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한국정보처리학회> JIPS(Journal of Information Processing Systems)

JIPS(Journal of Information Processing Systems) update

  • : 한국정보처리학회
  • : 공학분야  >  전자공학
  • : KCI등재
  • : SCOPUS
  • : 연속간행물
  • : 격월
  • : 1976-913x
  • : 2092-805X
  • : International journal of information processing systems(~2007)→Journal of information processing system(2008~)

수록정보
17권5호(2021) |수록논문 수 : 12
간행물 제목
18권1호(2022년 02월) 수록논문
최근 권호 논문
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KCI등재 SCOPUS

1Delivering Augmented Information in a Session Initiation Protocol-Based Video Telephony Using Real-Time AR

저자 : Sung-bong Jang , Young-woong Ko

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 1-11 (11 pages)

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Online video telephony systems have been increasingly used in several industrial areas because of coronavirus disease 2019 (COVID-19) spread. The existing session initiation protocol (SIP)-based video call system is being usefully utilized, however, there is a limitation that it is very inconvenient for users to transmit additional information during conversation to the other party in real time. To overcome this problem, an enhanced scheme is presented based on augmented real-time reality (AR). In this scheme, augmented information is automatically searched from the Internet and displayed on the user's device during video telephony. The proposed approach was qualitatively evaluated by comparing it with other conferencing systems. Furthermore, to evaluate the feasibility of the approach, we implemented a simple network application that can generate SIP call requests and answer with AR object pre-fetching. Using this application, the call setup time was measured and compared between the original SIP and pre-fetching schemes. The advantage of this approach is that it can increase the convenience of a user's mobile phone by providing a way to automatically deliver the required text or images to the receiving side.

KCI등재 SCOPUS

2Lightweight Single Image Super-Resolution by Channel Split Residual Convolution

저자 : Buzhong Liu

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 12-25 (14 pages)

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In recent years, deep convolutional neural networks have made significant progress in the research of single image super-resolution. However, it is difficult to be applied in practical computing terminals or embedded devices due to a large number of parameters and computational effort. To balance these problems, we propose CSRNet, a lightweight neural network based on channel split residual learning structure, to reconstruct highresolution images from low-resolution images. Lightweight refers to designing a neural network with fewer parameters and a simplified structure for lower memory consumption and faster inference speed. At the same time, it is ensured that the performance of recovering high-resolution images is not degraded. In CSRNet, we reduce the parameters and computation by channel split residual learning. Simultaneously, we propose a double-upsampling network structure to improve the performance of the lightweight super-resolution network and make it easy to train. Finally, we propose a new evaluation metric for the lightweight approaches named 100_FPS. Experiments show that our proposed CSRNet not only speeds up the inference of the neural network and reduces memory consumption, but also performs well on single image super-resolution.

KCI등재 SCOPUS

3Systematic Review on Chatbot Techniques and Applications

저자 : Dong-min Park , Seong-soo Jeong , Yeong-seok Seo

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 26-47 (22 pages)

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Chatbots were an important research subject in the past. A chatbot is a computer program or an artificial intelligence program that participates in a conversation via auditory or textual methods. As the research on chatbots progressed, some important issues regarding them changed over time. Therefore, it is necessary to review the technology with a focus on recent advancements and core research technologies. In this paper, we introduce five different chatbot technologies: natural language processing, pattern matching, semantic web, data mining, and context-aware computer. We also introduce the latest technology for the chatbot researchers to recognize the present situation and channelize it in the right direction.

KCI등재 SCOPUS

4A Windowed-Total-Variation Regularization Constraint Model for Blind Image Restoration

저자 : Ganghua Liu , Wei Tian , Yushun Luo , Juncheng Zou , Shu Tang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 48-58 (11 pages)

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Blind restoration for motion-blurred images is always the research hotspot, and the key for the blind restoration is the accurate blur kernel (BK) estimation. Therefore, to achieve high-quality blind image restoration, this thesis presents a novel windowed-total-variation method. The proposed method is based on the spatial scale of edges but not amplitude, and the proposed method thus can extract useful image edges for accurate BK estimation, and then recover high-quality clear images. A large number of experiments prove the superiority.

KCI등재 SCOPUS

5A Hierarchical Bilateral-Diffusion Architecture for Color Image Encryption

저자 : Menglong Wu , Yan Li , Wenkai Liu

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 59-74 (16 pages)

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During the last decade, the security of digital images has received considerable attention in various multimedia transmission schemes. However, many current cryptosystems tend to adopt a single-layer permutation or diffusion algorithm, resulting in inadequate security. A hierarchical bilateral diffusion architecture for color image encryption is proposed in response to this issue, based on a hyperchaotic system and DNA sequence operation. Primarily, two hyperchaotic systems are adopted and combined with cipher matrixes generation algorithm to overcome exhaustive attacks. Further, the proposed architecture involves designing pixelpermutation, pixel-diffusion, and DNA (deoxyribonucleic acid) based block-diffusion algorithm, considering system security and transmission efficiency. The pixel-permutation aims to reduce the correlation of adjacent pixels and provide excellent initial conditions for subsequent diffusion procedures, while the diffusion architecture confuses the image matrix in a bilateral direction with ultra-low power consumption. The proposed system achieves preferable number of pixel change rate (NPCR) and unified average changing intensity (UACI) of 99.61% and 33.46%, and a lower encryption time of 3.30 seconds, which performs better than some current image encryption algorithms. The simulated results and security analysis demonstrate that the proposed mechanism can resist various potential attacks with comparatively low computational time consumption.

KCI등재 SCOPUS

6User-to-User Matching Services through Prediction of Mutual Satisfaction Based on Deep Neural Network

저자 : Jinah Kim , Nammee Moon

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 75-88 (14 pages)

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With the development of the sharing economy, existing recommender services are changing from user-item recommendations to user-user recommendations. The most important consideration is that all users should have the best possible satisfaction. To achieve this outcome, the matching service adds information between users and items necessary for the existing recommender service and information between users, so higher-level data mining is required. To this end, this paper proposes a user-to-user matching service (UTU-MS) employing the prediction of mutual satisfaction based on learning. Users were divided into consumers and suppliers, and the properties considered for recommendations were set by filtering and weighting. Based on this process, we implemented a convolutional neural network (CNN)-deep neural network (DNN)-based model that can predict each supplier's satisfaction from the consumer perspective and each consumer's satisfaction from the supplier perspective. After deriving the final mutual satisfaction using the predicted satisfaction, a top recommendation list is recommended to all users. The proposed model was applied to match guests with hosts using Airbnb data, which is a representative sharing economy platform. The proposed model is meaningful in that it has been optimized for the sharing economy and recommendations that reflect user-specific priorities.

KCI등재 SCOPUS

7Multistage Pulse Jamming Suppression Algorithm for Satellite Navigation Receiver

저자 : Xiaobo Yang , Jining Feng , Ying Xu

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 89-96 (8 pages)

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A novel multistage pulse jamming suppression algorithm was proposed to solve the anti-pulse jamming problem encountered in navigation receivers. Based on the characteristics of the short duration of pulse jamming and distribution characteristics of satellite signals, the pulse jamming detection threshold was derived. From the experiments, it was found that the randomness of pulse jamming affects jamming suppression. On this basis, the principle of the multistage anti-pulse jamming algorithm was established. The effectiveness of the anti-jamming algorithm was verified through experiments. The characteristics of the algorithm include simple determination of jamming detection threshold, easy programming, and complete suppression of pulse jamming.

KCI등재 SCOPUS

8Improved Dynamic Programming in Local Linear Approximation Based on a Template in a Lightweight ECG Signal-Processing Edge Device

저자 : Seungmin Lee , Daejin Park

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 97-114 (18 pages)

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Interest is increasing in electrocardiogram (ECG) signal analysis for embedded devices, creating the need to develop an algorithm suitable for a low-power, low-memory embedded device. Linear approximation of the ECG signal facilitates the detection of fiducial points by expressing the signal as a small number of vertices. However, dynamic programming, a global optimization method used for linear approximation, has the disadvantage of high complexity using memoization. In this paper, the calculation area and memory usage are improved using a linear approximated template. The proposed algorithm reduces the calculation area required for dynamic programming through local optimization around the vertices of the template. In addition, it minimizes the storage space required by expressing the time information using the error from the vertices of the template, which is more compact than the time difference between vertices. When the length of the signal is L, the number of vertices is N, and the margin tolerance is M, the spatial complexity improves from O(NL) to O(NM). In our experiment, the linear approximation processing time was 12.45 times faster, from 18.18 ms to 1.46 ms on average, for each beat. The quality distribution of the percentage root mean square difference confirms that the proposed algorithm is a stable approximation.

KCI등재 SCOPUS

9A Novel Framework Based on CNN-LSTM Neural Network for Prediction of Missing Values in Electricity Consumption Time-Series Datasets

저자 : Syed Nazir Hussain , Azlan Abd Aziz , Jakir Hossen , Nor Azlina Ab Aziz , G. Ramana Murthy , Fajaruddin Bin Mustakim

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 115-129 (15 pages)

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Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS) could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.

KCI등재 SCOPUS

10Identifying Critical Factors for Successful Games by Applying Topic Modeling

저자 : Mookyung Kwak , Ji Su Park , Jin Gon Shon , Abstract

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 18권 1호 발행 연도 : 2022 페이지 : pp. 130-145 (16 pages)

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Games are widely used in many fields, but not all games are successful. Then what makes games successful? The question gave us the motivation of this paper, which is to identify critical factors for successful games with topic modeling technique. It is supposed that game reviews written by experts sit on abundant insights and topics of how games succeed. To excavate these insights and topics, latent Dirichlet allocation, a topic modeling analysis technique, was used. This statistical approach provided words that implicate topics behind them. Fifty topics were inferred based on these words, and these topics were categorized by stimulation-response-desiregoal (SRDG) model, which makes a streamlined flow of how players engage in video games. This approach can provide game designers with critical factors for successful games. Furthermore, from this research result, we are going to develop a model for immersive game experiences to explain why some games are more addictive than others and how successful gamification works.

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KCI등재SCOUPUS

1Multi-channel Long Short-Term Memory with Domain Knowledge for Context Awareness and User Intention

저자 : Dan-Bi Cho , Hyun-young Lee , Seung-Shik Kang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 867-878 (12 pages)

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In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multichannel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multichannel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.

KCI등재SCOUPUS

2Route Optimization Algorithm Based on Game Theory for Tourism Routes at Pseudo-Imperial Palace

저자 : Guangjie Liu , Jinlong Zhu , Qiucheng Sun , Jiaze Hu , Hao Yu

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 879-891 (13 pages)

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With improvements in living conditions, an increasing number of people are choosing to spend their time traveling. Comfortable tour routes are affected by the season, time, and other local factors. In this paper, the influencing factors and principles of scenic spots are analyzed, a model used to find the available routes is built, and a multi-route choice model based on a game theory utilizing a path recommendation weight is developed. A Monte Carlo analysis of a tourist route subjected to fixed access point conditions is applied to account for uncertainties such as the season, start time, end time, stay time, number of scenic spots, destination, and start point. We use the Dijkstra method to obtain multiple path plans and calculate the path evaluation score using the Monte Carlo method. Finally, according to the user preference in the input path, game theory generates path ordering for user choice. The proposed approach achieves a state-of-the-art performance at the pseudo-imperial palace. Compared with other methods, the proposed method can avoid congestion and reduce the time cost.

KCI등재SCOUPUS

3Image Semantic Segmentation Using Improved ENet Network

저자 : Chaoxian Dong

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 892-904 (13 pages)

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An image semantic segmentation model is proposed based on improved ENet network in order to achieve the low accuracy of image semantic segmentation in complex environment. Firstly, this paper performs pruning and convolution optimization operations on the ENet network. That is, the network structure is reasonably adjusted for better results in image segmentation by reducing the convolution operation in the decoder and proposing the bottleneck convolution structure. Squeeze-and-excitation (SE) module is then integrated into the optimized ENet network. Small-scale targets see improvement in segmentation accuracy via automatic learning of the importance of each feature channel. Finally, the experiment was verified on the public dataset. This method outperforms the existing comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU) values. And in a short running time, the accuracy of the segmentation and the efficiency of the operation are guaranteed.

KCI등재SCOUPUS

4Range Segmentation of Dynamic Offloading (RSDO) Algorithm by Correlation for Edge Computing

저자 : Jieun Kang , Svetlana Kim , Jae-ho Kim , Nak-myoung Sung , Yong-Ik Yoon

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 905-917 (13 pages)

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In recent years, edge computing technology consists of several Internet of Things (IoT) devices with embedded sensors that have improved significantly for monitoring, detection, and management in an environment where big data is commercialized. The main focus of edge computing is data optimization or task offloading due to data and task-intensive application development. However, existing offloading approaches do not consider correlations and associations between data and tasks involving edge computing. The extent of collaborative offloading segmented without considering the interaction between data and task can lead to data loss and delays when moving from edge to edge. This article proposes a range segmentation of dynamic offloading (RSDO) algorithm that isolates the offload range and collaborative edge node around the edge node function to address the offloading issue.The RSDO algorithm groups highly correlated data and tasks according to the cause of the overload and dynamically distributes offloading ranges according to the state of cooperating nodes. The segmentation improves the overall performance of edge nodes, balances edge computing, and solves data loss and average latency.

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5Supply Chain Collaboration Degree of Manufacturing Enterprises Using Matter-Element Method

저자 : Qiang Xiao , Shuangshuang Yao , Mengjun Qiang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 918-932 (15 pages)

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Evaluation of the collaboration of the upstream and downstream enterprises in the manufacturing supply chain is important to improve their synergistic effect. From the supply chain perspective, this study establishes the evaluation model of the manufacturing enterprise collaboration on the basis of fuzzy entropy according to synergistic theory. Downstream enterprises carry out coordinated capital, business, and information flows as subsystems and research enterprises as composite systems. From the three subsystems, the collaboration evaluation index is selected as the order parameter. The compound fuzzy matter-element matrix is established by using its improved algorithm. Subordinate membership and standard deviation fuzzy matter-element matrixes are constructed. Index weight is determined using the entropy weight method. The closeness of each matter element is then calculated. Through a representative of the home appliance industry, namely, Gree Electric Appliances Inc. of Zhuhai, empirical analysis of data in 2011-2017 from the company and its upstream and downstream enterprise collaboration shows a good trend, but the coordinated development has not reached stability. Gree Electric Appliances Inc. of Zhuhai need to strengthen the synergy with upstream and downstream enterprises in terms of cash, business, and information flows to enhance competitiveness. Experimental results show that this method can provide precise suggestions for enterprises, improve the degree of collaboration, and accelerate the development and upgrading of the manufacturing industry.

KCI등재SCOUPUS

6Joint Detection Method for Non-orthogonal Multiple Access System Based on Linear Precoding and Serial Interference Cancellation

저자 : Jianpo Li , Qiwei Wang

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 933-946 (14 pages)

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In the non-orthogonal multiple access (NOMA) system, multiple user signals on the single carrier are superimposed in a non-orthogonal manner, which results in the interference between non-orthogonal users and noise interference in the channel. To solve this problem, an improved algorithm combining regularized zero-forcing (RZF) precoding with minimum mean square error-serial interference cancellation (MMSE-SIC) detection is proposed. The algorithm uses RZF precoding combined with successive over-relaxation (SOR) method at the base station to preprocess the source signal, which can balance the effects of non-orthogonal inter-user interference and noise interference, and generate a precoded signal suitable for transmission in the channel. At the receiver, the MMSE-SIC detection algorithm is used to further eliminate the interference in the signal for the received superimposed signal, and reduce the calculation complexity through the QR decomposition of the matrix. The simulation results show that the proposed joint detection algorithm has good applicability to eliminate the interference of non-orthogonal users, and it has low complexity and fast convergence speed. Compared with other traditional method, the improved method has lower error rate under different signal-to-interference and noise ratio (SINR).

KCI등재SCOUPUS

7Research on Water Edge Extraction in Islands from GF-2 Remote Sensing Image Based on GA Method

저자 : Yan Bian , Yusheng Gong , Guopeng Ma , Ting Duan

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 947-959 (13 pages)

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Aiming at the problem of low accuracy in the water boundary automatic extraction of islands from GF-2 remote sensing image with high resolution in three bands, new water edges automatic extraction method in island based on GF-2 remote sensing images, genetic algorithm (GA) method, is proposed in this paper. Firstly, the GA-OTSU threshold segmentation algorithm based on the combination of GA and the maximal inter-class variance method (OTSU) was used to segment the island in GF-2 remote sensing image after pre-processing. Then, the morphological closed operation was used to fill in the holes in the segmented binary image, and the boundary was extracted by the Sobel edge detection operator to obtain the water edge. The experimental results showed that the proposed method was better than the contrast methods in both the segmentation performance and the accuracy of water boundary extraction in island from GF-2 remote sensing images.

KCI등재SCOUPUS

8The Needs Analysis of Software Safety Education Program for Common Competency Area

저자 : Ji-Woon Kang , Sung-Ryong Do

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 960-971 (12 pages)

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As the era of the 4th Industrial Revolution enters, the importance of software safety is increasing, but related systematic educational curriculum and trained professional engineers are insufficient. The purpose of this research is to propose the high priority elements for the software safety education program through needs analysis. For this purpose, 74 candidate elements of software safety education program were derived through contents analysis of literature and nominal group technique (NGT) process with five software safety professionals from various industries in South Korea. Targeting potential education participants including industrial workers and students, an on-line survey was conducted to measure the current and required level of each element. Using descriptive statistics, t-test, Borich needs assessment and Locus for focus model, 16 high priority elements were derived for software safety education program. Based on the results, suggestions were made to develop a more effective education program for software safety education.

KCI등재SCOUPUS

9SAT-Analyser Traceability Management Tool Support for DevOps

저자 : Iresha Rubasinghe , Dulani Meedeniya , Indika Perera

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 972-988 (17 pages)

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At present, DevOps environments are getting popular in software organizations due to better collaboration and software productivity over traditional software process models. Software artefacts in DevOps environments are vulnerable to frequent changes at any phase of the software development life cycle that create a continuous integration continuous delivery pipeline. Therefore, software artefact traceability management is challenging in DevOps environments due to the continual artefact changes; often it makes the artefacts to be inconsistent. The existing software traceability related research shows limitations such as being limited to few types of artefacts, lack of automation and inability to cope with continuous integrations. This paper attempts to overcome those challenges by providing traceability support for heterogeneous artefacts in DevOps environments using a prototype named SAT-Analyser. The novel contribution of this work is the proposed traceability process model consists of artefact change detection, change impact analysis, and change propagation. Moreover, this tool provides multi-user accessibility and is integrated with a prominent DevOps tool stack to enable collaborations. The case study analysis has shown high accuracy in SAT-Analyser generated results and have obtained positive feedback from industry DevOps practitioners for its efficacy.

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10Implementation of Search Engine to Minimize Traffic Using Blockchain-Based Web Usage History Management System

저자 : Sunghyun Yu , Cheolmin Yeom , Yoojae Won

발행기관 : 한국정보처리학회 간행물 : JIPS(Journal of Information Processing Systems) 17권 5호 발행 연도 : 2021 페이지 : pp. 989-1003 (15 pages)

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With the recent increase in the types of services provided by Internet companies, collection of various types of data has become a necessity. Data collectors corresponding to web services profit by collecting users' data indiscriminately and providing it to the associated services. However, the data provider remains unaware of the manner in which the data are collected and used. Furthermore, the data collector of a web service consumes web resources by generating a large amount of web traffic. This traffic can damage servers by causing service outages. In this study, we propose a website search engine that employs a system that controls user information using blockchains and builds its database based on the recorded information. The system is divided into three parts: a collection section that uses proxy, a management section that uses blockchains, and a search engine that uses a built-in database. This structure allows data sovereigns to manage their data more transparently. Search engines that use blockchains do not use internet bots, and instead use the data generated by user behavior. This avoids generation of traffic from internet bots and can, thereby, contribute to creating a better web ecosystem.

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