• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.779-788
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• 2002

There are many technically difficult issues in searching multimedia data such as image, video and audio because they are massive and more complex than simple text-based data. As a method of searching multimedia data, a similarity retrieval has been studied to retrieve automatically basic features of multimedia data and to make a search among data with retrieved features because exact match is not adaptable to a matrix of features of multimedia. In this paper, data clustering and its indexing are proposed as a speedy similarity-retrieval method of multimedia data. This approach clusters similar images on adjacent disk cylinders and then builds Indexes to access the clusters. To minimize the search cost, the hashing is adapted to index cluster. In addition, to reduce I/O time, the proposed searching takes just one I/O to look up the location of the cluster containing similar object and one sequential file I/O to read in this cluster. The proposed schema solves the problem of multi-dimension by using clustering and its indexing and has higher search efficiency than the content-based image retrieval that uses only clustering or indexing structure.

• International Journal of Internet, Broadcasting and Communication
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• v.4 no.1
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• pp.13-17
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• 2012

Indexing allows converting raw document collection into easily searchable representation. Web searching by Google or Yahoo provides subsecond response time which is made possible by efficient indexing of web-pages over the entire Web. Indexing process gets challenging when the scale gets bigger. Parallel techniques, such as MapReduce framework can assist in efficient large-scale indexing process. In this paper we propose PDFindexer, system for indexing scientific papers in PDF using MapReduce programming model. Unlike Web search engines, our target domain is scientific papers, which has pre-defined structure, such as title, abstract, sections, references. Our proposed system enables parsing scientific papers in PDF recreating their structure and performing efficient distributed indexing with MapReduce framework in a cluster of nodes. We provide the overview of the system, their components and interactions among them. We discuss some issues related with the design of the system and usage of MapReduce in parsing and indexing of large document collection.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• Journal of KIISE
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• v.44 no.11
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• pp.1236-1243
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• 2017

In this paper, we propose a novel way of producing keyword networks, named LSI-based ClusterTextRank, which extracts significant key words from a set of clusters with a mutual information metric, and constructs an association network using latent semantic indexing (LSI). The proposed method reduces the dimension of documents through LSI, decomposes documents into multiple clusters through k-means clustering, and expresses the words within each cluster as a maximal spanning tree graph. The significant key words are identified by evaluating their mutual information within clusters. Then, the method calculates the similarities between the extracted key words using the term-concept matrix, and the results are represented as a keyword association network. To evaluate the performance of the proposed method, we used travel-related blog data and showed that the proposed method outperforms the existing TextRank algorithm by about 14% in terms of accuracy.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.400-409
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• 2003

Collaborative filtering (CF) recommendation is a knowledge sharing technology for distribution of opinions and facilitating contacts in network society between people with similar interests. The main concerns of the CF algorithm are about prediction accuracy, speed of response time, problem of data sparsity, and scalability. In general, the efforts of improving prediction algorithms and lessening response time are decoupled. We propose a three-step CF recommendation model which is composed of profiling, inferring, and predicting steps while considering prediction accuracy and computing speed simultaneously. This model combines a CF algorithm with two machine learning processes, SOM (Self-Organizing Map) and CBR (Case Based Reasoning) by changing an unsupervised clustering problem into a supervised user preference reasoning problem, which is a novel approach for the CF recommendation field. This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference.

• Journal of KIISE:Databases
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• v.32 no.3
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• pp.254-262
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• 2005

This paper presents a new sequential indexing method called segment-page indexing (SP-indexing) for multidimensional range queries. The design objectives of SP-indexing are twofold:(1) improving the range query performance of multidimensional indexing methods (MIMs) and (2) providing a compromise between optimal index clustering and the full index reorganization overhead. Although more than ten years of database research has resulted in a great variety of MIMs, most efforts have focused on data-level clustering and there has been less attempt to cluster indexes. As a result, most relevant index nodes are widely scattered on a disk and many random disk accesses are required during the search. SP-indexing avoids such scattering by storing the relevant nodes contiguously in a segment that contains a sequence of contiguous disk pages and improves performance by offering sequential access within a segment. Experimental results demonstrate that SP-indexing improves query performance up to several times compared with traditional MIMs using small disk pages with respect to total elapsed time and it reduces waste of disk bandwidth due to the use of simple large pages.

• Journal of KIISE:Databases
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• v.29 no.2
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• pp.128-137
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• 2002

In this paper, we propose the LPC+-file for efficient indexing of high-dimensional image data. With the proliferation of multimedia data, there Is an increasing need to support the indexing and retrieval of high-dimensional image data. Recently, the LPC-file (5) that based on vector approximation has been developed for indexing high-dimensional data. The LPC-file gives good performance especially when the dataset is uniformly distributed. However, compared with for the uniformly distributed dataset, its performance degrades when the dataset is clustered. We improve the performance of the LPC-file for the strongly clustered image dataset. The basic idea is to adaptively partition the data space to find subspaces with high-density clusters and to assign more bits to them than others to increase the discriminatory power of the approximation of vectors. The total number of bits used to represent vector approximations is rather less than that of the LPC-file since the partitioned cells in the LPC+-file share the bits. An empirical evaluation shows that the LPC+-file results in significant performance improvements for real image data sets which are strongly clustered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1886-1908
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• 2020

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.

• Journal of Industrial Technology
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• v.20 no.B
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• pp.213-218
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• 2000

The nearest neighbor query is an important operation widely used in multimedia databases for finding the object that is most similar to a given query object. Most of techniques for processing nearest neighbor queries employ multidimensional indexes for effective indexing of objects. However, the performance of previous multidimensional indexes, which use N-dimensional rectangles or spheres for representing the capsule of the object cluster, deteriorates seriously as the number of dimensions gets higher. This paper proposes a new index structure based singular value decomposition resolving this problem and the query processing method using it. We also verify the superiority of our approach through performance evaluation by performing extensive experiments.

• Journal of Broadcast Engineering
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• v.12 no.4
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• pp.311-320
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• 2007

In this paper, an efficient detection algorithm of an anchor shot in news video is presented. We observed the audio visual characteristics of news video and proposed several low level features which are appropriate for detecting an anchor shot in news video. The overall structure of the proposed algorithm is composed of 3 stages: the pause detection, the audio cluster classification, and the matching with motion activity stage. We used the audio features as well as the motion feature in order to improve the indexing accuracy and the simulation results show that the performance of the proposed algorithm is quite satisfactory.