• 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.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1367-1383
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• 2007

A sensor network consists of a network of sensors that can perform computation and also communicate with each other through wireless communication. Some important characteristics of sensor networks are that the network should be self administered and the power efficiency should be greatly considered due to the fact that it uses battery power. In sensor networks, when large amounts of various stream data is produced and multiple queries need to be processed simultaneously, the power efficiency should be maximized. This work proposes a technique to create an index on multiple monitoring queries so that the multi-query processing performance could be increased and the memory and power could be efficiently used. The proposed SMILE tree modifies and combines the ideas of spatial indexing techniques such as k-d trees and R+-trees. The k-d tree can divide the dimensions at each level, while the R+-tree improves the R-tree by dividing the space into a hierarchical manner and reduces the overlapping areas. By applying the SMILE tree on multiple queries and using it on stream data in sensor networks, the response time for finding an indexed query takes in some cases 50% of the time taken for a linear search to find the query.

• 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.

• Journal of KIISE:Databases
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• v.30 no.1
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• pp.52-63
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• 2003

Spatiotemporal moving objects are changing their Positions and/or shape over time in real world. As most of the indices of moving object are based on the R-tree. they have defects of the R-tree which are dead space and overlap. Some of the indices amplify the defects of the R-tree. In the paper, to solve the problems, we propose the MPR-tree(Moving Point R-tree) using Projection operation which has more effective search than existing moving point indices on time slice query and spatiotemporal range query. The MPR-tree connects positions of the same moving objects over time by using linked list, so it processes the combined query about trajectory effectively. The usefulness of the Projection operation is confirmed during processing moving object queries and in practical use of space from experimentation to compare MPR-tree with existing indices of moving objects. The proposed MPR-tree would be useful in the LBS, the car management using GPS, and the navigation system.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.687-694
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• 2003

According to the rapid increase of multimedia data quantity recently, various means of video data search has been desired. In order to process video data effectively, it is required that the content information of video data is loaded in database and semantic-based retrieval method can be available for various query of users. Currently existent contents-based video retrieval systems search by single method such as annotation-based or feature-based retrieval, and show low search efficiency and requires many efforts of system administrator or annotator form less perfect automatic processing. In this paper, we propose semantic-based video retrieval system which support semantic retrieval of various users by feature-based retrieval and annotation-based retrieval of massive video data. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user become query image and searches the most similar key frame through feature based retrieval method that propose. Therefore, we design the system that can heighten retrieval efficiency of video data through semantic-based retrieval.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.879-888
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• 2006

Recently many researchers have focused on management of Historical trajectories of moving objects in Euclidean spaces due to numerous sizes of accumulated data over time. However, the movement of moving objects in real applications generally has some constraints, for example vehicles on roads can only travel along connected road networks. In this paper, we propose an indexing method for trajectories of moving objects on road networks in order to process the network-based spatiotemporal range query. Our method contains the connect information of road networks to use the network distance for query processing, deals with trajectories which are represented by road segments in road networks, and manages them using multiple R-trees assigned per each road segment. Furthermore, it has a structure to be able to share R-tree among several road segments in large road networks. Consequently, we show that our method takes about 30% less in node accesses for the network-based spatiotemporal range query processing than other methods based on the Euclidean distance by experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3102-3119
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• 2021

In the modern rapid growing web era, the scope of web publication is about accessing the web resources. Due to the increased size of web, the search engines face many challenges, in indexing the web pages as well as producing result to the user query. Methodologies discussed in literatures towards clustering web documents suffer in producing higher clustering accuracy. Problem is mitigated using, the proposed scheme, Semantic Conceptual Relational Similarity (SCRS) based clustering algorithm which, considers the relationship of any document in two ways, to measure the similarity. One is with the number of semantic relations of any document class covered by the input document and the second is the number of conceptual relation the input document covers towards any document class. With a given data set Ds, the method estimates the SCRS measure for each document Di towards available class of documents. As a result, a class with maximum SCRS is identified and the document is indexed on the selected class. The SCRS measure is measured according to the semantic relevancy of input document towards each document of any class. Similarly, the input query has been measured for Query Relational Semantic Score (QRSS) towards each class of documents. Based on the value of QRSS measure, the document class is identified, retrieved and ranked based on the QRSS measure to produce final population. In both the way, the semantic measures are estimated based on the concepts available in semantic ontology. The proposed method had risen efficient result in indexing as well as search efficiency also has been improved.

• Journal of KIISE:Databases
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• v.37 no.1
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• pp.44-49
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• 2010

In this paper, we first address the problems associated with indexing and searching for reduced-dimensional vectors, which are reduced by using a combination of angle approximation and dimension grouping. Then, we propose a novel method to solve the problems. We also show the superiority of the proposed method by performing extensive experiments with synthetic and real-life data sets.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.10-12
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• 2004

Nowadays, with the rapid emergence of XML as a standard for data exchange over the Internet had led to considerable interest In the problem of data management requirements such as the need to store and query XML documents in which the location path languages Xpath is of particular important for XML application since it is a core component of many XML processing standards such as XSLT or XQuery, This parer gives a brief overview about method and design by applying a new spatial-based indexing method namely Xp-free that used for supporting Xpath. Spatial indexing technique has been proved its capacity on searching in large databases. Based on accelerating a node using planar as combined with the numbering schema, we devise efficiently derivative algorithms, which are simple, but useful. Besides that, it also allows to trace all Its relative nodes of context node In a manner supporting queries natural to the types especially Xpath queries with predicates.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.107-116
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• 2012

The applying of semantic technologies that aim to let computers understand and automatically process the meaning of the interlinked data on the Web is spreading. In Semantic Web, understanding and accessing the associations between data that is, the meaning between data as well as accessing to the data itself is important. W3C recommended RDF (Resource Description Framework) as a standard format to represent both Semantic Web data and their associations and also proposed several RDF query languages in order to support query processing for RDF data. However further researches on the query language definition considering the semantic associations and query processing techniques are still required. In this paper, using the suffix array-based indexing scheme previously introduced for RDF query processing, we propose a query processing approach to handle ${\rho}$-path query which is the representative type of semantic associations. To evaluate the query processing performance of the proposed approach, we implemented two different types of query processing approaches and measured the average query processing times. The experiments show that the proposed approach achieved 1.8 to 2.5 and 3.8 to 11 times better performance respectively than others two.