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Journal articles on the topic 'FAIR software'

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Published: 5 June 2025

Last updated: 24 June 2025

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1

Barton, C. Michael, Daniel Ames, Min Chen, et al. "Making modeling and software FAIR." Environmental Modelling & Software 156 (October 2022): 105496. http://dx.doi.org/10.1016/j.envsoft.2022.105496.

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Hasselbring, Wilhelm, Leslie Carr, Simon Hettrick, Heather Packer, and Thanassis Tiropanis. "From FAIR research data toward FAIR and open research software." it - Information Technology 62, no. 1 (2020): 39–47. http://dx.doi.org/10.1515/itit-2019-0040.

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AbstractThe Open Science agenda holds that science advances faster when we can build on existing results. Therefore, research data must be FAIR (Findable, Accessible, Interoperable, and Reusable) in order to advance the findability, reproducibility and reuse of research results. Besides the research data, all the processing steps on these data – as basis of scientific publications – have to be available, too.For good scientific practice, the resulting research software should be both open and adhere to the FAIR principles to allow full repeatability, reproducibility, and reuse. As compared to research data, research software should be both archived for reproducibility and actively maintained for reusability.The FAIR data principles do not require openness, but research software should be open source software. Established open source software licenses provide sufficient licensing options, such that it should be the rare exception to keep research software closed.We review and analyze the current state in this area in order to give recommendations for making research software FAIR and open.

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Samuelson, Pamela. "Reimplementing software interfaces is fair use." Communications of the ACM 64, no. 7 (2021): 24–26. http://dx.doi.org/10.1145/3466607.

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Lamprecht, Anna-Lena, Leyla Garcia, Mateusz Kuzak, et al. "Towards FAIR principles for research software." Data Science 3, no. 1 (2020): 37–59. http://dx.doi.org/10.3233/ds-190026.

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5

Dakin, K. "Establishing a fair price for software." IEEE Software 12, no. 6 (1995): 105–6. http://dx.doi.org/10.1109/52.469769.

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6

Pfeil, Andreas, Thomas Jejkal, Sabrine Chelbi, and Nicolas Blumenröhr. "A FAIR Digital Object Lab Software Stack." Research Ideas and Outcomes 8 (October 12, 2022): e94408. https://doi.org/10.3897/rio.8.e94408.

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Preprocessing data for research, like finding, accessing, unifying or converting, takes up to large parts of research time spans (Wittenburg and Strawn 2018). The FAIR (Findability, Accessibility, Interoperability, Reusability) principles (Wilkinson 2016) aim to support and facilitate the (re)use of data, and will contribute to alleviating this problem. A FAIR Digital Object (FAIR DO) captures research data resources of all kinds (raw data, metadata, software, ...) in order to align them with the FAIR principles.FAIR Digital Objects are expressive, machine-actionable pointers to research data (De Smedt et al. 2020). As such, each FAIR DO points to one research data object. Additionally, they may link to other FAIR DOs, explaining their relations. The FAIR Digital Object Lab (Pfeil et al. 2022) is an extendable and adjustable architecture (a software stack) for generic FAIR Digital Object tasks. It consists of a set of interacting components with services and tools for creation, validation, discovery, curation, and more. In this talk, we will present our plans for the FAIR DO Lab and explain our decisions, which are mostly based on the experience gained in previous developments.The creation and maintenance of FAIR DOs is not trivial, as their persistent identifiers (PIDs) contain typed record information. When creating or maintaining PID records of FAIR DOs, the required information has to be validated, involving calls to a public Data Type Registry (DTR) (Lannom et al. 2015). After a successful validation, the information has to be transformed to a representation of a PID service. After a FAIR DO has been registered successfully, the PID should be documented locally and disseminated. Using these PIDs as a starting point, tools may use the machine-actionability of FAIR DOs to maintain search indexes or to create collections. This enables researchers to look up PIDs by searching for record information or timestamp.We are developing a set of services, offering a solution to support these use-cases, which we call the FAIR DO Lab. Its goal is to have a production-ready and configurable software stack, easing the development of FAIR-DO-aware tools and services by offering at least the described use-cases. We have already gained some experience by its predecessor, the FAIR DO Testbed (Pfeil et al. (2021a)), which was introduced at the Research Data Alliance (RDA) Virtual Plenary 17 Poster Session (Pfeil et al. 2021b). The Lab will be configurable similar to the Testbed, as each service can be omitted or replaced to satisfy specific needs while integrating the Lab on top of existing research infrastructures.The FAIR DO Lab enables PID record management and validation using the Typed PID Maker (Pfeil and Jejkal 2021), following the RDA PID Information Types (PIT) Working Group Recommendations (Weigel et al. 2015) and an external Data Type Registry (DTR), following the RDA Data Type Registry Working Group Recommendations (Lannom et al. 2015). The DTR stores profiles and types, enabling typed, machine-actionable PID records. The Typed PID Maker uses this information for the validation of PID records, and stores and disseminates PIDs after their creation.All created or modified PIDs are communicated to a message broker. This way, other services can be notified about such activities. Our first service making use of this will be an advanced indexing service. It will ingest the PIDs and their record information into a search index, but also try to extract information from the bit-sequence of the digital object itself. In a second step, we are considering the automated creation of collections utilizing our production-ready Collection Registry (Chelbi and Jejkal 2020), which the Testbed already includes. This will require a set of rules and a process to use those rules in order to place new PIDs in the correct collection. The Collection Registry is an implementation of the Collection API specification (Weigel et al. 2017), which was published by the corresponding RDA Research Data Collections Working Group.On the conceptual side, we hope to gain more insight about the required structure of PID records. There are ongoing discussions about this structure and to which degree standardization is required. Large talking points are the concepts of Digital Object Types (Lannom et al. 2015) and Kernel Information Profiles (Weigel et al. 2018). Working on the Lab and its predecessor, we recognized that there are large gaps regarding the structure of FAIR Digital Objects and the roles of the object types and profiles. To bring FAIR DOs into reality, research software will need to use them. But as FAIR DOs point to diverse kinds of research data, the software needs to make decisions. To what extent can the software use a specific FAIR DO? We observed that too much flexibility makes automated decisions harder. Our suggestion is therefore to consider FAIR DOs less from the infrastructure point of view, and more from the machine's point of view to improve the machine-actionability. We expect that we will gain insights about the feasibility in the development process to ease the development of further FAIR-aware tools for research, particularly for specialized tools that already exist and are in use. It will not be feasible to write every tool from scratch.On the practical side, the Lab will already have a stronger focus on interactive tools with user interfaces in order to provide an easy-to-use Lab for research. We consider our current work on granular base services for research data management to be a solid ground for such developments. These tools can of course not replace specialized tools, but will make the generic services in the Lab easy to use. We still expect that specialized tools will benefit from the integration of such services.The FAIR DO Lab development has been supported by the research program 'Engineering Digital Futures' of the Helmholtz Association of German Research Centers and the Helmholtz Metadata Collaboration Platform.

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7

Worthy, John. "The EC software directive. A fair balance?" IEE Review 37, no. 10 (1991): 351. http://dx.doi.org/10.1049/ir:19910157.

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8

Uhlig, F., M. Al-Turany, D. Bertini, and R. Karabowicz. "Software Development Infrastructure for the FAIR Experiments." Journal of Physics: Conference Series 331, no. 4 (2011): 042024. http://dx.doi.org/10.1088/1742-6596/331/4/042024.

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9

Al-Turany, M., P. Buncic, P. Hristov, et al. "ALFA: The new ALICE-FAIR software framework." Journal of Physics: Conference Series 664, no. 7 (2015): 072001. http://dx.doi.org/10.1088/1742-6596/664/7/072001.

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10

Were, Madeley, and Munsell. "Fair Trade Software: empowering people, enabling economies." Journal of Fair Trade 2, no. 1 (2020): 4. http://dx.doi.org/10.13169/jfairtrade.2.1.0004.

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11

Martinez-Ortiz, Carlos, Carole Goble, Daniel Katz, et al. "How does software fit into the FDO landscape?" Research Ideas and Outcomes 8 (October 12, 2022): e95724. https://doi.org/10.3897/rio.8.e95724.

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In academic research virtually every field has increased its use of digital and computational technology, leading to new scientific discoveries, and this trend is likely to continue. Reliable and efficient scholarly research requires researchers to be able to validate and extend previously generated research results. In the digital era, this implies that digital objectsKahn and Wilensky 2006 used in research should be Findable, Accessible, Interoperable and Reusable (FAIR). These objects include (but are not limited to) data, software, models (for example, machine learning), representations of physical objects, virtual research environments, workflows, etc. Leaving any of these digital objects out of the FAIR process may result in a loss of academic rigor and may have severe consequences in the long term for the field, such as a reproducibility crisis. In this extended abstract, we focus on research software as a FAIR digital object (FDO).The FDO framework De Smedt et al. 2020 describes FDOs as being actionable units of knowledge, which can be aggregated, analyzed, and processed by different types of algorithms. Such algorithms must be implemented by software in one form or another. The framework also describes large software stacks supporting FDOs enabling responsible data science and increasing reproducibility. This implies that software is a key ingredient of the FDO framework, and should adhere to the FAIR principles. Software plays multiple roles: it is a DO itself, it is responsible for creating new FDOs (e.g., data) and it helps to make them available to the public (e.g., via repositories and registries). However there is a need to specify in more detail how non-data DOs, in particular software, fit in this framework.Different classes of digital objects have different intrinsic properties and ways to relate to other DOs. This means that while they, in principle, are subject to the high-level FAIR principles, there are also differences depending on their type and properties, requiring an adaptation so FAIR implementations are more aligned to the digital object itself. This holds true in particular to software. Software has intrinsic properties (executability, composite nature, development practices, continuous evolution and versioning, and packaging and distribution) and specific needs that must be considered by the FDO framework. For example, open source software is typically developed in the open on social coding platforms, where releases are distributed through package management systems, unlike data that is typically published in archival repositories. These social coding platforms do not provide long term archiving, permanent identifiers, or metadata, and package management systems, while somewhat better, similarly do not make a commitment to long term archiving, do not use identifiers that fit the scholarly publication system well, and provide metadata that may be missing key elements. The FAIR for research software (FAIR4RS, Chue Hong et al. 2021) working group has dedicated significant effort in building a community consensus around developing FAIR principles that are customized for research software, providing methods for researchers to understand and address these gaps.In this presentation we will highlight the importance of software for the FAIR landscape and why different (but related) FAIR principles are needed for software (vs those originally developed for data). Our goal here is to contribute to building an FDO landscape together, where we consider all different types of digital objects that are essential in today's research, and we are enthusiastic about contributing our expertise on research software in helping shape this landscape.

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Sliz, Piotr. "Practicing FAIR research with SBGrid." Structural Dynamics 12, no. 2_Supplement (2025): A230. https://doi.org/10.1063/4.0000536.

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SBGrid (www.sbgrid.org) is a consortium of 516 research laboratories that pursue X-ray Crystallography, CryoEM and NMR projects. SBGrid “Factory” at Harvard Medical School, actively curates over 1000 software titles and makes them available for immediate execution on Mac and Linux computers in the SBGrid laboratories. All SBGrid software titles are multi-versioned, and the entire software collection dates back to 2007. All current members can easily select the most recent version of a given software, or switch back to an older version to reproduce projects completed in the past. By addressing the demand for efficient and accessible tools in structural biology and upholding the F.A.I.R. principles (Findable, Accessible, Interoperable and Reusable), the SBGrid collection ensures that all software versions are readily findable within version-specific subdirectories. Users can access different software versions by adding version-specific directives to the .sbgrid.conf file, promoting interoperability across various computers and operating systems. The collection's multi-version approach enables the reuse of applications, allowing for the retention of numerous versions of each software title. This facilitates the reinstallation of older versions, previously removed from the active collection, to replicate past results. To further support research reproducibility, SBGrid established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG, url: data.sbgrid.org). Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Datasets archived with the SBDG are freely available to the research community under a public domain dedication license and the metadata for all datasets is published under the DataCite schema. Datasets are accessible to researchers through the Data Access Alliance infrastructure, which facilitates global and institutional data access. The metadata archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the original published structures. In summary, by building an adaptable, community-driven framework SBGrid not only meets the intricate needs of structural biology, but by adhering to the FAIR principles promotes efficient, rigorous, and transparent research practices.

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13

Steiner, Christine. "Fair use." Archives and Museum Informatics 8, no. 3 (1994): 275–76. http://dx.doi.org/10.1007/bf02770365.

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14

Sonabend, Raphael, Hugo Gruson, Leo Wolansky, Agnes Kiragga, and Daniel S. Katz. "FAIR-USE4OS: Guidelines for creating impactful open-source software." PLOS Computational Biology 20, no. 5 (2024): e1012045. http://dx.doi.org/10.1371/journal.pcbi.1012045.

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This paper extends the FAIR (Findable, Accessible, Interoperable, Reusable) guidelines to provide criteria for assessing if software conforms to best practices in open source. By adding “USE” (User-Centered, Sustainable, Equitable), software development can adhere to open source best practice by incorporating user-input early on, ensuring front-end designs are accessible to all possible stakeholders, and planning long-term sustainability alongside software design. The FAIR-USE4OS guidelines will allow funders and researchers to more effectively evaluate and plan open-source software projects. There is good evidence of funders increasingly mandating that all funded research software is open source; however, even under the FAIR guidelines, this could simply mean software released on public repositories with a Zenodo DOI. By creating FAIR-USE software, best practice can be demonstrated from the very beginning of the design process and the software has the greatest chance of success by being impactful.

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15

Schellhorn, Gerhard. "Completeness of fair ASM refinement." Science of Computer Programming 76, no. 9 (2011): 756–73. http://dx.doi.org/10.1016/j.scico.2009.10.004.

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16

Greenberg, A. G., and N. Madras. "How fair is fair queuing." Journal of the ACM 39, no. 3 (1992): 568–98. http://dx.doi.org/10.1145/146637.146658.

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Hashim, Firas Ali, Qabas Abdal Zahraa, Nadia Mahmood Hussien, and Yasmin Makki Mohialden. "Enhanced Priority-Integrated Mult winner Voting Software." Babylonian Journal of Artificial Intelligence 2024 (April 7, 2024): 27–33. http://dx.doi.org/10.58496/bjai/2024/005.

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Modern democracy values efficient, fair elections. This paper offers Google Cola an embedded interactive multi-winner voting method to improve democracy. Candidates and values can be entered using Python in the Colab. Basic Python algorithms and classes simplify priority candidate multi-winner elections. According to user feedback, event-driven programming modifies candidate priority enabling accurate and effective elections. These embedded system functions prioritize, rank, and aggregate votes. These mathematics facilitate fair and transparent outcome processing. This research demonstrates the embedded system's technology deployment and potential to support inclusive elections. The proposed solution helps electoral democratization by integrating collaboration with cutting-edge technologies

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LI, Ji. "Timeslot Weighted Fair Scheduling in EPFTS." Journal of Software 17, no. 4 (2006): 822. http://dx.doi.org/10.1360/jos170822.

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LI, Ji. "Timeslot-Reservation Based Weighted Fair Scheduling." Journal of Software 18, no. 10 (2007): 2605. http://dx.doi.org/10.1360/jos182605.

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Goble, Carole, Sarah Cohen-Boulakia, Stian Soiland-Reyes, et al. "FAIR Computational Workflows." Data Intelligence 2, no. 1-2 (2020): 108–21. http://dx.doi.org/10.1162/dint_a_00033.

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Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.

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Carole, Goble, Cohen-Boulakia Sarah, Soiland-Reyes Stian, et al. "FAIR Computational Workflows." Data Intelligence 2, no. 1 (2019): 108–21. https://doi.org/10.5281/zenodo.3528076.

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Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development

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Hernandez, Serrano Pedro, and Vincent Emonet. "The FAIR extension: A web browser extension to evaluate Digital Object FAIRness." Research Ideas and Outcomes 8 (October 12, 2022): e95006. https://doi.org/10.3897/rio.8.e95006.

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The scientific community's efforts have increased regarding the application and assessment of the FAIR principles on Digital Objects (DO) such as publications, datasets, or research software. Consequently, openly available automated FAIR assessment services have been working on standardization, such as FAIR enough, the FAIR evaluator or FAIRsFAIR's F-UJI. Digital Competence Centers such as University Libraries have been paramount in this process by facilitating a range of activities, such as awareness campaigns, trainings, or systematic support. However, in practice, using the FAIR assessment tools is still an intricate process for the average researcher. It requires a steep learning curve since it involves performing a series of manual processes requiring specific knowledge when learning the frameworks, disengaging some some researchers in the process.We aim to use technology to close this gap and make this process more accessible by bringing the FAIR assessment to the researcher's profiles. We will develop "The FAIR extension", an open-source, user-friendly web browser extension that allows researchers to make FAIR assessment directly at the web source. Web browser extensions have been an accessible digital tool for libraries supporting scholarship (De Sarkar 2015). A remarkable example is the lightweight version of reference managers deployed as a browser service (Ferguson 2019). Moreover, it has been demonstrated that they can be a vehicle for open access, such as Lean Library Browser Extension.The FAIR extension is a service that builds on top of the community-accepted FAIR evaluator APIs, i.e. it does not intend to create yet another FAIR assessment framework from scratch. The objective of the FAIR Digital Objects Framework (FDOF) is for objects published in a digital environment to comply with a set of requirements, such as identifiability, and the use of a rich metadata record (Santos 2021, Schultes and Wittenburg 2019). The FAIR extension will connect via REST-like operations to individual FAIR metrics test endpoints, according to Wilkinson et al. (2018), Wilkinson et al. (2019) and ultimately display the FAIR metrics on the client side (Fig. 1). Ultimately, the user will get FAIR scores of articles, datasets and other DOs in real-time on a web source, such as a scholarly platform or DO repository. With the possibility of creating simple reports of the assessment.It is acknowledged that the development of web-based tools carries some constraints regarding platform versions releases, e.g. Chromium Development Calendar. Nevertheless, we are optimistic about the potential use cases. For example,A student wanting to make use of a DO (e.g. software package), but doesn't know which to choose. The FAIR extension will indicate which one is more FAIR and aid the decision making processA Data steward recommending sourcesA researcher who wants to display all FAIR metrics of her DOs on a research profileA PI that wants to evaluate an aggregated metric for a project.These use cases can be the means to bringing the open source community and FAIR DO interest groups to work together.

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Oeltjen, Wiebke, Kathleen Neumann, Ulrike Stahl, and Robert Stephan. "MyCoRe macht Forschungsdaten FAIR." Bibliothek Forschung und Praxis 43, no. 1 (2019): 82–90. http://dx.doi.org/10.1515/bfp-2019-2013.

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ZusammenfassungForschungsdaten werden in Repositorien gespeichert und zur Nachnutzung erhalten. Dies setzt voraus, dass die Repositorien bestimmte Prinzipien einhalten. Die FAIR-Leitprinzipien besagen, dass wissenschaftliche Daten auffindbar, zugänglich, interoperabel und wiederverwendbar sein sollen. In diesem Artikel wird gezeigt, dass die Open-Source-Software MyCoRe geeignet ist, Repositorien bereitzustellen, die die FAIR-Prinzipien erfüllen. Dazu werden drei MyCoRe-Anwendungen, die Forschungsdaten verwalten und bereitstellen, untersucht und bewertet.

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Weed, Richard M. "Quality Assurance Software for Personal Computer." Transportation Research Record: Journal of the Transportation Research Board 1544, no. 1 (1996): 116–24. http://dx.doi.org/10.1177/0361198196154400114.

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Demonstration Project 89 on Quality Management was created to provide guidance on the use of practical and effective procedures to assure that the level of quality designed into the plans and specifications is actually achieved in the constructed product. One part of this effort is the distribution of a software package consisting of several interactive programs developed for use on the personal computer. These extremely user-friendly programs make it possible to analyze both pass/fail and pay adjustment acceptance procedures, construct operating characteristic curves, plot control charts, experiment with computer simulation, perform statistical comparisons of data sets, demonstrate the unreliability of decisions based on a single test result, and explore the effectiveness of stratified random sampling. This comprehensive software package provides highway engineers with the necessary tools to learn why some statistical procedures are inherently superior to others and how to incorporate this knowledge into fair and effective construction specifications.

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Katz, Daniel S., Morane Gruenpeter, and Tom Honeyman. "Taking a fresh look at FAIR for research software." Patterns 2, no. 3 (2021): 100222. http://dx.doi.org/10.1016/j.patter.2021.100222.

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Katz, Daniel S., Morane Gruenpeter, and Tom Honeyman. "Taking a fresh look at FAIR for research software." Patterns 2, no. 5 (2021): 100267. http://dx.doi.org/10.1016/j.patter.2021.100267.

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Bellamy, Rachel K. E., Kuntal Dey, Michael Hind, et al. "Think Your Artificial Intelligence Software Is Fair? Think Again." IEEE Software 36, no. 4 (2019): 76–80. http://dx.doi.org/10.1109/ms.2019.2908514.

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Benincasa, Giacomo, Konstantin Pavlikov, and Donald Hearn. "Algorithms and Software for the Golf Director Problem." Journal of Sports Analytics 6, no. 3 (2020): 155–72. http://dx.doi.org/10.3233/jsa-200346.

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The golf director problem is a sports management problem that aims to find an allocation of golf players into fair teams for certain golf club competitions. The motivation for fairness as the objective is that club golf competitions are recreational events for which the golf director needs to form teams that are competitive even though they consist of players with different skill levels measured by their USGA (http://www.usga.org) or R&A (http://www.randa.org) handicaps. We formalize the concept of “fairness" of allocation of players into teams playing 18-hole golf games and argue that finding an optimal assignment of players to teams is intractable for even the fastest computers. Instead, we provide an efficient simulation and optimization-based procedure that finds a near-optimal fair team allocation. Computational tests show the approach to be better than standard methods. A computer implementation of the solution method is publicly available and located at http://www.fairgolfteams.com. The website provides a golf director with a variety of controls to manage and run club golf competitions in a fair way. This is described in the appendix.

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Lee, Dongjae, Minki Cho, Jinwoo Kim, Soonwon Moon, Youngju Song, and Chung-Kil Hur. "Fair Operational Semantics." Proceedings of the ACM on Programming Languages 7, PLDI (2023): 811–34. http://dx.doi.org/10.1145/3591253.

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Fairness properties, which state that a sequence of bad events cannot happen infinitely before a good event takes place, are often crucial in program verification. However, general methods for expressing and reasoning about various kinds of fairness properties are relatively underdeveloped compared to those for safety properties. This paper proposes FOS (Fair Operational Semantics), a theory capable of expressing arbitrary notions of fairness as an operational semantics and reasoning about these notions of fairness. In addition, FOS enables thread-local reasoning about fairness by providing thread-local simulation relations equipped with separation- logic-style resource algebras. We verify a ticket lock implementation and a client of the ticket lock under weak memory concurrency as an example, which requires reasoning about different notions of fairness including fairness of a scheduler, fairness of the ticket lock implementation, and even fairness of weak memory. The theory of FOS, as well as the examples in the paper, are fully formalized in Coq.

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Prabowo, Sidik, and Maman Abdurohman. "Studi Perbandingan Performa Algoritma Penjadwalan untuk Real Time Data Twitter pada Hadoop." Komputika : Jurnal Sistem Komputer 9, no. 1 (2020): 43–50. http://dx.doi.org/10.34010/komputika.v9i1.2848.

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Hadoop merupakan sebuah framework software yang bersifat open source dan berbasis java. Hadoop terdiri atas dua komponen utama, yaitu MapReduce dan Hadoop Distributed File System (HDFS). MapReduce terdiri atas Map dan Reduce yang digunakan untuk pemrosesan data, sementara HDFS adalah tempat atau direktori dimana data hadoop dapat disimpan. Dalam menjalankan job yang tidak jarang terdapat keragaman karakteristik eksekusinya, diperlukan job scheduler yang tepat. Terdapat banyak job scheduler yang dapat di pilih supaya sesuai dengan karakteristik job. Fair Scheduler menggunakan salah satu scheduler dimana prisnsipnya memastikan suatu jobs akan mendapatkan resource yang sama dengan jobs yang lain, dengan tujuan meningkatkan performa dari segi Average Completion Time. Hadoop Fair Sojourn Protocol Scheduler adalah sebuah algoritma scheduling dalam Hadoop yang dapat melakukan scheduling berdasarkan ukuran jobs yang diberikan. Penelitian ini bertujuan untuk melihat perbandingan performa kedua scheduler tersebut untuk karakteristik data twitter. Hasil pengujian menunjukan Hadoop Fair Sojourn Protocol Scheduler memiliki performansi lebih baik dibandingkan Fair Scheduler baik dari penanganan average completion time sebesar 9,31% dan job throughput sebesar 23,46%. Kemudian untuk Fair Scheduler unggul dalam parameter task fail rate sebesar 23,98%.

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Valmari, Antti, and Walter Vogler. "Fair testing and stubborn sets." International Journal on Software Tools for Technology Transfer 20, no. 5 (2017): 589–610. http://dx.doi.org/10.1007/s10009-017-0481-2.

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Rastegar, Seyed Hamed, Aliazam Abbasfar, and Vahid Shah-Mansouri. "On Fair Rule Caching in Software Defined Radio Access Networks." IEEE Wireless Communications Letters 7, no. 3 (2018): 460–63. http://dx.doi.org/10.1109/lwc.2017.2785256.

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Hoogeboom, Hendrik Jan, and Nikè van Vugt. "Fair sticker languages." Acta Informatica 37, no. 3 (2000): 213–25. http://dx.doi.org/10.1007/pl00006050.

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Cave, Andrew, Francisco Ferreira, Prakash Panangaden, and Brigitte Pientka. "Fair reactive programming." ACM SIGPLAN Notices 49, no. 1 (2014): 361–72. http://dx.doi.org/10.1145/2578855.2535881.

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Mitchell, Chris J., and Konstantinos Rantos. "A fair certification protocol." ACM SIGCOMM Computer Communication Review 29, no. 3 (1999): 47–49. http://dx.doi.org/10.1145/505724.505730.

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Goyal, Pawan, Harrick M. Vin, and Haichen Chen. "Start-time fair queueing." ACM SIGCOMM Computer Communication Review 26, no. 4 (1996): 157–68. http://dx.doi.org/10.1145/248157.248171.

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Stoica, Ion, Scott Shenker, and Hui Zhang. "Core -stateless fair queueing." ACM SIGCOMM Computer Communication Review 28, no. 4 (1998): 118–30. http://dx.doi.org/10.1145/285243.285273.

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Asarch, Chad G. "Is Turn about Fair Play? Copyright Law and the Fair Use of Computer Software Loaded into RAM." Michigan Law Review 95, no. 3 (1996): 654. http://dx.doi.org/10.2307/1290163.

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Stapor, Katarzyna, Paweł Ksieniewicz, Salvador García, and Michał Woźniak. "How to design the fair experimental classifier evaluation." Applied Soft Computing 104 (June 2021): 107219. http://dx.doi.org/10.1016/j.asoc.2021.107219.

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Maia, João Paulo Oliveira, Washington Sales Do Monte, and Francisco Marlon Carneiro Feijó. "Semiarid Potigaur Science Fair and intelectual proerty: a lexicometric analysis of technological potential." Concilium 24, no. 1 (2024): 447–66. http://dx.doi.org/10.53660/clm-2742-24b09.

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The objective of this work is to present the mapping of the technological and innovative potential of the Semiarid Potiguar Science Fair, 2023 edition. Technology has accompanied the development of humanity, being part of our daily lives and occupying a relevant space in the educational context using technology in schools. Science fairs are spaces for the construction and exhibition of knowledge. Technological development and connection with intellectual property occur in them, an area that protects the creations of human intellect and is strongly connected to innovation. The Potiguar Semiarid Science Fair, held at UFERSA in Mossoró/RN and region. The event has the participation of schools from western Potiguar. To achieve the objective, a qualitative methodology of an exploratory nature was used using a literature review and analysis of the summaries of the projects presented at the Science Fair using the IRAMUTEQ software. The analyzes showed that scientific methodology and the construction of knowledge by students occupy a relevant position in the Science Fair and that technology and innovation are focused on the production of knowledge by participating students.

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Sinclair, Sean R., Siddhartha Banerjee, and Christina Lee Yu. "Sequential Fair Allocation." ACM SIGMETRICS Performance Evaluation Review 50, no. 1 (2022): 95–96. http://dx.doi.org/10.1145/3547353.3526951.

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We consider the problem of dividing limited resources to individuals arriving over T rounds. Each round has a random number of individuals arrive, and individuals can be characterized by their type (i.e. preferences over the different resources). A standard notion of 'fairness' in this setting is that an allocation simultaneously satisfy envy-freeness and efficiency. For divisible resources, when the number of individuals of each type are known upfront, the above desiderata are simultaneously achievable for a large class of utility functions. However, in an online setting when the number of individuals of each type are only revealed round by round, no policy can guarantee these desiderata simultaneously. We show that in the online setting, the two desired properties (envy-freeness and efficiency) are in direct contention, in that any algorithm achieving additive counterfactual envy-freeness up to a factor of LT necessarily suffers a efficiency loss of at least 1 / LT. We complement this uncertainty principle with a simple algorithm, Guarded-Hope, which allocates resources based on an adaptive threshold policy and is able to achieve any fairness-efficiency point on this frontier.

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Liu, Yuezhou, Yuanyuan Li, Qian Ma, Stratis Ioannidis, and Edmund Yeh. "Fair Caching Networks." ACM SIGMETRICS Performance Evaluation Review 48, no. 3 (2021): 89–90. http://dx.doi.org/10.1145/3453953.3453973.

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We study fair content allocation strategies in caching networks through a utility-driven framework, where each request achieves a utility of its caching gain rate. The resulting problem is NP-hard. Submodularity allows us to devise a deterministic allocation strategy with an optimality guarantee factor arbitrarily close to 1-1/e. When 0 < α ≤ 1, we further propose a randomized strategy that attains an improved optimality guarantee, (1 - 1/e)1-α, in expectation. Through extensive simulations over synthetic and real-world network topologies, we evaluate the performance of our proposed strategies and discuss the effect of fairness.

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Shi, Lianjie, Xin Wang, Richard T. B. Ma, and Y. C. Tay. "Weighted Fair Caching." ACM SIGMETRICS Performance Evaluation Review 46, no. 3 (2019): 35–36. http://dx.doi.org/10.1145/3308897.3308913.

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Kurokawa, David, Ariel D. Procaccia, and Junxing Wang. "Fair Enough." Journal of the ACM 65, no. 2 (2018): 1–27. http://dx.doi.org/10.1145/3140756.

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Wallner, Johannes, Helmut Pottmann, and Michael Hofer. "Fair webs." Visual Computer 23, no. 1 (2006): 83–94. http://dx.doi.org/10.1007/s00371-006-0088-1.

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Aumuller, Martin, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, and Francesco Silvestri. "Fair near neighbor search via sampling." ACM SIGMOD Record 50, no. 1 (2021): 42–49. http://dx.doi.org/10.1145/3471485.3471496.

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Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. Given a set of points S and a radius parameter r > 0, the rnear neighbor (r-NN) problem asks for a data structure that, given any query point q, returns a point p within distance at most r from q. In this paper, we study the r-NN problem in the light of individual fairness and providing equal opportunities: all points that are within distance r from the query should have the same probability to be returned. In the low-dimensional case, this problem was first studied by Hu, Qiao, and Tao (PODS 2014). Locality sensitive hashing (LSH), the theoretically strongest approach to similarity search in high dimensions, does not provide such a fairness guarantee.

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Fitzgerald, Sue, and Mary Lou Hines. "The computer science fair." ACM SIGCSE Bulletin 28, no. 1 (1996): 368–72. http://dx.doi.org/10.1145/236462.236581.

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Parciak, Marcel, Theresa Bender, Ulrich Sax, and Christian Robert Bauer. "Applying FAIRness: Redesigning a Biomedical Informatics Research Data Management Pipeline." Methods of Information in Medicine 58, no. 06 (2019): 229–34. http://dx.doi.org/10.1055/s-0040-1709158.

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Abstract Background Managing research data in biomedical informatics research requires solid data governance rules to guarantee sustainable operation, as it generally involves several professions and multiple sites. As every discipline involved in biomedical research applies its own set of tools and methods, research data as well as applied methods tend to branch out into numerous intermediate and output data objects, making it very difficult to reproduce research results. Objectives This article gives an overview of our implementation status applying the Findability, Accessibility, Interoperability and Reusability (FAIR) Guiding Principles for scientific data management and stewardship onto our research data management pipeline focusing on the software tools that are in use. Methods We analyzed our progress FAIRificating the whole data management pipeline, from processing non-FAIR data up to data usage. We looked at software tools for data integration, data storage, and data usage as well as how the FAIR Guiding Principles helped to choose appropriate tools for each task. Results We were able to advance the degree of FAIRness of our data integration as well as data storage solutions, but lack enabling more FAIR Guiding Principles regarding Data Usage. Existing evaluation methods regarding the FAIR Guiding Principles (FAIRmetrics) were not applicable to our analysis of software tools. Conclusion Using the FAIR Guiding Principles, we FAIRificated relevant parts of our research data management pipeline improving findability, accessibility, interoperability and reuse of datasets and research results. We aim to implement the FAIRmetrics to our data management infrastructure and—where required—to contribute to the FAIRmetrics for research data in the biomedical informatics domain as well as for software tools to achieve a higher degree of FAIRness of our research data management pipeline.

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Ben-Menachem, Mordechai, and Ilanit Gavious. "Accounting Software Assets: A Valuation Model for Software." Journal of Information Systems 21, no. 2 (2007): 117–32. http://dx.doi.org/10.2308/jis.2007.21.2.117.

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Enterprise Information Technology Systems are major corporate assets upon which corporate management and operations are heavily dependent. Current accounting standards for the treatment of these assets have not kept pace with advances in technology, such as models for creating and evolving IT. Capitalizable costs according to the standards are principally primary development costs and exclude system evolution. This weakness of the standards creates a disproportionate downward bias in the book value of software and in current earnings, as costs incurred for on-going systems evolution is five to twenty times the costs incurred for first release. We present a quantitative valuation model for IT systems based upon engineering measurements of software that allow the fair value of software to be based on all costs incurred by the system. Costs are collected by an automatic tool and stored in an inventory system of enterprise software assets. To total costs is added the effect of each individual module's relative significance to the enterprise. The model also provides a systematic algorithm for software amortization, based on the decrease in its usability.

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Taubenfeld, Gadi. "Fair synchronization." Journal of Parallel and Distributed Computing 97 (November 2016): 1–10. http://dx.doi.org/10.1016/j.jpdc.2016.06.007.

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