I-Fang Su

Top- k combinatorial skyline queries

The problem of top-k skyline computation has attracted considerable research attention in the past few years. Given a dataset, a top-k skyline returns k “most interesting” skyline tuples based on some kind of preference specified by the user. We extend the concept of top-k skyline to a so-called top-k combinatorial skyline query (k-CSQ). In contrast to the existing top-k skyline query (which is mainly to find the interesting skyline tuples), a k-CSQ is to find the interesting skyline tuples from various kinds of combinations of the given tuples. The k-CSQ is an important tool for areas such as decision making, market analysis, business planning, and quantitative economics research. In this paper, we will formally define this new problem, propose an intelligent method to resolve this problem, and also conduct a set of experiments to show the effectiveness and efficiency of the proposed algorithm.

Efficient skyline query processing in wireless sensor networks

How to process a skyline query efficiently has received considerable attention in recent years. A skyline query identifies a set of non-dominated data records in a multidimensional dataset. Whereas most previous studies have resolved this problem in a centralized environment, this work considers it in a distributed sensor network environment. An algorithm, known as Skyline Sensor Algorithm (SkySensor), is presented to efficiently retrieve skyline results from a sensor network. A cluster-based architecture is designed in SkySensor to collect all sensor readings. A pruning method is then proposed to progressively sift out the skyline results from the sensor network. SkySensor avoids the need of collecting data from all sensors in the network, which is an extremely expensive action, when searching for the skyline results. The performance study indicates that SkySensor is highly efficient, and significantly outperforms previous methods in processing skyline queries.

Efficient computation of combinatorial skyline queries

Current skyline evaluation techniques are mainly to find the outstanding tuples from a large dataset. In this paper, we generalize the concept of skyline query and introduce a novel type of query, the combinatorial skyline query, which is to find the outstanding combinations from all combinations of the given tuples. The past skyline query is a special case of the combinatorial skyline query. This generalized concept is semantically more abundant when used in decision making, market analysis, business planning, and quantitative economics research. In this paper, we first introduce the concept of the combinatorial skyline query (CSQ) and explain the difficulty in resolving this type of query. Then, we propose two algorithms to solve the problem. The experiments manifest the effectiveness and efficiency of the proposed algorithms.

An efficient mechanism for processing similarity search queries in sensor networks

The similarity search problem has received considerable attention in database research community. In sensor network applications, this problem is even more important due to the imprecision of the sensor hardware, and variation of environmental parameters. Traditional similarity search mechanisms are both improper and inefficient for these highly energy-constrained sensors. A difficulty is that it is hard to predict which sensor has the most similar (or closest) data item such that many or even all sensors need to send their data to the query node for further comparison. In this paper, we propose a similarity search algorithm (SSA), which is a novel framework based on the concept of Hilbert curve over a data-centric storage structure, for efficiently processing similarity search queries in sensor networks. SSA successfully avoids the need of collecting data from all sensors in the network in searching for the most similar data item. The performance study reveals that this mechanism is highly efficient and significantly outperforms previous approaches in processing similarity search queries.

Supporting Multi-Dimensional Range Query for Sensor Networks

This paper presents the design of Pool, an efficient and scalable data storage scheme for supporting multidimensional queries. The foundation of the work that makes the Pool approach superior in executing multi-dimensional queries is that it provides a novel and elegant higher dimension to two-dimensional data mapping mechanism. Our performance study proves the efficiency of the design.

Radius reconfiguration for energy conservation in sensor networks

The sensing range of a sensor node significantly affects its energy consumption. This study focuses on allowing each sensor to dynamically adjust its sensing radius so that the global coverage of the whole detecting area remains unchanged, while minimising the sensing range of each sensor locally. The proposed solution minimises energy consumption and extends the lifetime of the sensor network while adjusting the sensing radius of each sensor node. Simulation results indicate that the proposed method can reduce the sensing radii of 90% of sensor nodes when sensors are randomly deployed with enough sensors to cover a designated area [i.e. density of 5 sensors/sensing radius (Shih et al., 2001)], and can reduce the sensing radii of 40% of the nodes to 0 (i.e. a sleep mode) when sensors are densely deployed.

Multiple k nearest neighbor search

The problem of kNN (k Nearest Neighbor) queries has received considerable attention in the database and information retrieval communities. Given a dataset D and a kNN query q, the k nearest neighbor algorithm finds the closest k data points to q. The applications of kNN queries are board, not only in spatio-temporal databases but also in many areas. For example, they can be used in multimedia databases, data mining, scientific databases and video retrieval. The past studies of kNN query processing did not consider the case that the server may receive multiple kNN queries at one time. Their algorithms process queries independently. Thus, the server will be busy with continuously reaccessing the database to obtain the data that have already been acquired. This results in wasting I/O costs and degrading the performance of the whole system. In this paper, we focus on this problem and propose an algorithm named COrrelated kNN query Evaluation (COKE). The main idea of COKE is an “information sharing” strategy whereby the server reuses the query results of previously executed queries for efficiently processing subsequent queries. We conduct a comprehensive set of experiments to analyze the performance of COKE and compare it with the Best-First Search (BFS) algorithm. Empirical studies indicate that COKE outperforms BFS, and achieves lower I/O costs and less running time.

Finding Similar Answers in Data-Centric Sensor Networks

Intensive study has been dedicated to wireless sensor networks and their applications in the last few years. However, similarity search problem in sensor network environments seems to have not attracted the deserved attention. In fact, sensor detected data are very likely imprecise due to the simplified hardware of the sensor itself and various environmental factors. Hence, queries requesting for similar result should be an often scenario and an important problem to resolve. In this paper, we propose a similarity search algorithm (SSA) for efficiently processing similarity search queries. We first present a data-centric storage structure based on the concept of Hilbert curve. Then, we propose an algorithm designed for efficiently probing the most similar data item for the sensor network. The performance study reveals that this mechanism is highly efficient and significantly outperforms other approaches in processing similarity search queries.

k -most suitable locations selection

Choosing the best location for starting a business or expanding an existing enterprize is an important issue. A number of location selection problems have been discussed in the literature. They often apply the Reverse Nearest Neighbor as the criterion for finding suitable locations. In this paper, we apply the Average Distance as the criterion and propose the so-called k-most suitable locations (k-MSL) selection problem. Given a positive integer k and three datasets: a set of customers, a set of existing facilities, and a set of potential locations. The k-MSL selection problem outputs k locations from the potential location set, such that the average distance between a customer and his nearest facility is minimized. In this paper, we formally define the k-MSL selection problem and show that it is NP-hard. We first propose a greedy algorithm which can quickly find an approximate result for users. Two exact algorithms are then proposed to find the optimal result. Several pruning rules are applied to increase computational efficiency. We evaluate the algorithms’ performance using both synthetic and real datasets. The results show that our algorithms are able to deal with the k-MSL selection problem efficiently.

An efficient distributed range query processing algorithm on LiDAR data

With the rapid development of the LiDAR (Light Detection and Ranging) remote sensing technology in the past decade, LiDAR sensing systems have become an important source for acquiring environmental data. The LiDAR system can be equipped on an aircraft to collect geographic information in a wide area. One characteristic of the LiDAR system is that it usually produces huge volumes of data. Thus, how to efficiently manage, store, process and visualize the LiDAR data sources has become an important and challenging research issue in the spatial database community. In this paper, we propose a distributed algorithm to process a remarkable spatial query (i.e., range queries) over massive LiDAR data points. Different from existing range query processing approaches which assume all data points are stored in a centralized server, our method adopts a decentralized fashion. Our query processing system is a master/slave architecture. A large data set is split into smaller partitions that are distributed among several slave machines. Therefore, each slave machine only process a small part of data points. We also develop index structures over LiDAR data sets to further enhance the efficiency of query processing. Our performance study proves the efficiency of the design.