• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.989-997
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• 2018

Massive event stream brings us great challenges in its volume, velocity, variety, value and veracity. Picking up some valuable information from it often faces with long detection time, high memory consumption and low detection efficiency. Aiming to solve the problems above, an efficient complex event detection method based on NFA_HTS (Nondeterministic Finite Automaton_Hash Table Structure) is proposed in this paper. The achievement of this paper lies that we successfully use NFA_HTS to realize the detection of complex event from massive RFID event stream. Specially, in our scheme, after using NFA to capture the related RFID primitive events, we use HTS to store and process the large matched results, as a result, our scheme can effectively solve the problems above existed in current methods by reducing lots of search, storage and computation operations on the basis of taking advantage of the quick classification and storage technologies of hash table structure. The simulation results show that our proposed NFA_HTS scheme in this paper outperforms some general processing methods in reducing detection time, lowering memory consumption and improving event throughput.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.1166-1171
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• 2000

기존의 멀티미디어 연구의 실현에 있어 가장 큰 문제라 할 수 있던 성능의 문제가 하드웨어의 급속한 발달로 해결되어 감에 따라 멀티미디어 및 제반 관련기술도 함께 발전되었으며 이에 기반한 multimedia stream에서의 event를 검출하기 위한 다양한 연구들이 진행되어 왔다. 그러나 지금까지의 연구는 주로 전송 및 저장, 검색에 집중되어 연구되어 왔으며 영상인식 등의 Vision관련 연구에서는 멀티미디어 스트리밍 기술과의 연동을 고려하지 않은 연구를 수행함에 따라 검출 가능한 event가 있다고 하더라도 응용영역에 종속적인 인테페이스만을 고려함에 따라 사용자가 이를 기술(記述, description)하거나, 사용자에게 검출 가능한 event를 제시하기 위해 일반화된 방법이 제시되어 있지 않았다. 본 연구에서는 사용자가 검출을 원하는 event를 기술하는 방법과, 시스템에서 검출 가능한 event를 제시하기 위한 방법을 제안하고, 제시되는 방법이 응용영역에 독립적이기 위해 요구되는 사항들과 객체 단위인 이벤트/행위와 처리기 사이의 인터페이스에 관하여 정의한 후 기본적인 동작방식을 제안한다.

• ETRI Journal
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• v.31 no.4
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• pp.463-465
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• 2009

In this letter, we present an event stream processing system that can evaluate a pattern query for a data sequence with predicates. We propose a pattern query language and develop a pattern query processing system. In our system, we propose novel techniques for run-time aggregation and negation processing and apply our system to stream data generated from vehicles to monitor unusual driving patterns.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.799-806
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• 2021

In the IoT environment, event data from sensors is continuously reported over time. Event data obtained in this trend is accumulated indefinitely, so a method for efficient analysis and management of data is required. In this study, a data stream segmentation method was proposed to support the effective selection and utilization of event data from sensors that are continuously reported and received. An identifier for identifying the point at which to start the analysis process was selected. By introducing the role of these identifiers, it is possible to clarify what is being analyzed and to reduce data throughput. The identifier for stream segmentation proposed in this study is a semantic-oriented data stream segmentation method based on the event occurrence of each stream. The existence of identifiers in stream processing can be said to be useful in terms of providing efficiency and reducing its costs in a large-volume continuous data inflow environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1385-1402
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• 2019

Quickly picking up some valuable information from massive manufacturing event stream usually faces with the problem of long detection time, high memory consumption and low detection efficiency due to its stream characteristics of large volume, high velocity, many variety and small value. Aiming to solve the problem above for the current complex event processing methods because of not sharing detection during the detecting process for massive manufacturing event streams, an efficient complex event processing method based on multipattern sharing is presented in this paper. The achievement of this paper lies that a multipattern sharing technology is successfully used to realize the quick detection of complex event for massive manufacturing event streams. Specially, in our scheme, we firstly use pattern sharing technology to merge all the same prefix, suffix, or subpattern that existed in single pattern complex event detection models into a multiple pattern complex event detection model, then we use the new detection model to realize the quick detection for complex events from massive manufacturing event streams, as a result, our scheme can effectively solve the problems above by reducing lots of redundant building, storing, searching and calculating operations with pattern sharing technology. At the end of this paper, we use some simulation experiments to prove that our proposed multiple pattern processing scheme outperforms some general processing methods in current as a whole.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.74-82
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• 2004

In digital broadcasting, applications are computer programs executed by the set-top box(TV receiver) , and synchronized applications are those that perform tasks at the specified moments in the underlying video. This paper describes important concepts, standards, and skills needed to implement synchronized applications and explains how to integrate them to implement these applications. This Paper assumes the European data broadcasting standard, DVB-MHP. In DVB-MHP, scheduled stream events are recommended as a means of synchronizing applications with video streams. In this method, the application receives each stream event, and executes the action associated with the stream event at the time specified in the stream event. Commercially available stream generators, i.e., multiplexers, do not generate transport streams that support scheduled stream events. So we used a stream generator implemented in our lab. We implemented a synchronized application where the actions triggered by stream events are to display graphic images. We found that our synchronized application processes scheduled stream events successfully. In our experimentation, the stream events were synchronized with the video and the deviation from the intended time was within 240 ㎳, which is a tolerance for synchronization skew between graphic images and video.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4307-4325
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• 2016

With the aim of solving the problems of long processing times, high memory consumption and low event throughput in the current processing approaches in out-of-order RFID event streams, an efficient complex event processing method based on INFA-HTS (Improved Nondeterministic Finite Automaton-Hash Table Structure) is presented in this paper. The contribution of this paper lies in the fact that we use INFA and HTS to successfully realize the detection of complex events for out-of-order RFID event streams. Specifically, in our scheme, to detect the disorder of out-of-order event streams, we expand the traditional NFA model into a new INFA model to capture the related RFID primitive events from the out-of-order event stream. To high-efficiently manage the large intermediate capturing results, we use the HTS to store and process them. As a result, these problems in the existing methods can be effectively solved by our scheme. The simulation results of our experiments show that our proposed method in this paper outperforms some of the current general processing approaches used to process out-of-order RFID event streams.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.99-110
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• 2010

An event means a flow which has a time attribute such as a symptom of patient. Stream data collected by sensors can be summarized as an interval event which has a time interval between the start-time point and the end-time point in multiple stream data environment. Most of temporal mining techniques have considered only the frequent events. However, these approaches may ignore the infrequent event even if it is important. In this paper, we propose a new temporal data mining that can find association rules for the significant temporal relation based on interval events in multidimensional stream data environment. Our method considers the weight of events and stream data on the sensing time point of abnormal events. And we can discover association rules on the significant temporal relation regardless of the occurrence frequency of events. The experimental analysis has shown that our method provide more useful knowledge than other conventional methods.

• The KIPS Transactions:PartD
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• v.14D no.5
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• pp.483-490
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• 2007

Recently mining techniques that analyze the data stream to discover potential information, have been widely studied. However, most of the researches based on the support are concerned with the frequent event, but ignore the infrequent event even if it is crucial. In this paper, we propose SM-AF method discovering association rules to an abnormal event. In considering the window that an abnormal event is sensed, SM-AF method can discover the association rules to the critical event, even if it is occurred infrequently. Also, SM-AF method can discover the significant rare itemsets associated with abnormal event and periodic event itemsets. Through analysis and experiments, we show that SM-AF method is superior to the previous methods of mining association rules.

• The KIPS Transactions:PartD
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• v.18D no.4
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• pp.215-224
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• 2011

U-Health collects patient information with various kinds of sensor. Stream data can be summarized as an interval event which has aninterval between start-time-point and end-time-point. Most of temporal mining techniques consider only the event occurrence-time-point and ignore stream data change of state. In this paper, we propose the temporal mining technique considering the event change of state in U-Health. Our method overcomes the restrictions of the environment by sending a significant event in U-Health from sensors to a server. We define four event states of stream data and perform the temporal data mining considered the event change of state. Finally, we can remove an ambiguity of discovered rules by describing cause-and-effect relations among events in temporal relation sequences.