as mobile devices are required. With a mobile device with limited amount of memory, the techniques of XML stream query processing should be employed to process queries over a large volume of XML data. Recently, several techniques were proposed which fragment XML documents into XML fragments and stream them for query processing at client. During query processing, there could be great difference in resource usage (query processing time and memory usage) depending on how the source XML documents are fragmented. As such, an efficient fragmentation technique is needed. In this paper, we propose an XML fragmentation technique whereby resource efficiency in query processing at client could be enhanced. For this, we first present a cost model of query processing over XML fragment stream. Then, we propose an algorithm for resource-efficient XML fragmentation. Through implementation and experiments, we showed that our fragmentation technique outperformed previous techniques both in processing time and memory usage. The contribution of this paper is to have made the techniques of query processing over XML fragment stream more feasible for practical use.

• 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.35 no.6
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• pp.505-517
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• 2008

This paper proposes a rewriting algorithm of OWL-DL ontology query in SPARQL. Currently, to obtain inference results of given SPARQL queries, Web ontology repositories construct inference ontology models and match the SPARQL queries with the models. However, an inference model requires much larger space than its original base model, and reusability of the model is not available for other inferrable SPARQL queries. Therefore, the aforementioned approach is not suitable for large scale SPARQL query processing. To resolve tills issue, this paper proposes a novel SPARQL query rewriting algorithm that can obtain results by rewriting SPARQL queries and accomplishing query operations against the base ontology model. To achieve this goal, we first define OWL-DL inference rules and apply them on rewriting graph pattern in queries. The paper categorizes the inference rules and discusses on how these rules affect the query rewriting. To show the advantages of our proposal, a prototype system based on lena is implemented. For comparative evaluation, we conduct an experiment with a set of test queries and compare of our proposal with the previous approach. The evaluation result showed the proposed algorithm supports an improved performance in efficiency of the inferrable SPARQL query processing without loss of completeness and soundness.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.1-14
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• 2008

This paper is on query processing in the mobile devices where memory capacity is limited. In case that a query against a large volume of XML data is processed in such a mobile device, techniques of fragmenting the XML data into chunks and of streaming and processing them are required. Such techniques make it possible to process queries without materializing the XML data in its entirety. The previous schemes such as XFrag[4], XFPro[5], XFLab[6] are not scalable with respect to the increase of the size of the XML data because they lack proper memory management capability. After some information on XML fragments necessary for query processing is stored, it is not deleted even after it becomes of no use. As such, when the XML fragments are dynamically generated and infinitely streamed, there could be no guarantee of normal completion of query processing. In this paper, we address scalability of query processing over dynamic XML fragment stream, proposing techniques of deleting information on XML fragments accumulated during query processing in order to extend the previous schemes. The performance experiments through implementation showed that our extended schemes considerably outperformed the previous ones in memory efficiency and scalability with respect to the size of the XML data.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.5
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• pp.543-552
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• 1999

The iterator technique which is used for implementing physical operators of the query executor is known for its efficiency and extensibility. The most widely used technique for processing an operator on spatial objects is to process by dividing it into the filter step and the refinement step. Recently, there was a research for an optimizer which can generate more efficient query execution plans than those of traditional methods by separating a spatial operator into filter and refinement steps in the level of the object algebra. But, traditional query executors were not designed considering such query execution plans. So they have no function of transmitting the result of the filter operation between operators. We propose two methods, the probe technique and the tag technique, which transmit the result of the filter operator when using the iterator in the query execution plan in which operators are separated by filter/refinement steps and other operators can be allowed between the steps. Whereas the probe technique extends the state record within an operator, the tag technique stores the result of a filter step in an intermediate result in the form of the tag. Based on the comparison of these methods, we design and implement a query executor using the tag technique that is superior in extensibility. The implemented query executor can execute operations defined in the Spatial Object Algebra(SOA) to process an extended OQL for spatial queries.

• 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.