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Efficient Iceberg Query Processing in Sensor Networks

2015-11-26  iceberg query. In this paper, we propose an energy-efficient iceberg query processing technique in sensor networks. COUNT, SUM and AVG are considered as aggregate functions of the iceberg queries. To reduce the size of data transmitted, a lossless data compression method is devised using prime numbers. The size

Efficient Iceberg Query Processing in Sensor

Efficient Iceberg Query Processing in Sensor Networks Efficient Iceberg Query Processing in Sensor Networks Yang, Heejung; Chung, Chin-Wan 2014-12-21 00:00:00 The iceberg query finds data whose aggregate values exceed a pre-specified threshold. To process an iceberg query in sensor networks, all sensor data have to be aggregated and then sensor data whose aggregate values are smaller than

IJA: an efficient algorithm for query processing in

IJA: an efficient algorithm for query processing in sensor networks. Lee HC(1), Lee YJ, Lim JH, Kim DH. such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental

SafeQ: Secure and Efficient Query Processing in Sensor

2010-7-9  Query Processing in Sensor Networks Fei Chen Alex X. Liu Dept. of Computer Science and Engineering Michigan State University East Lansing, MI 48824-1266, U.S.A. Email: {feichen, alexliu}@cse.msu.edu Abstract—The architecture of two-tiered sensor networks, where storage nodes serve as an intermediate tier between sensors

SafeQ: Secure and Efficient Query Processing in

The architecture of two-tiered sensor networks, where storage nodes serve as an intermediate tier between sensors and a sink for storing data and processing queries, has been widely adopted because of the benefits of power and storage saving for sensors as well as the efficiency of query processing. However, the importance of storage nodes also makes them attractive to attackers.

In-network processing of an iceberg join query in

2015-3-12  In-network processing of an iceberg join query in wireless sensor networks based on 2-way fragment semijoins. Kang H(1). Author information: (1)School of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, Korea. [email protected]

Energy-Efficient Data Organization and Query

Energy-Efficient Data Organization and Query Processing in Sensor Networks,Ramakrishna Gummadi,Xin Li,Ramesh Govindan,Cyrus Shahabi,Wei Hong,{,gummadi, xinl, ramesh, cshahabi,},@usc.edu,[email protected],Abstract,Recent sensor networks research has produced a class,of data storage and query processing techniques called,Data-Centric Storage,that leverages

(PDF) Efficient Time Triggered Query Processing in

In this paper we introduce a novel system that comprises techniques advancing the query processing in wireless sensor networks. Our system facilitates time triggered queries that are scheduled in

Efficient Continuous Skyline Query Processing in

Owing to the rapid advent of wireless technology and proliferation of smart sensors, wireless sensor networks (WSNs) have been widely used to monitor and query the physical world in many applications based on the Internet of Things (IoT), such as environmental monitoring and event surveillance. A WSN can be treated as a distributed database to respond to user queries. Skyline query, as one of

Efficient Continuous Skyline Query Processing in Wireless

2019-6-30  sensors Article E cient Continuous Skyline Query Processing in Wireless Sensor Networks Yingyuan Xiao 1,2,*,Xu Jiao 2, Hongya Wang 3, Ching-Hsien Hsu 4,*, Li Liu 2 and Wenguang Zheng 2 1 Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China 2 Tianjin Key Laboratory of Intelligence Computing and Novel Software

In-Network Processing of an Iceberg Join Query in

We investigate the in-network processing of an iceberg join query in wireless sensor networks (WSNs). An iceberg join is a special type of join where only those joined tuples whose cardinality exceeds a certain threshold (called iceberg threshold) are qualified for the result. Processing such a join involves the value matching for the join predicate as well as the checking of the cardinality

In-network processing of an iceberg join query in

We investigate the in-network processing of an iceberg join query in wireless sensor networks (WSNs). An iceberg join is a special type of join where only those joined tuples whose cardinality exceeds a certain threshold (called iceberg threshold) are qualified for the result.

OPEN ACCESS sensors MDPI

2015-3-12  Abstract: We investigate the in-network processing of an iceberg join query in wireless sensor networks (WSNs). An iceberg join is a special type of join where only those joined tuples whose cardinality exceeds a certain threshold (called iceberg threshold) are qualified for the result. Processing such a join involves the value matching for the

Iceberg Clique queries in large graphs ScienceDirect

2017-9-20  1. Introduction 1.1. Motivations. As a fundamental problem, clique query in graphs has many significant applications, such as social recommendation,,,network routing,and community detection,, .However, a practical issue of clique queries in real-world graphs is just to extract the cliques with the required number of vertices from a given large graph.

E[equation]STA: An Energy-Efficient Spatio-Temporal

2018-12-11  Coman, A., Nascimento, M.A., Sander, J.: A framework for spatio-temporal query processing over wireless sensor networks. In: Proceeedings of the 1st International Workshop on Data Management for Sensor Networks: in Conjunction with VLDB 2004, pp.

International Journals ISLAB

[62] D.W. Choi and C.W. Chung, "REQUEST+: A Framework for Efficient Processing of Region-based Queries in Sensor Networks," Information Sciences, Vol. 248, Issue 1, November 2013, pp. 151-167. [61] J.H. Yun and C.W. Chung,"Efficient Probabilistic XML Query Processing using An Extended Labeling Scheme and A Lightweight Index," Information

A filtering technique for n-way stream joins in wireless

Purpose The join operations between data streams need more time and request more energy than traditional joins. In wireless sensor networks, energy is a critical factor. The survival of the network depends on this energy, thus it is necessary to consider, for this type of queries in such networks, the reduction of the ;sensors’ energy consumption.

dblp: Chin-Wan Chung

Heejung Yang, Chin-Wan Chung, Myoung Ho Kim: Erratum to 'An efficient top-k query processing framework in mobile sensor networks' [Data Knowl. Eng. 102(2016) 78-95]. Data Knowl.

Computer Science Final Projects Thesis123Thesis123

21. Efficient Iceberg Query Evaluation Using Compressed Bitmap Index 22. Efficient Multidimensional Fuzzy Search for Personal Information Management Systems 23. A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication. DISTRIBUTED SYSTEMS. 1. Catching Packet Droppers and Modifiers in Wireless Sensor Networks 2.

A Fast and Efficient Algorithm for Finding Frequent Items

2016-1-15  processing one input data item. Moreover, the computation time for answering each query is O(log O), and for answering query about the frequentness of a given data item is O(1). Through extensive experiments, we show that λ-Count outperforms other methods such as LC and EC in terms of the accuracy, memory requirement, and processing speed.

Iceberg Clique queries in large graphs ScienceDirect

2017-9-20  1. Introduction 1.1. Motivations. As a fundamental problem, clique query in graphs has many significant applications, such as social recommendation,,,network routing,and community detection,, .However, a practical issue of clique queries in real-world graphs is just to extract the cliques with the required number of vertices from a given large graph.

A filtering technique for n-way stream joins in wireless

Purpose The join operations between data streams need more time and request more energy than traditional joins. In wireless sensor networks, energy is a critical factor. The survival of the network depends on this energy, thus it is necessary to consider, for this type of queries in such networks, the reduction of the ;sensors’ energy consumption.

Information Systems Design and Intelligent

Efficient Iceberg Query Evaluation in Distributed Databases by Developing Deferred Strategies. Vuppu Shankar, C. V. Guru Rao and covers a variety of topics, including natural language processing, artificial intelligence, security and privacy, communications, wireless and sensor networks, microelectronics, circuit and systems, machine

Answering ad-hoc continuous aggregate queries over

The cougar approach to in-network query processing in sensor networks, ACM SIGMOD Record 31(3) (Sep 2002), 9-18. [14] Yao Y., and Gehrke J., Query processing for sensor networks, Asilomar, 2003.

PPT Research issues in wireless Sensor networks

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Instrumenting the Earth: MICHAEl lEHNING Next

2018-1-4  efficient query processing to help users quickly explore the dataset to find features for detailed analysis [5-7]. But even with these capabilities, SenseWeb hits just the tip of the iceberg of the challenging data management tasks facing environmental scientists. Additional tools are necessary to efficiently integrate sensor data with

IEEE Transactions on Knowledge and Data

Computing Iceberg Cubes by Top-Down and Bottom-Up Integration: The StarCubing Approach. 111-126; Jiang He, Tong Gao, Wei Hao, I-Ling Yen, Farokh B. Bastani: A Flexible Content Adaptation System Using a Rule-Based Approach. 127-140. Volume 19, Number 2, February 2007 Special Issue on the Semantic Web Era

Computer Science Final Projects Thesis123Thesis123

21. Efficient Iceberg Query Evaluation Using Compressed Bitmap Index 22. Efficient Multidimensional Fuzzy Search for Personal Information Management Systems 23. A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication. DISTRIBUTED SYSTEMS. 1. Catching Packet Droppers and Modifiers in Wireless Sensor Networks 2.

A Fast and Efficient Algorithm for Finding Frequent Items

2016-1-15  processing one input data item. Moreover, the computation time for answering each query is O(log O), and for answering query about the frequentness of a given data item is O(1). Through extensive experiments, we show that λ-Count outperforms other methods such as LC and EC in terms of the accuracy, memory requirement, and processing speed.

Jun Yang: Publications Duke University

Adam Silberstein, Gregory Filpus, Kamesh Munagala, and Jun Yang. "Data-driven processing in sensor networks." In Proceedings of the 2007 Conference on Innovative Data Systems Research, pages 10-21, Asilomar, California, USA, January 2007. Acceptance rate: 34 out of 98. Junyi Xie and Jun Yang. "A survey of join processing in data streams."