• Proceedings of the Korea Information Processing Society Conference
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• 2003.05c
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• pp.1575-1578
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• 2003

이동통신 기술의 발달로 인하여 무선 이동 기기의 사용이 보편화되면서 LBS(Location Based System)의 요구가 나날이 증대되고 있다. 이와 같은 위치 기반 서비스에서 클라이언트인 이동체들은 일정한 보고 주기를 가지고 서버에 위치 데이터를 보고하게 되는데, 빈번한 보고 데이터를 실시간으로 처리하기 위해서 서버에서는 메인 메모리 DBMS를 유지하는 것이 필요하다. 기존에 제시된 메인 메모리 색인으로는 T-tree 가 있는데, 이는 1차원 데이터를 위한 것이므로 이동체 데이터베이스 환경에 적합하지 못하다. 그리고, 디스크 기반의 다차원 색인으로는 R-tree 계열이 있는데, 이는 메인 메모리에서 효율적인 사용을 보장하지 못한다. 이 논문에서는 이동체 데이터베이스 환경에 적합한 메인 메모리 색인을 고려함에 있어서, 기존의 디스크 기반의 다차원 색인으로 가장 널리 알려진 R-tree 계열의 색인을 메인 메모리에 적재 후 메인 메모리 환경에서 성능에 영향을 주는 요소를 실험을 통하여 제시한다. 실험은 메인 메모리에서는 간단한 알고리즘을 사용하는 것이 성능에 좋고, 삽입 시에는 삽입할 노드를 찾기 위해서 비교하는 엔트리의 수가, 검색 시에는 노드간의 중첩으로 인하여 비교하는 노드의 수와 엔트리의 수가 성능에 영향을 주는 요소임을 보여준다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.46
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• pp.33-49
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• 1998

In order to improve the performance of relational databases, one has to reduce the number of disk accesses necessary to transfer data from disk to main memory. The paper proposes to reduce the number of disk I/O accesses by vertically partitioning relation into fragments and allowing attribute replication to fragments if necessary. When zero-one integer programming model is solved by the branch-and-bound method, it requires much computing time to solve a large sized problem. Therefore, heuristic solutions using genetic algorithm(GA) are presented. GA in this paper adapts a few ideas which are different from traditional genetic algorithms, for examples, a rank-based sharing fitness function, elitism and so on. In order to improve performance of GA, a set of optimal parameter levels is determined by the experiment and makes use of it. As relations are vertically partitioned allowing attribute replications and saved in disk, an attribute replicating vertical partition method by GA can attain less access cost than non-attribute-replication one and require less computing time than the branch-and-bound method in large-sized problems. Also, it can acquire a good solution similar to the optimum solution in small-sized problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1590-1609
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• 2021

Laughter is one of the most important nonverbal sound that human generates. It is a means for expressing his emotions. The acoustic and contextual features of this specific sound are different from those of speech and many difficulties arise during their modeling process. During this work, we propose an audio laughter generation system based on unsupervised generative models: the autoencoder (AE) and its variants. This procedure is the association of three main sub-process, (1) the analysis which consist of extracting the log magnitude spectrogram from the laughter database, (2) the generative models training, (3) the synthesis stage which incorporate the involvement of an intermediate mechanism: the vocoder. To improve the synthesis quality, we suggest two hybrid models (LSTM-VAE, GRU-VAE and CNN-VAE) that combine the representation learning capacity of variational autoencoder (VAE) with the temporal modelling ability of a long short-term memory RNN (LSTM) and the CNN ability to learn invariant features. To figure out the performance of our proposed audio laughter generation process, objective evaluation (RMSE) and a perceptual audio quality test (listening test) were conducted. According to these evaluation metrics, we can show that the GRU-VAE outperforms the other VAE models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.689-698
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• 2005

Various systems are used to provide the location based services. But, the existing systems have some problems which have difficulties in dealing with faster services for above million people. In order to solve it, a multi-level DBMS which supports both fast data processing and large data management support should be used. The multi-level DBMS with snapshots has all the data existing in disk database and the data which are required to be processed for fast processing are managed in main memory database as snapshots. To optimize performance of this system for location based services, the query classification component which classifies the queries for efficient snapshot usage is needed. In this paper, the query classification component in multi-level DBMS for location based services is designed and implemented. The proposed component classifies queries into three types: (1) memory query, (2) disk query, (3) hybrid query, and increases the rate of snapshot usage. In addition, it applies division mechanisms which divide aspatial and spatial filter condition for partial snapshot usage. Hence, the proposed component enhances system performance by maximizing the usage of snapshot as a result of the efficient query classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1113-1120
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• 2017

As the size of the data is getting larger and larger due to improvement of the telecommunication techniques, it would be main issues to develop and process the database. The bloom filter used to lookup a particular element under the given set is very useful structure because of the space efficiency. In this paper, we analyse the main factor of the false positive and propose the new parallel type bloom filter in order to minimize the false positive which is caused by other hash functions. The proposed method uses the memory as large as the conventional bloom filter use, but it can improve the processing speed using parallel processing. In addition, if we use the perfect hash function, the insertion and deletion function in the proposed bloom filter would be possible.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.7B
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• pp.1313-1321
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• 1999

The two-phase locking protocol(2PL) is the most widely-used concurrency control mechanism for guaranteeing logical consistency of data in a database environment where a number of transactions perform concurrently. The problem inherent in the 2PL protocol is a deadlock, where a set of transactions holding some locks indefinitely wait an additional lock that is already held by other transactions in the set. The deadlock detector is a DBMS sub-component that examines periodically whether a system is in a deadlock state based on lock waiting information of transactions. The deadlock detector and transactions execute concurrently in a DBMS and read and/or write the lock waiting information simultaneously. Since the lock waiting information is a shared one, we need an efficient method guaranteeing its physical consistency by using mutual exclusion. The efficiency of the mutual exclusion method is crucial especially in a main memory DBMS with high performance since it seriously affects the performance of an entire system. In this paper, we propose a new method that effectively guarantees physical consistency of lock waiting information. Two primary goals of our method are to minimize the processing overhead and to maximize system concurrency.

• The Transactions of the Korea Information Processing Society
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• v.4 no.4
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• pp.895-907
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• 1997

The mining of assciation rules disovers the trndency of events ocuring simultaneously in large databases. Previous announced research on association rules deals with associations with associations with respect to the whole transaction. However, xome association rules could have very high confidence in a sub-range of the time domain, even though they do not have quite high confidence in the whole time domain. Such kind of association rules are ecpected to be very usdful in various decion making problems.In this paper, we define transient association rule, as an association with high cimfidence worthy of special attention in a partial time interval, and propose an dfficeint algorithm wich finds out the time intervals appropriate to transient association rules from large-databases.We propose the data-driven retrival method excluding unecessary interval search, and design an effective data structure manageable in main memory obtined by one scanning of database, which offers the necessary information to next retrieval phase. In addition, our simulation shows that the suggested algorithm has reliable performance at the time cost acceptable in application areas.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.625-632
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• 2001

The electronic stores have realized that they need to understand their customers and to quickly response their wants and needs. To be successful in increasingly competitive Internet marketplace, recommender systems are adapting data mining techniques. One of most successful recommender technologies is collaborative filtering (CF) algorithm which recommends products to a target customer based on the information of other customers and employ statistical techniques to find a set of customers known as neighbors. However, the application of the systems, however, is not very suitable for seasonal products which are sensitive to time or season such as refrigerator or seasonal clothes. In this paper, we propose a new adjusted item-based recommendation generation algorithms called the exponentially weighted collaborative filtering recommendation (EWCFR) one that computes item-item similarities regarding seasonal products. Finally, we suggest the recommendation system with relatively high quality computing time on main memory database (MMDB) in XML since the collaborative filtering systems are needed that can quickly produce high quality recommendations with very large-scale problems.

• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.639-648
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• 1997

As the areas of computer application are expanded, the real-time applicition enviroments that must process as many transactions as possible within their deadlines have been increased recently. Conventional disk based databaes system is not appropriate in real-time transaction processing due to delying time for disk I/O processing. When the system is overloaede, the performance of transaction scheduling technique using earliest deadline first deteriorates rapidly because it can assign the highest priority ot a transaction that has already missed or is about to miss tis deadline. Therfore, the performance of suggested transaction secheduling technique is made to improved by propos-ing the doule locking mechanism based on priority. Finally, in order to evaluate the performance of the proposed priority-based double locking techniques under single proessor and main memory database system environments, the simulation model was developed using the SLAM II language.