Finding Frequent Structures in XML Stream Data

2009 ◽  
Author(s):  
Jeong Hee Hwang ◽  
Mi Sug Gu
Keyword(s):  
Stream Data ◽  
Xml Stream

scholarly journals Association Rule Extraction from XML Stream Data for Wireless Sensor Networks

Sensors ◽  
2014 ◽  
Vol 14 (7) ◽  
pp. 12937-12957 ◽  
Author(s):  
Juryon Paik ◽  
Junghyun Nam ◽  
Ung Kim ◽  
Dongho Won
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Association Rule ◽  
Rule Extraction ◽  
Wireless Sensor ◽  
Stream Data ◽  
Xml Stream

A Query Algebra for Fragmented XML Stream Data

2004 ◽  
pp. 195-215 ◽  
Author(s):  
Sujoe Bose ◽  
Leonidas Fegaras ◽  
David Levine ◽  
Vamsi Chaluvadi
Keyword(s):  
Stream Data ◽  
Xml Stream

The Adaptive Fragmentation for XML Stream Dissemination

2010 ◽  
Vol 33 (10) ◽  
pp. 1953-1962
Author(s):  
Huan HUO ◽  
Qing-Kui CHEN ◽  
Guo-Ren WANG ◽  
Dun-Lu PENG ◽  
Ju-Tao HAO ◽  
...  
Keyword(s):  
Xml Stream

scholarly journals s2p: Provenance Research for Stream Processing System

2021 ◽  
Vol 11 (12) ◽  
pp. 5523
Author(s):  
Qian Ye ◽  
Minyan Lu
Keyword(s):  
Data Storage ◽  
Stream Processing ◽  
Processing System ◽  
Coarse Grained ◽  
Stream Data ◽  
Related Data ◽  
Dsp System ◽  
Provenance Research ◽  
Dsp Systems ◽  
Abnormal Results

The main purpose of our provenance research for DSP (distributed stream processing) systems is to analyze abnormal results. Provenance for these systems is not nontrivial because of the ephemerality of stream data and instant data processing mode in modern DSP systems. Challenges include but are not limited to an optimization solution for avoiding excessive runtime overhead, reducing provenance-related data storage, and providing it in an easy-to-use fashion. Without any prior knowledge about which kinds of data may finally lead to the abnormal, we have to track all transformations in detail, which potentially causes hard system burden. This paper proposes s2p (Stream Process Provenance), which mainly consists of online provenance and offline provenance, to provide fine- and coarse-grained provenance in different precision. We base our design of s2p on the fact that, for a mature online DSP system, the abnormal results are rare, and the results that require a detailed analysis are even rarer. We also consider state transition in our provenance explanation. We implement s2p on Apache Flink named as s2p-flink and conduct three experiments to evaluate its scalability, efficiency, and overhead from end-to-end cost, throughput, and space overhead. Our evaluation shows that s2p-flink incurs a 13% to 32% cost overhead, 11% to 24% decline in throughput, and few additional space costs in the online provenance phase. Experiments also demonstrates the s2p-flink can scale well. A case study is presented to demonstrate the feasibility of the whole s2p solution.

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