Yuan Ko Huang

Continuous K-Nearest Neighbor Query for Moving Objects with Uncertain Velocity

One of the most important queries in spatio-temporal databases that aim at managing moving objects efficiently is the continuous K-nearest neighbor (CKNN) query. A CKNN query is to retrieve the K-nearest neighbors (KNNs) of a moving user at each time instant within a user-given time interval [ts, te]. In this paper, we investigate how to process a CKNN query efficiently. Different from the previous related works, our work relieves the past assumption, that an object moves with a fixed velocity, by allowing that the velocity of the object can vary within a known range. Due to the introduction of this uncertainty on the velocity of each object, processing a CKNN query becomes much more complicated. We will discuss the complications incurred by this uncertainty and propose a cost-effective P2KNN algorithm to find the objects that could be the KNNs at each time instant within the given query time interval. Besides, a probability-based model is designed to quantify the possibility of each object being one of the KNNs. Comprehensive experiments demonstrate the efficiency and the effectiveness of the proposed approach.

Continuous K-Nearest Neighbor Query over Moving Objects in Road Networks

Continuous K -Nearest Neighbor (CK NN) query is an important type of spatio-temporal queries. A CK NN query is to find among all moving objects the K -nearest neighbors (K NNs) of a moving query object at each timestamp. In this paper, we focus on processing such a CK NN query in road networks, where the criterion for determining the K NNs is the shortest network distance between objects. We first highlight the limitations of the existing approaches, and then propose a cost-effective algorithm, namely the Continuous KNN algorithm , to overcome these limitations. Comprehensive experiments are conducted to demonstrate the efficiency of the proposed approach.

Efficient evaluation of continuous spatio-temporal queries on moving objects with uncertain velocity

Continuous Range (CR) query and Continuous K-Nearest Neighbor (CKNN) query are two important types of spatio-temporal queries. Given a time interval [ts, te] and a moving query object q, a CR query is to find the moving objects whose Euclidean distances to q are within a user-given distance at each time instant within [ts, te]. A CKNN query is to retrieve the K-Nearest Neighbors (KNNs) of this query object q at each time instant within [ts, te]. In this paper, we investigate how to process these spatio-temporal queries efficiently under the situation that the velocity of each object is not fixed. This uncertainty on the velocity of object inevitably results in high complexity in processing spatio-temporal queries. We will discuss the complications incurred by this uncertainty and propose two algorithms, namely the Possibility-based possible within objects searching algorithm and the Possibility-based possible KNN searching algorithm, for the CR query and the CKNN query, respectively. A Possibility-based model is designed accordingly to quantify the possibility of each object being the result of a CR query or a CKNN query. Comprehensive experiments are performed to demonstrate the effectiveness and the efficiency of the proposed approaches.

Efficient Continuous K-Nearest Neighbor Query Processing over Moving Objects with Uncertain Speed and Direction

One of the important types of queries in spatio-temporal databases is the Continuous K-Nearest Neighbor (CKNN) query, which is to find among all moving objects the K-Nearest Neighbors (KNNs) of a mobile user at each time instant within a user-given time interval [t s , t e ]. In this paper, we focus on how to process such a CKNN query efficiently when the moving speedand directionof each moving object are uncertain. We thoroughly analyze the complicated problems incurred by this uncertainty and propose a Continuous PKNN(CPKNN)algorithmto effectively tackle these problems.

Incremental In-Network RNN Search in Wireless Sensor Networks

With the rapid advances of wireless communication and sensor technologies, spatial queries on moving objects are increasingly important in many sensor applications. One of the most frequently used spatial queries is the reverse nearest neighbor (RNN) query that returns the objects whose nearest neighbor is the query object. RNN answers are particularly essential to the users who are under emergency circumstances. In this paper, we propose an incremental RNN search (IRS) method to answer RNN queries for moving objects in sensor networks. IRS employs a filter- verification framework to achieve the energy-efficient query processing by filtering out most objects irrelevant to the query results. To support the execution of IRS, we also design a distributed object location management scheme to reduce the amount of communication in managing moving objects. Our experimental results reveal that IRS is indeed quite promising, as it requires only a moderate amount of communications for managing moving objects as well as for processing RNN queries.

Shortest Average-Distance Query on Heterogeneous Neighboring Objects

Currently, most of the processing techniques for the conventional location-based queries focus only on a single type of objects. However, in real-life applications the user may be interested in obtaining information about different types of objects, in terms of their neighboring relationship. We term the different types of objects closer to each other the heterogeneous neighboring objects (HNOs for short). Efficient processing of the location-based queries on the HNOs is more complicated than that on a single data source, because the neighboring relationship between the HNOs inevitably affects the query result. In this paper, we present a novel and important query on the HNOs, namely the shortest average-distance query (SAvgDQ for short), which can provide useful object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects. Given a query object q and a distance d, the SAvgDQ retrieves a set of HNOs, such that the distances between any two objects in this set are less than or equal to d and its average distance to q is the smallest among all HNOs sets. To efficiently process the SAvgDQ, we develop an algorithm, the SAvgDQ processing algorithm, which operates based on three devised heuristics, the HNOs-qualifying heuristic, the HNOs-pruning heuristic, and the SAvgD-pruning heuristic, to reduce the number of distance computations required for query processing. Comprehensive experiments are conducted to demonstrate the effectiveness of the heuristics and the efficiency of the proposed algorithm.

Continuous Possible K-Nearest Skyline Query in Euclidean Spaces

Knowledge reuse is an urge requirement of shortening cycle, improving quality and reducing cost in the rockets design process. In this paper, the knowledge application content in rockets overall design process is analyzed firstly. Then a design process model throughout requirement analyzing, function design, principle solving and structure design is proposed, in which the main purpose is to reuse knowledge effectively. Finally modeling technologies are studied comprehensively in each phase of rockets design process and a universal expression of design process is put forward based on computer aided design technologies. Our work provides a reference and guidance of integration methodology for knowledge reuse and sharing in aerospace products design process.Continuous K-nearest skyline query (CKNSQ) is an important type of the spatio-temporal queries. Given a query time interval [ts, te] and a moving query object q, a CKNSQ is to retrieve the K-nearest skyline points of q at each time instant within [ts, te]. Different from the previous works, our work devotes to overcoming the past assumption that each object is static with certain dimensional values and located in road networks. In this paper, we focus on processing the CKNSQ over moving objects with uncertain dimensional values in Euclidean space and the velocity of each object (including the query object) varies within a known range. Such a query is called the continuous possible K-nearest skyline query (CPKNSQ). We first discuss the difficulties raised by the uncertainty of object and then propose the CPKNSQ algorithm operated with a data partitioning index, called the uncertain TPR-tree (UTPR-tree), to efficiently answer the CPKNSQ.The current works about task scheduling with deadline-constraint in homogeneous environment rarely take the differences of Map and Reduce task and data locality into account in the same scheduler. To address this problem, we introduce a scheduling algorithm that Map and Reduce are regarded as two separated stages of scheduling problem in homogeneous environment. For the sake of realizing this algorithm, five aspects that are average execution time of map/reduce tasks, map/reduce stage deadline, remaining time of map/reduce stage, jobs priority and data locality must be taken into consider. Compared with other real-time scheduling algorithm, we propose several methods which are one-to-one sampling, estimating requirements of resource and compromised task-data matching strategy to solve above five aspects. The experimental results show the sampling method can get accurate map/reduce task execution time and the proposed scheduling algorithm not only satisfies the jobs real-time requirement but also improves the throughput of cluster.

Skyline Query Processing System for Taiwan Maps

With the fast advance of GPS technology and mobile devices, Location-Based Services (LBS for short) are useful in many real-life applications. In this paper, we develop a location-based system which processes the skyline queries for the Taiwan maps. We dedicate to improve the usability, design the user-friendly interface, and provide a consistent user experience for all cross-devices. We try to implement a general and useful system for the skyline queries.

Efficient K NN processing over moving objects with uncertain velocity

Spatio-temporal databases aim at combining the spatial and temporal characteristics of data. The continuous K-Nearest Neighbor (CKNN) query is an important type of spatio-temporal query that finds the K-Nearest Neighbors (KNNs) of a moving query object at each time instant within a given time interval [ts, te]. In this paper, we investigate how to process a CKNN query efficiently under the situation that each object moves with an uncertain velocity. This uncertainty on the velocity of each object inevitably results in high complexity of the CKNN problem. We propose a cost-effective PKNN algorithm to tackle the complicated problem incurred by this uncertainty.