Regularly Frequent Patterns Mining from Sensor Data Stream

2013 ◽  
pp. 417-424 ◽  
Author(s):  
Md. Mamunur Rashid ◽  
Iqbal Gondal ◽  
Joarder Kamruzzaman
Keyword(s):  
Data Stream ◽  
Sensor Data ◽  
Frequent Patterns

Mining Frequent Patterns over Data Stream under the Time Decaying Model

2010 ◽  
Vol 36 (5) ◽  
pp. 674-684 ◽  
Author(s):  
Feng WU ◽  
Yan ZHONG ◽  
Quan-Yuan WU
Keyword(s):  
Data Stream ◽  
Frequent Patterns

Research on Sequence Query Processing Techniques over Data Streams

2013 ◽  
Vol 284-287 ◽  
pp. 3507-3511 ◽  
Author(s):  
Edgar Chia Han Lin
Keyword(s):  
Query Processing ◽  
Data Streams ◽  
Management System ◽  
Data Stream ◽  
Sensor Data ◽  
Data Management System ◽  
Content Filtering ◽  
Data Stream Management ◽  
Stream Management ◽  
Great Progress

Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since the current DSMS does not support queries on sequence data, this project will study the issues related to two types of data. First, we will focus on the content filtering on single-attribute streams, such as sensor data. Second, we will focus on multi-attribute streams, such as video films. We will discuss the related issues such as how to build an efficient index for all queries of different streams and the corresponding query processing mechanisms.

An Efficient Frequent Patterns Mining Algorithm over Data Streams Based on FPD-Graph

2012 ◽  
Vol 433-440 ◽  
pp. 4457-4462 ◽  
Author(s):  
Jun Shan Tan ◽  
Zhu Fang Kuang ◽  
Guo Gui Yang
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Data Generation ◽  
Experiment Data ◽  
Mining Algorithm ◽  
Head Node

The design of synopses structure is an important issue of frequent patterns mining over data stream. A data stream synopses structure FPD-Graph which is based on directed graph is proposed in this paper. The FPD-Graph contains list head node FPDG-Head and list node FPDG-Node. The operations of FPD-Graph consist of insert operation and deletion operation. A frequent pattern mining algorithm DGFPM based on sliding window over data stream is proposed in this paper. The IBM synthesizes data generation which output customers shopping a data are adopted as experiment data. The DGFPM algorithm not only has high precision for mining frequent patterns, but also has low processing time.

scholarly journals Correction to: Semantic annotation of summarized sensor data stream for effective query processing

2018 ◽  
Vol 76 (6) ◽  
pp. 4040-4040
Author(s):  
Shobharani Pacha ◽  
Suresh Ramalingam Murugan ◽  
R. Sethukarasi
Keyword(s):  
Query Processing ◽  
Data Stream ◽  
Semantic Annotation ◽  
Sensor Data

Sensor Data Hiding Based on Image Watermarking Using Interpolation Technique over Inter-Packet Delays

2014 ◽  
Vol 573 ◽  
pp. 543-548 ◽  
Author(s):  
K.P. Ramya ◽  
R. Chithra Devi ◽  
M.K. Revathi ◽  
P. Annapandi
Keyword(s):  
Data Stream ◽  
Image Watermarking ◽  
Location Based Services ◽  
Sensor Data ◽  
Storage Efficiency ◽  
Interpolation Technique ◽  
Packet Delays ◽  
Watermarking Scheme ◽  
Secure Transmission ◽  
Efficient Transmission

Large number of application areas, like location-based services, transaction logs, sensor networks are qualified by uninterrupted data stream from many. Sensor data handling of continuous data needs to cover various issues, admitting the storage efficiency, processing throughput, bandwidth conception and secure transmission. This paper addresses the challenges by providing secure and efficient transmission of sensor data by embedding it over the inter-packet delays (IPDs). The embedding of sensor data within a host medium makes this technique reminiscent of watermarking. Interpolation technique is used to hide the sensor data into an image which is send to another node. By enforcing linear enlargement to interpolation-errors, a extremely effective reversible watermarking scheme is achieved, which can ensure high image quality without sacrificing embedding capacity. Time-Based flow watermarking technique is proposed, that avoids data degradation due to traditional watermarking. Sensor data is extracted effectively based on the inter-packet delays that minimizes the probability of decoding error. The outcome of the observation depicts that this system is scalable and highly resilient in sensor data.

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