Query Processing Method Research Articles

Top-k Query Processing and Malicious Node Identification Based on Node Grouping in MANETs

In mobile ad hoc networks (MANETs), it is effective to retrieve data items using top-k query. However, accurate results may not be acquired in environments when malicious nodes are present. In this paper, we assume that malicious nodes attempt to replace necessary data items with unnecessary ones (we call these data replacement attacks), and propose methods for top-k query processing and malicious node identification based on node grouping in MANETs. In order to maintain the accuracy of the query result, nodes reply with k data items with the highest score along multiple routes, and the query-issuing node tries to detect attacks from the information attached to the reply messages. After detecting attacks, the query-issuing node tries to identify the malicious nodes through message exchanges with other nodes. When multiple malicious nodes are present, the query-issuing node may not be able to identify all malicious nodes at a single query. It is effective for a node to share information about the identified malicious nodes with other nodes. In our method, each node divides all nodes into groups by using the similarity of the information about the identified malicious nodes. Then, it identifies malicious nodes based on the information on the groups. We conduct simulation experiments by using a network simulator, QualNet5.2, to verify that our method achieves high accuracy of the query result and identifies malicious nodes.

Skyline Discovery and Composition of Multi-Cloud Mashup Services

A cloud mashup is composed of multiple services with shared datasets and integrated functionalities. For example, the elastic compute cloud (EC2) provided by Amazon Web Service (AWS), the authentication and authorization services provided by Facebook, and the Map service provided by Google can all be mashed up to deliver real-time, personalized driving route recommendation service. To discover qualified services and compose them with guaranteed quality of service (QoS), we propose an integrated skyline query processing method for building up cloud mashup applications. We use a similarity test to achieve optimal localized skyline. This mashup method scales well with the growing number of cloud sites involved in the mashup applications. Faster skyline selection, reduced composition time, dataset sharing, and resources integration assure the QoS over multiple clouds. We experiment with the quality of Web service (QWS) benchmark over 10,000 Web services along six QoS dimensions. By utilizing block-elimination, data-space partitioning, and service similarity pruning, the skyline process is shortened by three times, when compared with two state-of-the-art methods.

On the application of fuzzy set theory in relational database

In this paper, we introduced a model of retrieval system based on the theory of fuzzy sets in which we constructed a suitable storage structure with incomplete inverted files (they contain even some incomplete inverted files for a collection of fuzzy data of database). In this concept, we introduced a method for fuzzy query processing in the system and shown some retrieval strategies. The general response system was also defined and the ability of these strategies was investigated.

Performance and scalability of indexed subgraph query processing methods

Graph data management systems have become very popular as graphs are the natural data model for many applications. One of the main problems addressed by these systems is subgraph query processing; i.e., given a query graph, return all graphs that contain the query. The naive method for processing such queries is to perform a subgraph isomorphism test against each graph in the dataset. This obviously does not scale, as subgraph isomorphism is NP-Complete. Thus, many indexing methods have been proposed to reduce the number of candidate graphs that have to underpass the subgraph isomorphism test. In this paper, we identify a set of key factors-parameters, that influence the performance of related methods: namely, the number of nodes per graph, the graph density, the number of distinct labels, the number of graphs in the dataset, and the query graph size. We then conduct comprehensive and systematic experiments that analyze the sensitivity of the various methods on the values of the key parameters. Our aims are twofold: first to derive conclusions about the algorithms' relative performance, and, second, to stress-test all algorithms, deriving insights as to their scalability, and highlight how both performance and scalability depend on the above factors. We choose six well-established indexing methods, namely Grapes, CT-Index, GraphGrepSX, gIndex, Tree+Δ, and gCode, as representative approaches of the overall design space, including the most recent and best performing methods. We report on their index construction time and index size, and on query processing performance in terms of time and false positive ratio. We employ both real and synthetic datasets. Specifically, four real datasets of different characteristics are used: AIDS, PDBS, PCM, and PPI. In addition, we generate a large number of synthetic graph datasets, empowering us to systematically study the algorithms' performance and scalability versus the aforementioned key parameters.

SigMR: MapReduce-based SPARQL query processing by signature encoding and multi-way join

Large numbers of Resource Description Framework triples are available in Linked Data which can grow exponentially. It makes SPARQL query processing engines infeasible on a single machine. To address this scalability issue, MapReduce framework-based SPARQL engines have been proposed, but we note that these methods are limited in terms of join evaluations. The two-way join-based approach evaluates joins via a sequence of binary multiplications that require multiple MapReduce jobs, which involves costly disk accesses between MapReduce jobs. The multi-way join-based approach combines multiple two-way join operations, which allows the simultaneous evaluation of joins during one MapReduce job. However, the size of data for the MapReduce job might increase exponentially if a complex query is given. In this study, we propose SigMR, a pruning method for multi-way join-based SPARQL query processing in MapReduce. In the proposed approach, a SPARQL query can be evaluated in a single MapReduce job, where the size of data is reduced dramatically by pruning based on our signature encoding technique, thereby overcoming the weaknesses of the previous approaches. In experiments, we showed that the query processing time required was lower with our approach than existing MapReduce-based methods.

Design and Implementation of Spatial Operators and Energy-Efficient Query Processing Strategy in Wireless Sensor Network Database System

Database applications in wireless sensor networks very often demand data collection from sensor nodes of specific target regions. Design and development of spatial query expressions and energy-efficient query processing strategy are important issues for sensor network database systems. The existing sensor network database systems lack the needed sophistication for the space calculation of the target sensor nodes; hence, unnecessary query/data transmissions are required between the sensor nodes and the server. This paper describes our spatial operations and energy-efficient query processing methods that are designed and implemented in our sensor network database system called [Formula: see text]. With a set of spatial operators based on geometric parameters, such as Envelope, NearBy, Distance, Direction, and set theoretic operators, [Formula: see text] allows sensor network applications to easily specify the target space of interest. Our energy-efficient query processing strategy implements an in-network query management based on the lowest common ancestor (LCA) algorithm, so that the query processing cost for calculating the target spaces is greatly reduced by avoiding the need of heavy query/data transmissions between the base-station and target nodes. Performance evaluation shows that our proposed design and implementation of spatial query expressions and processing strategy achieve improved energy efficiency for database operations in the wireless sensor network.

A continuous reverse skyline query processing method in moving objects environments

Many studies on reverse skyline query processing have been done for various services. The existing reverse skyline query processing methods are based on dynamic skylines. There are no reverse skyline query processing algorithms based on metric spaces for location-based services. The existing methods for processing a reverse skyline query have the limitation of service domains and require the high costs of computation to provide various location-based services. In this paper, we propose a new reverse skyline query processing method that efficiently processes a query over the moving objects. In addition, the proposed method processes a continuous reverse skyline query efficiently. In order to show the superiority of the proposed method, we compare it with the previous reverse skyline query processing method in various environments. As a result, the proposed method achieves better performance than the existing method.

Network Voronoi Diagram on uncertain objects for nearest neighbor queries

In the past decade, probabilistic nearest neighbor (PNN) query processing has received significant research attention due to the development of mobile smart terminals and the advances in wireless communication technologies. However, to the best of our knowledge, most of the existing PNN-oriented studies are aimed at the Euclidean space and cannot be readily extended to road networks. This paper takes the first step toward processing PNN queries in road Networks (NPNN). We first present an efficient method to construct Network Voronoi Diagram on Uncertain objects (UNVD), in which we first find the possible NNs of all the vertices, and then compute the u-edges as well as their corresponding possible NNs. Next, to process NPNN queries, we first present a computational method to calculate the probabilities for each possible nearest object, and then propose two data structures, namely gIndex and qIndex, to index the u-edges in UNVD. Finally, we evaluate the performance of our NPNN query processing methods via extensive experiments on both real road networks and synthetic 2-dimensional grid networks. Experimental results demonstrate the effectiveness and efficiency of our methods in terms of I/O cost and query time.

PHƯƠNG PHÁP XỬ LÍ TRUY VẤN ĐA ĐIỀU KIỆN TRONG CƠ SỞ DỮ LIỆU HƯỚNG ĐỐI TƯỢNG MỜ DỰA TRÊN NGỮ NGHĨA ĐẠI SỐ GIA TỬ

PHƯƠNG PHÁP XỬ LÍ TRUY VẤN ĐA ĐIỀU KIỆN TRONG CƠ SỞ DỮ LIỆU HƯỚNG ĐỐI TƯỢNG MỜ DỰA TRÊN NGỮ NGHĨA ĐẠI SỐ GIA TỬ

Scaling Hop-Based Reachability Indexing for Fast Graph Pattern Query Processing

Graphs are becoming increasingly dominant in modeling real-life networked data including social and biological networks, the WWW and the Semantic Web, etc. Graph pattern queries are useful for gathering information with expressive semantics from these graph-structured data. Current methods for graph pattern query processing have performance deficiency caused by inefficiencies of the underlying reachability index and costly merge-join operations on huge amounts of tuple-formatted intermediate results. To overcome the above problems, this paper contributes in the following aspects to boost graph pattern query evaluation. First, we propose an improved hop-based reachability indexing scheme 3-Hop which gains faster reachability query evaluation, less indexing costs and better scalabilities than state-of-the-art hop-based methods. Second, we propose a two-stage node filtering algorithm based on 3-Hop to answer tree pattern queries more efficiently. Tree pattern queries serve as the underlying facility for graph pattern query evaluation. Furthermore, we use a graph representation of the intermediate results during node filtering and final results enumeration. Experiments on real-life and synthetic datasets demonstrate the effectiveness of the proposed methods.

A Formal Description of XML Tree Pattern Query for XQuery Language

In order to express tree pattern query in query plan and take advantage of formal method to analyze its behavioral characteristics, this paper present a formal description of tree pattern query based on functional language and denotational semantics. This description major focuses on behavior of a tree pattern query on matching against an eXtensible Markup Language (XML) document tree. First, we introduce a formal definition for a kind of extended generalized tree pattern (GTP++). Then we present a functional tree pattern description language (XTPL) for GTP++ and give its complete denotational semantics based on a novel data structure, named WTree, which efficiently organizes this typical XML data query results and provides flexible data access method. In the end, we present the formal semantics of identifying tree pattern from path expressions. By using formal methods, the semantics of tree pattern query is consistent and analyzable. As the core operation of XML query, this formal description can provide an initial step for analyzing the correctness of XML queries, and improves the reliability and robustness of query processing methods.

빅데이터 환경에서 연속 질의 처리를 위한 리버스 k-최근접 질의 처리 기법

빅데이터 환경에서 연속 질의 처리를 위한 리버스 k-최근접 질의 처리 기법

<inline-formula> <tex-math notation="LaTeX">\(K\) </tex-math></inline-formula> NN Query Processing Methods in Mobile Ad Hoc Networks

In this paper, we propose two beacon-less kNN query processing methods for reducing traffic and maintaining high accuracy of the query result in mobile ad hoc networks (MANETs). In these methods, the query-issuing node first forwards a kNN query using geo-routing to the nearest node from the point specified by the query (query point). Then, the nearest node from the query point forwards the query to other nodes close to the query point, and each node receiving the query replies with the information on itself. In this process, we adopt two different approaches: the Explosion (EXP) method and the Spiral (SPI) method. In the EXP method, the nearest node from the query point floods the query to nodes within a specific circular region, and each node receiving the query replies with information on itself. In the SPI method, the nearest node from the query point forwards the query to other nodes in a spiral manner, and the node that collects a satisfactory kNN result transmits the result to the query-issuing node. Experimental results show that our proposed methods reduce traffic and achieve high accuracy of the query result, in comparison with existing methods.

View field nearest neighbor: A novel type of spatial queries

In this paper, we introduce a novel spatial query called the view field nearest neighbor query. Given the view field and location of a user, the view field nearest neighbor query retrieves a data object that is nearest to the user’s location and falls within the user’s view field. This query can be employed for applications such as augmented reality systems, tour guide systems, and CCTV-based surveillance systems. We propose a view field nearest neighbor query processing method that considers moving data objects (i.e., continuous view field nearest neighbor query processing), where we utilize the grid index. Continuous view field nearest neighbor query processing consists of two phases: (1) initial phase and (2) update phase. The first phase computes the initial result of a view field nearest neighbor query (i.e., snapshot query result) and the second phase continuously updates the result according to the movement of data objects. For the initial phase, we propose two algorithms: Naive Exploration Algorithm and Fan-shaped Exploration Algorithm. For the update phase, we propose the Fan-shaped Monitoring Algorithm to process the moving data objects efficiently. Through extensive experiments, we investigate the performance of our proposed algorithms on synthetic and real data sets.

K-Nearest Neighbor Search based on Node Density in MANETs

In a kNN query processing method, it is important to appropriately estimate the range that includes kNNs. While the range could be estimated based on the node density in the entire network, it is not always appropriate because the density of nodes in the network is not uniform. In this paper, we propose two kNN query processing methods in MANETs where the density of nodes is ununiform; the One-Hop (OH) method and the Query Log (QL) method. In the OH method, the nearest node from the point specified by the query acquires its neighbors' location and then determines the size of a circle region (the estimated kNN circle) which includes kNNs with high probability. In the QL method, a node which relays a reply of a kNN query stores the information on the query result for future queries.

Top-k query processing for replicated data in mobile peer to peer networks

In mobile ad hoc peer to peer (M-P2P) networks, since nodes are highly resource constrained, it is effective to retrieve data items using a top-k query, in which data items are ordered by the score of a particular attribute and the query-issuing node acquires data items with the k highest scores. However, when network partitioning occurs, the query-issuing node cannot connect to some nodes having data items included in the top-k query result, and thus, the accuracy of the query result decreases. To solve this problem, data replication is a promising approach. However, if each node sends back its own data items (replicas) responding to a query without considering replicas held by others, same data items are sent back to the query-issuing node more than once through long paths, which results in increase of traffic. In this paper, we propose a top-k query processing method considering data replication in M-P2P networks. This method suppresses duplicate transmissions of same data items through long paths. Moreover, an intermediate node stops transmitting a query message on-demand.

Continuous clustering trajectory stream of moving objects

The clustering of trajectories over huge volumes of streaming data has been recognized as critical for many modern applications. In this work, we propose a continuous clustering of trajectories of moving objects over high speed data streams, which updates online trajectory clusters on basis of incremental line-segment clustering. The proposed clustering algorithm obtains trajectory clusters efficiently and stores all closed trajectory clusters in a bi-tree index with efficient search capability. Next, we present two query processing methods by utilising three proposed pruning strategies to fast handle two continuous spatio-temporal queries, threshold-based trajectory clustering queries and threshold-based trajectory outlier detections. Finally, the comprehensive experimental studies demonstrate that our algorithm achieves excellent effectiveness and high efficiency for continuous clustering on both synthetic and real streaming data, and the proposed query processing methods utilise average 90% less time than the naive query methods.

A Histogram Based Analytical Approximate Query Processing for Massive Data

In this paper, we study the characteristics of analytical query processing and proposed a histogram based approximate method for query processing over massive data. We implemented this approach into Hive system and evaluate it with Hive and BlinkDB cluster, the experimental results verified that our method is significantly fast than these existing techniques.

A New Approach of Distributed Query Processing Based on Database Grid

Distributed query processing technology is a difficult problem of data query, at the moment, the research and result of the query processing technology which is in view of grid environment is seldom. On the basis of the current grid standard and technology and aiming at the research of distributed relation join algorithm which adapts to grid environment, the paper designs the relevant solutions, achieves the efficient distributed query processing. For the characteristics of database grid, come up with the algorithm of relation join operation in database grid-symmetrical same-ordered semi-join algorithm, this algorithm can reduces the cost of communication and improves the parallelism, and is suitable for multi-join; Second, for the algorithm characteristics, researching the relevant SQL query processing method in order to generates efficient execution plan of symmetrical same-ordered semi-join algorithm.

Probabilistic nearest neighbor queries of uncertain data via wireless data broadcast

Most existing location-dependent query processing methods are based on the client-server model. However, due to the increasing nubmer of smart mobile devices, there can be a large volume of data being processed on the server side and the server can be system performance bottleneck. This paper takes the first step towards processing probabilistic nearest neighbor queries of uncertain data objects via wireless data broadcast (BPNN). Our method leverages the key properties of Voronoi Diagrams for Uncertain Data (UV-Diagram). To preserve the good properties of UV-Diagram, according to the property of Hilbert curve, UV-Hilbert-Partition is proposed to partition the UV-Diagram into several grid cells, called Hilbert-Cells, which have good locality-preserving behavior. Then a special organizing method is proposed. For a certain UV-Diagram, the CellFrame structure, which can be located based on the coordinates of a query client, is used to efficiently minimize the broadcast cycle and keep the probabilistic nearest neighbor results. Based on the sequence of the CellFrames, a distributed index, called UVHilbert-DI, is proposed to support BPNN query processing. Finally, the efficient BPNN algorithms based on UVHilbert-DI is presented and extensive experiments have been conducted to demonstrate the performance of our approaches.