• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1259-1276
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• 2017

As technologies related to sensor network are currently emerging and the use of GeoSensor is increasing along with the development of Internet of Things (IoT) technology, spatial query processing systems to efficiently process spatial sensor data are being actively studied. However, existing spatial query processing systems do not support a spatial-temporal data type and a spatial-temporal operator for processing spatialtemporal sensor data. Therefore, they are inadequate for processing spatial-temporal sensor data like GeoSensor. Accordingly, this paper developed a spatial-temporal query processing system, for efficient spatial-temporal query processing of spatial-temporal sensor data in a sensor network. Lastly, this paper verified the utility of System through a scenario, and proved that this system's performance is better than existing systems through performance assessment of performance time and memory usage.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.239-250
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• 2019

An individual's health data is very sensitive and private. Such data are usually stored on a private or community owned cloud, where access is not restricted to the owners of that cloud. Anyone within the cloud can access this data. This data may not be read only and multiple parties can make to it. Thus, any unauthorized modification of health-related data will lead to incorrect diagnosis and mistreatment. However, we cannot restrict semipublic access to this data. Existing security mechanisms in e-health systems are competent in dealing with the issues associated with these systems but only up to a certain extent. The indigenous technologies need to be complemented with current and future technologies. We have put forward a method to complement such technologies by incorporating the concept of blockchain to ensure the integrity of data as well as its provenance.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2804-2807
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• 2011

As computer-based train control system became used widely, reliability and safety assessment of the computer is getting more important. A fault on a computer can cause a malfunction of train control system, and this can lead an accident. So where reliability and safety is highly required TMR is used. In this paper, the method of data sharing and voting for TMR processing is proposed, designed and verified.

• Journal of Computing Science and Engineering
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• v.5 no.2
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• pp.111-120
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• 2011

Data mining algorithms should exploit new hardware technologies to accelerate computations. Such goal is difficult to achieve in database management system (DBMS) due to its complex internal subsystems and because data mining numeric computations of large data sets are difficult to optimize. This paper explores taking advantage of existing multithreaded capabilities of multicore CPUs as well as caching in RAM memory to efficiently compute summaries of a large data set, a fundamental data mining problem. We introduce parallel algorithms working on multiple threads, which overcome the row aggregation processing bottleneck of accessing secondary storage, while maintaining linear time complexity with respect to data set size. Our proposal is based on a combination of table scans and parallel multithreaded processing among multiple cores in the CPU. We introduce several database-style and hardware-level optimizations: caching row blocks of the input table, managing available RAM memory, interleaving I/O and CPU processing, as well as tuning the number of working threads. We experimentally benchmark our algorithms with large data sets on a DBMS running on a computer with a multicore CPU. We show that our algorithms outperform existing DBMS mechanisms in computing aggregations of multidimensional data summaries, especially as dimensionality grows. Furthermore, we show that local memory allocation (RAM block size) does not have a significant impact when the thread management algorithm distributes the workload among a fixed number of threads. Our proposal is unique in the sense that we do not modify or require access to the DBMS source code, but instead, we extend the DBMS with analytic functionality by developing User-Defined Functions.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.137-139
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• 2014

Objective: Data exchange, interoperability, and access as a service in healthcare information management systems (HIMS) is the basic need to provision health-services. Data existing in various HIMS not only differ in the basic underlying structure but also in data processing systems. Data interoperability can only be achieved when following a common structure or standard which is shareable such as semantics based structures. We propose web-enabler: A Hadoop MapReduce based distributed approach to transform the existing huge variety data in variety formats to a conformed and flexible ontological format that enables easy access to data, sharing, and providing various healthcare services. Results: For proof of concept, we present a case study of general patient record in conventional system to be enabled for analysis on the web by transforming to semantics based structure. Conclusion: This work achieves transformation of stale as well as future data to be web-enabled and easily available for analytics in healthcare systems.

• International Journal of Contents
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• v.5 no.1
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• pp.46-50
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• 2009

Logistics is in the limelight as one of a variety of RFID applications. The RFID technology is actively being applied to improve the competitiveness power of companies through the synthetic management of products and information. The RFID system generates large volume of stream data. It has problems which occur waste of storage and long processing time when storing large data and processing queries. Recently, many studies have been done to solve the problems which are generated in RFID system. In this thesis, we propose an efficient data management scheme for path queries and containment queries which are occurred frequently. The proposed data management scheme considers a change of the containment of products during a transport and supports a path of changed products by representing a path of various containments. Also, the compression utilizing the structure of supply chain reduces the stored data volumes. In order to show the superiority of our approach, we compare it with the existing schemes. As a result, our experimental results show that our scheme outperforms the existing scheme in terms of storage efficiency and query processing time.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.870-872
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• 2007

Sensor networks have been widely applied for data collection. Due to the energy limitation of the sensor nodes and the most energy consuming data transmission, we should allocate as much work as possible to the sensors, such as data compression and aggregation, to reduce data transmission and save energy. Querying extreme values is a general query type in wireless sensor networks. In this paper, we propose a novel querying method called Two-Level Filter (TLF) for querying extreme values in wireless sensor networks. We first divide the whole sensor network into domains using the Distributed Data Aggregation Model (DDAM). The sensor nodes report their data to the cluster heads using push method. The advantages of two-level filter lie in two aspects. When querying extreme values, the number of pull operations has the lower boundary. And the query results are less affected by the topology changes of the wireless sensor network. Through this method, the sensors preprocess the data to share the burden of the base station and it combines push and pull to be more energy efficient.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.122-129
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• 2014

Effective information gathering and retrieval of the most relevant web documents on the topic of interest is difficult due to the large amount of information that exists in various formats. Current information gathering and retrieval techniques are unable to exploit semantic knowledge within documents in the "big data" environment; therefore, they cannot provide precise answers to specific questions. Existing commercial big data analytic platforms are restricted to a single data type; moreover, different big data analytic platforms are effective at processing different data types. Therefore, the development of a common big data platform that is suitable for efficiently processing various data types is needed. Furthermore, users often possess more than one intelligent device. It is therefore important to find an efficient preference profile construction approach to record the user context and personalized applications. In this way, user needs can be tailored according to the user's dynamic interests by tracking all devices owned by the user.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1141-1155
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• 2019

Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities' detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.149-163
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• 2023

Affected by external factors, errors in time series data collected by sensors are common. Using the traditional method of constraining the speed change rate to clean the errors can get good performance. However, they are only limited to the data of stable changing speed because of fixed constraint rules. Actually, data with uneven changing speed is common in practice. To solve this problem, an online cleaning algorithm for time series data based on dynamic speed change rate constraints is proposed in this paper. Since time series data usually changes periodically, we use the extreme learning machine to learn the law of speed changes from past data and predict the speed ranges that change over time to detect the data. In order to realize online data repair, a dual-window mechanism is proposed to transform the global optimal into the local optimal, and the traditional minimum change principle and median theorem are applied in the selection of the repair strategy. Aiming at the problem that the repair method based on the minimum change principle cannot correct consecutive abnormal points, through quantitative analysis, it is believed that the repair strategy should be the boundary of the repair candidate set. The experimental results obtained on the dataset show that the method proposed in this paper can get a better repair effect.