• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.137-145
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• 2010

Digital vessel have to accurate and efficient mange the digital data from various sensors in the digital vessel. But, In sensor network, it is difficult to transmit and analyze the entire stream data depending on limited networks, power and processor. Therefore it is suitable to use alternative stream data processing after classifying the continuous stream data. In this paper, We propose efficient processing method that arrange some sensors (temperature, humidity, lighting, voice) and process query based on sliding window for efficient input stream and pre-clustering using multiple Support Vector Machine(SVM) algorithm and manage hash table to summarized information. Processing performance improve as store and search and memory using hash table and usage reduced so maintain hash table in memory. We obtained to efficient result that accuracy rate and processing performance of proposal method using 35,912 data sets.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.285-293
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• 2008

In Semantic Web, metadata and ontology for representing semantics and conceptual relationships of information resources are essential factors. RDF and RDF Schema are W3C standard models for describing metadata and ontology. Therefore, many studies to store and retrieve RDF and RDF Schema documents are required. In this paper, we focus on some results of analyzing available query patterns considering both RDF and RDF Schema and classify queries on RDF and RDF Schema into the three patterns. RDF and RDF Schema can be represented as graph models. So, we proposed some strategies to store and retrieve using the graph models of RDF and RDF Schema. We can retrieve entities that can be arrived from a certain class or property in RDF and RDF Schema without a loss of performance on account of multiple joins with tables.

• Journal of Korea Spatial Information System Society
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• v.7 no.1 s.13
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• pp.53-62
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• 2005

A challenging task in the LBS system engineering is to implement a highly scalable system architecture which can manage moderate-size configurations handling thousands of moving items as well as upper-end configurations handling distributed computing system architecture that consists of multiple data processors, each dedicated to keeping records relevant to a different geographical zone and a different time zone. In this paper, we explain a prototype location data server structuring major components of GALIS by employing the TMO programming scheme, including the execution engine middleware developed to support real-time distributed object programming and real-time distributed computing system design. We present how to generate realistic location sensing reports and how to process such location reports and location-related queries. Some experimental results showing performance factors regarding distributed query processing are also explained.

• Korean Journal of Construction Engineering and Management
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• v.13 no.3
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• pp.56-66
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• 2012

The Recent Specialty Contractor in construction of the reduced quantity due to the recession, but competitive in order to increase the number of companies that are increasingly competitive. The field work of one person to perform the work of employees and multiple sites, workforce supply flow that occurs at each stage of information transfer is not made a late staffing. Therefore this research has proposed a new alternative that can overcome them and the entire industry in recent issue smart phone features that take advantage of GPS(Global Positioning System). This paper presents a Diligence and Indolence Management System using GPS of smart phone. offering the advantage of Web-based PMIS(Project Management Information System) through integration with real-time information transfer and smart phone applications for query by building a prototype of a professional construction company as their own on-site management measures are proposed to contribute time and attendance.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.407-411
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• 2005

KUGI (Korean UniGene Information) database contains the annotation information of the cDNA sequences obtained from the disease samples prevalent in Korean. A total of about 157,000 5'-EST high throughput sequences collected from cDNA libraries of stomach, liver, and some cancer tissues or established cell lines from Korean patients were clustered to about 35,000 contigs. From each cluster a representative clone having the longest high quality sequence or the start codon was selected. We stored the sequences of the representative clones and the clustered contigs in the KUGI database together with their information analyzed by running Blast against RefSeq, human mRNA, and UniGene databases from NCBI. We provide a web-based search engine fur the KUGI database using two types of user interfaces: attribute-based search and similarity search of the sequences. For attribute-based search, we use DBMS technology while we use BLAST that supports various similarity search options. The search system allows not only multiple queries, but also various query types. The results are as follows: 1) information of clones and libraries, 2) accession keys, location on genome, gene ontology, and pathways to public databases, 3) links to external programs, and 4) sequence information of contig and 5'-end of clones. We believe that the KUGI database and search system may provide very useful information that can be used in the study for elucidating the causes of the disease that are prevalent in Korean.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1843-1850
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• 2004

If the location information of a great number of cars kept for business with telematics terminals is acquired and managed efficiently, this information forms the foundation for controlling cars and traffic flows. The studies on the pure spatial indices have focused on the efficient retrievals. However, the acquisition and management of the terminal location of moving objects are more important than the efficiency of the query processing in the moving object databases. Therefore, it will be need to adopt parallel processing system for the moving object databases which should maintain the object's current location as precise as possible. This paper proposes a location management system using CDMA communications of telematics terminals. More precisely, we propose a architecture of spatial indexing mobile objects using multiple processors, and also newly propose a method of splitting buckets using the properties of moving objects in order to minimize the number of database updates. We also propose a acquisition method for gathering the location information of moving objects and passing the information of the bucket extents in order to reduce the amount of passed messages between processors.

• Journal of KIISE
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• v.42 no.10
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• pp.1247-1253
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• 2015

An inverted index structure is widely used for efficient string similarity search. One of the main requirements of similarity search is a fast response time; to this end, most techniques use an in-memory index structure. Since the size of an inverted index structure usually very large, however, it is not practical to assume that an index structure will fit into the main memory. To alleviate this problem, we propose a novel technique that reduces the size of an inverted index. In order to reduce the size of an index, the proposed technique rearranges data strings so that the data strings containing the same q-grams can be placed close to one other. Then, the technique encodes those multiple strings into a range. Through an experimental study using real data sets, we show that our technique significantly reduces the size of an inverted index without sacrificing query processing time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2599-2613
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• 2015

Multi-index hashing (MIH) is the state-of-the-art method for indexing binary codes, as it di-vides long codes into substrings and builds multiple hash tables. However, MIH is based on the dataset codes uniform distribution assumption, and will lose efficiency in dealing with non-uniformly distributed codes. Besides, there are lots of results sharing the same Hamming distance to a query, which makes the distance measure ambiguous. In this paper, we propose a data-oriented multi-index hashing method (DOMIH). We first compute the covariance ma-trix of bits and learn adaptive projection vector for each binary substring. Instead of using substrings as direct indices into hash tables, we project them with corresponding projection vectors to generate new indices. With adaptive projection, the indices in each hash table are near uniformly distributed. Then with covariance matrix, we propose a ranking method for the binary codes. By assigning different bit-level weights to different bits, the returned bina-ry codes are ranked at a finer-grained binary code level. Experiments conducted on reference large scale datasets show that compared to MIH the time performance of DOMIH can be improved by 36.9%-87.4%, and the search accuracy can be improved by 22.2%. To pinpoint the potential of DOMIH, we further use near-duplicate image retrieval as examples to show the applications and the good performance of our method.

• Journal of Computing Science and Engineering
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• v.9 no.4
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• pp.204-213
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• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.

• The Journal of Society for e-Business Studies
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• v.14 no.1
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• pp.143-160
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• 2009

In determining the performance of OLAP database applications, the structure and the effective access methods to the underlying disk system is a significant factor. In recent years, hard disks are designed with multiple physical zones where seek times and data transfer rates vary across the zones. However, there is little consideration of multi-zone disks in previous works. Instead, they assumed a traditional disk model that comes with many simplifying assumptions such as an average seek-time and a single data transfer rate. In this paper, we propose a technique storing a set of materialized views into the multi-zoned disks in OLAP environment dealing with large sets of data. We first present the disk zoning algorithm of materialized views according to the access probabilities of each views. Also, we address the problem of storing views in the dynamic environment where data are updated continuously. Finally, through experiments, we prove the performance improvement of the proposed algorithm against the conventional methods.