scholarly journals Efficient Processing of ContinuousSkylineQuery over Smarter Traffic Data Stream for Cloud Computing

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Wang Hanning ◽  
Xu Weixiang ◽  
Jiulin Yang ◽  
Lili Wei ◽  
Jia Chaolong
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Data Stream ◽  
Sliding Window ◽  
Continuous Query ◽  
Traffic Data ◽  
Stream Data ◽  
Smart Transportation ◽  
Efficient Processing ◽  
Query Algorithm

The analyzing and processing of multisource real-time transportation data stream lay a foundation for the smart transportation's sensibility, interconnection, integration, and real-time decision making. Strong computing ability and valid mass data management mode provided by the cloud computing, is feasible for handlingSkylinecontinuous query in the mass distributed uncertain transportation data stream. In this paper, we gave architecture of layered smart transportation about data processing, and we formalized the description about continuous query over smart transportation dataSkyline. Besides, we proposedmMR-SUDSalgorithm (Skylinequery algorithm of uncertain transportation stream data based onmicro-batchinMap Reduce) based on sliding window division and architecture.

Analysis Engine Based on Cloud Computing

2014 ◽  
Vol 602-605 ◽  
pp. 3478-3480
Author(s):  
Shan Hong Zhu ◽  
Wei Liu
Keyword(s):  
Cloud Computing ◽  
Behavior Analysis ◽  
Real Time ◽  
User Behavior ◽  
User Behavior Analysis ◽  
Efficient Processing ◽  
Processing And Storage ◽  
Mining Model ◽  
Analysis Engine ◽  
And Storage

In this paper come up with the idea of user behavior analysis engine, the combination of the static analysis of user behavior and real-time monitoring, real-time acquisition of Web log and user to access the context information of the page, apply to the improved data mining model analysis, which based on cloud computing technology, meanwhile efficient processing and storage, cloud database test showed that, the system can significantly improve the effect and efficiency of user behavior analysis.

Linked-tree: An aggregate query algorithm based on sliding window over data stream

2006 ◽  
Vol 11 (5) ◽  
pp. 1114-1119
Author(s):  
Yu Yaxin ◽  
Wang Guoren ◽  
Su Dong ◽  
Zhu Xinhua
Keyword(s):  
Data Stream ◽  
Sliding Window ◽  
Aggregate Query ◽  
Query Algorithm

An improved algorithm for clustering uncertain traffic data streams based on Hadoop platform

2019 ◽  
Vol 33 (19) ◽  
pp. 1950203
Author(s):  
Weixiang Xu ◽  
Jiaojiao Li
Keyword(s):  
Data Streams ◽  
Intelligent Transportation Systems ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Transportation Systems ◽  
Traffic Data ◽  
Distributed Structure ◽  
Streaming Traffic ◽  
Query Algorithm ◽  
The Impact

During the development of intelligent transportation systems, traffic data has the characteristics of streaming, high dimension and uncertainty. In order to realize the query of uncertain traffic data streams in a distributed environment, the authors design the algorithm of Uncertain Traffic Data Stream Parallel Continuous Query algorithm (UTDSPCQ). Firstly, the sliding window mode is applied to realize the data receiving and buffering in the data stream environment, so as to adapt to the MapReduce computing framework of the Hadoop distributed structure. Then, the impact of the high dimensionality and uncertainty of the data on the feature analysis of the dataset is reduced, through the dimension reduction and data rewriting. Finally, a multi-attribute data point RePoint is newly defined, to solve the problem of data dimension increase caused by data rewriting. Experiments show that this algorithm optimizes the traditional density-based clustering algorithm, and make it more adaptable to parallel continuous queries for uncertain traffic data streams, and can fully consider the newly generated streaming traffic data.

Processing Data Streams

2020 ◽  
pp. 20-39
Author(s):  
Parimala N.
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Continuous Queries ◽  
Stream Data ◽  
Continuous Sequence ◽  
Static Data ◽  
Transactional Data ◽  
Events Data

A data stream is a real-time continuous sequence that may be comprised of data or events. Data stream processing is different from static data processing which resides in a database. The data stream data is seen only once. It is too voluminous to store statically. A small portion of data called a window is considered at a time for querying, computing aggregates, etc. In this chapter, the authors explain the different types of window movement over incoming data. A query on a stream is repeatedly executed on the new data created by the movement of the window. SQL extensions to handle continuous queries is addressed in this chapter. Streams that contain transactional data as well as those that contain events are considered.

SBASH Stack Based Allocation of Sheer Window Architecture for Real Time Stream Data Processing

2020 ◽  
Vol 1 (1) ◽  
pp. 1-21
Author(s):  
Devesh Kumar Lal ◽  
Ugrasen Suman
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Data Streams ◽  
Time Complexity ◽  
Data Stream ◽  
Stream Data ◽  
Time Data ◽  
Processing Data ◽  
Stream Data Processing ◽  
Window Selection

The processing of real-time data streams is complex with large number of volume and variety. The volume and variety of data streams enhances a number of processing units to run in real time. The required number of processing units used for processing data streams are lowered by using a windowing mechanism. Therefore, the appropriate size of window selection is vital for stream data processing. The coarse size window will directly affect the overall processing time. On the other hand, a finely sized window has to deal with an increased number of management costs. In order to manage such streams of data, we have proposed a SBASH architecture, which can be helpful for determining a unipartite size of a sheer window. The sheer window reduces the overall latency of data stream processing by a certain extent. The time complexity to process such sheer window is equivalent to w log n w. These windows are allocated and retrieved in a stack-based manner, where stacks ≥ n, which is helpful in reducing the number of comparisons made during retrieval.

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