• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.91-107
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• 2006

To handling the extreme situation that must manage positional information of a very large volume, at least millions of moving objects. A cluster-based sealable distributed computing system architecture, called the GALIS which consists of multiple data processors, each dedicated to keeping records relevant to a different geographical zone and a different time zone, was proposed. In this paper, we proposed a valid time management and time-zone shifting scheme, which are essential in realizing the long-term location data subsystem of GALIS, but missed in our previous prototype development. We explain how to manage valid time of moving objects to avoid ambiguity of location information. We also describe time-zone shifting algorithm with three variations, such as Real Time-Time Zone Shifting, Batch-Time Zone Shifting, Table Partitioned Batch-Time Zone Shifting, Through experiments related with query processing time and CPU utilization, we show the efficiency of the proposed time-zone shifting schemes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.2
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• pp.52-62
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• 2013

Design Hourly Factor(DHF) is defined as the ratio of design hourly volume(DHV) to Average Annual Daily Traffic(AADT). Generally DHV used the 30th rank hourly volume. But this case DHV is affected by holiday volumes so the road is at risk for overdesigning. Computing K factor is available for counting 8,760 hour traffic volume, but it is impossible except permanent traffic counts. This study applied three method to make DHF, using 30th rank hourly volume to make DHF(method 1), using peak hour volume to make DHF(method 2). Another way to make DHF, rank hourly volumes ordered descending connect a curve smoothly to find the point which changes drastic(method 3). That point is design hour, thus design hourly factor is able to be computed. In addition road classified 3 type for national highway using factor analysis and cluster analysis, so we can analyze the characteristic of DHF by road type. DHF which was used method 1 is the largest at any other method. There is no difference in DHF by road type at method 2. This result shows for this reason because peak hour is hard to describe the characteristic of hourly volume change. DHF which was used method 3 is similar to HCM except recreation road but 118th rank hourly volume is appropriate.

• The Journal of the Korea Contents Association
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• v.13 no.2
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• pp.211-219
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• 2013

The analysis method using Big Data has evolved based on the Big data Management Technology. There are quite a few researching institutions anticipating new era in data analysis using Big Data and IT vendors has been sided with them launching standardized technologies for Big Data management technologies. Big Data is also affected by improvements of IT gadgets IT environment. Foreran by social media, analyzing method of unstructured data is being developed focusing on diversity of analyzing method, anticipation and optimization. In the past, data analyzing methods were confined to the optimization of structured data through data mining, OLAP, statics analysis. This data analysis was solely used for decision making for Chief Officers. In the new era of data analysis, however, are evolutions in various aspects of technologies; the diversity in analyzing method using new paradigm and the new data analysis experts and so forth. In addition, new patterns of data analysis will be found with the development of high performance computing environment and Big Data management techniques. Accordingly, this paper is dedicated to define the possible analyzing method of social media using Big Data. this paper is proposed practical use analysis for social media analysis through data mining analysis methodology.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.101-108
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• 2015

This paper presents the implementation of a system that recommends empirical best routes based on classification of large trajectory data. As many location-based services are used, we expect the amount of location and trajectory data to become big data. Then, we believe we can extract the best empirical routes from the large trajectory repositories. Large trajectory data is clustered into similar route groups using Hadoop MapReduce framework. Clustered route groups are stored and managed by a DBMS, and thus it supports rapid response to the end-users' request. We aim to find the best routes based on collected real data, not the ideal shortest path on maps. We have implemented 1) an Android application that collects trajectories from users, 2) Apache Hadoop MapReduce program that can cluster large trajectory data, 3) a service application to query start-destination from a web server and to display the recommended routes on mobile phones. We validated our approach using real data we collected for five days and have compared the results with commercial navigation systems. Experimental results show that the empirical best route is better than routes recommended by commercial navigation systems.

• Journal of Internet Computing and Services
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• v.12 no.1
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• pp.99-110
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• 2011

Mobile environments rapidly changing and digital convergence widely employed, mobile devices including smart phones have been playing a critical role that changes users' lifestyle in the areas of entertainments, businesses and information services. The various services using mobile devices are developing to meet the personal needs of users in the mobile environments. Especially, an LBS (Location-Based Service) is combined with other services and contents such as augmented reality, mobile SNS (Social Network Service), games, and searching, which can provide convenient and useful services to mobile users. In this paper, we design and implement the prototype of mobile personal assistant (PA) agents. Our personal assistant agent helps users do some tasks by hiding the complexity of difficult tasks, performing tasks on behalf of the users, and reflecting the preferences of users. To identify user's preferences and provide personalized services, clustering and classification algorithms of data mining are applied. The clusters of the log data using clustering algorithms are made by measuring the dissimilarity between two objects based on usage patterns. The classification algorithms produce user profiles within each cluster, which make it possible for PA agents to provide users with personalized services and contents. In the experiment, we measured the classification accuracy of user model clustered using clustering algorithms. It turned out that the classification accuracy using our method was increased by 17.42%, compared with that using other clustering algorithms.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.415-426
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• 2005

Wireless internet, which becomes a hot social issue, has limitations due to the following characteristics, as different from wired internet. It has low bandwidth, frequent disconnection, low computing power, and small screen in user terminal. Also, it has technical issues to Improve in terms of user mobility, network protocol, security, and etc. Wireless internet server should be scalable to handle a large scale traffic due to rapidly growing users. In this paper, wireless internet proxy server clusters are used for the wireless Internet because their caching, distillation, and clustering functions are helpful to overcome the above limitations and needs. TranSend was proposed as a clustering based wireless internet proxy server but it has disadvantages; 1) its scalability is difficult to achieve because there is no systematic way to do it and 2) its structure is complex because of the inefficient communication structure among modules. In our former research, we proposed the All-in-one structure which can be scalable in a systematic way but it also has disadvantages; 1) data sharing among cache servers is not allowed and 2) its communication structure among modules is complex. In this paper, we proposed its improved scheme which has an efficient communication structure among modules and allows data to be shared among cache servers. We performed experiments using 16 PCs and experimental results show 54.86$\%$ and 4.70$\%$ performance improvement of the proposed system compared to TranSend and All-in-one system respectively Due to data sharing amount cache servers, the proposed scheme has an advantage of keeping a fixed size of the total cache memory regardless of cache server numbers. On the contrary, in All-in-one, the total cache memory size increases proportional to the number of cache servers since each cache server should keep all cache data, respectively.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.105-112
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• 2017

PCI Express is a bus technology that connects the processor and the peripheral I/O devices that widely used as an industry standard because it has the characteristics of high-speed, low power. In addition, PCI Express is system interconnect technology such as Ethernet and Infiniband used in high-performance computing and computer cluster. PGAS(partitioned global address space) programming model is often used to implement the one-sided RDMA(remote direct memory access) from multi-host systems, such as computer clusters. In this paper, we design and implement a OpenSHMEM API based on PCI Express maintaining the existing features of OpenSHMEM to implement RDMA based on PCI Express. We perform experiment with implemented OpenSHMEM API through a matrix multiplication example from system which PCs connected with NTB(non-transparent bridge) technology of PCI Express. The PCI Express interconnection network is currently very expensive and is not yet widely available to the general public. Nevertheless, we actually implemented and evaluated a PCI Express based interconnection network on the RDK evaluation board. In addition, we have implemented the OpenSHMEM software stack, which is of great interest recently.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.2
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• pp.27-37
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• 2009

Sensor nodes forming a sensor network have limited energy capacity such as small batteries and when these nodes are placed in a specific field, it is important to research minimizing sensor nodes' energy consumption because of difficulty in supplying additional energy for the sensor nodes. Clustering has been in the limelight as one of efficient techniques to reduce sensor nodes' energy consumption in sensor networks. However, energy saving results can vary greatly depending on election of cluster heads, the number and size of clusters and the distance among the sensor nodes. /This research has an aim to find the optimal set of clusters which can reduce sensor nodes' energy consumption. We use a Genetic Algorithm(GA), a stochastic search technique used in computing, to find optimal solutions. GA performs searching through evolution processes to find optimal clusters in terms of energy efficiency. Our results show that GA is more efficient than LEACH which is a clustering algorithm without evolution processes. The two-dimensional GA (2D-GA) proposed in this research can perform more efficient gene evolution than one-dimensional GA(1D-GA)by giving unique location information to each node existing in chromosomes. As a result, the 2D-GA can find rapidly and effectively optimal clusters to maximize lifetime of the sensor networks.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.580-586
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• 2017

The establishment of big data service environment requires both cloud-based network technology and clustering technology to improve the efficiency of information access. These cloud-based networks and clustering environments can provide variety of valuable information in real-time, which can be an intensive target of attackers attempting illegal access. In particular, attackers attempting IP spoofing can analyze information of mutual trust hosts constituting clustering, and attempt to attack directly to system existing in the cluster. Therefore, it is necessary to detect and respond to illegal attacks quickly, and it is demanded that the security policy is stronger than the security system that is constructed and operated in the existing single system. In this paper, we investigate routing pattern changes and use them as detection information to enable active correspondence and efficient information service in illegal attacks at this network environment. In addition, through the step-by -step encryption based on the routing information generated during the detection process, it is possible to manage the stable service information without frequent disconnection of the information service for resetting.

• Journal of KIISE
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• v.43 no.1
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• pp.87-95
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• 2016

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.