• Journal of Digital Convergence
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• v.12 no.9
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• pp.201-207
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• 2014

In the cloud environment, the company needs data integration and analysis to make decision and policy. If new system is added to this environment, a lot of time and cost is needed due to disparate properties among systems when data is integrated. Therefore, in this paper, we propose a DBaaS hub system for multi-database service. The DBaaS may require a different database and need data integration for relevant service. Using the ontology, we propose a metadata query to resolve the interoperability issues between data of DBaaS. The meta-query is not a query to access the real data, but the query for the upper level. This method provides data integration by accessing the data with the converted query through an ontology when we access the actual database.We also constructs a document-oriented database system using the metadata.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.16-22
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• 2016

The database integration is being emphasized to one way of the companies collaboration. To database integration, companies are use like one database what their own, it can be provided more efficient service to customer. However, there exist some difficulty to database integration. that is the database security and database heterogeneity problems. In this paper, we proposed the MapReduce based p2p DBaaS hub system to solve database heterogeneity problem. The proposed system provides an environment for companies in the P2P cloud to integrate a database of each other. The proposed system uses DBaaS Hub for a collection of data in the P2P cloud, and use MapReduce for integrating the collected data.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.97-102
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• 2019

As the number of "Mobility Handicapped" increases, the incidence of "Mobility Handicapped" traffic accidents is also increasing. In order to reduce the incidence of traffic accidents in the "Mobility Handicapped", a service providing system for "Mobility Handicapped" is required. Since these services have different data formats, data heterogeneity occurs. Therefore, the system should resolve the data heterogeneity by mapping the format of the data. In this paper, we design DBaaS as a mobility handicapped system for data interoperability. This system provides a service to extend the flashing time of the traffic lights according to the condition of "Mobility Handicapped" on the occurrence of a fall or a crosswalk in a crosswalk where there is a risk of a traffic accident. These services can reduce the incidence of traffic accidents in "Mobility Handicapped".

• International journal of advanced smart convergence
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• v.4 no.1
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• pp.88-92
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• 2015

Today, the medical information system has evolved in the way of integrated healthcare IT information systems. Therefore, it is trying to build advanced U-Healthcare service. Though the U-Healthcare environments is exchanged the information between systems in many cases, however since the each system is different, the integration and exchange of data is difficult. To overcome this problem, in this paper it proposes that we suggests a possible DBaaS(DataBase as a Service) for the heterogeneous integration of medical information management and data exchange. First, the proposed system builds DBaaS cloud by integrating the meta-DB Schema level and DB Schema for each hospital. And, the mapping the schema data and the existing hospital information system is possible using the International Standard HL7. By applying the proposed method to the hospital system, it comes true the efficient exchange of information between the patients, doctors, staffs through the data mapping of the one to multi-system.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.59-66
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• 2017

Cloud computing has evolved into a core computing infrastructure for the internet that encompasses content, as well as communications, applications and commerce. By providing powerful computing and communications capabilities in the palm of the hand everywhere with a variety of smart devices, mobile applications such as virtual reality, sensing and navigation have emerged and radically changed the patterns people live. The data that is generated is getting bigger. Cloud computing, on the other hand, has problems with system load and speed due to the collection, processing and control of remote data. To solve this problem, fog computing has been proposed in which data is collected and processed at an edge. In this paper, we propose a system that dynamically selects a fog server that acts as a cloud in the edge. It serves as a mediator in the cloud, and provides information on the services and systems belonging to the cloud to the mobile device so that the mobile device can act as a fog. When the role of the fog system is complete, we provide it to the cloud to virtualize the fog. The heterogeneous problem of data of mobile nodes can be solved by using mDBaaS (Mobile DataBase as a Service) and we propose a system design method for this.

• International journal of advanced smart convergence
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• v.5 no.2
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• pp.59-65
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• 2016

With the recent growth in cloud computing, big data processing and collaboration between businesses are emerging as new paradigms in the IT industry. In an environment where a large amount of data is generated in real time, such as SNS, big data processing techniques are useful in extracting the valid data. MapReduce is a good example of such a programming model used in big data extraction. With the growing collaboration between companies, problems of duplication and heterogeneity among data due to the integration of old and new information storage systems have arisen. These problems arise because of the differences in existing databases across the various companies. However, these problems can be negated by implementing the MapReduce technique. This paper proposes a collaboration system based on Database as a Service, or DBaaS, to solve problems in data integration for collaboration between companies. The proposed system can reduce the overhead in data integration, while being applied to structured and unstructured data.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.83-90
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• 2016

The emergence of cloud has been able to provide a variety of IT service to the user. As organizations and companies are increased that provide these cloud service, many problems arises on integration. However, with the advent of latest technologies such as big data, document-oriented database, and MapReduce, this problem can be easily solved. This paper is intended to design the Cloud Aggregator to provide them as a service to collect information of the cloud system providing each service. To do this, we use the DBaaS(DataBase as a Service) and MapReduce techniques. This makes it possible to maintain the functionality of existing system and correct the problem that may occur depending on the combination.

• International journal of advanced smart convergence
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• v.6 no.3
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• pp.29-37
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• 2017

The current behavior recognition system don't match data formats between sensor data measured by user's sensor module or device. Therefore, it is necessary to support data processing, sharing and collaboration services between users and behavior recognition system in order to process sensor data of a large capacity, which is another formats. It is also necessary for real time interaction with users and behavior recognition system. To solve this problem, we propose fog cloud based behavior recognition system for human body sensor data processing. Fog cloud based behavior recognition system solve data standard formats in DbaaS (Database as a System) cloud by servicing fog cloud to solve heterogeneity of sensor data measured in user's sensor module or device. In addition, by placing fog cloud between users and cloud, proximity between users and servers is increased, allowing for real time interaction. Based on this, we propose behavior recognition system for user's behavior recognition and service to observers in collaborative environment. Based on the proposed system, it solves the problem of servers overload due to large sensor data and the inability of real time interaction due to non-proximity between users and servers. This shows the process of delivering behavior recognition services that are consistent and capable of real time interaction.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.1-7
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• 2016

Recently, introduced a cloud computing technology to the IT industry, smart phones, it has become possible connection between mobility terminal such as a tablet PC. For dissemination and popularization of movable wireless terminal, the same operation have focused on a viable mobile cloud in various terminal. Also, it evolved Wireless Sensor Network(WSN) technology, utilizing a Body Sensor Network(BSN), which research is underway to build large Ubiquitous Sensor Network(USN). BSN is based on large-scale sensor networks, it integrates the state information of the patient's body, it has been the need to build a managed system. Also, by transferring the acquired sensor information to HIS(Hospital Information System), there is a need to frequently monitor the condition of the patient. Therefore, In this paper, possible sensor information exchange between terminals in a mobile cloud environment, by integrating the data obtained by the body sensor HIS and interoperable data DBaaS (DataBase as a Service) it will provide a base of mBodyCloud System. Therefore, to provide an integrated protocol to include the sensor data to a standard HL7(Health Level7) medical information data.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.120-126
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• 2020

Studies are being conducted regarding falling recognition using sensors on smartphonesto recognize falling in human daily life. These studies use a number of sensors, mostly acceleration sensors, gyro sensors, motion sensors, etc. Falling recognition system processes the values of sensor data by using a falling recognition algorithm and classifies behavior based on thresholds. If the threshold is ambiguous, the accuracy will be reduced. To solve this problem, Deep learning was introduced in the behavioral recognition system. Deep learning is a kind of machine learning technique that computers process and categorize input data rather than processing it by man-made algorithms. Thus, in this paper, we propose a falling recognition application system using deep learning based on smartphones. The proposed system is powered by apps on smartphones. It also consists of three layers and uses DataBase as a Service (DBaaS) to handle big data and address data heterogeneity. The proposed system uses deep learning to recognize the user's behavior, it can expect higher accuracy compared to the system in the general rule base.