• Journal of Information Technology Services
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• v.16 no.1
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• pp.73-82
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• 2017

Recently in-memory data stream processing has been actively applied to various subjects such as query processing, OLAP, data mining, i.e., frequent item sets, association rules, clustering. However, finding regular periodic patterns of events in an infinite data stream gets less attention. Most researches about finding periods use autocorrelation functions to find certain changes in periodic patterns, not period itself. And they usually find periodic patterns in time-series databases, not in data streams. Literally a period means the length or era of time that some phenomenon recur in a certain time interval. However in real applications a data set indeed evolves with tiny differences as time elapses. This kind of a period is called as a pseudo-period. This paper proposes a new scheme called FPMH (Finding Periods using Multiple Hash functions) algorithm to find such a set of pseudo-periods over a data stream based on multiple hash functions. According to the type of pseudo period, this paper categorizes FPMH into three, FPMH-E, FPMH-PC, FPMH-PP. To maximize the performance of the algorithm in the data stream environment and to keep most recent periodic patterns in memory, we applied decay mechanism to FPMH algorithms. FPMH algorithm minimizes the usage of memory as well as processing time with acceptable accuracy.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.335-344
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• 2005

Recently open data mining methods focusing on a data stream that is a massive unbounded sequence of data elements continuously generated at a rapid rate are proposed actively. Knowledge embedded in a data stream is likely to be changed over time. Therefore, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. This paper proposes a sliding window technique for finding recently frequent itemsets, which is applied efficiently in open data mining. In the proposed technique, its memory usage is kept in a small space by delayed-insertion and pruning operations, and its mining result can be found in a short time since the data elements within its target range are not traversed repeatedly. Moreover, the proposed technique focused in the recent data elements, so that it can catch out the recent change of the data stream.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.117-128
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• 2006

In this paper, we propose a streaming method for XML data that supports energy-efficient processing of queries over the stream in mobile clients. We propose new stream organizations for XML data which have different kinds of addresses to related data in a stream. We describe event-driven stream generation algorithms for the proposed stream structures and provide search algorithms for simple XML path queries which leverage the access mechanisms incorporated in the stream. Experimental results show that our approaches can effectively improve the tuning time performance of user queries in a wireless broadcasting environment.

• Environmental Engineering Research
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• v.16 no.2
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• pp.69-79
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• 2011

Rapid population growth and land use changes have severely degraded streams across the United States. In response, there has been a surge in the number of stream restoration projects, including stream restoration for mitigation purposes. Currently, most projects do not include evaluation and monitoring, which are critical in the success of stream restoration projects. The goal of this study is to review the current status of assessment methodologies and restoration approaches for streams in Virginia, with the aim of assisting the restoration community in making sound decisions. As part of the study, stream restoration projects data from a project in Fairfax County, Virginia was assessed. This review revealed that the stream assessment methodologies currently applied to restoration are visuallybased and do not include biological data collection and/or a method to incorporate watershed information. It was found from the case study that out of the twenty nine restoration projects that had occurred between 1995 and 2003 in Fairfax County, nineteen projects reported bank stabilization as a goal or the only goal, indicating an emphasis on a single physical component rather than on the overall ecological integrity of streams. It also turned out that only seven projects conducted any level of monitoring as part of the restoration, confirming the lack of evaluation and monitoring. However, Fairfax County has recently improved its stream restoration practices by developing and incorporating watershed management plans. This now provides one of the better cases that might be looked upon by stakeholders when planning future stream restoration projects.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.24-33
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• 2009

Stream data, gathered from ubiquitous sensor networks, change continuously over time. Because they have quite different characteristics from traditional databases, we need new techniques for storing and querying/analyzing these stream data, which are research issues recently emerging. In this research, we implemented a storage manager gathering stream data and storing them into databases, which are sampled continuously from sensor networks. The storage manager cleans faulty data occurred in mobile sensors and it also reduces the size of stream data by merging repeatedly-sampled values into one and by employing the tilted time frame which stores stream data with several different sampling rates. In this research furthermore, we measured the performance of the storage manager in the context of a sensor network monitoring fires of a building. The experimental results reveal that the storage manager reduces significantly the size of storage spaces and it is effective to manage the data stream for real applications monitoring buildings and their fires.

• Journal of Advanced Navigation Technology
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• v.6 no.4
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• pp.290-295
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• 2002

In Mpeg-4 system, objects are composed of ES(Elementary Stream). Each of the objects is managed independently by object-based coding and is transmitted via DMIF(Delivery Multimedia Integration Framework). The data streams in Mpeg-4 are transmitted with using DVB-Data Carousel which can improve the reliability and efficiency by cyclic retransmission. This paper describes a system which transmits some part of data stream of Mpeg-4 object with using DVB-Data Carousel to clients via satellite. This also uses a performance enhancing proxy server for reducing round trip time between ground network and satellite.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1242-1251
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• 2009

A DSMS(Data Stream Management System) in ubiquitous environment processes huge data that input from a number of sensor. The existed system is used with a load shedding method that is eliminated with a part of huge data stream when it doesn't process the huge data stream. The Load shedding method has to filter a part of input data. This is because, data completeness or reliability is decreased. In this paper, we proposed the overload prediction load balancing to maintain data completeness when the system has an overload. The proposed method predicts the overload time. and than it is decreased with data loss when achieves the prediction overload time. The performance evaluation shows that the proposed method performs better than the existed method.

• Journal of KIISE
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• v.42 no.7
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• pp.840-845
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• 2015

In recent years, Big Data has been a topic of great interest for the production and operation work of offshore plants as well as for enterprise resource planning. The ability to predict future equipment performance based on historical results can be useful to shuttling assets to more productive areas. Specifically, a centrifugal compressor is one of the major piece of equipment in offshore plants. This machinery is very dangerous because it can explode due to failure, so it is necessary to monitor its performance in real time. In this paper, we present stream data processing architecture that can be used to compute the performance of the centrifugal compressor. Our system consists of two major components: a virtual tag stream generator and a real-time data stream manager. In order to provide scalability for our system, we exploit a parallel programming approach to use multi-core CPUs to process the massive amount of stream data. In addition, we provide experimental evidence that demonstrates improvements in the stream data processing for the centrifugal compressor.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.2
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• pp.103-110
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• 2011

It is necessary to accurate and efficient management for measured digital data from sensors in u-health system. It is not efficient that sensor network process input stream data of mass storage stored in database the same time. We propose to improve the processing performance of multidimensional stream data continuous incoming from multiple sensor. We propose process query based on sliding window for efficient input stream and found multiple query plan to Mjoin method and we reduce stored data using backpropagation algorithm. As a result, we obtained to efficient result about 18.3% reduction rate of database using 14,324 data sets.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.717-732
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• 2011

Classification in biomedical applications is an important task that predicts or classifies an outcome based on a given set of input variables such as diagnostic tests or the symptoms of a patient. Traditionally the classification algorithms would have to digest a stationary set of historical data in order to train up a decision-tree model and the learned model could then be used for testing new samples. However, a new breed of classification called stream-based classification can handle continuous data streams, which are ever evolving, unbound, and unstructured, for instance--biosignal live feeds. These emerging algorithms can potentially be used for real-time classification over biosignal data streams like EEG and ECG, etc. This paper presents a pioneer effort that studies the feasibility of classification algorithms for analyzing biosignals in the forms of infinite data streams. First, a performance comparison is made between traditional and stream-based classification. The results show that accuracy declines intermittently for traditional classification due to the requirement of model re-learning as new data arrives. Second, we show by a simulation that biosignal data streams can be processed with a satisfactory level of performance in terms of accuracy, memory requirement, and speed, by using a collection of stream-mining algorithms called Optimized Very Fast Decision Trees. The algorithms can effectively serve as a corner-stone technology for real-time classification in future biomedical applications.