scholarly journals Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Metabolites ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 51 ◽  
Author(s):  
Nguyen Phuoc Long ◽  
Tran Diem Nghi ◽  
Yun Pyo Kang ◽  
Nguyen Hoang Anh ◽  
Hyung Min Kim ◽  
...  
Keyword(s):  
Internal Validity ◽  
Future Research ◽  
Data Mining Algorithms ◽  
Control Procedures ◽  
Comprehensive Metabolomics ◽  
Computational Resources ◽  
Five Phases ◽  
Mining Algorithms ◽  
Clinical Metabolomics

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional “pre-pre-” and “post-post-” analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Big Data Quality for Data Mining in Business Intelligence Applications

2021 ◽  
pp. 64-91
Author(s):  
Arun Thotapalli Sundararaman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Quality ◽  
Business Intelligence ◽  
Future Research ◽  
Quality Factors ◽  
Data Volume ◽  
Mining Algorithms ◽  
Data Mining Application

Study of data quality for data mining application has always been a complex topic; in the recent years, this topic has gained further complexity with the advent of big data as the source for data mining and business intelligence (BI) applications. In a big data environment, data is consumed in various states and various forms serving as input for data mining, and this is the main source of added complexity. These new complexities and challenges arise from the underlying dimensions of big data (volume, variety, velocity, and value) together with the ability to consume data at various stages of transition from raw data to standardized datasets. These have created a need for expanding the traditional data quality (DQ) factors into BDQ (big data quality) factors besides the need for new BDQ assessment and measurement frameworks for data mining and BI applications. However, very limited advancement has been made in research and industry in the topic of BDQ and their relevance and criticality for data mining and BI applications. Data quality in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in business intelligence applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI system has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of BDQ definitions and measurement for data mining for BI, analyzes the gaps therein, and provides a direction for future research and practice in this area.

Data Quality for Data Mining in Business Intelligence Applications

2015 ◽  
pp. 34-59
Author(s):  
Arun Thotapalli Sundararaman
Keyword(s):  
Data Mining ◽  
Data Quality ◽  
Business Intelligence ◽  
Objective Measures ◽  
Future Research ◽  
Subjective Measures ◽  
Research And Practice ◽  
Modeling Approaches ◽  
Mining Algorithms

Data Quality (DQ) in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in Business Intelligence (BI) applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI System has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of DQ definition and measurement for data mining for BI, analyzes the gaps therein, besides reviewing proposed solutions and providing a direction for future research and practice in this area.

scholarly journals Learning analytics made in France: the METAL project

2019 ◽  
Vol 36 (4) ◽  
pp. 299-313 ◽  
Author(s):  
Armelle Brun ◽  
Geoffray Bonnin ◽  
Sylvain Castagnos ◽  
Azim Roussanaly ◽  
Anne Boyer
Keyword(s):  
Data Mining ◽  
Secondary School ◽  
Data Storage ◽  
Learning Analytics ◽  
Content Type ◽  
Data Mining Algorithms ◽  
School Based ◽  
Source Data ◽  
Mining Algorithms

Purpose The purpose of this paper is to present the METAL project, a French open learning analytics (LA) project for secondary school, that aims at improving the quality of teaching. The originality of METAL is that it relies on research through exploratory activities and focuses on all the aspects of a learning analytics environment. Design/methodology/approach This work introduces the different concerns of the project: collection and storage of multi-source data owned by a variety of stakeholders, selection and promotion of standards, design of an open-source LRS, conception of dashboards with their final users, trust, usability, design of explainable multi-source data-mining algorithms. Findings All the dimensions of METAL are presented, as well as the way they are approached: data sources, data storage, through the implementation of an LRS, design of dashboards for secondary school, based on co-design sessions data mining algorithms and experiments, in line with privacy and ethics concerns. Originality/value The issue of a global dissemination of LA at an institution level or at a broader level such as a territory or a study level is still a hot topic in the literature, and is one of the focus and originality of this paper, associated with the large spectrum of different concerns.

Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms

2000 ◽  
Vol 1718 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Geert Wets ◽  
Koen Vanhoof ◽  
Theo Arentze ◽  
Harry Timmermans
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Logit Model ◽  
Goodness Of Fit ◽  
Travel Demand ◽  
Activity Patterns ◽  
Future Research ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The utility-maximizing framework—in particular, the logit model—is the dominantly used framework in transportation demand modeling. Computational process modeling has been introduced as an alternative approach to deal with the complexity of activity-based models of travel demand. Current rule-based systems, however, lack a methodology to derive rules from data. The relevance and performance of data-mining algorithms that potentially can provide the required methodology are explored. In particular, the C4 algorithm is applied to derive a decision tree for transport mode choice in the context of activity scheduling from a large activity diary data set. The algorithm is compared with both an alternative method of inducing decision trees (CHAID) and a logit model on the basis of goodness-of-fit on the same data set. The ratio of correctly predicted cases of a holdout sample is almost identical for the three methods. This suggests that for data sets of comparable complexity, the accuracy of predictions does not provide grounds for either rejecting or choosing the C4 method. However, the method may have advantages related to robustness. Future research is required to determine the ability of decision tree-based models in predicting behavioral change.

Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
Haiyang Zheng ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Wind Farm ◽  
Multivariate Time Series ◽  
Future Research ◽  
Support Vector ◽  
Time Series Models ◽  
Ramp Rate ◽  
Data Mining Algorithms ◽  
Mining Algorithms

In this paper, multivariate time series models were built to predict the power ramp rates of a wind farm. The power changes were predicted at 10 min intervals. Multivariate time series models were built with data-mining algorithms. Five different data-mining algorithms were tested using data collected at a wind farm. The support vector machine regression algorithm performed best out of the five algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10–60 min. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy of the power ramp rate. The data used in this research originated at a wind farm of 100 turbines. The test results of multivariate time series models were presented in this paper. Suggestions for future research were provided.

scholarly journals A Testbed for QOS-Based Data Analytic Service Selection in the Cloud

2021 ◽  
Author(s):  
Md Shahinur Rahman
Keyword(s):  
Service Selection ◽  
Selection Models ◽  
Data Mining Algorithms ◽  
Traditional Service ◽  
Monitoring Service ◽  
Mining Algorithms ◽  
Infrastructure Services ◽  
Software Services ◽  
Data Analytic

With the emergence of Big Data and Cloud Computing, more and more data analytic software services have become available through a Cloud platform. Compared to the traditional service selection problem, selecting this type of services has additional challenges, which requires new selection models being proposed. It is the purpose of this work to “create a testbed” to benefit the research community in this area so that different selection models with consideration of different performance-influencing factors such as algorithms implemented, datasets to be processed, hosting infrastructure, can be tested and compared. We created a cloud-based platform for publishing and invoking analytic services as well as monitoring service performance during invocation. We implemented various data mining algorithms from different packages as example analytic services and hosted them on different infrastructure services. We also ran these services on some real datasets to collect a sample dataset of their Quality of Service (QoS) values.

Future Networked Healthcare Systems

2015 ◽  
pp. 531-558 ◽  
Author(s):  
Rashid Mehmood ◽  
Muhammad Ali Faisal ◽  
Saleh Altowaijri
Keyword(s):  
Performance Studies ◽  
Healthcare Systems ◽  
Future Research ◽  
Network Architectures ◽  
Organizational Boundaries ◽  
Health Level 7 ◽  
And Performance ◽  
Qos Performance ◽  
Computational Resources

Future healthcare systems and organizations demand huge computational resources, and the ability for the applications to interact and communicate with each other, within and across organizational boundaries. This chapter aims to explore state-of-the-art of the healthcare landscape and presents an analysis of networked healthcare systems with a focus on networking traffic and architectures. To this end, the relevant technologies including networked healthcare architectures and performance studies, Health Level 7 (HL7), big data, and cloud computing, are reviewed. Subsequently, a study of healthcare systems, applications and traffic over local, metro, and wide area networks is presented using multi-hospital cross-continent scenarios. The network architectures for these systems are described. A detailed study to explore quality of service (QoS) performance for these healthcare systems with a range of applications, system sizes, and network sizes is presented. Conclusions are drawn regarding future healthcare systems and internet designs along with directions for future research.

Multi-Relational Data Mining A Comprehensive Survey

2015 ◽  
pp. 32-53
Author(s):  
Ali H. Gazala ◽  
Waseem Ahmad
Keyword(s):  
Data Mining ◽  
Search Space ◽  
Data Representation ◽  
Research Area ◽  
Relational Data ◽  
Future Research ◽  
Relational Data Mining ◽  
Data Mining Algorithms ◽  
Table Data ◽  
Mining Algorithms

Multi-Relational Data Mining or MRDM is a growing research area focuses on discovering hidden patterns and useful knowledge from relational databases. While the vast majority of data mining algorithms and techniques look for patterns in a flat single-table data representation, the sub-domain of MRDM looks for patterns that involve multiple tables (relations) from a relational database. This sub-domain has received an increased research attention during the last two decades due to the wide range of possible applications. As a result of that growing attention, many successful multi-relational data mining algorithms and techniques were presented. This chapter presents a comprehensive review about multi-relational data mining. It discusses the different approaches researchers have followed to explore the relational search space while highlighting some of the most significant challenges facing researchers working in this sub-domain. The chapter also describes number of MRDM systems that have been developed during the last few years and discusses some future research directions in this sub-domain.

Analyzing Customer Behavior Using Online Analytical Mining (OLAM)

2015 ◽  
pp. 894-910 ◽  
Author(s):  
Thanachart Ritbumroong
Keyword(s):  
Data Mining ◽  
Behavior Analysis ◽  
Customer Behavior ◽  
Future Research ◽  
Technical Performance ◽  
Data Mining Algorithms ◽  
Integration Data ◽  
Mining Algorithms ◽  
Provided Examples

Online Analytical Mining (OLAM) is an architecture integrating data mining into OLAP. With this integration, data mining algorithms can be performed with OLAP abilities. OLAM enables users to choose a particular portion of data and analyze them with data mining models. Previous studies have provided examples of OLAM applications with the motivation to improve technical performance. This chapter reviews the capabilities of OLAM and discusses the well-known concept encompassing the analysis of customer behavior. The underlying motivation of this chapter is to present the opportunities for the development of OLAM to support the customer behavior analysis. Three main directions of the advancement in OLAM are proposed for future research.

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