Junchang Xin

Continuously maintaining sliding window skylines in a sensor network

Currently, wireless sensor network has been widely used in environment monitoring. The skyline query, as an important operator for multiple criteria decision making and data mining, plays an important role in many sensing applications. Though skyline queries have been well-studied in traditional database system, the existing solutions designed for data stored in a centralized site are not directly applicable to sensor environment due to the unique characteristics of wireless sensor network. In this paper, we propose an energy-efficient algorithm, called Sliding Window Skyline Monitoring Algorithm (SWSMA), to continuously maintain sliding window skylines over a wireless sensor network. Specifically, SWSMA employs two types of filters within each sensor to reduce the amount of data transferred and save the energy consumption as a consequence. In addition to SWSMA, a set of optimization mechanisms are also discussed to improve the performance of SWSMA. Our extensive simulation studies show that SWSMA together with the optimization techniques performs effectively on reducing communication cost and saving the energy on monitoring sliding window skylines.

Energy-Efficient Reverse Skyline Query Processing over Wireless Sensor Networks

Reverse skyline query plays an important role in many sensing applications, such as environmental monitoring, habitat monitoring, and battlefield monitoring. Due to the limited power supplies of wireless sensor nodes, the existing centralized approaches, which do not consider energy efficiency, cannot be directly applied to the distributed sensor environment. In this paper, we investigate how to process reverse skyline queries energy efficiently in wireless sensor networks. Initially, we theoretically analyzed the properties of reverse skyline query and proposed a skyband-based approach to tackle the problem of reverse skyline query answering over wireless sensor networks. Then, an energy-efficient approach is proposed to minimize the communication cost among sensor nodes of evaluating range reverse skyline query. Moreover, optimization mechanisms to improve the performance of multiple reverse skylines are also discussed. Extensive experiments on both real-world data and synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings.

ELM * : distributed extreme learning machine with MapReduce

Extreme Learning Machine (ELM) has been widely used in many fields such as text classification, image recognition and bioinformatics, as it provides good generalization performance at a extremely fast learning speed. However, as the data volume in real-world applications becomes larger and larger, the traditional centralized ELM cannot learn such massive data efficiently. Therefore, in this paper, we propose a novel Distributed Extreme Learning Machine based on MapReduce framework, named ELM ∗ , which can cover the shortage of traditional ELM whose learning ability is weak to huge dataset. Firstly, after adequately analyzing the property of traditional ELM, it can be found out that the most expensive computation part of the matrix Moore-Penrose generalized inverse operator in the output weight vector calculation is the matrix multiplication operator. Then, as the matrix multiplication operator is decomposable, a Distributed Extreme Learning Machine (ELM ∗ ) based on MapReduce framework can be developed, which can first calculate the matrix multiplication effectively with MapReduce in parallel, and then calculate the corresponding output weight vector with centralized computing. Therefore, the learning of massive data can be made effectively. Finally, we conduct extensive experiments on synthetic data to verify the effectiveness and efficiency of our proposed ELM ∗  in learning massive data with various experimental settings.

Holistically Twig Matching in Probabilistic XML

Traditional databases manage only deterministic information, but now many applications that use databases involve uncertain data. For example, it is infeasible for a sensor database to contain only the exact value of each sensor at all points in time. The uncertainty is inherent in these systems due to measurement and sampling errors, and resource limitations. This paper aims at the query processing algorithm of twig patterns on probabilistic XML documents. The existing algorithms evaluate twig patterns in a traversal way. The main shortcoming of this way is scanning the whole probabilistic XML document to get the final results. In this paper, we first represent a probabilistic XML document in the form of probabilistic tag streams and then match them in a holistic way. Extensive experiments are conducted and show that the proposed holistic way has the higher performance than the traversal way.

ComMapReduce: an improvement of mapreduce with lightweight communication mechanisms

As a parallel programming model, MapReduce processes scalable and parallel applications with huge amounts of data on large clusters. In MapReduce framework, there are no communication mechanisms among Mappers, neither are among Reducers. When the amount of final results is much smaller than the original data, it is a waste of time processing the unpromising intermediate data objects. We observe that this waste can be avoided by simple communication mechanisms. In this paper, we propose ComMapReduce, a framework that extends and improves MapReduce for efficient query processing of massive data in the cloud. With efficient lightweight communication mechanisms, ComMapReduce can effectively filter the unpromising intermediate data objects in Map phase so as to decrease the input of Reduce phase specifically. Three communication strategies, Lazy, Eager and Hybrid, are proposed to filter the unpromising intermediate results of Map phase. In addition, two optimization strategies, Prepositive and Postpositive, are presented to enhance the performance of query processing by filtering more candidate data objects. Our extensive experiments on different synthetic datasets demonstrate that ComMapReduce framework outperforms the original MapReduce framework in all metrics without affecting its existing characteristics.

Energy-efficient skyline queries over sensor network using mapped skyline filters

In recent years, wireless sensor network has been widely used in military and civil applications. For many wireless sensor applications, the skyline query is a very important operator for retrieving data according to multiple criteria. In traditional database system skyline queries have been well studied, but in sensor environment the existing solutions are not suitable, because of the essential characteristics of wireless sensor network, such as wireless, multi-hop communication, resource-constrained and distributed environment. An Energy-Efficient Sliding Window Skyline Maintaining Algorithm (EES), which continuously maintains sliding window skylines over a wireless sensor network, is proposed in this paper. In particular, we propose a mapped skyline filter (MSF) in EES. MSF resides in each sensor node and filters the tuples having no contribution to the final result, therefore energy consumption is saved significantly. Our extensive performance studies show that EES can effectively reduce communication cost and save the energy on maintaining sliding window skylines over wireless sensor network.

Alternative Tuples Based Probabilistic Skyline Query Processing in Wireless Sensor Networks

As uncertainty is the inherent character of sensing data, the processing and optimization techniques for Probabilistic Skyline (PS) in wireless sensor networks (WSNs) are investigated. It can be proved that PS is not decomposable after analyzing its properties, so in-network aggregation techniques cannot be used directly to improve the performance. In this paper, an efficient algorithm, called Distributed Processing of Probabilistic Skyline (DPPS) query in WSNs, is proposed. The algorithm divides the sensing data into candidate data (CD), irrelevant data (ID), and relevant data (RD). The ID in each sensor node can be filtered directly to reduce data transmissions cost, since, only according to both CD and RD, PS result can be correctly obtained on the base station. Experimental results show that the proposed algorithm can effectively reduce data transmissions by filtering the unnecessary data and greatly prolong the lifetime of WSNs.

Probabilistic reverse skyline query processing over uncertain data stream

Reverse skyline plays an important role in market decision-making, environmental monitoring and market analysis. Now the flow property and uncertainty of data are more and more apparent, probabilistic reverse skyline query over uncertain data stream has become a new research topic. Firstly, a novel pruning technique is proposed to reduce the number of uncertain tuples reserved for processing continuous probabilistic reverse skyline query. Then some probability pruning techniques are proposed to reduce some redundant calculations. Next, an efficient algorithm, called Optimization Probabilistic Reverse Skyline (OPRS), is proposed to process continuous probabilistic reverse skyline queries. Finally, the performance of OPRS is verified through a large number of simulation experiments. The experimental results show that OPRS is an effective way to solve the problem of continuous probabilistic reverse skyline, and it could significantly reduce the executionx time of continuous probabilistic reverse skyline queries and meet the requirements of practical applications.

PeerLearning: A Content-Based e-Learning Material Sharing System Based on P2P Network

Managing and retrieving reusable learning materials in a content-based way is a big challenge in e-Learning material sharing systems. E-Learning materials are highly heterogeneous; they may exist in the form of video, audio, image, slide or plain text. Furthermore, the learning systems are highly dynamic in the presence of massively increasing multimedia materials. P2P network seems to be one of the most promising infrastructures to deal with the challenge in such highly dynamic environments. In this paper we propose a Peer-to-Peer (P2P) infrastructure based on the trie tree and the deBruijn structure. It can support efficiently query processing in highly dynamic scenarios. Furthermore we develop a P2P e-Learning system PeerLearning to provide two content-based learning material sharing services: a keyword search component for supporting content-based document sharing and a content-based retrieval method for multimedia materials. Extensive experiments are conducted in this study to verify the superiority of our methods over the existing works.

Energy-Efficient Evaluation of Multiple Skyline Queries over a Wireless Sensor Network

Though skyline queries in wireless sensor networks have been intensively studied in recent years, existing solutions are not optimized for multiple skyline queries as they focus on single full space skyline queries. It is not efficient to individually evaluate skyline queries especially in a wireless sensor network environment where power consumption should be minimized. In this paper, we propose an energy-efficient multi-skyline evaluation (EMSE) algorithm to effectively evaluate multiple skyline queries in wireless sensor networks. EMSE first utilizes a global optimization mechanism to reduce the number of skyline queries and save on query propagation cost and parts of redundant result transmission cost as a consequence. Then, it utilizes a local optimization mechanism to share the skyline results among skyline queries and uses some filtering policies to further eliminate unnecessary data transmission and save the skyline result transmission cost as a consequence. The experimental results show that the proposed algorithm is energy-efficient when evaluating multiple skyline queries over wireless sensor networks.