scholarly journals Semantic Data Mining in Ubiquitous Sensing: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4322
Author(s):  
Grzegorz J. Nalepa ◽  
Szymon Bobek ◽  
Krzysztof Kutt ◽  
Martin Atzmueller
Keyword(s):  
Data Mining ◽  
Domain Knowledge ◽  
Large Scale ◽  
Industrial Applications ◽  
Levels Of Abstraction ◽  
Current Application ◽  
Collective Behaviors ◽  
Semantic Data ◽  
Ubiquitous Sensing ◽  
Semantic Data Mining

Mining ubiquitous sensing data is important but also challenging, due to many factors, such as heterogeneous large-scale data that is often at various levels of abstraction. This also relates particularly to the important aspects of the explainability and interpretability of the applied models and their results, and thus ultimately to the outcome of the data mining process. With this, in general, the inclusion of domain knowledge leading towards semantic data mining approaches is an emerging and important research direction. This article aims to survey relevant works in these areas, focusing on semantic data mining approaches and methods, but also on selected applications of ubiquitous sensing in some of the most prominent current application areas. Here, we consider in particular: (1) environmental sensing; (2) ubiquitous sensing in industrial applications of artificial intelligence; and (3) social sensing relating to human interactions and the respective individual and collective behaviors. We discuss these in detail and conclude with a summary of this emerging field of research. In addition, we provide an outlook on future directions for semantic data mining in ubiquitous sensing contexts.

Semantic Data Mining of Financial News Articles

2013 ◽  
pp. 294-307 ◽  
Author(s):  
Anže Vavpetič ◽  
Petra Kralj Novak ◽  
Miha Grčar ◽  
Igor Mozetič ◽  
Nada Lavrač
Keyword(s):  
Data Mining ◽  
Financial News ◽  
Semantic Data ◽  
Semantic Data Mining

scholarly journals Machine Learning for Fluid Mechanics

2020 ◽  
Vol 52 (1) ◽  
pp. 477-508 ◽  
Author(s):  
Steven L. Brunton ◽  
Bernd R. Noack ◽  
Petros Koumoutsakos
Keyword(s):  
Machine Learning ◽  
Fluid Mechanics ◽  
Domain Knowledge ◽  
Large Scale ◽  
Past History ◽  
Fluid Flows ◽  
Field Measurements ◽  
Industrial Applications ◽  
Current Lines ◽  
Large Scale Simulations

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. Moreover, ML algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of ML for fluid mechanics. We outline fundamental ML methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experiments, and simulations. ML provides a powerful information-processing framework that can augment, and possibly even transform, current lines of fluid mechanics research and industrial applications.

Pattern Based Feature Construction in Semantic Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 27-65 ◽  
Author(s):  
Agnieszka Ławrynowicz ◽  
Jędrzej Potoniec
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Semantic Features ◽  
Semantic Data ◽  
Data Mining Approach ◽  
Meta Learning ◽  
New Type ◽  
Domain Ontologies ◽  
Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

scholarly journals Using Ontologies in Semantic Data Mining with SEGS and g-SEGS

2011 ◽  
pp. 165-178 ◽  
Author(s):  
Nada Lavrač ◽  
Anže Vavpetič ◽  
Larisa Soldatova ◽  
Igor Trajkovski ◽  
Petra Kralj Novak
Keyword(s):  
Data Mining ◽  
Semantic Data ◽  
Semantic Data Mining

Semantic data mining of short utterances

2005 ◽  
Vol 13 (5) ◽  
pp. 672-680 ◽  
Author(s):  
Lee Begeja ◽  
H. Drucker ◽  
D. Gibbon ◽  
P. Haffner ◽  
Zhu Liu ◽  
...  
Keyword(s):  
Data Mining ◽  
Semantic Data ◽  
Semantic Data Mining ◽  
Short Utterances

Semantic Data Mining

2008 ◽  
pp. 3524-3530
Author(s):  
Protima Banerjee ◽  
Xiaohua Hu ◽  
Illhio Yoo
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Transaction Processing ◽  
Data Sets ◽  
Ordered Trees ◽  
Semantic Data ◽  
Symbolic Sequences ◽  
Semantic Data Mining ◽  
Enterprise Data Warehouse ◽  
Mining Algorithms

Over the past few decades, data mining has emerged as a field of research critical to understanding and assimilating the large stores of data accumulated by corporations, government agencies, and laboratories. Early on, mining algorithms and techniques were limited to relational data sets coming directly from Online Transaction Processing (OLTP) systems, or from a consolidated enterprise data warehouse. However, recent work has begun to extend the limits of data mining strategies to include “semi-structured data such as HTML and XML texts, symbolic sequences, ordered trees and relations represented by advanced logics” (Washio & Motoda, 2003).

Pattern Based Feature Construction in Semantic Data Mining

2016 ◽  
pp. 823-864
Author(s):  
Agnieszka Ławrynowicz ◽  
Jędrzej Potoniec
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Semantic Features ◽  
Semantic Data ◽  
Data Mining Approach ◽  
Meta Learning ◽  
New Type ◽  
Domain Ontologies ◽  
Semantic Data Mining

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.

An Ontology-Based Semantic Data Mining in Mobile Environment

2017 ◽  
Vol 23 (10) ◽  
pp. 10241-10245
Author(s):  
Mi-Sug Gu ◽  
Jeong-Hee Hwang
Keyword(s):  
Data Mining ◽  
Mobile Environment ◽  
Semantic Data ◽  
Semantic Data Mining
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