A Theoretical Comprehensive Framework for the Process of Theories Formation

Scientists rely more and more upon computerized data mining and artificial intelligence to analyze data sets and identify association rules, which serve as the basis of evolving theories. This tendency is likely to expand, and computerized intelligence is likely to take a leading role in scientific theorizing. While the ever-advancing technology could be of great benefit, scientists with expertise in many research fields do not necessarily understand thoroughly enough the various assumptions, which underlie different data mining methods and which pose significant limitations on the association rules that could be identified in the first place. There seems to be a need for a comprehensive framework, which should present the various possible technological aids in the context of our neurocognitive process of theorizing and identifying association rules. Such a framework can be hopefully used to understand, identify, and overcome the limitations of the currently fragmented processes of technology-based theorizing and the formation of association rules in any research field. In order to meet this end, we divide theorizing into underlying neurocognitive components, describe their current technological expansions and limitations, and offer a possible comprehensive computational framework for each such component and their combination.

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Fundamental resource trade-offs for encoded distributed optimization

Information and Inference A Journal of the IMA ◽

10.1093/imaiai/iaaa026 ◽

2020 ◽

Author(s):

A Salman Avestimehr ◽

Seyed Mohammadreza Mousavi Kalan ◽

Mahdi Soltanolkotabi

Keyword(s):

Computational Time ◽

Massive Data ◽

Data Sets ◽

Massive Data Sets ◽

Computational Framework ◽

Data Set ◽

Trade Offs ◽

Major Bottleneck ◽

Computing Environments ◽

Analyze Data

Abstract Dealing with the shear size and complexity of today’s massive data sets requires computational platforms that can analyze data in a parallelized and distributed fashion. A major bottleneck that arises in such modern distributed computing environments is that some of the worker nodes may run slow. These nodes a.k.a. stragglers can significantly slow down computation as the slowest node may dictate the overall computational time. A recent computational framework, called encoded optimization, creates redundancy in the data to mitigate the effect of stragglers. In this paper, we develop novel mathematical understanding for this framework demonstrating its effectiveness in much broader settings than was previously understood. We also analyze the convergence behavior of iterative encoded optimization algorithms, allowing us to characterize fundamental trade-offs between convergence rate, size of data set, accuracy, computational load (or data redundancy) and straggler toleration in this framework.

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Stamping Plant 4.0 – Basics for the Application of Data Mining Methods in Manufacturing Car Body Parts

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.639.21 ◽

2015 ◽

Vol 639 ◽

pp. 21-30 ◽

Cited By ~ 7

Author(s):

Stephan Purr ◽

Josef Meinhardt ◽

Arnulf Lipp ◽

Axel Werner ◽

Martin Ostermair ◽

...

Keyword(s):

Data Mining ◽

Data Acquisition ◽

Data Driven ◽

Quality Analysis ◽

Process Conditions ◽

Data Sets ◽

Body Parts ◽

Car Body ◽

Sample Data ◽

Mining Methods

Data-driven quality evaluation in the stamping process of car body parts is quite promising because dependencies in the process have not yet been sufficiently researched. However, the application of data mining methods for the process in stamping plants would require a large number of sample data sets. Today, acquiring these data represents a major challenge, because the necessary data are inadequately measured, recorded or stored. Thus, the preconditions for the sample data acquisition must first be created before being able to investigate any correlations. In addition, the process conditions change over time due to wear mechanisms. Therefore, the results do not remain valid and a constant data acquisition is required. In this publication, the current situation in stamping plants regarding the process robustness will be first discussed and the need for data-driven methods will be shown. Subsequently, the state of technology regarding the possibility of collecting the sample data sets for quality analysis in producing car body parts will be researched. At the end of this work, an overview will be provided concerning how this data collection was implemented at BMW as well as what kind of potential can be expected.

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The R package “eseis” – a software toolbox for environmental seismology

Earth Surface Dynamics ◽

10.5194/esurf-6-669-2018 ◽

2018 ◽

Vol 6 (3) ◽

pp. 669-686 ◽

Cited By ~ 8

Author(s):

Michael Dietze

Keyword(s):

Data Science ◽

Earth Science ◽

Science Research ◽

Worked Examples ◽

R Package ◽

Research Field ◽

Data Sets ◽

Research Fields ◽

Scientific Disciplines ◽

Analysis Environment

Abstract. Environmental seismology is the study of the seismic signals emitted by Earth surface processes. This emerging research field is at the intersection of seismology, geomorphology, hydrology, meteorology, and further Earth science disciplines. It amalgamates a wide variety of methods from across these disciplines and ultimately fuses them in a common analysis environment. This overarching scope of environmental seismology requires a coherent yet integrative software which is accepted by many of the involved scientific disciplines. The statistic software R has gained paramount importance in the majority of data science research fields. R has well-justified advances over other mostly commercial software, which makes it the ideal language to base a comprehensive analysis toolbox on. The article introduces the avenues and needs of environmental seismology, and how these are met by the R package eseis. The conceptual structure, example data sets, and available functions are demonstrated. Worked examples illustrate possible applications of the package and in-depth descriptions of the flexible use of the functions. The package has a registered DOI, is available under the GPL licence on the Comprehensive R Archive Network (CRAN), and is maintained on GitHub.

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The Integral of Spatial Data Mining in the Era of Big Data

Advances in Business Information Systems and Analytics - Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence ◽

10.4018/978-1-5225-2031-3.ch006 ◽

2017 ◽

pp. 90-126

Author(s):

Gebeyehu Belay Gebremeskel ◽

Chai Yi ◽

Zhongshi He

Keyword(s):

Data Mining ◽

Data Warehouse ◽

Spatial Data ◽

High Volume ◽

Spatial Data Mining ◽

Research Field ◽

Data Sets ◽

Data Types ◽

Basic Principles ◽

Gis Data

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

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Finding Persistent Strong Rules

Knowledge Discovery Practices and Emerging Applications of Data Mining - Advances in Data Mining and Database Management ◽

10.4018/978-1-60960-067-9.ch005 ◽

2010 ◽

pp. 85-107

Author(s):

Anthony Scime ◽

Karthik Rajasethupathy ◽

Kulathur S. Rajasethupathy ◽

Gregg R. Murray

Keyword(s):

Data Mining ◽

Association Rules ◽

Strong Association ◽

National Election ◽

Data Sets ◽

Rule Discovery ◽

Discovery Process ◽

Data Set ◽

Rule Sets ◽

Election Studies

Data mining is a collection of algorithms for finding interesting and unknown patterns or rules in data. However, different algorithms can result in different rules from the same data. The process presented here exploits these differences to find particularly robust, consistent, and noteworthy rules among much larger potential rule sets. More specifically, this research focuses on using association rules and classification mining to select the persistently strong association rules. Persistently strong association rules are association rules that are verifiable by classification mining the same data set. The process for finding persistent strong rules was executed against two data sets obtained from the American National Election Studies. Analysis of the first data set resulted in one persistent strong rule and one persistent rule, while analysis of the second data set resulted in 11 persistent strong rules and 10 persistent rules. The persistent strong rule discovery process suggests these rules are the most robust, consistent, and noteworthy among the much larger potential rule sets.

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Visual Data Mining for Discovering Association Rules

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch125 ◽

2008 ◽

pp. 2105-2120

Author(s):

Kesaraporn Techapichetvanich ◽

Amitava Datta

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Large Data ◽

Data Sets ◽

Visual Data Mining ◽

Useful Knowledge ◽

Large Databases ◽

A New Technique ◽

Mining Association Rule

Both visualization and data mining have become important tools in discovering hidden relationships in large data sets, and in extracting useful knowledge and information from large databases. Even though many algorithms for mining association rules have been researched extensively in the past decade, they do not incorporate users in the association-rule mining process. Most of these algorithms generate a large number of association rules, some of which are not practically interesting. This chapter presents a new technique that integrates visualization into the mining association rule process. Users can apply their knowledge and be involved in finding interesting association rules through interactive visualization, after obtaining visual feedback as the algorithm generates association rules. In addition, the users gain insight and deeper understanding of their data sets, as well as control over mining meaningful association rules.

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Overview of Predictive Modeling Approaches in Health Care Data Mining

Business Intelligence ◽

10.4018/978-1-4666-9562-7.ch004 ◽

2016 ◽

pp. 73-95 ◽

Cited By ~ 1

Author(s):

Sunita Soni

Keyword(s):

Data Mining ◽

Predictive Modeling ◽

Clinical Information ◽

Ease Of Use ◽

Classification Model ◽

Computational Time ◽

Data Sets ◽

Advantages And Disadvantages ◽

Mining Methods ◽

Predictive Data Mining

Medical data mining has great potential for exploring the hidden pattern in the data sets of the medical domain. A predictive modeling approach of Data Mining has been systematically applied for the prognosis, diagnosis, and planning for treatment of chronic disease. For example, a classification system can assist the physician to predict if the patient is likely to have a certain disease, or by considering the output of the classification model, the physician can make a better decision on the treatment to be applied to the patient. Once the model is evaluated and verified, it may be embedded within clinical information systems. The objective of this chapter is to extensively study the various predictive data mining methods to evaluate their usage in terms of accuracy, computational time, comprehensibility of the results, ease of use of the algorithm, and advantages and disadvantages to relatively naive medical users. The research has shown that there is not a single best prediction tool, but instead, the best performing algorithm will depend on the features of the dataset to be analyzed.

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Visual Data Mining for Discovering Association Rules

Business Applications and Computational Intelligence ◽

10.4018/978-1-59140-702-7.ch011 ◽

2011 ◽

pp. 209-226

Author(s):

Kesaraporn Techapichetvanich ◽

Amitava Datta

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

Large Data ◽

Data Sets ◽

Visual Data Mining ◽

Useful Knowledge ◽

Large Databases ◽

A New Technique ◽

Mining Association Rule

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Scientific Web Intelligence

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch187 ◽

2011 ◽

pp. 995-999

Author(s):

Mike Thelwall

Keyword(s):

Data Mining ◽

End Users ◽

Research Field ◽

Data Sources ◽

Web Pages ◽

Web Intelligence ◽

Research Fields ◽

Patterns Of Communication ◽

The Impact ◽

Citation Databases

Scientific Web Intelligence (SWI) is a research field that combines techniques from data mining, Web intelligence, and scientometrics to extract useful information from the links and text of academic-related Web pages using various clustering, visualization, and counting techniques. Its origins lie in previous scientometric research into mining off-line academic data sources such as journal citation databases. Typical scientometric objectives are either evaluative (assessing the impact of research) or relational (identifying patterns of communication within and among research fields). From scientometrics, SWI also inherits a need to validate its methods and results so that the methods can be justified to end users, and the causes of the results can be found and explained.

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A Data Warehouse Cleansing Approach Based on Mathematical Association Rules

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.490-495.1878 ◽

2012 ◽

Vol 490-495 ◽

pp. 1878-1882

Author(s):

Yu Xiang Song

Keyword(s):

Data Mining ◽

Data Warehouse ◽

Association Rules ◽

Data Sets ◽

Mathematical Association ◽

Manual Intervention ◽

Mining Association Rules ◽

High Degree

The alliance rules stated above based on the principle of data mining association rules provide a solution for detecting errors in the data sets. The errors are detected automatically. The manual intervention in the proposed algorithm is highly negligible resulting in high degree of automation and accuracy. The duplicity in the names field of the data warehouse has been remarkably cleansed and worked out. Domain independency has been achieved using the concept of integer domain which even adds on to the memory saving capability of the algorithm.

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