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.

최근 페이스북, 트위터 등의 SNS(Social Networking Service)가 발전함에 따라, 사용자가 생성하는 데이터가 급격히 증가하고 있다. 사용자 데이터는 민감한 개인정보를 포함하기 때문에, 원본 데이터를 공격자로부터 보호하기 위해서는 데이터를 암호화하는 것이 필요하다. 따라서 암호화된 데이터의 복호화 없이 질의를 처리하는 암호화 질의 처리 기법이 제안되었다. 그러나 기존의 질의처리 기법은 암호화 데이터에 대한 색인 구조를 구축하고 이를 순차적 으로 탐색하기 때문에, 데이터의 크기가 증가함에 따라 질의탐색 비용이 증가하는 문제점이 존재한다. 이를 위해, P.B.Volk, et al.은 prefix 트리 기반 병렬 질의처리 알고리즘을 제안하였다. 제안하는 알고리즘은 암호화된 데이 터를 위해 prefix 트리 구조를 구축하고, 트리를 부분 트리로 분할하여 생성된 모든 부분 트리를 병렬적으로 탐색한 다. 그러나 이 알고리즘은 모든 부분 트리를 탐색하기 때문에, 트리 깊이에 따라 연산 비용이 급격히 증가하는 문제 점이 존재한다. 아울러, 이 알고리즘은 범위 질의나 부분 매칭 등의 다양한 질의를 지원하지 못하는 문제점이 존재 한다. 이러한 문제를 해결하기 위해, 본 논문에서는 prefix 트리 및 해시 테이블을 사용하는 GPU 기반 병렬 질의처 리 알고리즘을 제안한다. 제안하는 알고리즘은 prefix 트리 loop-up 테이블을 사용하여 범위 질의 및 부분매칭 질 의를 지원한다. 아울러 제안하는 알고리즘이 기존 P.B.Volk, et al. 의 알고리즘보다 검색 시간 측면에서 약 30% 우수한 성능을 나타냄을 보인다.

Recently, social networking services, such as Facebook and Twitter, have been widely used, so the amount of the data created by users has been dramatically increased. Because the user-created data can contain privacy information, it is required to encrypt the data for protecting the original data from adversaries. Thus, an encrypted query processing scheme has been proposed to process the query without the decryption of the encrypted data. The existing schemes construct an index for the encrypted data, so they can process the query by sequentially accessing the index. As a result, the query processing cost increases as the amount of the data is increased. For this, P.B.Volk, et al. proposed a prefix-tree based parallel query processing algorithm. The algorithm constructs a prefix-tree structure for the encrypted data and searches all sub-trees on GPU in parallel by dividing the tree into sub-trees. However, the algorithm has a problem that its computational cost is highly increased according to the depth of the tree because it searches all sub-trees. In addition, the algorithm does not support the various types of queries, such as a range query and a partial matching query. To solve these problems, we, in this paper, propose a GPU-based parallel query processing algorithm using both a prefix-tree and a hash table. By using the prefix-tree look-up table, the proposed algorithm can support both a range query and a partial matching query. In addition, we show that the proposed algorithm is about 30% better on retrieval performance than the existing algorithm by P.B.Volk, et al.

본 연구에서는 객체지향 데이터베이스 모델링과 추론적 기법을 사용하여 체육 우수선수 조기 발굴을 위한 객체 지향 데이터베이스 시스템의 지능적 질의 처리 기법에 대하여 논의한다. 먼저 우수선수 조기 발굴 데이터베이스 시스템 을 객체지향 기법을 사용하여 분석 대상을 계층 구조적으로 분류한다. 이런 분류 후 질의의 내포적 답을 구하기 위하여 규칙 생성, 선 분석, 분석의 세 단계로 구성된 알고리즘을 제안한다. 우수선수를 조기에 발굴하기 위한 알고리즘은 우수 선수 발굴과 육성에 활용할 수 있으며 운동종목, 학년, 성별 등 우수선수 선발 목적에 따라 기준 점수를 다르게 적용할 수 있을 뿐 아니라 다른 체육 영역으로 확대 적용할 수 있다. 더 나아가 경기력 진단 및 훈련에도 이용될 수 있다. 기존 종목별 우수선수 중에서 국가대표선수를 선발 할 때도 지능적 질의 처리 알고리즘을 사용하면 빠르고 객관적으로 선수 들을 선발 할 수 있다. 본 연구는 융합 연구가 아니면 할 수 없는 영역으로 융합 연구의 필요성이 절실한 스포츠영역의 발전에도 크게 기여할 것이다.

In this paper, we present an intelligent query processing in object-oriented databases systems for early search of gifted athletics using object-oriented databases modeling and deductive approach. We first classify the analysis target as hierarchical databases systems for early search of gifted athletics using object-oriented techniques. After such a classification, we present an algorithm to obtain implicit answers which consists of three phases: rule representation and reformation, pre-resolution, and resolution. The algorithm for early search of gifted athletes can be utilized for cultivating outstanding athletes, can be modified depending on the selection criteria of athletes, grades, and gender, and can be applied to other sports areas. Furthermore, it can be used for athletic diagnoses and training. Even for selection of national athletes among the outstanding athletes, the intelligent question processing algorithm can be used for fast and objective process. It can also be applied for evaluation of athletic performance and training. This research will make an important contribution to the development of interdisciplinary research on various areas of sports, where integrated research is essential.

최근 클라우드 컴퓨에 한 심이 고조됨에 따라, 클라우드에 아웃소싱된 암호화 데이터베이스 상에서의 역 질 의처리 연구가 활발히 진행되고 있다. 그러나 기존 연구 데이터 근 패턴 보호를 지원하는 연구는 무하다. 따 라서 본 논문에서는 데이터 보호, 사용자 질의 보호 데이터 근 패턴 보호를 모두 지원하는 암호화 데이터베이 스 상에서의 역 질의처리 알고리즘을 제안한다. 성능평가를 통해, 제안하는 기법이 정보보호를 지원하는 동시에 효율인 처리 성능을 제공함을 보인다.

As the cloud computing has attracted much interests recently, the study on a range query processing on encrypted databases outsourced in the cloud have been actively performed. However, there is no existing work that hides data access patterns. Therefore, in this paper we propose a new range query processing algorithm on the encrypted database that not only preserves the confidentiality of data and user query but also conceals the data access patterns. Through the performance analysis, we show that the proposed range query processing algorithm can efficiently process a range query while preserving privacy.

최근 클라우드 컴퓨팅이 발전함에 따라 데이터베이스 아웃소싱에 대한 관심이 증가하고 있다. 그러나 데이터베이스 를 아웃소싱하는 경우, 데이터 소유자의 정보가 내부 및 외부 공격자에게 노출되는 문제점을 지닌다. 따라서 본 논 문에서는 프라이버시 보호를 지원하는 십진수 기반의 암호화 연산 프로토콜을 제안한다. 제안하는 프로토콜은 이진 수 기반의 암호화 연산 프로토콜과 달리 bit length 만큼 반복 연산을 수행하지 않기 때문에 연산의 효율성을 향상 시킨다. 아울러 십진수 기반의 암호화 연산 프로토콜을 이용한 kNN 질의처리 알고리즘을 제안한다. 제안하는 kNN 질의처리 알고리즘은 암호화 연산의 효율성을 향상시킨 십진수 기반의 프로토콜을 사용함으로써 높은 질의 처 리 성능을 제공한다. 한편 제안하는 알고리즘의 보안 분석을 수행하여, 데이터 보호, 질의 보호, 접근 패턴 보호를 지원함을 증명한다. 마지막으로 성능평가를 통해, 제안하는 kNN 질의처리 알고리즘이 기존 알고리즘에 비해서 약 5배의 질의처리 성능 향상이 있음을 보인다.

With the development of cloud computing, interest in database outsourcing has recently increased. However, when the database is outsourced, there is a problem in that the information of the data owner is exposed to internal and external attackers. Therefore, in this paper, we propose decimalbased encryption operation protocols that support privacy preservation. The proposed protocols improve operational efficiency compared to binary-based encryption operation protocols by eliminating the need for repetitive operations based on bit length. In addition, we propose a privacy-preserving kNN query processing algorithm using decimal-based encryption operation protocols. The proposed kNN query processing algorithm provides high query processing performance by utilizing efficient decimal-based protocols which enhance the efficiency of the encryption operations. Meanwhile, the security analysis of the proposed algorithm is performed to prove its data protection, query protection, and access pattern protection. Through our performance analysis, the proposed kNN query processing algorithm shows about 5 times better query processing performance, compared with the existing algorithms.

세 대 이상의 빠르게 이동하는 차량들은 때론 서로 모이기 위해 모일 장소를 알아야 될 필요가 있다. 이때 각 차량들은 다른 속도를 가지고 있으며, 여러 대의 차량이 짧은 거리를 이동해 빠르게 모이게 하기 위한 방법이 필요하다. 이러한 방법은 그룹기반의 최근접 질의로서 기존의 연구가 진행되어 왔으나, 기존 연구는 이동하지 않는 객체들을 다루고 있어 움직이는 차량에 적용하기엔 어려운 점이 있다. 본 논문에서는 이동하는 차량들에게 효율적인 차량간 최근접 질의 처리 기법을 제안한다. 본 기법은 각 차량의 움직이는 방향과 속도를 기반으로 모든 차량이 최소 시간에 모일 수 있는 최근접 질의점을 찾을 수 있다. 본 기법은 효율적으로 질의점의 그룹을 표현하는 센트로이드를 통해 그룹기반의 최근접을 계산한다. 실험 결과는 제안하는 기법이 움직이는 차량의 최근접 질의 처리에 효율적임을 보여준다.

그래는 생물학, 화학, 사회 계망 데이터 등 복잡한 구조의 데이터를 모델링하는데 리 사용되는 자료구조이다. 이러한 그래 데이터에서 서로 유사한 데이터를 찾아내는 것은 매우 다양한 응용에서 필수으로 요구되는 작업이 다. 본 논문에서는 화학 그래에 을 맞추어, 필터링-검증 임워크 기반의 유사도 기반 그래 질의 처리 기 법을 제안한다. 계산량이 많은 그래 데이터의 특징에 맞는 필터링 방법을 제안하고, 후보 그래들을 효율으로 검증하기 한 기법을 제안한다. 한, 규모 데이터에서 실시간 분석이 가능할 수 있도록 MapReduce를 이용 하는 분산처리 기법을 제안하고, 실 데이터에 한 실험을 통해 본 논문에서 제안하는 방법이 유사도 기반 그래 질의 처리의 성능을 크게 개선함을 입증한다.

Graph is widely used to model complex structured data such as biomedical data, chemical data, and social network data. It is essential to find graphs similar to a given query in many applications. In this paper, we focus on chemical graph data and propose a graph similarity query processing technique based on a filtering and verification framework. We propose a filtering technique considering the heavy computation requirement of graph data and develop a novel technique to efficiently verify candidate graphs. We also propose a distributed processing framework using MapReduce to support large scale graph data. Through the experiments on real chemical data, we show that the proposed technique substantially improves the performance of query processing.

대용량 민감 데이터에 대한 아웃소싱이 각광받음에 따라, 이를 보호하기 위한 데이터 암호화 기법이 요구되고 있다. 이에 따라, 대용량 암호화 데이터 관리를 지원하는 분산 색인 구조 및 암호화된 데이터 상에서의 질의처리 알고리즘 이 요구되고 있다. 그러나 기존 분산 색인 구조 중 암호화 데이터의 특성을 고려한 연구는 존재하지 않는다. 또한, 기존 암호화 질의처리 알고리즘은 지원 가능한 질의 타입이 한정적이며, 상이한 방식으로 암호화된 컬럼 간 연산을 지원하지 못하는 문제점이 존재한다. 이를 해결하기 위해, 본 논문에서는 비트맵 기반 분산 색인 구조 및 암호화 질 의처리 기법을 제안한다. 제안하는 분산 암호화 색인 구조는 데이터 프라이버시를 보장하며, 다양한 종류의 질의에 대해 성능 향상을 제공한다. 아울러, 제안하는 암호화 질의처리 기법은 복호화를 수행하지 않고 질의처리를 수행함 으로써 데이터 보호 수준을 향상시키며, 높은 질의 처리 성능 및 정확도를 보장한다. 아울러 성능평가를 통해 제안 하는 색인구조 및 암호화 질의처리 기법이 대용량 민감 데이터 보호에 적합함을 보인다.

As the outsourcing of the large sensitive data has been highlighted, data encryption schemes to protect the sensitive data are required. Accordingly, it is necessary to develop not only a distributed index structure to manage the large amount of encrypted data, but also a query processing scheme over the encrypted data. However, there has been no index structure considering the encrypted data. Existing query processing schemes over the encrypted data can support limited types of queries. In addition, the schemes cannot support operations among data with different columns because they use different types of encryption schemes depending on their attribute type. To solve these problems, in this paper, we propose a bitmap-based distributed index structure and a query processing scheme for the encrypted data. The proposed distributed index structure guarantees data privacy preservation and performance improvement for the various types of queries. In addition, by processing a query over the encrypted data without data decryption, the proposed query processing scheme guarantees the high query performance and accuracy while preserving the data privacy. Finally, we show from our performance evaluation that our proposed index structure and query processing scheme are suitable for protecting the data privacy of the large sensitive data.

아파치 스파크(Apache Spark)는 오픈 소스, 분산, 인메모리 컴퓨팅 프레임워크 및 아키텍처이다. 아파치 스파크를 통해 데이터 병렬 및 내결함성을 갖춘 분산 시스템 클러스터에서 실행되기 때문에 농업, 정보통신 산업 등 다양한 분야에 적용 및 활용할 수 있다. 한편, 빅데이터 시대를 맞아 많은 시공간 데이터가 발생하고 있다. 그러나, 아파치 스파크를 통한 분산 컴퓨팅 환경에서 많은 연산이 필요한 조인과 같은 연산을 제공하고 있지 않기 때문에 효율적으로 처리하지 못한다. 따라서, 본 논문에서는 대용량 시공간 데이터를 이용하여 그리드 분할 기법에 근거한 withindistance, contain 조인 질의처리 알고리즘을 제안한다. 성능평가 결과, 제안한 알고리즘이 기존 알고리즘보다 약 20%의 우수한 성능을 보인다.

Apache Spark is an open source, distributed, in-memory computing framework and architecture. Because it runs on a distributed system cluster with data parallelism and fault tolerance through Apache Spark, it can be applied and utilized in various fields such as agriculture, information and communication industries. Meanwhile, in the era of big data, large amount of spatio-temporal data is being generated. However, Apache Spark cannot efficiently process join operation because it does not support join operation that requires many computations in a distributed computing environment. Therefore, in this paper, we proposed an join query processing algorithm, i.e., withindistance and contain join, based on grid partitioning technique using large-scale spatio-temporal data. As a result of performance evaluation, our algorithm shows 20% better performance than the existing algorithm in terms of query processing time.

Query optimization is a very complex task for commercial databases in case of performance issue that needs to be well known of entire structure of database. In desktop or web application at the back end, query processing aspires to be major factor for finding the better execution. We address the problem of SQL query optimization merely from the perspective of query response time in different databases invoked multiple queries are imparted in admission database management system, leveraging join and complex queries. Our proposed method adhering with respect to the underlying topics, to tune (Select, complex and join SQL) queries with optimized execution plan using PL/SQL features by incorporating database objects such as procedures, triggers and methods to improve query performance, instead amendment of query semantics which lessen time of developer or administrator to do manual tuning.

Due to the proliferation of low cost wireless sensors, there is growing research interest in their applications, for example, in home healthcare and location tracking. However, due to sensors’ energy resource constraint, some possible applications of sensors have been restricted. In particular, in applications concerning deployment of mobile sensors in dynamic environments, high amounts of energy are consumed by sensors to maintain routing tables. Although existing methods have been proposed to query data from sensors without the use of any routing tables, these methods typically require redundant data to be sent back to the sink and not all of the aggregation functions could be executed precisely. In this paper, we modify an existing method to provide more accurate query answers and extend the lifetime of a wireless sensor network (WSN). According to our simulation, this method outperforms the existing method our approach modifies.

This paper proposes a novel secure query processing model for semantic sensor networks. A semantic sensor network (SSN) is a sensor network which includes semantics of sensory data and context information, and relationships between the semantics. In brief, SSN is an extension of the current Sensor Network (SN) and realized by using Semantic Web (SW) technologies. Although various researches have been activated on SSN, there is little activity on how to access data in semantic sensor networks in safe. This paper proposes a new access control model for secure query processing under semantic sensor networks. The proposed security model is based on RBAC (Role-Based Access Control) and enables secure access control of ontology for SSNs.

XML message brokers have a lot of importance because XML has become a practical standard for data exchange in many applications. Message brokers covered in this document store many users. This paper is a study of the processing of twig pattern queries in XML documents using branching node streams in XML message broker structures. This work is about query processing in XML documents, especially for query processing with XML twig patterns in the XML message broker structure and proposed a method to reduce query processing time when parsing documents with XML twig patterns by processing information. In this paper, the twig pattern query processing method of documents using the branching node stream removes the twigging value of the branch node that does not include the labeling value of the branch node stream when it receives a twig query from the client. In this paper, the leaf node discovery time can be reduced by reducing the navigation time of nodes in XML documents that are matched to leaf nodes in twig queries for client twig queries. Overall, the overall processing time to respond to queries is reduced, allowing for rapid questionanswer processing.

The conventional researches on a distributed On-Line Analytical Processing (OLAP) system have been in hardship to be adapted to real business environment. However, the recent spread of Cloud PaaS (Platform as a Services) provides new chances in the field of a distributed OLAP. OLAP query execution costs many minutes by its enormous data and OLAP query properties. On the other hand, MOLAP has fast responses. But it has a physical space limit to materialize all cells in possible combinations. Therefore, MOLAP is unsuited to analyze large data. In this paper, to provide not only flexibility and expandability of ROLAP but also the speediness of MOLAP, the cloud server architecture is proposed which shares clients’ cube cache by P2P and manages central index on cube data on P2P nodes. In this paper, each node acts as not only a P2P duplicator of aggregated data cubes but also a hybrid server to process queries on sub-cubes. Also, the requests on server are confined to non-aggregated areas and multiple client nodes exchange data simultaneously and asynchronously in the proposed Cloud P2P OLAP. In particular, data are retrieved from physically or logically adjacent nodes. With time series properties, the volume of requested data is minimized and the reuse of past cache data is focused. While central managed P2P has blocked the extendibility, Cloud P2P OLAP guarantees the performance and the extendibility. While Grid OLAP should keep self-distributed system, Cloud use services as much as used by public services. Therefore, Cloud P2P OLAP solves a lot of theoretical limits of conventional distributed OLAP.

Sensors have a function to gather environmental data in sensor networks. They operate by limited, small-size battery depending on applications. Thus there are several kinds of research efforts to make better use of local energy. In this paper, we propose multi-attribute query processing algorithm (Section Bit based scheme (SB)). In the initial setup, routing path is determined based on parent-child relationship by two phases and some partial information (section bits) about the sensing data of their descendant nodes are delivered to the parent in second phase (parent selection phase); that is, an attribute is divided into the same, small size, N sections and one bit is used to each section. By using a small volume of local information, it is possible to protect nodes from propagating messages who does not become the help during query processing. Finally, we measure energy efficiency by counting the number of messages. Experimental results show the improved energy efficiency of the proposed algorithm.

Query processing technology has recently received a lot of attention in the business intelligence and information service communities. However, the existing approaches can not efficiently optimize the query performance in the uncertain big data environment. In this paper, we propose QPPUBG, a novel and efficient query processing platform for uncertain big data. QPPUBG mainly includes four modules: (i) query equivalence reconstructing for uncertain big data; (ii) multiple query optimization over probability relation components; (iii) query execution plan constructing over probability relation components, and (iv) physical implementation solution of query for uncertain big data. Specially, QPPUBG can support the possible world instance semantics and efficiently handle arbitrary decision spaces. Moreover, QPPUBG can seamlessly integrate the above four modules into the modern parallel computation frameworks. We present the extensive experiments that demonstrate QPPUBG is both efficient and effective.

Peer-to-peer (P2P) databases are becoming prevalent on the Internet for distribution and sharing of documents, applications, and other digital media. The problem of answering large-scale ad hoc analysis queries, for example, aggregation queries, on these databases poses unique challenges. Exact solutions can be time consuming and difficult to implement, for the distributed and dynamic nature of P2P databases. In this paper, we have presented novel sampling-based techniques for approximate answering of ad hoc aggregation queries in such databases. Computing a high-quality random sample of the database efficiently in the P2P environment is complicated due to several factors: the data is distributed (usually in uneven quantities) across many peers, within each peer the data is often highly correlated and moreover, even collecting a random sample of the peers is difficult to accomplish. To formulate these problems, developed software uses approach, based on random walks of the P2P graph, as well as block-level sampling techniques. We have presented here extensive experimental evaluations to demonstrate the feasibility of our solution. The modules that are included in this project are: 1. Peer-to-Peer Node Construction 2. Random Selection of Node 3. Selection of Records 4. Performance Evaluation

With the recent development of the ubiquitous computing technology, there are increasing interest and research in technologies such as sensors and RFID related to information recognition and location positioning in various ubiquitous fields. Especially, RTLS (Real-Time Locating Services) dealing with spatio-temporal data is emerging as a promising technology. For these reasons, the ISO/IEC published RTLS standard specification for compatibility and interoperability in RTLS. Therefore, in this paper, we designed and implemented Spatio-temporal Query Processing Systems for efficiently managing and searching the incoming Spatio-temporal data stream of moving objects. Spatio-temporal Query Processing Systems's spatio-temporal middleware maintains interoperability among heterogeneous devices and guarantees data integrity in query processing through real time processing of unceasing spatio-temporal data streams and two way synchronization of spatio-temporal DBMSs. Web Server uses the SOAP(Simple Object Access Protocol) message between client and server for interoperability and translates client's SOAP message into CQL(Continuous Query Language) of the spatio-temporal middleware.