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Axioms
Special Issues
Data Farming: Mathematical Foundations and Applications
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Data Farming: Mathematical Foundations and Applications

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (31 December 2016) | Viewed by 15791

Special Issue Editors

Prof. Dr. Matthias M. Dehmer

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Guest Editor
1. Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Campus Steyr, Wehrgrabengasse 1, 4040 Steyr, Austria
2. College of Artificial Intelligence, Nankai University, Tianjin 300071, China
Interests: applied mathematics; bioinformatics; data mining; machine learning; systems biology; graph theory; complexity and information theory
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Frank Emmert-Streib

E-Mail Website
Guest Editor
Predictive Society and Data Analytics Lab, Tampere University, 33720 Tampere, Finland
Interests: data science; network science; machine learning; computational biology; computational social science
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Stefan Pickl

E-Mail Website
Guest Editor
Department of Computer Science, Core Competence Center for Operations Research, Universität der Bundeswehr München, Neubiberg-Munich, Germany
Interests: operations research; systems biology; graph theory and discrete optimization

Special Issue Information

Dear Colleagues,

Data farming is a relatively new field and, hence, there have been many challenges in Operations Research and Simulation when using Data Farming approaches. Typical problems are so-called “What If” scenarios in military decision support where a mathematical model needs to be developed and high-performance simulations must be performed. Afterwards, the results are explored regarding normal and abnormal trends (how do the data grow?). Also, the definition of Data Farming is not always unique because many researchers have used it in different ways and contexts.

In this special issue, we would like to address mathematical and practical contributions when dealing with Data Farming. In Particular:

  1. Strategic data farming
  2. Novel practical applications by using Data Farming
  3. Interrelations of data farming and optimization
  4. Social networks in the context of Data Farming
  5. Knowledge networks in the context of Data Farming

Prof. Dr. Matthias Dehmer
Dr. Frank Emmert-Streib
Prof. Dr. Stefan Pickl
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


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Published Papers (3 papers)

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2468 KiB  
Article
Summary of Data Farming
by Gary Horne and Klaus-Peter Schwierz
Axioms 2016, 5(1), 8; https://doi.org/10.3390/axioms5010008 - 1 Mar 2016
Cited by 4 | Viewed by 5476
Abstract
Data Farming is a process that has been developed to support decision-makers by answering questions that are not currently addressed. Data farming uses an inter-disciplinary approach that includes modeling and simulation, high performance computing, and statistical analysis to examine questions of interest with [...] Read more.
Data Farming is a process that has been developed to support decision-makers by answering questions that are not currently addressed. Data farming uses an inter-disciplinary approach that includes modeling and simulation, high performance computing, and statistical analysis to examine questions of interest with a large number of alternatives. Data farming allows for the examination of uncertain events with numerous possible outcomes and provides the capability of executing enough experiments so that both overall and unexpected results may be captured and examined for insights. Harnessing the power of data farming to apply it to our questions is essential to providing support not currently available to decision-makers. This support is critically needed in answering questions inherent in the scenarios we expect to confront in the future as the challenges our forces face become more complex and uncertain. This article was created on the basis of work conducted by Task Group MSG-088 “Data Farming in Support of NATO”, which is being applied in MSG-124 “Developing Actionable Data Farming Decision Support for NATO” of the Science and Technology Organization, North Atlantic Treaty Organization (STO NATO). Full article
(This article belongs to the Special Issue Data Farming: Mathematical Foundations and Applications)
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2725 KiB  
Article
Tactical Size Unit as Distribution in a Data Farming Environment
by Esa Lappi and Bernt Åkesson
Axioms 2016, 5(1), 7; https://doi.org/10.3390/axioms5010007 - 22 Feb 2016
Cited by 1 | Viewed by 4398
Abstract
In agent based models, the agents are usually platforms (individual soldiers, tanks, helicopters, etc.), not military units. In the Sandis software, the agents can be platoon size units. As there are about 30 soldiers in a platoon, there is a need for [...] Read more.
In agent based models, the agents are usually platforms (individual soldiers, tanks, helicopters, etc.), not military units. In the Sandis software, the agents can be platoon size units. As there are about 30 soldiers in a platoon, there is a need for strength distribution in simulations. The contribution of this paper is a conceptual model of the platoon level agent, the needed mathematical models and concepts, and references earlier studies of how simulations have been conducted in a data farming environment with platoon/squad size unit agents with strength distribution. Full article
(This article belongs to the Special Issue Data Farming: Mathematical Foundations and Applications)
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2416 KiB  
Article
Data Farming Process and Initial Network Analysis Capabilities
by Gary Horne and Theodore Meyer
Axioms 2016, 5(1), 4; https://doi.org/10.3390/axioms5010004 - 27 Jan 2016
Cited by 3 | Viewed by 4692
Abstract
Data Farming, network applications and approaches to integrate network analysis and processes to the data farming paradigm are presented as approaches to address complex system questions. Data Farming is a quantified approach that examines questions in large possibility spaces using modeling and simulation. [...] Read more.
Data Farming, network applications and approaches to integrate network analysis and processes to the data farming paradigm are presented as approaches to address complex system questions. Data Farming is a quantified approach that examines questions in large possibility spaces using modeling and simulation. It evaluates whole landscapes of outcomes to draw insights from outcome distributions and outliers. Social network analysis and graph theory are widely used techniques for the evaluation of social systems. Incorporation of these techniques into the data farming process provides analysts examining complex systems with a powerful new suite of tools for more fully exploring and understanding the effect of interactions in complex systems. The integration of network analysis with data farming techniques provides modelers with the capability to gain insight into the effect of network attributes, whether the network is explicitly defined or emergent, on the breadth of the model outcome space and the effect of model inputs on the resultant network statistics. Full article
(This article belongs to the Special Issue Data Farming: Mathematical Foundations and Applications)
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