Holistic thermal-aware workload management and infrastructure control for heterogeneous data centers using machine learning

2021 ◽  
Vol 118 ◽  
pp. 208-218
Author(s):  
SeyedMorteza MirhoseiniNejad ◽  
Ghada Badawy ◽  
Douglas G. Down
Keyword(s):  
Machine Learning ◽  
Data Centers ◽  
Heterogeneous Data ◽  
Workload Management

scholarly journals Entity Type Recognition for Heterogeneous Semantic Graphs

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 75-86 ◽  
Author(s):  
Jennifer Sleeman ◽  
Tim Finin ◽  
Anupam Joshi
Keyword(s):  
Machine Learning ◽  
Background Knowledge ◽  
Knowledge Bases ◽  
Heterogeneous Data ◽  
Unstructured Data ◽  
Supervised Machine Learning ◽  
Coreference Resolution ◽  
Multiple Sources ◽  
Fine Grained ◽  
High Level

We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. Big data problems that involve integrating data from multiple sources can benefit from our approach when the datas ontologies are unknown, inaccessible or semantically trivial. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

UPS Node-Based Workload Management for Data Centers Considering Flexible Service Requirements

2019 ◽  
Vol 55 (6) ◽  
pp. 5533-5542
Author(s):  
Fang Cao ◽  
Yajing Wang ◽  
Feng Zhu ◽  
Yujie Cao ◽  
Zhaohao Ding
Keyword(s):  
Data Centers ◽  
Workload Management

scholarly journals Critical Parameter Identification for Safety Events in Commercial Aviation Using Machine Learning

Aerospace ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 73 ◽  
Author(s):  
HyunKi Lee ◽  
Sasha Madar ◽  
Santusht Sairam ◽  
Tejas G. Puranik ◽  
Alexia P. Payan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Heterogeneous Data ◽  
Use Cases ◽  
Critical Parameters ◽  
Analytical Methodology ◽  
Commercial Aviation ◽  
Related Data ◽  
Flight Operations ◽  
Commercial Airline

In recent years, there has been a rapid growth in the application of data science techniques that leverage aviation data collected from commercial airline operations to improve safety. This paper presents the application of machine learning to improve the understanding of risk factors during flight and their causal chains. With increasing complexity and volume of operations, rapid accumulation and analysis of this safety-related data has the potential to maintain and even lower the low global accident rates in aviation. This paper presents the development of an analytical methodology called Safety Analysis of Flight Events (SAFE) that synthesizes data cleaning, correlation analysis, classification-based supervised learning, and data visualization schema to streamline the isolation of critical parameters and the elimination of tangential factors for safety events in aviation. The SAFE methodology outlines a robust and repeatable framework that is applicable across heterogeneous data sets containing multiple aircraft, airport of operations, and phases of flight. It is demonstrated on Flight Operations Quality Assurance (FOQA) data from a commercial airline through use cases related to three safety events, namely Tire Speed Event, Roll Event, and Landing Distance Event. The application of the SAFE methodology yields a ranked list of critical parameters in line with subject-matter expert conceptions of these events for all three use cases. The work concludes by raising important issues about the compatibility levels of machine learning and human conceptualization of incidents and their precursors, and provides initial guidance for their reconciliation.

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