scholarly journals Interannual to decadal variability in the southern Okhotsk Sea based on a new gridded upper water temperature data set

2004 ◽  
Vol 109 (C9) ◽  
Author(s):  
Shoshiro Minobe
Keyword(s):  
Water Temperature ◽  
Decadal Variability ◽  
Temperature Data ◽  
Okhotsk Sea ◽  
Data Set ◽  
Water Temperature Data

scholarly journals A Note on the Collection and Cleaning of Water Temperature Data

Water ◽  
2012 ◽  
Vol 4 (3) ◽  
pp. 597-606 ◽  
Author(s):  
Colin Sowder ◽  
E. Ashley Steel
Keyword(s):  
Water Temperature ◽  
Temperature Data ◽  
Water Temperature Data

EXPERIENCE: Algorithms and Case Study for Explaining Repairs with Uniform Profiles over IoT Data

2021 ◽  
Vol 13 (3) ◽  
pp. 1-17
Author(s):  
Zhicheng Liu ◽  
Yang Zhang ◽  
Ruihong Huang ◽  
Zhiwei Chen ◽  
Shaoxu Song ◽  
...  
Keyword(s):  
Water Temperature ◽  
Decision Maker ◽  
Block Size ◽  
Temperature Data ◽  
Prototype System ◽  
Time Intervals ◽  
Incremental Computation ◽  
Water Temperature Data ◽  
Gps Trajectories

IoT data with timestamps are often found with outliers, such as GPS trajectories or sensor readings. While existing systems mostly focus on detecting temporal outliers without explanations and repairs, a decision maker may be more interested in the cause of the outlier appearance such that subsequent actions would be taken, e.g., cleaning unreliable readings or repairing broken devices or adopting a strategy for data repairs. Such outlier detection, explanation, and repairs are expected to be performed in either offline (batch) or online modes (over streaming IoT data with timestamps). In this work, we present TsClean, a new prototype system for detecting and repairing outliers with explanations over IoT data. The framework defines uniform profiles to explain the outliers detected by various algorithms, including the outliers with variant time intervals, and take approaches to repair outliers. Both batch and streaming processing are supported in a uniform framework. In particular, by varying the block size, it provides a tradeoff between computing the accurate results and approximating with efficient incremental computation. In this article, we present several case studies of applying TsClean in industry, e.g., how this framework works in detecting and repairing outliers over excavator water temperature data, and how to get reasonable explanations and repairs for the detected outliers in tracking excavators.

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