• Journal of Information Processing Systems
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• v.15 no.3
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• pp.500-519
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• 2019

The blooming of social media has simulated interest in sentiment analysis. Sentiment analysis aims to determine from a specific piece of content the overall attitude of its author in relation to a specific item, product, brand, or service. In sentiment analysis, the focus is on the subjective sentences. Hence, in order to discover and extract the subjective information from a given text, researchers have applied various methods in computational linguistics, natural language processing, and text analysis. The aim of this paper is to provide an in-depth up-to-date study of the sentiment analysis algorithms in order to familiarize with other works done in the subject. The paper focuses on the main tasks and applications of sentiment analysis. State-of-the-art algorithms, methodologies and techniques have been categorized and summarized to facilitate future research in this field.

• The Journal of Society for e-Business Studies
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• v.14 no.1
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• pp.13-34
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• 2009

As ubiquitous computing technology is proliferating, a wider range of u-services applying ubiquitous computing technology has been implemented and some of the u-services have been actively considered for commercialization. Accordingly, establishing corresponding business models based on u-services also have been increased. However, structured methodologies to build u-service business models are still in its very early age. Moreover, since most of the legacy business model formulation and analysis methodologies have stressed on conventional or digital services, not u-services. Hence, the purpose of this paper is to propose a structured methodology of formulating and analyzing business models for u-services. An illustrative example is described to show the feasibility of the proposed methodology. To do so, Business Model Diagram (BMD), which adopts model components for u-services, is newly developed.

• Convergence Security Journal
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• v.24 no.2
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• pp.19-29
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• 2024

A crypto-jacking attack is an attack that infringes on the availability of users by stealing computing resources required for cryptocurrency mining. The target of the attack is gradually diversifying from general desktop or server environments to cloud environments. Therefore, it is essential to apply a crypto-minor detection technique suitable for various computing environments. However, since the existing detection methodologies have only been detected in a specific environment, comparative analysis has not been properly performed on the methodologies that can be applied to each environment. Therefore, in this study, classification criteria for conventional crypto-minor detection techniques are established, and a complex and integrated detection framework applicable to the cloud environment is presented through in-depth comparative analysis of existing crypto-minor detection techniques based on different experimental environments and datasets.

• Journal of Computing Science and Engineering
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• v.7 no.4
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• pp.251-262
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• 2013

Bad smells are usually related to program source code, arising from bad design and programming practices. Refactoring activities are often motivated by the detection of bad smells. With the increasing adoption of Design-by-Contract (DBC) methodologies in formal software development, evidence of bad design practices can similarly be found in programs that combine actual production code with interface contracts. These contracts can be written in languages, such as the Java Modeling Language (JML), an extension to the Java syntax. This paper presents a catalog of bad smells that appear during DBC practice, considering JML as the language for specifying contracts. These smells are described over JML constructs, although several can appear in other DBC languages. The catalog contains 6 DBC smells. We evaluate the recurrence of DBC smells in two ways: first by describing a small study with graduate student projects, and second by counting occurrences of smells in contracts from the JML models application programming interface (API). This API contains classes with more than 1,600 lines in contracts. Along with the documented smells, suggestions are provided for minimizing the impact or even removing a bad smell. It is believed that initiatives towards the cataloging of bad smells are useful for establishing good design practices in DBC.

• Journal of Computing Science and Engineering
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• v.6 no.1
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• pp.60-66
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• 2012

It has been proved that information and communication technology (ICT) solutions for personalized health (PHealth) and ambient assisted living (AAL) can support people in their daily life activities. Several solutions have been demonstrated to empower different levels of services through seamless data acquisition and specific users' interaction modalities. Usually services usability and accessibility are handled in the design process and are validated with small users' groups. Moreover, while service design and systems development have been extensively described in literature, service deployment methodologies are not properly addressed and documented. Proper reference guidelines are also missing. The most common methodologies like unified process (UP) or ICONX can cover only the design and the development of PHealth services without a clear description on the following phases such as deployment, service provision and maintenance. These phases present several risks to be taken into account right from the beginning of the implementation of PHealth or AAL services. This paper focuses on the description of a structured methodology to deploy PHealth services and how this process can be supported by integrated software routines and adapting the UP framework in particular the "Transition phase."

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• Journal of Internet Computing and Services
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• v.3 no.2
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• pp.39-56
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• 2002

In distributed computing environments, there are many database applications that should share data each other such as data warehousing and data mining with autonomy on local databases. The first step to such applications is the integration of heterogeneous database schema, but there is no accepted common data model for the integration and also are difficulties on the construction of integration program. In this paper, we use the XML Schema for the representation of common data model and exploit XSLT for reducing the programming difficulties. We define the schema integration operations and develop a methodology for the semi-automatic schema integration according to schema conflicts types. Our integration method has benefits on standardization, extendibility on schema integration process comparing to existing methodologies.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2007.10a
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• pp.247-256
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• 2007

Accessibility is a field of study that has primarily been applied to urban or transportation problems two dimensionally. However, in large complex buildings as shopping centers or hospitals, inter-spatial accessibility among compartments has to be taken into account such as in building layouts or evacuation planning. This study expands space syntax theory, one of accessibility-related methodologies used for computing connectivity in urban or architectural spaces, into 3D indoor spaces. Although space syntax is basically a topology-based theory that does not consider general costs such as distance or time, this study suggests modification that incorporates different types of impedances in moving between places including distances, turns and transfers between floors. The proposed method is applied to a 3D campus building model in computing and displaying the accessibility to exit doors or cohesive accessibility among similar functions.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1041-1045
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• 2010

Clusters can provide scalable and flexible architectures for parallel computing servers and data centers. Their performance prediction has been a very challenging issue. Existing performance measurement methodologies are able to measure the performance of servers already constructed. Thus they cannot provide a way to predict the overall system performance in advance when designing the system at the initial phase or adding more nodes for more capacity. Therefore, the performance modeling and prediction methodology for large-scale clusters is highly required. In this paper, we suggest a methodology to predict the performance of large-scale clusters, which consists of measurement, modeling and prediction steps. We apply the methodology to a real cluster server and show its usefulness.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.11-25
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• 2024

The proliferation of IoT devices has presented an unprecedented challenge in managing device identities securely and efficiently. In this paper, we introduce an innovative Hybrid Blockchain-Based Approach for IoT Identity Management that prioritizes both security and efficiency. Our hybrid solution, strategically combines the advantages of direct and indirect connections, yielding exceptional performance. This approach delivers reduced latency, optimized network utilization, and energy efficiency by leveraging local cluster interactions for routine tasks while resorting to indirect blockchain connections for critical processes. This paper presents a comprehensive solution to the complex challenges associated with IoT identity management. Our Hybrid Blockchain-Based Approach sets a new benchmark for secure and efficient identity management within IoT ecosystems, arising from the synergy between direct and indirect connections. This serves as a foundational framework for future endeavors, including optimization strategies, scalability enhancements, and the integration of advanced encryption methodologies. In conclusion, this paper underscores the importance of tailored strategies in shaping the future of IoT identity management through innovative blockchain integration.