Domain-Specific Particularities of Data Mining: Lessons Learned

Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1582-2 ◽

2019 ◽

Vol 2019 (1) ◽

Cited By ~ 2

Author(s):

Shadi Aljawarneh ◽

Aurea Anguera ◽

John William Atwood ◽

Juan A. Lara ◽

David Lizcano

Keyword(s):

Data Mining ◽

Time Series ◽

Knowledge Discovery ◽

Time Series Data ◽

Medical Patient ◽

Lessons Learned ◽

Physiological Signals ◽

Knowledge Discovery In Databases ◽

Series Data ◽

Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

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Pattern-based design applied to cultural heritage knowledge graphs

Semantic Web ◽

10.3233/sw-200422 ◽

2020 ◽

pp. 1-45

Author(s):

Valentina Anita Carriero ◽

Aldo Gangemi ◽

Maria Letizia Mancinelli ◽

Andrea Giovanni Nuzzolese ◽

Valentina Presutti ◽

...

Keyword(s):

Cultural Heritage ◽

Design Patterns ◽

Lessons Learned ◽

Knowledge Graph ◽

Unit Testing ◽

Domain Specific ◽

Rigorous Testing ◽

Ontology Design ◽

Domain Specific Knowledge ◽

Knowledge Graphs

Ontology Design Patterns (ODPs) have become an established and recognised practice for guaranteeing good quality ontology engineering. There are several ODP repositories where ODPs are shared as well as ontology design methodologies recommending their reuse. Performing rigorous testing is recommended as well for supporting ontology maintenance and validating the resulting resource against its motivating requirements. Nevertheless, it is less than straightforward to find guidelines on how to apply such methodologies for developing domain-specific knowledge graphs. ArCo is the knowledge graph of Italian Cultural Heritage and has been developed by using eXtreme Design (XD), an ODP- and test-driven methodology. During its development, XD has been adapted to the need of the CH domain e.g. gathering requirements from an open, diverse community of consumers, a new ODP has been defined and many have been specialised to address specific CH requirements. This paper presents ArCo and describes how to apply XD to the development and validation of a CH knowledge graph, also detailing the (intellectual) process implemented for matching the encountered modelling problems to ODPs. Relevant contributions also include a novel web tool for supporting unit-testing of knowledge graphs, a rigorous evaluation of ArCo, and a discussion of methodological lessons learned during ArCo’s development.

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Editorial: Data Mining Lessons Learned

Machine Learning ◽

10.1023/b:mach.0000035515.33403.6b ◽

2004 ◽

Vol 57 (1/2) ◽

pp. 5-11 ◽

Cited By ~ 2

Author(s):

Nada Lavrač ◽

Hiroshi Motoda ◽

Tom Fawcett

Keyword(s):

Data Mining ◽

Lessons Learned

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Lessons Learned From Data Mining, Decision Support and Collaboration

Data Mining and Decision Support ◽

10.1007/978-1-4615-0286-9_20 ◽

2003 ◽

pp. 237-246

Author(s):

Dunja Mladenić ◽

Nada Lavrač ◽

Marko Bohanec

Keyword(s):

Data Mining ◽

Decision Support ◽

Lessons Learned

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Comparison Between Internal and External DSLs via RubyTL and Gra2MoL

Formal and Practical Aspects of Domain-Specific Languages ◽

10.4018/978-1-4666-2092-6.ch005 ◽

2012 ◽

pp. 109-131 ◽

Cited By ~ 3

Author(s):

Jesús Sánchez Cuadrado ◽

Javier Luis Cánovas Izquierdo ◽

Jesús García Molina

Keyword(s):

Empirical Studies ◽

Lessons Learned ◽

The Other ◽

Model Transformations ◽

Domain Specific Languages ◽

Model Driven ◽

Domain Specific ◽

Advantages And Disadvantages

Domain Specific Languages (DSL) are becoming increasingly more important with the emergence of Model-Driven paradigms. Most literature on DSLs is focused on describing particular languages, and there is still a lack of works that compare different approaches or carry out empirical studies regarding the construction or usage of DSLs. Several design choices must be made when building a DSL, but one important question is whether the DSL will be external or internal, since this affects the other aspects of the language. This chapter aims to provide developers confronting the internal-external dichotomy with guidance, through a comparison of the RubyTL and Gra2MoL model transformations languages, which have been built as an internal DSL and an external DSL, respectively. Both languages will first be introduced, and certain implementation issues will be discussed. The two languages will then be compared, and the advantages and disadvantages of each approach will be shown. Finally, some of the lessons learned will be presented.

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An Overview of Biological Data Mining

Biotechnology ◽

10.4018/978-1-5225-8903-7.ch005 ◽

2019 ◽

pp. 120-139

Author(s):

Seetharaman Balaji

Keyword(s):

Data Mining ◽

Information Retrieval ◽

Biological Networks ◽

Web Mining ◽

Biological Data ◽

Biological Research ◽

Digital Repository ◽

Domain Specific ◽

Novice Learner ◽

Integration Data

The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.

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An Overview of Biological Data Mining

Library and Information Services for Bioinformatics Education and Research - Advances in Library and Information Science ◽

10.4018/978-1-5225-1871-6.ch007 ◽

2017 ◽

pp. 130-154

Author(s):

Seetharaman Balaji

Keyword(s):

Data Mining ◽

Information Retrieval ◽

Biological Networks ◽

Web Mining ◽

Biological Data ◽

Biological Research ◽

Digital Repository ◽

Domain Specific ◽

Novice Learner ◽

Integration Data

The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.

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