• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1446-1465
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• 2022

Diversified SaaS applications allow users more choices to use, according to their own preferences. However, the diversification of SaaS applications also makes it impossible for users to choose the best one. Furthermore, users can't take advantage of the functionality between SaaS applications. In this paper, we propose a platform that provides an SaaS mashup service, by extracting interoperable service functions from SaaS-based applications that independent vendors deploy and supporting a customized service recommendation function through log data binding in the cloud environment. The proposed SaaS mashup service platform consists of a SaaS aggregation framework and a log data binding framework. Each framework was concreted by using Apache Kafka and rule matrix-based recommendation techniques. We present the theoretical basis of implementing the high-performance message-processing function using Kafka. The SaaS mashup service platform, which provides a new type of mashup service by linking SaaS functions based on the above technology described, allows users to combine the required service functions freely and access the results of a rich service-utilization experience, using the SaaS mashup function. The platform developed through SaaS mashup service technology research will enable various flexible SaaS services, expected to contribute to the development of the smart-contents industry and the open market.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.171-178
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• 2017

Data mashup is a web technology that combines information from multiple sources into a single web application. Mashup applications create a new horizon for new services, like environmental monitoring. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations utilize a data mashup to merge datasets from different Internet of multimedia things (IoMT) context-based services in order to leverage its data analytics performance and the accuracy of the predictions. However, mashup different datasets from multiple sources is a privacy hazard as it might reveal citizens specific behaviors in different regions. The ability to preserve privacy in mashuped datasets and at the same time provide accurate insights becomes a key success for the spread of mashup services. In this paper, we present our efforts to build a fog-based middleware for private data mashup (FMPM) to serve a centralized environmental monitoring service. The proposed middleware is equipped with concealment mechanisms to preserve the privacy of the merged datasets from multiple IoMT networks involved in the mashup application. Also, these mechanisms preserve the aggregates in the dataset to maximize the usability of information to attain accurate analytical results. We also provide a scenario for IoMT-enabled data mashup service and experimentation results.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.4
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• pp.65-71
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• 2009

Web Contents have been evolved from statics, dynamics contents to Mashup service. In recent years, Mashup which can creates new contents by mixing different services is topic on the web service. But, Mashup is only concentrate on making new contents to provide for client. In this paper, we propose the policy of composing services and client-friendly UI.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.45-53
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• 2014

UI-Mashup is widely used as a service method to add value, which is composed of distributed various contents on the internet and has turned out to be one of the latest trends in web application program development. Previous UI-Mashup-related studies have focused primarily on the dynamic service composition and have not been able to adapt to a rapidly changing Web Standard, thusthe end users conclude that UI-Mashups are slow, incompatible and poor security services. In this study, We propose an architecture for the performance improvements of UI-Mashup.In order to provide fast services and security enhancements, the proposed architecture collects UI fragments on the server in parallel, and sends layouts and contents of Mashups UI to the client through a special delivery channel supporting fast reaction and response time. In this study, the implementation and performance tests were proceeded to verify the proposed architecture experimentally. As a result of the performance testing, the proposed architecture has two to three times faster response time and more than four times throughput compared to the previous UI-Mashup technology.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.2
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• pp.79-85
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• 2009

This research study and design, implement the digital multi-media description technique of Mashup Web which will be evolved into visible visual structure in form of cooperation studying. This technique is study user connect location information and service using pattern, then this information description technique of Mashup pre-operate inference data by tracking user service data.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.163-170
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• 2017

Internet of things (IoT) is a new paradigm for collecting, processing and analyzing various contents in order to detect anomalies and to monitor particular patterns in a specific environment. The collected data can be used to discover new patterns and to offer new insights. IoT-enabled data mashup is a new technology to combine various types of information from multiple sources into a single web service. Mashup services create a new horizon for different applications. Environmental monitoring is a serious tool for the state and private organizations, which are located in regions with environmental hazards and seek to gain insights to detect hazards and locate them clearly. These organizations may utilize IoT - enabled data mashup service to merge different types of datasets from different IoT sensor networks in order to leverage their data analytics performance and the accuracy of the predictions. This paper presents an IoT - enabled data mashup service, where the multimedia data is collected from the various IoT platforms, then fed into an environmental cognition service which executes different image processing techniques such as noise removal, segmentation, and feature extraction, in order to detect interesting patterns in hazardous areas. The noise present in the captured images is eliminated with the help of a noise removal and background subtraction processes. Markov based approach was utilized to segment the possible regions of interest. The viable features within each region were extracted using a multiresolution wavelet transform, then fed into a discriminative classifier to extract various patterns. Experimental results have shown an accurate detection performance and adequate processing time for the proposed approach. We also provide a data mashup scenario for an IoT-enabled environmental hazard detection service and experimentation results.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.1
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• pp.89-98
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• 2009

The existing mashup service providers have induced users to participate in their own made services and have grown their services by user participations and contributing contents. Recent services based on Web2.0 are called by 'Open (Content) Service' which means anyone can provide new services to the others without any of their own contents. But unfortunately the most of these days services are not opened. They just using mashups for increasing their service's traffics. We introduce the way to make a simple service to provide searching videos present it with extracted keyword from external content without our own contents at all. this show that how to combine the content with another contents or services.

• Journal of KIISE
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• v.44 no.9
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• pp.908-917
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• 2017

As web service technologies are widely used, there have been many efforts to develop approaches for recommending appropriate web services to users in complex and dynamic service environments. In addition, for the effective development of service mashups, service recommender systems that are specialized for service composition have been developed. However, existing service recommender systems for service mashups are not effective at recommending services in a personalized manner that reflect developers' preferences. To deal with this issue, we propose an approach that recommends services based on the similarities between mashup developers who have developed similar service mashups. The proposed approach is then evaluated by using the mashup data retrieved from ProgrammableWeb. The evaluation results clearly show that the proposed approach is an effective way of improving service recommendations compared to the traditional user-based collaborative filtering algorithm.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.4
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• pp.745-754
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• 2019

As GeoWeb 2.0 technologies are widely used, various kinds of services that mashup spatial data and user data are being developed. In particular, various spatial information platforms such as Google Maps, OpenStreetMap, Daum Map, Naver Map, olleh Map, and VWorld based on GeoWeb 2.0 technologies support mashup service. The mashup service which is supported through the Open APIs of the platforms, provides various kinds of spatial data such as 2D map, 3D map, and aerial image. Also, application fields using the mashup service are greatly expanded. Recently, as user data for mashup have been greatly increased, there was a problem in mashup performance. However, the research on the mashup performance improvement is currently insufficient, even the research on the mashup performance comparison of various platforms has not been performed. In this paper, we perform comparative analysis of the mashup performance for large amounts of user data and spatial data using various spatial information platforms available in Korea. Specifically, we propose two performance analysis indexes of mashup time and user interaction time in order to analyze the mashup performance efficiently. Also, we implement a system for the performance analysis. Finally, from the performance analysis result, we propose a spatial information platform that can be efficiently applied to cases when user data increases greatly and user interaction occurs frequently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.10
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• pp.2292-2298
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• 2010

A mashup is a web page or application that uses and combines information, contents or Open APIs available on the web to create a new service. The mashup developed by the need not combination of simple information can contribute to new added-values with practicality and convenience. Thus, in this paper, we want to develop a RIA-based dynamic mashup service for the users considering weather forecast and marine information importantly. We design and implement the system that it can register a number of information about a domain dynamically through registration process based on the map and present a mark of domain location on the map and the information including internal environment, external environment and weather of related to the domain within a webpage. Implemented service need not require a tedious process visiting other web sites every time to confirm the relevant information because we can see simultaneously related information with a map within a page.