• The Transactions of the Korea Information Processing Society
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• v.6 no.2
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• pp.315-325
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• 1999

Indexing techniques for distributed resources have much effect on an information service system based on distributed environment like digital library. There is a centralized indexing technique, a distributed technique, and a mixed technique for distributed indexing techniques. In this paper, we propose new distributed indexing technique using EIF(extended Inverted File) structure that mix the centralized technique and t도 distributed technique. And we propose management techniques using EIF structure and retrieval technique using EIF structure. This distributed indexing technique proposed is able to fast process retrieval request and reduce network overload and select servers relevant to query terms. This paper investigated performance of a proposed distributed indexing technique.

• 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.

• Journal of KIISE:Databases
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• v.37 no.5
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• pp.228-237
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• 2010

Although conventional index structures provide various nearest-neighbor search algorithms for high-dimensional data, there are additional requirements to increase search performances as well as to support index scalability for large scale data. To support these requirements, we propose a distributed high-dimensional indexing structure based on cluster systems, called a Distributed Vector Approximation-tree (DVA-tree), which is a two-level structure consisting of a hybrid spill-tree and VA-files. We also describe the algorithms used for constructing the DVA-tree over multiple machines and performing distributed k-nearest neighbors (NN) searches. To evaluate the performance of the DVA-tree, we conduct an experimental study using both real and synthetic datasets. The results show that our proposed method contributes to significant performance advantages over existing index structures on difference kinds of datasets.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.45-48
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• 2013

In this paper, we proposed an Indexing Structure System (ISS) based on Apache Hadoop in Wireless Sensor Network (WSN). Nowadays sensors data continuously keep growing that need to control. Data constantly update in order to provide the newest information to users. While data keep growing, data retrieving and storing are face some challenges. So by using the ISS, we can maximize processing quality and minimize data retrieving time. In order to design ISS, Indexing Types have to be defined depend on each sensor type. After identifying, each sensor goes through the Indexing Structure Processing (ISP) in order to be indexed. After ISP, indexed data are streaming and storing in Hadoop Distributed File System (HDFS) across a number of separate machines. Indexed data are split and run by MapReduce tasks. Data are sorted and grouped depend on sensor data object categories. Thus, while users send the requests, all the queries will be filter from sensor data object and managing the task by MapReduce processing framework.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1807-1824
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• 2015

As the technologies advance, mobile peer-to-peer (MP2P) networks or systems become one of the major ways to share resources and information. On such a system, the information retrieval (IR), including the development of scalable infrastructures for indexing, becomes more complicated due to a huge increase on the amount of information and rapid information change. To keep the systems on MP2P networks more reliable and consistent, the index structures need to be updated frequently. For a semantic IR system, the index structure is even more complicated than a classic IR system and generally has higher update cost. The most well-known indexing technique used in semantic IR systems is Latent Semantic Indexing (LSI), of which the index structure is generated by singular value decomposition (SVD). Although LSI performs well, updating the index structure is not easy and time consuming. In an MP2P environment, which is fully distributed and dynamic, the update becomes more challenging. In this work, we consider how to update the sematic index generated by LSI and keep the index consistent in the whole MP2P network. The proposed Concept Space Update (CSU) protocol, based on distributed 2-Phase locking strategy, can effectively achieve the objectives in terms of two measurements: coverage speed and update cost. Using the proposed effective synchronization mechanism with the efficient updates on the SVD, re-computing the whole index on the P2P overlay can be avoided and the consistency can be achieved. Simulated experiments are also performed to validate our analysis on the proposed CSU protocol. The experimental results indicate that CSU is effective on updating the concept space with LSI/SVD index structure in MP2P semantic IR systems.

• Journal of Korea Multimedia Society
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• v.17 no.10
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• pp.1182-1188
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• 2014

Recently, the research on database outsourcing has been actively done with the popularity of cloud computing. However, because users' data may contain sensitive personal information, such as health, financial and location information, the data encryption methods have attracted much interest. Existing data encryption schemes process a query without decrypting the encrypted databases in order to support user privacy protection. On the other hand, to efficiently handle the large amount of data in cloud computing, it is necessary to study the distributed index structure. However, existing index structure and query processing algorithms have a limitation that they only consider single-column query processing. In this paper, we propose a grid-based multi column indexing scheme and an encrypted query processing algorithm. In order to support multi-column query processing, the multi-dimensional index keys are generated by using a space decomposition method, i.e. grid index. To support encrypted query processing over encrypted data, we adopt the Hilbert curve when generating a index key. Finally, we prove that the proposed scheme is more efficient than existing scheme for processing the exact and range query.

• Spatial Information Research
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• v.19 no.5
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• pp.63-74
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• 2011

Recently, there has been much interest in the spatial query which energy-efficiently acquires sensor readings from sensor nodes inside specified geographical area of interests. The centralized approach which performs the spatial query at a server after acquiring all sensor readings, though simple, it incurs high wireless transmission cost in accessing all sensor nodes. In order to remove the high wireless transmission cost, various in-network spatial indexing schemes have been proposed. They have focused on reducing the transmission cost by performing distributed spatial filtering on sensor nodes. However, these in-network spatial indexing schemes have a problem which cannot optimize both the spatial filtering and the wireless routing among sensor nodes, because these schemes have been developed by simply applying the existing spatial indexing schemes into the in-network environment. Therefore, we propose a new distributed spatial indexing scheme of the GR-tree. The GR-tree which form s a MBR-based tree structure, can reduce the wireless transmission cost by optimizing both the efficient spatial filtering and the wireless routing. Finally, we compare the existing spatial indexing scheme through extensive experiments and clarify our approach's distinguished features.

• International Journal of Contents
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• v.11 no.1
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• pp.69-72
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• 2015

Generally, existing parallel main-memory spatial index structures to avoid the trade-off between query freshness and CPU cost uses light-weight locking techniques. However, still, the lock based methods have some limits such as thrashing which is a well-known problem in lock based methods. In this paper, we propose a distributed index structure for moving objects exploiting the parallelism in multiple machines. The proposed index is a lock free multi-version concurrency technique based on the D-Stream model of Spark Stream. The proposed method exploits the multiversion nature of D-Stream of Spark Streaming.

• Journal of KIISE:Databases
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• v.36 no.1
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• pp.50-62
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• 2009

In wireless and mobile environments, data broadcasting is recognized as an effective way for data dissemination due to its benefits to bandwidth efficiency, energy-efficiency, and scalability. In this paper, we address the problem of delayed query processing raised by tree-based index structures in wireless broadcast environments, which increases the access time of the mobile clients. We propose a novel distributed index structure and a clustering strategy for streaming XML data which enable energy and latency-efficient broadcast of XML data. We first define the DIX node structure to implement a fully distributed index structure which contains tag name, attributes, and text content of an element as well as its corresponding indices. By exploiting the index information in the DIX node stream, a mobile client can access the wireless stream in a shorter latency. We also suggest a method of clustering DIX nodes in the stream, which can further enhance the performance of query processing over the stream in the mobile clients. Through extensive performance experiments, we demonstrate that our approach is effective for wireless broadcasting of XML data and outperforms the previous methods.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.718-722
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• 2008

Ontology plays an important role of the Semantic Web to describe meaning and reasoning of resources. Ontology has more rich expressive power through OWL that is a next standard representation language recommended by W3C. As the Semantic Web is widely known, an amount of information resources on the Web is growing rapidly and the related information resources are placed in distributed systems on the Web. So, for providing seamless services without the awareness of far distance, efficient management of the distributed information resources is required. Especially, sear ching fast for local repositories that include data related to user's queries is important to the performance of systems in the distributed environment. In this paper, first, we propose an index structure to search local repositories related to queries in the distributed Semantic Web. Second, we propose a query rewriting strategy to extend given user's query using various expression of OWL. Through the proposed index and query strategy, we can utilize various expressions of OWL and find local repositories related to all query patterns on the Semantic Web.