• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.720-726
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• 2009

There are coexisting various kinds of data in the large scale database system, the maintenance problem of freshness of data is emerging important issue that provide correctness and usefulness information to users. Most solution of this problem depends on how execute effectively required refreshing query within timely time. In this paper, we propose the refreshing scheduling algorithm to retain the freshness of data and fairness of starvation of requested refresh queries. Our algorithm recompute a rate of goal refreshing a every period to assign execution time of requested refreshing query so that we can keep the freshness and fairness of data by using proposed algorithm. We implement the web sites to showing the results of refreshing process of dynamic and semi-dynamic and static data.

• Journal of KIISE:Databases
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• v.37 no.2
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• pp.121-126
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• 2010

This paper proposes an efficient index scheduling technique for kNN query processing in multiple wireless broadcast channel environment. Previous works have to wait for the next cycle if the required child nodes of the same parent node are allocated in the same time slot on multiple channel. Our proposed method computes the access frequencies of each node of R tree at the server before the generation of the R-tree index broadcast schedule. If they have high frequencies, we allocate them serially on the single channel. If they have low frequencies, we allocate them in parallel on the multiple channels. As a result, we can reduce the index node access conflicts and the long broadcast cycle. The performance evaluation shows that our scheme gives the better performance than the existing schemes.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.10
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• pp.239-244
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• 2019

In recent mobile environments, query processing costs have been rapidly increasing as users request large amounts of queries. In addition, the server's performance is increasing for many users to handle high-capacity queries, but the workload is increasing continuously. To solve these problems, we use the wireless broadcasting environment. However, in a existing wireless broadcasting environment, servers have a problem sending all the objects they manage to their clients. Therefore, we propose a new R-Bcast combining the advantages of demand-based and wireless broadcasting. R-Bcast is a technique that protects query information and reduces query processing time. Experiments have proved that R-Bcast is superior to conventional techniques.

• Journal of Computing Science and Engineering
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• v.7 no.4
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• pp.220-230
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• 2013

In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time. The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control.

• Journal of the Korea Society of Computer and Information
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• v.10 no.1 s.33
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• pp.113-124
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• 2005

The work of investigation in the laboratory medicine includes various kinds of investigations and microbes and it is too complicated to draw needed results in time. So, we aim to improve work performance of the laboratory medicine. For this study, we implemented the scheduling system in microbe investigation using agent environment and the workflow management system to manage the schedule of investigation, and the query system to check the schedule. And preliminary report and final report of microbe investigation can be announced automatically using agent. The scheduling system implemented could identify the lack or waste of resources and thus enable efficient management and distribution of resources. The query system could check the schedule and retrieve the Processing status in short time, enabled the automated report, and reduced possible interrupts and the delay of work that can be occurred in confirming process. It also enables users to access from local and remote sites. Also, this system can reduce the conflicts among People that may occur in unexpected situations because it enables doctors to confirm those situations such as the malfunction of instrument and the lack of agar or reagent, and the efficiency of work process can be expected.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1035-1043
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• 2021

An Internet of Things (IOT) sensor network is an effective solution for monitoring environmental conditions. However, IOT sensor networks generate massive data such that the abilities of massive data storage, processing, and query become technical challenges. To solve the problem, a Hadoop cloud platform is proposed. Using the time and workload genetic algorithm (TWLGA), the data processing platform enables the work of one node to be shared with other nodes, which not only raises efficiency of one single node but also provides the compatibility support to reduce the possible risk of software and hardware. In this experiment, a Hadoop cluster platform with TWLGA scheduling algorithm is developed, and the performance of the platform is tested. The results show that the Hadoop cloud platform is suitable for big data processing requirements of IOT sensor networks.

• Journal of KIISE:Databases
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• v.37 no.4
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• pp.203-208
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• 2010

This paper deals with an efficient index scheduling technique based on Hilbert curve and MBR for k-NN query in a single wireless broadcast channel environment. Previous works have two major problems. One is that they need a long time to process queries due to the back-tracking problem. The other is that they have to download too many spatial data since they can not reduce search space rapidly. Our proposed method broadcasts spatial data based on Hilbert curve order where a distributed index table is also broadcast with each spatial data. Each entry of index table represents the MBR which groups spatial data. By predicting the unknown location of spatial data, our proposed index scheme allows mobile clients to remove unnecessary data and to reduce search space rapidly. As a result, our method gives the decreased tuning time and access latency.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.605-612
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• 2001

In order to solve the overload of academic affairs management system, we propose a method builds a distributed Replication server and uses this server with the present centralized system. Normal query transactions which are not required for data modification are composed of almost all DML sentences. So we construct the distributed replication servers according to the data characteristics and make them perform the query transaction without modification. In this way, we can simultaneously distribute users and data, and cut down processing time for every transaction. Also Replication server has the advantages of implemental efficiency and economical because it uses resources of present centralized system without and additional configurations. Usually, to distribute the overload of server, they can use way, Client-side overload distribution that user program get present overload status then can choose a less overloaded server, and the other way, Server-side overload distribution that make use of Application Layer Scheduling Technique and IP Layer Scheduling Technique. Our Replication server can reduce the overload of centralized system by eliminating or complementing those defects of overload distribution, referred to in the forehead.

• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.22-32
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• 2008

To overcome the weakness of the image retrieval system using the existing Ontology and the distributed image based on the database having a simple structure, HERMES was suggested to ensure the self-control of various image suppliers and support the image retrieval based on semantic, the mentioned framework could not solve the problems which are not considered the deterioration in the capacity and scalability when many users connect to broker server simultaneously. In this paper the tables are written which in the case numerous users connect at the same time to the supply analogous level of services without the deterioration in the capacity installs Broker servers and then measures the performance time of each inner Broker Component through Monitoring System and saved and decides the ranking in saved data. As many Query performances are dispersed into several Servers User inputted from the users Interface with reference to Broker Ranking Table, Load Balancing system improving reliability in capacity is proposed. Through the experiment, the scheduling technique has proved that this schedule is faster than existing techniques.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.163-173
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• 2016

Recently, massive amounts of stream data have been studied for distributed processing. In this paper, we propose an incremental stream data processing method based on in-memory in big data environments. The proposed method stores input data in a temporary queue and compare them with data in a master node. If the data is in the master node, the proposed method reuses the previous processing results located in the node chosen by the master node. If there are no previous results of data in the node, the proposed method processes the data and stores the result in a separate node. We also propose a job scheduling technique considering the load and performance of a node. In order to show the superiority of the proposed method, we compare it with the existing method in terms of query processing time. Our experimental results show that our method outperforms the existing method in terms of query processing time.