SparQLing : SparkSQL 기반 대용량 트리플 데이터를 위한 SPARQL 질의 시스템 구축

전명중(MyungJoong Jeon),홍진영(JinYoung Hong),박영택(YoungTack Park) Korean Institute of Information Scientists and Eng 2016 정보과학회논문지 Vol.43 No.4

Every year, RDFS data tends further toward scalability; hence, the manner of SPARQL processing needs to be changed for fast query. The query processing method of SPARQL has been studied using a scalable distributed processing framework. Current studies indicate that the query engine based on the scalable distributed processing framework i.e., Hadoop(MapReduce) is not suitable for real-time processing because of the repetitive tasks; in addition, it is difficult to construct a query engine based on an In-memory Distributed Query engine, because distributed structure on the low-level is required to be considered. In this paper, we proposed a method to construct a query engine for improving the speed of the query process with the mass triple data. The query engine processes the query of SPARQL using the SparkSQL, which is an In-memory based, distributed query processing framework. SparkSQL is a high-level distributed query engine that facilitates existing SQL statement. In order to process the SPARQL query, after generating the Algebra Tree using Jena, the Algebra Tree is required to be translated to Spark Algebra Tree for application in the Spark system, and construction of the system that generated the SparkSQL query. Furthermore, we proposed the design of triple property table based on DataFrame for more efficient query processing in the Spark system. Finally, we verified the validity through comparative evaluation with the query engine, which is the existing distributed processing framework.

대용량 데이터 기반 SPARQL 질의결과에 대한 통합 설명 시스템

전명중(MyungJoong Jeon),박현규(HyunKyu Park),박영택(YoungTack Park) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.10

Recently, there has been an increasing demand for an explanation of query results in a variety of QA systems and expert systems. However, the systems being studied today only focus on the scalable query processing. Therefore, this paper proposes an integrated system that explains the causal relationship to the query results based on large volumes of retrievable data. The system uses a distributed rule-based SWRL engine for reasoning about large amounts of knowledge. And in this case uses evidence of reasoning as input for a distributed ATMS to express the structure of the causal relationship. Finally, after obtaining the answers using SPARQLGX, and a scalable SPARQL query processor, this system explains the evidence of answers using a reference to the previously established dependency structure. The evaluation of the proposed explanation system used the benchmark data(Lehigh University Benchmark) and used 14 test queries provided by the LUBM for evaluating the response time and explanation time in this case.

온톨로지 기반 무인기의 자율 위협 상황 인지 시스템

전명중(MyungJoong Jeon),박현규(HyunKyu Park),박영택(YoungTack Park),윤형식(Hyung-Sik Yoon),김윤근(Yun-Geun Kim) Korean Institute of Information Scientists and Eng 2019 정보과학회논문지 Vol.46 No.10

An autonomous threat situational awareness system is necessary for Unmanned Aerial Vehicles(UAVs) in a variety of fields. Although various of approaches to autonomous threat situational awareness have been proposed, most of them involved reasoning of the semantic information of the object. Therefore, in this paper, based on the existing semantic information of an object, we propose a method to achieve threat situational awareness for a UAV based on reasoning of the relationship between the objects. In this paper, there are three main ways that are used to recognize a threat to a UAV: First, information on the recognized objects is expressed using an LOD(Level of Detail)-based grid map. Second, the concepts of objects around the UAV are defined as ontology while the relationships and situations between objects are defined as SWRL(Semantic Web Rule Language). Third, through the ontology reasoning, the simulator visualizes the recognition of the relationships of objects and threat situations for the UAV.

온톨로지와 CNN 기반의 무인기와 주변 개체 간 위협 관계 추론

전명중(MyungJoong Jeon),이민호(MinHo Lee),박현규(HyunKyu Park),박영택(YoungTack Park),윤형식(Hyung-Sik Yoon),김윤근(Yun-Geun Kim) Korean Institute of Information Scientists and Eng 2020 정보과학회논문지 Vol.47 No.4

The technology that identifies the relationship between surrounding objects and recognizes the situation is considered as an important and necessary technology in various areas. Numerous methodologies are being studied for this purpose. Most of the studies have solved the problem by building the domain knowledge into ontology for reasoning of situation awareness. However, based on the existing approach; it is difficult to deal with new situations in the absence of domain experts due to the dependency of experts on relevant domain knowledge. In addition, it is difficult to build the knowledge to infer situations that experts have not considered. Therefore, this study proposes a model for using ontology and CNN for reasoning of the relationships between UAVs and surrounding objects to solve the existing problems. Based on the assumption that the accuracy of ontology reasoning is insufficient, first, the reasoning was performed using the information from the detected surrounding objects. Later, the results of ontology reasoning are revised by CNN inference. Due to the limitations of actual data acquisition, data generator was built to generate data similar to real data. For evaluation of this study, two models of relationships between two objects were built and evaluated; both the models showed over 90% accuracy.

인메모리 기반 병렬 컴퓨팅 그래프 구조를 이용한 대용량 RDFS 추론

전명중(MyungJoong Jeon),소치승(ChiSeoung So),바트셀렘(Batselem Jagvaral),김강필(KangPil Kim),김진(Jin Kim),홍진영(JinYoung Hong),박영택(YoungTack Park) Korean Institute of Information Scientists and Eng 2015 정보과학회논문지 Vol.42 No.8

In recent years, there has been a growing interest in RDFS Inference to build a rich knowledge base. However, it is difficult to improve the inference performance with large data by using a single machine. Therefore, researchers are investigating the development of a RDFS inference engine for a distributed computing environment. However, the existing inference engines cannot process data in real-time, are difficult to implement, and are vulnerable to repetitive tasks. In order to overcome these problems, we propose a method to construct an in-memory distributed inference engine that uses a parallel graph structure. In general, the ontology based on a triple structure possesses a graph structure. Thus, it is intuitive to design a graph structure-based inference engine. Moreover, the RDFS inference rule can be implemented by utilizing the operator of the graph structure, and we can thus design the inference engine according to the graph structure, and not the structure of the data table. In this study, we evaluate the proposed inference engine by using the LUBM1000 and LUBM3000 data to test the speed of the inference. The results of our experiment indicate that the proposed in-memory distributed inference engine achieved a performance of about 10 times faster than an in-storage inference engine.

GPU 클러스터 기반 대용량 온톨로지 추론

홍진영(JinYung Hong),전명중(MyungJoong Jeon),박영택(YoungTack Park) Korean Institute of Information Scientists and Eng 2016 정보과학회논문지 Vol.43 No.1

In recent years, there has been a need for techniques for large-scale ontology inference in order to infer new knowledge from existing knowledge at a high speed, and for a diversity of semantic services. With the recent advances in distributed computing, developments of ontology inference engines have mostly been studied based on Hadoop or Spark frameworks on large clusters. Parallel programming techniques using GPGPU, which utilizes many cores when compared with CPU, is also used for ontology inference. In this paper, by combining the advantages of both techniques, we propose a new method for reasoning large RDFS ontology data using a Spark in-memory framework and inferencing distributed data at a high speed using GPGPU. Using GPGPU, ontology reasoning over high-capacity data can be performed as a low cost with higher efficiency over conventional inference methods. In addition, we show that GPGPU can reduce the data workload on each node through the Spark cluster. In order to evaluate our approach, we used LUBM ranging from 10 to 120. Our experimental results showed that our proposed reasoning engine performs 7 times faster than a conventional approach which uses a Spark in-memory inference engine.

미디어 온톨로지의 시공간 정보 확장을 위한 분산 인메모리 기반의 대용량 RDFS 추론 및 질의 처리 엔진

이완곤(Wan-Gon Lee),이남기(Nam-Gee Lee),전명중(MyungJoong Jeon),박영택(Young-Tack Park) Korean Institute of Information Scientists and Eng 2016 정보과학회논문지 Vol.43 No.9

Providing a semantic knowledge system using media ontologies requires not only conventional axiom reasoning but also knowledge extension based on various types of reasoning. In particular, spatio-temporal information can be used in a variety of artificial intelligence applications and the importance of spatio-temporal reasoning and expression is continuously increasing. In this paper, we append the LOD data related to the public address system to large-scale media ontologies in order to utilize spatial inference in reasoning. We propose an RDFS/Spatial inference system by utilizing distributed memory-based framework for reasoning about large-scale ontologies annotated with spatial information. In addition, we describe a distributed spatio-temporal SPARQL parallel query processing method designed for large scale ontology data annotated with spatio-temporal information. In order to evaluate the performance of our system, we conducted experiments using LUBM and BSBM data sets for ontology reasoning and query processing benchmark.

개인 경로 학습을 위한 GPS 좌표 기반 이동 궤적 추출

양승국(SeoungKuk Yang ),백혜정 (HaeJung Baek),김제민 (JeMin Kim),전명중 (Myungjoong Jeon),박영택(Youngtack Park) 한국정보과학회 2011 한국정보과학회 학술발표논문집 Vol.38 No.2D

개인 경로의 학습은 사용자의 위치에 대한 경험과 지식을 가지고 있음으로써 사용자에게 지능형 서비스를 제공하기에 적합하다. 이러한 경로 학습을 위해서는 사용자의 단순 위치정보와 더불어 사용자의 경로를 인지할 수 있는 이동 궤적이 필요하다. 현재 사용자의 위치정보를 파악하는 지표로 사용되는 GPS 좌표는 오류로 인한 잘못된 좌표 추출이 발생하며 대용량의 누적된 GPS 좌표로 경로 계산 시 높은 시간 복잡도를 유발한다. 본 논문에서는 이러한 단점을 개선하기 위하여 GPS 좌표의 오류 제거 경로의 단순화 경로의 유사성 검출의 세 단계로 구성된 이동 궤적 추출 기법을 제안한다. 각 단계에서의 기법은 첫 단계로 초단위로 수집된 GPS 좌표간의 이웃하는 좌표와의 속도 각도 WiFi SSID 차이를 통하여 부정확하게 수신된 좌표를 제거하고 다음단계에서 수신된 좌표 중에서 사용자의 이동 궤적의 특징을 잘 반영하는 좌표를 결정하여 경로를 추출하였다. 또한 추출된 특징이 되는 좌표에 가중치를 부여하여 가중치에 따라 좌표의 수를 결정 가능하게 하였다. 세 번째 단계에서는 단순화된 경로의 거리를 통한 유사성을 검출하여 높은 형상유사도를 가지는 이동 궤적을 추출하였다. 추출된 이동 궤적은 GPS오류 검출 전의 경로보다 평균 2M의 거리의 오차를 줄이고 특징을 가지는 좌표로 단순화되었다. 본 논문에서 제안하는 기법은 특히 학교와 같은 근거리 범위 내에서 사람의 경로를 파악하는데 효과적으로 적용될 수 있다.