• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1471-1480
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• 2006

• Journal of Information Processing Systems
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• v.12 no.2
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• pp.234-248
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• 2016

Due to the proliferation of data being exchanged and the increase of dependency on this data for critical decision-making, it has become imperative to ensure the trustworthiness of the data at the receiving end in order to obtain reliable results. Data provenance, the derivation history of data, is a useful tool for evaluating the trustworthiness of data. Various frameworks have been proposed to evaluate the trustworthiness of data based on data provenance. In this paper, we briefly review a history of these frameworks for evaluating the trustworthiness of data and present an overview of some prominent state-of-the-art evaluation frameworks. Moreover, we provide a comparative analysis of two key frameworks by evaluating various aspects in an executional environment. Our analysis points to various open research issues and provides an understanding of the functionalities of the frameworks that are used to evaluate the trustworthiness of data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.460-470
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• 2017

We address how to handle big data mining effectively using the intelligent SSD (iSSD). ISSD is a storage device equipped with computing power inside SSD for reducing the transferring cost and for processing data nearby SSD where the data is stored. We first introduce the structural characteristics of iSSD for efficient data processing. Then, we present how to process data mining algorithms by using iSSD. Finally, we discuss how to improve the performance of data mining algorithms significantly by exploiting heterogeneous computing environment where host CPUs and GPU coexist for maximizing the performance.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1277-1298
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• 2017

Related to the maximum vector problem, a skyline query is to discover dominating tuples from a set of tuples, where each defines an object (such as a hotel) in several dimensions (such as the price and the distance to the beach). A tuple, an instance of an object, dominates another tuple if it is equally good or better in all dimensions and better in at least one dimension. Traditionally, skyline queries are defined upon single-instance data or upon objects each of which is associated with an instance. However, in some cases, an object is not associated with a single instance but rather by multiple instances. For example, on a review website, many users assign scores to a product or a service, and a user's score is an instance of the object representing the product or the service. Such data is an example of multi-instance data. Unlike most (if not all) others considering the traditional setting, we consider skyline queries defined upon multi-instance data. We define the dominance calculation and propose an algorithm to reduce its computational cost. We use synthetic and real data to evaluate the proposed methods, and the results demonstrate their utility.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1447-1450
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• 2006

단순한 데이터통신을 위한 넷(Net)이 정보 교환의 혁명을 일으켰던 웹(Web)으로 발전하였고, 현재 웹은 신경망 형태의 인터넷 구조를 갖는 그리드(grid)를 향해 발전하고 있다. 정보의 교환 및 분산된 자원을 공유하기 위한 그리드 컴퓨팅은 자원의 발견 뿐 만 아니라, 접근 속도와 제한된 자원의 공유를 비롯한 여러 문제점을 가지고 있다. 특히, 데이터에 대한 접근 속도와 제한적인 데이터 공유 문제를 해결하기 위해 Replica 서비스가 제안되었으나, 이러한 Replica 서비스를 원활하게 지원하기 위해서는 Replica 경로 및 정보들을 목록으로 구성해야만 한다. 현재 그리드 컴퓨팅 분야에서 이러한 목록을 구현하는 것과 동시에 최적의 조건을 찾아가는 기법에 대한 연구가 활발히 진행 중이다. 따라서, 본 논문에서는 Replica 서비스를 최적화하기 위한 기존의 연구들을 분석하고 Data Location Service를 이용하여 Replica 서비스를 최적화하는 방법을 제안하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.473-474
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• 2019

Recently, data security and convenience are critical requirements to the interaction and collaboration of tourism service systems for the tourism industry. However, there are still many challenges for current tourism service systems to fulfill these requirements since they have inconsistent structures with different access control models and security policies. Blockchain has the potential to evolve the conventional tourism industry benefiting by its unique features such as data privacy and transparency. This paper mainly aims the interworking mechanism of heterogenous blockchain platforms for secure tourism service. We propose interworking scheme to connect multi-blockchain platforms for enhancing data integrity in the tourism industry. A proof of concept design and implement based on Hyperledger Fabric and Winding Tree.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.472-473
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• 2013

The growth in number and efficiency of smart devices such as GPS enabled smart phones and PDAs present an unparalleled opportunity for diverse areas of life. However extraction of GPS traces for provision of services demand a huge storage space as well as computation overhead. This is a challenging task especially for the applications which provide runtime services. In this paper we provide a simplified model to extract GPS traces of moving objects at runtime. Road segment partitioning and measure of deviation in angle of trajectory path is incorporated to identify the significant data points. The number of these data points is minimized by our proposed approach in an efficient manner to overwhelm the storage and computation overhead. Further, the competent reconstruction of complete itinerary based on gathered data, is also ensured by proposed method.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.95-103
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• 2011

The actual utilization of Semantic Web technology which aims to provide more intelligent and automated service for information retrieval over the Web becomes gradually reality. RDF is widely used as the one of standard formats to present and manage the voluminous data on the Web. Efficient query processing on RDF data, therefore, is one of the ongoing research topics. Retrieving resources having a specific association from a given resource is the typical query processing type and several researches for this have done. However the most of previous researches have not fully considered discovering the complex relationship among resources such as returning the association between resources as the query processing result. This paper introduces the indexing and query processing for ${\rho}$-intersect operation which is one of the semantic association retrieval types. It includes an indexing scheme using suffix array and optimal processing approaches for handling ${\rho}$-intersect operation. The experimental evaluations shows that the average execution times for the proposed approach is 3~7 times faster than the previous approach.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.1-9
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• 2017

Smart Cities intelligently manage numerous infrastructures, including Smart-City IoT devices, and provide a variety of smart-city applications to citizen. In order to provide various information needed for smart-city applications, Smart Cities require a function to intelligently process large-scale streamed big data that are constantly generated from a large number of IoT devices. To provide smart services in Smart-City, the Smart-City Consortium uses stream reasoning. Our stream reasoning requires real-time processing of big data. However, there are limitations associated with real-time processing of large-scale streamed big data in Smart Cities. In this paper, we introduce one of our researches on cloud computing based real-time distributed-parallel-processing to be used in stream-reasoning of IoT big data in Smart Cities. The Smart-City Consortium introduced its previously developed smart-city middleware. In the research for this paper, we made cloud computing based real-time distributed-parallel-processing available in the cloud computing platform of the smart-city middleware developed in the previous research, so that we can perform real-time distributed-parallel-processing with them. This paper introduces a real-time distributed-parallel-processing method and system for stream reasoning with IoT big data transmitted from various sensors of Smart Cities and evaluate the performance of real-time distributed-parallel-processing of the system where the method is implemented.