Baichen Chen

Finding maximal k-edge-connected subgraphs from a large graph

In this paper, we study how to find maximal k-edge-connected subgraphs from a large graph. k-edge-connected subgraphs can be used to capture closely related vertices, and finding such vertex clusters is interesting in many applications, e. g., social network analysis, bioinformatics, web link research. Compared with other explicit structures for modeling vertex clusters, such as quasi-clique, k-core, which only set the requirement on vertex degrees, k-edge-connected subgraph further requires high connectivity within a subgraph (a stronger requirement), and hence defines a more closely related vertex cluster. To find maximal k-edge-connected subgraphs from a graph, a basic approach is to repeatedly apply minimum cut algorithm to the connected components of the input graph until all connected components are k-connected. However, the basic approach is very expensive if the input graph is large. To tackle the problem, we propose three major techniques: vertex reduction, edge reduction and cut pruning. These speed-up techniques are applied on top of the basic approach. We conduct extensive experiments and show that the speed-up techniques are very effective.

Energy-efficient skyline query processing and maintenance in sensor networks

The skyline query, as an important operator in databases for multi-preference analysis and decision making, has received much attention recently due to its wide application backgrounds. In this paper, we consider the skyline query problem in Wireless Sensor Network with an objective to maximize the network lifetime by proposing filter-based distributed algorithms for skyline evaluation and maintenance. We also conduct preliminary experiments to evaluate the performance of the proposed algorithms. The experimental results demonstrate that the proposed algorithms significantly outperform existing algorithms on various datasets.

Energy-efficient top-k query processing in wireless sensor networks

Technological advances have enabled the deployment of large-scale sensor networks for environmental monitoring and surveillance purposes. The large volume of data generated by sensors needs to be processed to respond to the users queries. However, efficient processing of queries in sensor networks poses great challenges due to the unique characteristics imposed on sensor networks including slow processing capability, limited storage, and energy-limited batteries, etc. Among various queries, top-k query is one of the fundamental operators in many applications of wireless sensor networks for phenomenon monitoring. In this paper we focus on evaluating top-k queries in an energy-efficient manner such that the network lifetime is maximized. To achieve that, we devise a scalable, filter-based localized evaluation algorithm for top-k query evaluation, which is able to filter out as many unlikely top-k results as possible within the network from transmission. We also conduct extensive experiments by simulations to evaluate the performance of the proposed algorithm on real datasets. The experimental results show that the proposed algorithm outperforms existing algorithms significantly in network lifetime prolongation.

Top-k query evaluation in sensor networks under query response time constraint

Top-k query in a wireless sensor network is to find the k sensor nodes with the highest sensing values. To evaluate the top-k query in such an energy-constrained network poses great challenges, due to the unique characteristics imposed on its sensors. Existing solutions for top-k query in the literature mainly focused on energy efficiency but little attention has been paid to the query response time and its effect on the network lifetime. In this paper we address the query response time and its effect on the network lifetime through the study of the top-k query problem in sensor networks with the response time constraint. We aim at finding an energy-efficient routing tree and evaluating top-k queries on the tree such that the network lifetime is significantly prolonged, provided that the query response time constraint is met too. To do so, we first present a cost model of energy consumption for answering top-k queries and introduce the query response time definition. We then propose a novel joint query optimization framework, which consists of finding a routing tree in the network and devising a filter-based evaluation algorithm for top-k query evaluation on the tree. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithms, in terms of the total energy consumption, the maximum energy consumption among nodes, the query response time, and the network lifetime. The experimental results showed that there is a non-trivial tradeoff between the query response time and the network lifetime, and the joint query optimization framework can prolong the network lifetime significantly under a specified query response time constraint.

Progressive Skyline Query Processing in Wireless Sensor Networks

With the further development of sensor techniques in wireless sensor networks (WSNs), it is becoming urgent that they should be able to support complicated queries like skyline query for multi-preference and decision making. In this paper, we consider skyline query evaluation in WSNs by devising evaluation algorithms for finding skyline points on a dataset progressively. The core techniques adopted are to partition the dataset into several disjoint subsets and output the skyline points by examining each subsequent subset progressively, using some of the skyline points obtained so far to filter out those unlikely skyline points in the current processing subset from transmission. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on synthetic and real datasets. The experimental results show that the proposed algorithms outperform existing algorithms significantly in network lifetime prolongation.

Energy-efficient skyline query optimization in wireless sensor networks

With the deployment of wireless sensor networks (WSNs) for environmental monitoring and event surveillance, WSNs can be treated as virtual databases to respond to user queries. It thus becomes more urgent that such databases are able to support complicated queries like skyline queries. Skyline query which is one of popular queries for multi-criteria decision making has received much attention in the past several years. In this paper we study skyline query optimization and maintenance in WSNs. Specifically, we first consider skyline query evaluation on a snapshot dataset, by devising two algorithms for finding skyline points progressively without examining the entire dataset. Two key strategies are adopted: One is to partition the dataset into several disjoint subsets and produce the skyline points in each subset progressively. Another is to employ a global filter that consists of some skyline points in the processed subsets to filter out unlikely skyline points from the rest of unexamined subsets. We then consider the query maintenance issue by proposing an algorithm for incremental maintenance of the skyline in a streaming dataset. A novel maintenance mechanism is proposed, which is able to identify which skyline points from past skylines to be the global filter and determine when the global filter is broadcast. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on both synthetic and real sensing datasets, and the experimental results demonstrate that the proposed algorithms significantly outperform existing algorithms in terms of network lifetime prolongation.

Online Time Interval Top-k Queries in Wireless Sensor Networks

Motivated by many applications, top-k query is a fundamental operation in modern database systems. Technological advances have enabled the deployment of large-scale sensor networks for environmental monitoring and surveillance purposes, efficient processing of top-k query in such networks poses great challenges due to the unique characteristics of sensors and a vast amount of data generated by sensor networks. In this paper, we first introduce the concept of time interval top-k query that is to return k highest sensed values from the sensory data generated within a specified time interval. We then propose a filter-based algorithm for time interval top-k query evaluation, which is capable to filter out nearly a half unlikely top-k data from transmission in comparison with a well known existing solution. We also develop a novel online algorithm for answering time interval top-k queries with various ks and time intervals one by one through maintaining a materialized view that consists of historical top-k query results. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on real sensory datasets The experimental results show that the proposed algorithms outperform existing algorithms significantly to prolong the network lifetime.

Response Time Constrained Top-k Query Evaluation in Sensor Networks

Existing solutions for top-k queries in wireless sensor networks mainly focused on energy efficiency and little attention has been paid to the response time to answer a top-k query as well as the relationship between the response time and the network lifetime. In this paper we address this issue explicitly by studying the top-k query problem in sensor networks with the response time constraint. We aim at finding an energy-efficient routing tree and devising an evaluation algorithm for top-k queries on the tree such that the network lifetime is significantly prolonged, provided that the query response time constraint is met too. To do so, we propose a novel joint optimization framework of finding a routing tree and devising a filter-based evaluation algorithm on the tree. We also conduct extensive experiments by simulation to evaluate the performance of the proposed algorithms. The experimental results showed that the joint optimization framework prolongs the network lifetime significantly under a given response time constraint.

Progressive skyline query evaluation and maintenance in wireless sensor networks

Skyline query has been received much attention due to its wide application backgrounds for multi-preference and decision making. In this paper we consider skyline query evaluation and maintenance in wireless sensor networks. We devise an evaluation algorithm for finding skyline points progressively and a maintenance algorithm for skyline maintenance incrementally. We also conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on various datasets. The experimental results show that the proposed algorithms significantly outperform existing algorithms in terms of network lifetime prolongation.

Top-k query evaluation in sensor networks with the guaranteed accuracy of query results

In many applications of sensor networks including environmental monitoring and surveillance, a large volume of sensed data generated by sensors needs to be either collected at the base station or aggregated within the network to respond to user queries. However, due to the unreliable wireless communication, robust query processing in such networks becomes a great challenge in the design of query evaluation algorithms for some mission-critical tasks. In this paper we propose an adaptive, localized algorithm for robust top-k query processing in sensor networks, which trades off between the energy consumption and the accuracy of query results. In the proposed algorithm, whether a sensor is to forward the collected data to the base station is determined in accordance with the calculation of a proposed local function, which is the estimation of the probability of transmitting the data successfully. We also conduct extensive experiments by simulations on real datasets to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm is energy-efficient while achieving the specified accuracy of the query results.