Earticle

This paper proposes a hash index for spatiotemporal continuous query processing rules for filtering, classifying, analyzing, and responding to consecutively collected target objects. The Rete technique for improving the performance of rule-based complex event processing shows better performance than the rule interpretation method as it creates a compiled data structure for the rule. This paper proposes a performance improvement method that eliminates the rule search overhead by creating a spatiotemporal index for the Rete nodes expressing spatiotemporal continuity query rules and stabbing the Rete node of the rule by employing hash indexing on the stream data.

Event mean concentration (EMC) of nitrogen (N) and phosphorus (P) is primary information for non-point source pollution assessment of a watershed. The EMCs for various types of agriculture such as dairy and crop farming under different climate and geologic conditions are not fully investigated. A diary- and cropfarming complex agricultural watershed in Piedmont region in Maryland, USA has been monitored for 10 years as a section 319 national monitoring program of US EPA. Dairy manure was the main source of fertilizer for crop farming in this watershed. Observed mean concentrations of N and P for each event were analyzed. Distribution of EMCs for N and P showed a wide range of variations. Representative EMCs of T-N and $NO_{3}-N$ tended to be higher than those reported for other agricultural watersheds. This study confirmed that site-specific EMC information for various agricultural practices is required for better assessment of non-point source pollution using EMC method.

이 연구는 석유화학산단의 화학사고 발생으로 인한 지역주민의 불안 가중과 사고 확산에 대비한 주민 대피구역의 확보방안을 제안하는 데 목적이 있다. 정부가 화학물질관리법과 화학물질 등록평 가관련 법률을 개정하여 위해관리계획서 상에 주민 소산 계획을 포함하고 있으나, 이를 위한 전용대 피소 지정은 전무한 실정이다. 화학사고 발생 시 최적의 대피를 위해서는 이동을 최대한 자제하는 것이 최선이므로 현재 위치에서 대피할 수 있는 공간적 활용이 선행되어야 한다. 대피 장소는 현재 민방위 시설과 재난대피 시설이 지정되어 있는데, 화학사고가 많이 발생하는 지역의 특성을 고려하 여 현재 시설을 화학전용시설로 전환하여 사용할 수 있도록 화학물질의 특성에 적합한 방재물품을 보완하고 차단시설을 추가할 수 있다. 이를 위해서는 광역단위의 화학물질 안전관리 종합계획 수립 이 요구되며, 이를 바탕으로 사고예방 측정 모니터링을 비롯한 예방관리와 사후 대피지원 및 이동훈 련 기능을 강화하여야 한다.

In this study, we tried to suggest a plan to secure an evacuation site for local residents in preparation for the spread of anxiety and accidents caused by chemical accidents in petrochemical complexes. In the event of a chemical accident, it is recommended to limit movement as much as possible for optimal evacuation. Therefore, it is of paramount importance to utilize an evacuation space in your current location. Currently, civil defense facilities are designated as evacuation sites, and chemical disaster prevention equipment must be stored in existing evacuation facilities and air blocking facilities must be installed. To this end, it is necessary to establish comprehensive measures for chemical safety management of local governments. After that, preventive management and movement training functions such as accident prevention, measurement and monitoring, and follow-up evacuation support should be strengthened.

최근 다양한 응용에서 실시간으로 발행하는 이벤트들을 신속하고 효율적으로 처리하기 위해 복합 이벤트 처리 시스템을 도입하고 있다. 기존 복합 이벤트 처리 연구는 연산자의 유사성과 중복성을 고려하지 않기 때문에 많은 연산과 많은 메모리를 소비하는 문제점이 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 유사 연산과 중복 연산을 고려한 효율적인 복합 이벤트 처리 기법을 제안한다. 제안하는 기법은 동일한 기본 이벤트들이 유사한 연산을 가지면 그 연산들을 하나의 가상 연산자로 결합한다. 중복된 연산을 가질 경우에는 우선 하나의 연산만을 처리하고 이후에 같은 연산이 나오면 먼저 처리된 연산의 결과를 이용하여 처리한다. 기존 기법과의 성능 비교를 통하여 전체 연산 처리량 측면에서 제안하는 기법의 성능이 우수함을 보인다.

Recently, a complex event processing system has been introduced to quickly and efficiently process real-time events in various applications. Since the existing complex event processing schemes do not consider the similarity and duplication of operators, they perform a lot of computations and consume memory. To solve such problems, this paper proposes an efficient complex event processing scheme considering similar operators and duplication operations. When the same primitive events have similar operations, the proposed scheme combines them into one virtual operator. For duplicated operations, the proposed scheme processes only one of them first, and then processes the others using the results of the already processed operation when the same operation is subsequently performed. It is shown through performance comparison that the proposed scheme outperforms the existing scheme in terms of the whole operation throughput.

본 연구는 다양한 교통 주체가 동시에 상호작용하고 불확실성이 높은 도시 교차로 환경에서, 복합 이벤트 기반 충돌 위험 예측 프레임워크를 제안한다. 기존의 단일 예측값 또는 단일 신호 중심 방법론과 달리, 본 방법은 급정지, 차선 변경, 근접주행 등 주요 위험 행동, TTC·PET과 같은 정량 지표, 그리고 예측 신뢰구간 중첩(IoU)을 통합적으로 결합하여 위험을 평가한다. 분석은 DRIFT 데이터셋의 site A, B, C 세 개의 실제 교차로 데이터셋에서 추출된 다중 차량 궤적을 기반으로 하였으며, 위험 점수는 정규화·시각화되어 각 지점을 4단계 등급으로 분류하였다. 실증 결과, 고위험 hotspot은 합류부·교차부·병목 구간 등 특정 영역에 뚜렷하게 집중되며, 복합 이벤트가 빈번·반복적으로 발생하는 곳에서 위험 점수가 급격히 누적됨을 확인하였다. 복합 신호 조합 기준은 기존 단일 기준 대비 정밀도·F1-score를 대폭 개선해, 실시간 위험 모니터링과 정책적 개입 타깃 대상으로서 실효성을 입증하였다. 다만, 위험 이벤트 점수 부여의 가중치가 경험적 기준에 의존한 한계가 있어, 향후 실제 사고 통계와 도메인 전문성에 기반한 추가 보정이 필요하다. 본 프레임워크는 과학적 교차로 안전 평가와 선제적 교통관리, 자율주행 시스템 개발에 활용될 수 있는 실질적 근거를 제시한다.

This study proposes a composite event-based collision risk prediction framework for complex, high-uncertainty urban intersections involving multiple interacting road users. Unlike conventional approaches based on single-point estimates or isolated risk signals, the proposed method integrates major unsafe behaviors (sudden braking, lane changing, close following), quantitative risk indices (TTC, PET), and overlap of predicted uncertainty intervals (IoU) to assess risk. The analysis leverages multi-vehicle trajectories extracted from three real intersections (Sites A, B, C) in the DRIFT dataset, with risk scores normalized and visualized spatially, and each location classified into four risk levels. Empirical results show that high-risk hotspots are strongly concentrated at specific locations-merging zones, crossings, and bottlenecks-where composite events frequently and repeatedly occur, resulting in rapid risk accumulation. The composite signal criteria substantially improve precision and F1-score over traditional single-feature approaches, validating practical utility for real-time risk monitoring and targeted intervention. However, a limitation is that event score weighting currently relies on heuristics; future studies should calibrate using accident statistics and domain expertise. This framework provides actionable support for scientific intersection.

“사건의 인과관계를 사건이 일어나기 전 완전히 알 수 있다는 단순한 영역”에서 부터 “사건의 인과관계는 사건이 일어날 때 알 수 있다는 복잡한 영역”으로 패러다임 변화가 일어나고 있다. 자연 재해(태풍, 쓰나미, 지진), 원전, 테러 등은 “일어난 후 반응” 할 수 있다. 미군은 9.11 테러 후 미래전 장을 “불확실한 위협들과 예측할 수 없는 많은 갈등으로 가득한 VUCA(Volatility, Uncertainty, Complexity, Ambiguity)라고 정의하고, 제한된 능력으로 어떻게 세계질서를 유지할 것인가”에 관한 연구를 지속적으로 하고 있으며, “복잡성”과 “민첩성”의 개념과, “복잡성 관련 모델” 등도 활발하게 연 구하고 있다. 그 결과 “민첩성에 의한 자기조직화”가 이 영역에 필수요소라는 것도 알게 되었다. 본 연구에서는 사건을 X축(복잡도: 단순한, 복잡한, 카오스), Y축(시간: 과거, 현재, 미래), Z축(자원: 수단, 방법)을 중심으로 분석하는 “민첩성기반 복잡한 영역 사건 모델”을 제안한다. 제안하는 모델을 사용하 면 복잡성 영역의 사건에 민첩하게 대응할 수 있을 뿐 아니라 복잡한 영역의 자원을 민첩하게 개발 및 배치할 수 있을 것이다.

The paradigm is shifting from a simple area which anticipates that “the cause and effect of an event can be predicted before an event occurs” to a complex area which anticipates that “the cause and effect of an event can be known when an event occurs.” We can only “respond after events” such as natural disasters (typhoon, tsunami, earthquake), nuclear accidents, or acts of terrorism occur. After 9/11, the United States defined the future battlefield as “a VUCA situation full of uncertain threats and countless unpredictable conflicts and have continued research on maintaining order under limited resources. Much research has been made on the concepts of Complexity, Agility, and Complexity Model. As such, we have come to understand that “Self-Organization Agility” is a vital concept. Our proposed model is an “Agility-based Event Model of Complex Area” which consists of a X axis (Complexity: Simple area, Complex ares, Chaos area), a Y axis (Time: Past, Current, Future), and a Z axis (Resources: Process, Procedure). Using this model we will be able to not only respond with agility against events in the complex area but also develop and deploy resources in the complex area as well.

This paper investigates the composition patterns of the event structures of the complex verb constructions, V1-e V2{nohta/twuta}, based on Pustejovsky’s(1995) Generative Lexicon, and the meanings of V2{nohta/twuta} in the constructions. The V1-e V2{nohta/twuta} constructions fall largely into five groups depending on whether the ‘ey’-marked Goal argument is introduced by V2, which is affected by whether the two verbs share the [+concrete, +mobility] object(Theme) argument: (i) ‘yele-nohta/twuta’ and ‘iphye-nohta/twuta’ are the typical auxiliary verb constructions in which V1 and V2 share only the subject, and the ‘ey’-marked Goal argument cannot be introduced by V2. In these constructions, ‘-e V2{nohta/twuta}’ means the ‘continuation of the resultative state’; (ii) the other three groups, i.e. ‘kele-nohta/twuta’, ‘kkenay-nohta/twuta’, and ‘mantule-nohta/twuta’ are the constructions in which V1 and V2 share not only the subject but the [+concrete, +mobility] object(Theme) argument as well, and ‘ey’-marked Goal argument is syntactically selected by V2. In such cases, V2{nohta/twuta} can be construed as having the aspectual or the lexical meaning, a ‘change of location’, according to the context.

Events are crammed with numerous basic elements in today’s management and manufacturing system. Especially, for a large-scale system, there are thousands of independent messages (events) happening every second. How to integrate the information and how to deal with such massive events are faced by most of the system. Event monitoring and processing has been posed in manufacturing corporations to tackle this challenge. However, well-prepared methodology and processing are lack for this challenge. In this article, we propose an extensible event-driven manufacturing platform for handling those massive events. First, holistic architecture of the platform is depicted. Next, the principle of primitive and complex event is detailed. Eventually, Complex Event Processing (CEP) approach is applied to the platform.

Events are crammed with numerous basic elements in today’s management and manufacturing system. Especially, for a large-scale system, there are thousands of independent messages (events) happening every second. How to integrate the information and how to deal with such massive events are faced by most of the system. Event monitoring and processing has been posed in manufacturing corporations to tackle this challenge. However, well-prepared methodology and processing are lack for this challenge. In this article, we propose an extensible event-driven manufacturing platform for handling those massive events. First, holistic architecture of the platform is depicted. Next, the principle of primitive and complex event is detailed. Eventually, Complex Event Processing (CEP) approach is applied to the platform.

Complex Event Processing (CEP) is a promising technology that deals with event stream in a cenralized manner. But in some application scenario such as the express delivery business, business-related data is scattered in the network and must be obtained by request. For the purpose of resolving the difficulty to utilize and process such distributed infomation, the paper comprehensively utilizes various advanced technologies to design an automation Internet of Things (IoT) framework which can provide the rich-semantic complex events to the senior application. Firstly, this paper proposes the framework which can realize the automatic recognition of the parcel and positive request for the associated semantic data. Secondly, a hierarchical modularized ontology is established for the sake of sharing the background knowledge. Finally, we have implemented a prototype system with the proposed object and semantic event model and detection method. Test results from simulation data indicate that the system can operation stably and response quickly to express delivery business.

Intelligent Transportation Systems (ITS) is one of the important application areas of the Internet of Things (IoT). The key issue is how to process the huge events generated by IoT system to support ITS. In this paper a proactive parallel complex event processing method is proposed for congestion control in large-scale ITS. A Bayesian model averaging method is used to obtain accurate predictions under different event context. Based on the predictive analysis, a parallel Markov decision processes model is designed to support decision making for large-scale ITS. An optimized parallel policy iteration algorithm is proposed based on state partition and policy decomposition. The experimental evaluations show that this method has good accuracy and scalability when used to process congestion control in large-scale ITS.

시점기반 복합 이벤트 처리는 각 이벤트에 하나의 타임스탬프를 사용하여 즉각적인 이벤트를 처리한다. 하지만, 시점기반의 이벤트 처리로는 이벤트의 활동 기간이 중요한 역할을 하는 금융, 멀티미디어, 의학, 기상학 같은 분야에서 복합적인 시제 관계를 표현하기에는 불충분하다. 실세계의 애플리케이션 분야에서, 이벤트는 기간을 가지며, 두 종류 이상의 이벤트는 시간적으로 겹쳐질 수도 있고, 하나의 이벤트가 다른 이벤트를 포함할 수도 있다. 이런 종류의 이벤트들에 대한 관계는 시점기반 이벤트처럼 연속적이지 않을 수도 있다. 본 논문에서는 기간기반 이벤트를 사용하여 복합 이벤트의 패턴을 검출하는 방법을 설계하고 구현한다. 기간기반 이벤트는 시점기반 이벤트가 다룰 수 없는 이벤트들 사이의 겹침과 포함관계를 표현할 수 있다. 기간기반 이벤트 연산자는 시작 끝점과 종료 끝점을 사용하여 이벤트의 기간을 나타내고, 기간기반 이벤트의 시퀀스를 표현하여 복합 이벤트 패턴을 검출할 수 있다.본 논문에서는 복합 이벤트 패턴 검출의 효율성을 높이기 위해 활성 인스턴스 스택을 사용하는 알고리즘을 제시하며, 이벤트의 시퀀스를 구성할 때 중간 결과의 개수를 줄이기 위해 윈도우 푸시다운 기법을 적용하여 수행시간과 메모리의 효율을 높인다.

The point-based complex event processing handled an instantaneous event by using one time stamp in each event. However, the activity period of the event plays the important role in the field which is the same as the finance, multimedia, medicine, and meteorology. The point-based event is insufficient for expressing the complex temporal relationship in this field. In the application field of the real-time world, the event has the period. The events more than two kinds can be temporally overlapped. In addition, one event can include the other event. The relation about the events of kind of these can not be successive like the point-based event. This thesis designs and implements the method detecting the patterns of the complex event by using the interval-based events. The interval-based events can express the overlapping relation between events. Futhermore, it can include the others. By using the end point of beginning and end point of the termination, the operator of interval-based events shows the interval-based events. It expresses the sequence of the interval-based events and can detect the complex event patterns. This thesis proposes the algorithm using the active instance stack in order to raise efficiency of detection of the complex event patterns. When comprising the event sequence, this thesis applies the window push down technique in order to reduce the number of intermediate results. It raises the utility factor of the running time and memory.

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.

In recent through development of IOT owing to that mass stream data is being generated in variety of application complex event processing technology is being watched with keen interest as a technology to analyze this kind of real-time continuous data. However, the existing study related with complex event processing only comes to an end at simple event processing based on low-level event or comes to an end at service defect discovery with providing limited operator and so on. Accordingly, there would be limitation to provide useful analysis information. In this paper in consideration of complex event along with aspect-oriented programming an extended complex event model is provided, which is possible to provide more valuable and useful information. Specifically, we extend the model to support hierarchical event structures and let the model recognize point-cuts of aspect-oriented programming as events. We provide the event operators designed to specify the events on instances and handle temporal relations of the instances. It is presented that syntax and semantics of constructs in our event processing language including various and progressive event operators, complex event pattern, etc. In addition, an event context mechanism is proposed to analyze more delicate events. Finally, through application studies application possibility of this study would be shown and merits of this event model would be present through comparison with other event model.

웹이 성장함에 따라 데이터처리 기술도 발전하고 있다. 차세대 웹 환경에서는 다양한 유무선 사용자, USN, RFID를 위한 고속, 대용량데이터 처리기술 또한 발전하고 있다. 본 논문에서는 CEP(Complex Event Processing) 엔진을 이용하여 대용량데이터를 처리하는 기술을 제안한다. CEP는 복잡한 이벤트를 처리하는 기술로 CEP 엔진은 다음과 같은 특정이 있다. 첫째 대용량의 이벤트(데이터)를 받는 작업, 둘째 이를 분석하는 작업, 최종적으로 새로운 액션으로 연결시키는 작업으로 나눌 수 있다. 즉 대용량데이터를 수집하고 이벤트들을 분석, 필터링한다. 또한 이벤트 엔진에 미리 등록해 놓은 이벤트와 새로운 이벤트를 패턴매칭하여 데이터를 추출한다. 추출된 결과를 다른 작업의 입력 이벤트로 사용하거나 요청된 이벤트에 대해 실시간으로 응답할 수 있고 유효한 데이터만 데이터베이스에 트리거할 수도 있다.

According to growth of web, data processing technology is developing. In the Web of next generation, high-speed or high-volume data processing technologies for various wire-wireless users, USN and RFID are developing too. In this paper, we propose a high-volume data processing technology using Complex Event Processing(CEP) engine. CEP is the technology to process complex events. CEP Engine is the following characteristics. First it collects a high-volume event(data). Secondly it analyses events. Finally it lets event connect to new actions. In other words, CEP engine collects, analyses, filters high-volume events. Also it extracts events using pattern-matching for registered events and new events. As the results extracted. We use it by an input event of other work, real-time response for demanded event and can trigger to database for only valid data.

The concepts of phylogeny and floral genetics play a crucial role in understanding the origin and diversification of flowers in angiosperms. Angiosperms evolved a great diversity of ways to display their flowers for reproductive success with variations in floral color, size, shape, scent, arrangements, and flowering time. The various innovations in floral forms and the aggregation of flowers into different kinds of inflorescences have driven new ecological adaptations, speciation, and angiosperm diversification. Evolutionary developmental biology seeks to uncover the developmental and genetic basis underlying morphological diversification. Advances in the developmental genetics of floral display have provided a foundation for insights into the genetic basis of floral and inflorescence evolution. A number of regulatory genes controlling floral and inflorescence development have been identified in model plants such as Arabidopsis thaliana and Antirrhinum majus using forward genetics, and conserved functions of many of these genes across diverse non-model species have been revealed by reverse genetics. Transcription factors are vital elements in systems that play crucial roles in linked gene expression in the evolution and development of flowers. Therefore, we review the sex-linked genes, mostly transcription factors, associated with the complex and dynamic event of floral development and briefly discuss the sex-linked genes that have been characterized through next-generation sequencing.

본 연구에서 우리는 복합 이벤트(complex event)와 관점지향 프로그래밍(aspect-oriented programming)을 함께 고려하여 확장된 복합 이벤트 모델을 제시한다. 우리는 이 두 모델의 통합을 통해 이벤트 상관 기반의 네트워크 관리 시스템에 적합한 진보된 이벤트 명세 방법을 제안한다. 구체적으로, 계층적 이벤트 구조를 지원하도록 모델을 확장하고 관점지향 프로그래밍의 교차점(point cut)을 이벤트로 인식하도록 한다. 또한 이벤트 명세를 인스턴스(instance) 단위로 할 수 있도록 이벤트 연산자를 제공하고 시간적 관계를 원활하게 표현할 수 있도록 한다. 마지막으로 다른 이벤트 모델과의 비교를 통해 본 이벤트 모델의 장점을 제시한다.

In this study, we present an extended complex event model by considering both of the complex event and the aspect-oriented programming. We propose an advanced scheme for the event specification suited for the event correlation based network management systems by merging these two models. Specifically, we extend the model to support hierarchical event structures and let the model recognize point-cuts of aspect-oriented programming as events. We provide the event operators designed to specify the events on instances and handle temporal relations of the instances. Lastly, we compare the proposed model with other event models and present the benefits of it.

이벤트 스트림에 대한 Top-k 질의는 현재까지 유입된 이벤트 중 주어진 속성값이 큰 상위 k의 이벤트를 내어주는 것이다. 본 논문에서는 오픈소스 이벤트스트림 처리기인 Esper 엔진이 Top-k 질의를 효율적으로 지원할 수 있도록 확장하는 방법을 소개한다. 이를 위해 먼저 Esper 질의 언어의 문법을 Top-k 질의 지원을 위해 확장하였고, 상위 k개의 이벤트만 정렬하여 사용하는 효과적인 처리기를 제작하였다. 제작한 처리기와 Esper와의 결합을 위해서 추출 값 뷰(Derived View)를 확장하여 Top-k 기능을 구현하였다. 또한 제안한 방법은 기존의 Esper에서 제공되는 질의문 만을 통해서 작성한 질의에 비해 월등한 성능을 보이는 것을 실험으로 보였다.

A Top-k query on an event stream yields k events, with the highest values among the entire input events. This paper introduces our work of extending and integrating the open source event stream processing system, Esper, with a new querying facility so that its engine would efficiently process a Top-k query. For this purpose, we extend Esper query language to support Top-k queries and implement the corresponding module to process queries in an efficient way by caching the highest k elements. Our query processing module is integrated to Esper as an extension of a Derived View of Esper in order to support Top-k queries. Finally, the proposed query shows enormous improvement in performance compared to the built-in query facilities provided by Esper.

최근 소셜 미디어의 발달과 스마트폰의 확산으로 SNS(Social Network Service)가 활성화가 되면서 데이터양이 폭발적으로 증가하였다. 이에 맞춰 빅데이터 개념이 새롭게 대두되었으며, 빅데이터를 활용하기 위한 많은 방안이 연구되고 있다. 여러 기업이 보유한 빅데이터의 가치창출을 극대화하기 위해 기존 데이터와의 융합이 필요하며, 물리적, 논리적 저장구조가 다른 이기종 데이터 소스를 통합하고 관리하기 위한 시스템이 필요하다. 빅데이터를 처리하기 위한 시스템인 맵리듀스는 분산처리를 활용하여 빠른게 데이터를 처리한다는 이점이 있으나 모든 키워드에 대해 시스템을 구축하여 저장 및 검색 등의 과정을 거치므로 실시간 처리에 어려움이 따른다. 또한, 이기종 데이터를 처리하는 구조가 없어 복합 이벤트를 처리하는데 추가 비용이 발생할 수 있다. 이를 해결하는 방안으로 기존에 연구된 복합 이벤트 처리 시스템을 활용하여 실시간 복합 이벤트 탐지를 위한 기법을 제안하고자 한다. 복합 이벤트 처리 시스템은 서로 다른 이기종 데이터 소스로부터 각각의 데이터들을 통합하고 이벤트들의 조합이 가능하며 스트림 데이터를 즉시 처리할 수 있어 실시간 처리에 유용하다. 그러나 SNS, 인터넷 기사 등 텍스트 기반의 비정형 데이터를 텍스트형으로 관리하고 있어 빅데이터에 대한 질의가 요청될 때마다 문자열 비교를 해야 하므로 성능저하가 발생할 여지가 있다. 따라서 복합 이벤트 처리 시스템에서 비정형 데이터를 관리하고 질의처리가 가능하도록 문자열의 논리적 스키마를 부여하고 데이터 통합 기능을 제안한다. 그리고 키워드 셋을 이용한 필터링 기능으로 문자열의 키워드를 정수형으로 변환함으로써 반복적인 비교 연산을 줄인다. 또한, 복합 이벤트 처리 시스템을 활용하면 인 메모리(In-memory)에서 실시간 스트림 데이터를 처리함으로써 디스크에 저장하고 불러들이는 시간을 줄여 성능 향상을 가져온다.

Recently, due to the growth of social media and spread of smart-phone, the amount of data has considerably increased by full use of SNS (Social Network Service). According to it, the Big Data concept is come up and many researchers are seeking solutions to make the best use of big data. To maximize the creative value of the big data held by many companies, it is required to combine them with existing data. The physical and theoretical storage structures of data sources are so different that a system which can integrate and manage them is needed. In order to process big data, MapReduce is developed as a system which has advantages over processing data fast by distributed processing. However, it is difficult to construct and store a system for all key words. Due to the process of storage and search, it is to some extent difficult to do real-time processing. And it makes extra expenses to process complex event without structure of processing different data. In order to solve this problem, the existing Complex Event Processing System is supposed to be used. When it comes to complex event processing system, it gets data from different sources and combines them with each other to make it possible to do complex event processing that is useful for real-time processing specially in stream data. Nevertheless, unstructured data based on text of SNS and internet articles is managed as text type and there is a need to compare strings every time the query processing should be done. And it results in poor performance. Therefore, we try to make it possible to manage unstructured data and do query process fast in complex event processing system. And we extend the data complex function for giving theoretical schema of string. It is completed by changing the string key word into integer type with filtering which uses keyword set. In addition, by using the Complex Event Processing System and processing stream data at real-time of in-memory, we try to reduce the time of reading the query processing after it is stored in the disk.