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Journal articles on the topic 'PROCESSING FRAMEWORK'

Author: Grafiati

Published: 11 September 2023

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1

Soller, Dominik, Thomas Jaumann, Gerd Kilian, Jörg Robert, and Albert Heuberger. "DFC++ Processing Framework Concept." Journal of Signal Processing Systems 89, no. 1 (August 18, 2016): 181–90. http://dx.doi.org/10.1007/s11265-016-1174-x.

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2

Patel, Karan, Yash Sakaria, and Chetashri Bhadane. "Real Time Data Processing Framework." International Journal of Data Mining & Knowledge Management Process 5, no. 5 (September 30, 2015): 49–63. http://dx.doi.org/10.5121/ijdkp.2015.5504.

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3

Zhuo, Youwei, Jingji Chen, Gengyu Rao, Qinyi Luo, Yanzhi Wang, Hailong Yang, Depei Qian, and Xuehai Qian. "Distributed Graph Processing System and Processing-in-memory Architecture with Precise Loop-carried Dependency Guarantee." ACM Transactions on Computer Systems 37, no. 1-4 (June 2021): 1–37. http://dx.doi.org/10.1145/3453681.

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To hide the complexity of the underlying system, graph processing frameworks ask programmers to specify graph computations in user-defined functions (UDFs) of graph-oriented programming model. Due to the nature of distributed execution, current frameworks cannot precisely enforce the semantics of UDFs, leading to unnecessary computation and communication. It exemplifies a gap between programming model and runtime execution. This article proposes novel graph processing frameworks for distributed system and Processing-in-memory (PIM) architecture that precisely enforces loop-carried dependency; i.e., when a condition is satisfied by a neighbor, all following neighbors can be skipped. Our approach instruments the UDFs to express the loop-carried dependency, then the distributed execution framework enforces the precise semantics by performing dependency propagation dynamically. Enforcing loop-carried dependency requires the sequential processing of the neighbors of each vertex distributed in different nodes. We propose to circulant scheduling in the framework to allow different nodes to process disjoint sets of edges/vertices in parallel while satisfying the sequential requirement. The technique achieves an excellent trade-off between precise semantics and parallelism—the benefits of eliminating unnecessary computation and communication offset the reduced parallelism. We implement a new distributed graph processing framework SympleGraph, and two variants of runtime systems— GraphS and GraphSR —for PIM-based graph processing architecture, which significantly outperform the state-of-the-art.

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4

Ramamoorthi, Ravi, and Pat Hanrahan. "A signal-processing framework for reflection." ACM Transactions on Graphics 23, no. 4 (October 2004): 1004–42. http://dx.doi.org/10.1145/1027411.1027416.

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5

Sivaswamy, Jayanthi. "Framework for practical hexagonal-image processing." Journal of Electronic Imaging 11, no. 1 (January 1, 2002): 104. http://dx.doi.org/10.1117/1.1426078.

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6

Ye, Yinghao, Meilin Wang, Shuhong Yao, Jarvis N. Jiang, and Qing Liu. "Big data processing framework for manufacturing." Procedia CIRP 83 (2019): 661–64. http://dx.doi.org/10.1016/j.procir.2019.04.109.

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7

Chan, Daniel K. C., and Philip W. Trinder. "A processing framework for object comprehensions." Information and Software Technology 39, no. 9 (January 1997): 641–51. http://dx.doi.org/10.1016/s0950-5849(97)00014-1.

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8

Soto, David, Usman Ayub Sheikh, and Clive R. Rosenthal. "A Novel Framework for Unconscious Processing." Trends in Cognitive Sciences 23, no. 5 (May 2019): 372–76. http://dx.doi.org/10.1016/j.tics.2019.03.002.

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9

Haochen Zou, Haochen Zou, Dejian Wang Haochen Zou, and Yang Xiao Dejian Wang. "Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data." 電腦學刊 34, no. 2 (April 2023): 081–97. http://dx.doi.org/10.53106/199115992023043402007.

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<p>Data annotation is the categorization and labelling of data for applications, such as machine learning, artificial intelligence, and data integration. The categorization and labelling are done to achieve a specific use case in relation to solving problems. Existing data annotation systems and modules face imperfections such as knowledge and annotation not being formally integrated, narrow application range, and difficulty to apply on existing database management applications. To analyze and process annotated data, obtain the relationship between different annotations, and capture metainformation in data provenance and probabilistic databases, in this paper, we design a back-end query processing framework as a supplementary interface for the database management system to extend operation to datasets and boost efficiency. The framework utilizes Java language and the MVC model for development to achieve lightweight, cross-platform, and high adaptability identities. The contribution of this paper is mainly reflected in two aspects. The first contribution is to implement query processing, provenance semiring, and semiring homomorphism over annotated data. The second contribution is to combine query processing and provenance with SQL statements in order to enable the database manager to invoke operations to annotation.</p> <p> </p>

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10

POWELL, MARK W., and DMITRY GOLDGOF. "SOFTWARE TOOLKIT FOR TEACHING IMAGE PROCESSING." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 05 (August 2001): 833–44. http://dx.doi.org/10.1142/s0218001401001180.

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We introduce a software framework called the Java Vision Toolkit (JVT) for teaching image processing and computer vision. The toolkit provides over 50 image operations and presents them to the user in a GUI that can render grayscale, color and 3D range images. The software is written in Java, enabling it to be integrated into HTML documents and interactive course materials. The framework is designed for extensibility using a source code template that supports the implementation of any new operation with a minimal amount of supporting code. For students, this framework encapsulates the GUI, file I/O and other trivial programming details and allows them the maximum amount of time to spend on understanding computer vision. We compare the JVT with other computer vision software frameworks that are used for teaching and research. We also discuss the use of the JVT in an undergraduate image processing course at the University of South Florida.

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11

Leow, Kang-Ren, Meng-Chew Leow, and Lee-Yeng Ong. "A New Big Data Processing Framework for the Online Roadshow." Big Data and Cognitive Computing 7, no. 3 (June 27, 2023): 123. http://dx.doi.org/10.3390/bdcc7030123.

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The Online Roadshow, a new type of web application, is a digital marketing approach that aims to maximize contactless business engagement. It leverages web computing to conduct interactive game sessions via the internet. As a result, massive amounts of personal data are generated during the engagement process between the audience and the Online Roadshow (e.g., gameplay data and clickstream information). The high volume of data collected is valuable for more effective market segmentation in strategic business planning through data-driven processes such as web personalization and trend evaluation. However, the data storage and processing techniques used in conventional data analytic approaches are typically overloaded in such a computing environment. Hence, this paper proposed a new big data processing framework to improve the processing, handling, and storing of these large amounts of data. The proposed framework aims to provide a better dual-mode solution for processing the generated data for the Online Roadshow engagement process in both historical and real-time scenarios. Multiple functional modules, such as the Application Controller, the Message Broker, the Data Processing Module, and the Data Storage Module, were reformulated to provide a more efficient solution that matches the new needs of the Online Roadshow data analytics procedures. Some tests were conducted to compare the performance of the proposed frameworks against existing similar frameworks and verify the performance of the proposed framework in fulfilling the data processing requirements of the Online Roadshow. The experimental results evidenced multiple advantages of the proposed framework for Online Roadshow compared to similar existing big data processing frameworks.

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12

Fang, Hong, Bo Zhao, Xiao-Wang Zhang, and Xuan-Xing Yang. "A United Framework for Large-Scale Resource Description Framework Stream Processing." Journal of Computer Science and Technology 34, no. 4 (July 2019): 762–74. http://dx.doi.org/10.1007/s11390-019-1941-9.

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13

Beedkar, Kaustubh, David Brekardin, Jorge-Anulfo Quiané-Ruiz, and Volker Markl. "Compliant geo-distributed data processing in action." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2843–46. http://dx.doi.org/10.14778/3476311.3476359.

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In this paper we present our work on compliant geo-distributed data processing. Our work focuses on the new dimension of dataflow constraints that regulate the movement of data across geographical or institutional borders. For example, European directives may regulate transferring only certain information fields (such as non personal information) or aggregated data. Thus, it is crucial for distributed data processing frameworks to consider compliance with respect to dataflow constraints derived from these regulations. We have developed a compliance-based data processing framework, which (i) allows for the declarative specification of dataflow constraints, (ii) determines if a query can be translated into a compliant distributed query execution plan, and (iii) executes the compliant plan over distributed SQL databases. We demonstrate our framework using a geo-distributed adaptation of the TPC-H benchmark data. Our framework provides an interactive dashboard, which allows users to specify dataflow constraints, and analyze and execute compliant distributed query execution plans.

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14

Alslaihat, Ahmad A., Abdullah A. Alsous, Nasser M. Al-Dwaik, and Radi A. Al-Khlaifat. "Bio-Signals: Conceptual Framework and Significance Processing." International Journal of Scientific and Research Publications (IJSRP) 9, no. 9 (September 24, 2019): p93119. http://dx.doi.org/10.29322/ijsrp.9.09.2019.p93119.

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15

van Elk, Michiel. "A predictive processing framework of tool use." Cortex 139 (June 2021): 211–21. http://dx.doi.org/10.1016/j.cortex.2021.03.014.

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16

Wu, Hao, Yongqiang Cheng, and Hongqiang Wang. "Isometric Signal Processing under Information Geometric Framework." Entropy 21, no. 4 (March 27, 2019): 332. http://dx.doi.org/10.3390/e21040332.

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Information geometry is the study of the intrinsic geometric properties of manifolds consisting of a probability distribution and provides a deeper understanding of statistical inference. Based on this discipline, this letter reports on the influence of the signal processing on the geometric structure of the statistical manifold in terms of estimation issues. This letter defines the intrinsic parameter submanifold, which reflects the essential geometric characteristics of the estimation issues. Moreover, the intrinsic parameter submanifold is proven to be a tighter one after signal processing. In addition, the necessary and sufficient condition of invariant signal processing of the geometric structure, i.e., isometric signal processing, is given. Specifically, considering the processing with the linear form, the construction method of linear isometric signal processing is proposed, and its properties are presented in this letter.

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17

Trushkowsky, Beth, Tim Kraska, and Michael Franklin. "A Framework for Adaptive Crowd Query Processing." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 1 (November 3, 2013): 74–75. http://dx.doi.org/10.1609/hcomp.v1i1.13131.

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Search engines can yield poor results for information retrieval tasks when they cannot interpret query predicates. Such predicates are better left for humans to evaluate. We propose an adaptive processing framework for deciding (a) which parts of a query should be processed by machines and (b) the order the crowd should process the remaining parts, optimizing for result quality and processing cost. We describe an algorithm and experimental results for the first framework component.

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18

Ewoldsen, David R., Jennifer Hoewe, and Sarah Grady. "A Cognitive Processing Framework for Media Interpretation." Journal of Media Psychology 34, no. 2 (March 2022): 65–76. http://dx.doi.org/10.1027/1864-1105/a000326.

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Abstract. The same media content can be interpreted by different people in radically different ways. We propose a framework that considers both the cognitive processes and the associated mental representations implicated in the interpretation of media content. The foundation of this argument stems from a constraint satisfaction approach to coherence, and it explains the dynamic relationship between media content and media consumers’ processing and interpreting of that content. By integrating parallel constraint satisfaction and coherence with reflective imaginative involvement, we present an explanation of how people interpret media stories and how they may engage with these stories in the future. We believe this framework has significant implications for media scholars interested in message processing and effects.

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19

Wang, Yuanjun, Fan Jiang, and Yu Liu. "Spectrum-sine interpolation framework for DTI processing." Medical & Biological Engineering & Computing 60, no. 1 (November 29, 2021): 279–95. http://dx.doi.org/10.1007/s11517-021-02471-2.

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20

Duchaine, Brad, and Galit Yovel. "A Revised Neural Framework for Face Processing." Annual Review of Vision Science 1, no. 1 (November 24, 2015): 393–416. http://dx.doi.org/10.1146/annurev-vision-082114-035518.

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21

Vonrhein, C., and G. Bricogne. "AutoPROC– a framework for automated data processing." Acta Crystallographica Section A Foundations of Crystallography 64, a1 (August 23, 2008): C78. http://dx.doi.org/10.1107/s010876730809750x.

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22

Kravitz, D., K. Saleem, C. Baker, and M. Mishkin. "A new neural framework for visuospatial processing." Journal of Vision 11, no. 11 (September 23, 2011): 319. http://dx.doi.org/10.1167/11.11.923.

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23

Kravitz, Dwight J., Kadharbatcha S. Saleem, Chris I. Baker, and Mortimer Mishkin. "A new neural framework for visuospatial processing." Nature Reviews Neuroscience 12, no. 4 (March 18, 2011): 217–30. http://dx.doi.org/10.1038/nrn3008.

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24

Nikias, C. L., and M. R. Raghuveer. "Bispectrum estimation: A digital signal processing framework." Proceedings of the IEEE 75, no. 7 (1987): 869–91. http://dx.doi.org/10.1109/proc.1987.13824.

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25

Chakravarthy, S., A. Aved, S. Shirvani, M. Annappa, and E. Blasch. "Adapting Stream Processing Framework for Video Analysis." Procedia Computer Science 51 (2015): 2648–57. http://dx.doi.org/10.1016/j.procs.2015.05.372.

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26

Tian, Xinhui, and Jianfeng Zhan. "GraphDuo: A Dual-Model Graph Processing Framework." IEEE Access 6 (2018): 35057–71. http://dx.doi.org/10.1109/access.2018.2848291.

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27

Ritchie, J. Brendan. "The content of Marr’s information-processing framework." Philosophical Psychology 32, no. 7 (September 4, 2019): 1078–99. http://dx.doi.org/10.1080/09515089.2019.1646418.

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28

Kaczmarczyk, Vaclav, Radek Kuchta, Zdenka Kuchtova, Jaroslav Kadlec, and Ondrej Bastan. "Data processing platform for indoor localization framework." IFAC-PapersOnLine 51, no. 6 (2018): 508–13. http://dx.doi.org/10.1016/j.ifacol.2018.07.111.

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29

Mordkowitz, Eliott. "Applied cognitive psychology: An information-processing framework." New Ideas in Psychology 8, no. 3 (January 1990): 414–15. http://dx.doi.org/10.1016/0732-118x(94)90034-5.

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30

Zhao, Gang. "A Query Processing Framework based on Hadoop." International Journal of Database Theory and Application 7, no. 4 (August 31, 2014): 261–72. http://dx.doi.org/10.14257/ijdta.2014.7.4.21.

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31

Schwengerer, Lukas. "Self-Knowledge in a Predictive Processing Framework." Review of Philosophy and Psychology 10, no. 3 (August 23, 2018): 563–85. http://dx.doi.org/10.1007/s13164-018-0416-1.

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32

Schill, Alexander, and Martina Zitterbart. "A system framework for open distributed processing." Journal of Network and Systems Management 1, no. 1 (March 1993): 71–93. http://dx.doi.org/10.1007/bf01026829.

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33

Ete, Remi, Frank Gaede, Julian Benda, and Hadrian Grasland. "MarlinMT - parallelising the Marlin framework." EPJ Web of Conferences 245 (2020): 05022. http://dx.doi.org/10.1051/epjconf/202024505022.

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Marlin is the event processing framework of the iLCSoft [1] ecosystem. Originally developed for the ILC more than 15 years ago, it is now widely used also by other communities, such as CLICdp, CEPC and many test beam projects such as CALICE, LCTPC and EU-Telescope. While Marlin is lightweight and flexible it was originally designed for sequential processing only. With MarlinMT we have now evolved Marlin for parallel processing of events on multi-core architectures based on multi-threading. We report on the necessary developments and issues encountered, within Marlin as well as with the underlying LCIO [4] event data model (EDM). A focus will be put on the new parallel event processing (PEP) scheduler. We conclude with first performance estimates, like the application speedup and a discussion on histogram handling in parallel applications.

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34

Ajibade Lukuman Saheed, Abu Bakar Kamalrulnizam, Ahmed Aliyu, and Tasneem Darwish. "Latency-aware Straggler Mitigation Strategy in Hadoop MapReduce Framework: A Review." Systematic Literature Review and Meta-Analysis Journal 2, no. 2 (October 19, 2021): 53–60. http://dx.doi.org/10.54480/slrm.v2i2.19.

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Processing huge and complex data to obtain useful information is challenging, even though several big data processing frameworks have been proposed and further enhanced. One of the prominent big data processing frameworks is MapReduce. The main concept of MapReduce framework relies on distributed and parallel processing. However, MapReduce framework is facing serious performance degradations due to the slow execution of certain tasks type called stragglers. Failing to handle stragglers causes delay and affects the overall job execution time. Meanwhile, several straggler reduction techniques have been proposed to improve the MapReduce performance. This study provides a comprehensive and qualitative review of the different existing straggler mitigation solutions. In addition, a taxonomy of the available straggler mitigation solutions is presented. Critical research issues and future research directions are identified and discussed to guide researchers and scholars

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35

III, Henry L. Roediger, David A. Gallo, and Lisa Geraci. "Processing approaches to cognition: The impetus from the levels-of-processing framework." Memory 10, no. 5-6 (September 2002): 319–32. http://dx.doi.org/10.1080/09658210224000144.

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36

Khalid, Madiha, and Muhammad Murtaza Yousaf. "A Comparative Analysis of Big Data Frameworks: An Adoption Perspective." Applied Sciences 11, no. 22 (November 22, 2021): 11033. http://dx.doi.org/10.3390/app112211033.

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The emergence of social media, the worldwide web, electronic transactions, and next-generation sequencing not only opens new horizons of opportunities but also leads to the accumulation of a massive amount of data. The rapid growth of digital data generated from diverse sources makes it inapt to use traditional storage, processing, and analysis methods. These limitations have led to the development of new technologies to process and store very large datasets. As a result, several execution frameworks emerged for big data processing. Hadoop MapReduce, the pioneering framework, set the ground for forthcoming frameworks that improve the processing and development of large-scale data in many ways. This research focuses on comparing the most prominent and widely used frameworks in the open-source landscape. We identify key requirements of a big framework and review each of these frameworks in the perspective of those requirements. To enhance the clarity of comparison and analysis, we group the logically related features, forming a feature vector. We design seven feature vectors and present a comparative analysis of frameworks with respect to those feature vectors. We identify use cases and highlight the strengths and weaknesses of each framework. Moreover, we present a detailed discussion that can serve as a decision-making guide to select the appropriate framework for an application.

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37

Van Veen, B., and R. Roberts. "A framework for beamforming structures." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 4 (April 1987): 584–86. http://dx.doi.org/10.1109/tassp.1987.1165150.

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38

Tianfei, Dong. "Food Physical Processing Technology and its Basic Framework." E3S Web of Conferences 185 (2020): 04037. http://dx.doi.org/10.1051/e3sconf/202018504037.

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During the development of traditional food processing technology, there are great challenges in actual processing efficiency and product quality, which also makes the application of modern physical technology one of the effective measures to solve the above challenges. To this end, the relevant departments and staff need to strengthen the research emphasis on food physical processing technology. This article summarizes the problems faced by my country’s food industry based on previous work experience, and discusses the basic framework of food physical processing technology from two aspects of physical methods and food processing methods.

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39

Kamburugamuve, Supun, Leif Christiansen, and Geoffrey Fox. "A Framework for Real Time Processing of Sensor Data in the Cloud." Journal of Sensors 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/468047.

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We describe IoTCloud, a platform to connect smart devices to cloud services for real time data processing and control. A device connected to IoTCloud can communicate with real time data analysis frameworks deployed in the cloud via messaging. The platform design is scalable in connecting devices as well as transferring and processing data. With IoTCloud, a user can develop real time data processing algorithms in an abstract framework without concern for the underlying details of how the data is distributed and transferred. For this platform, we primarily consider real time robotics applications such as autonomous robot navigation, where there are strict requirements on processing latency and demand for scalable processing. To demonstrate the effectiveness of the system, a robotic application is developed on top of the framework. The system and the robotics application characteristics are measured to show that data processing in central servers is feasible for real time sensor applications.

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40

Tianxing, Man, Nataly Zhukova, Alexander Vodyaho, and Tin Tun Aung. "A Meta-Mining Ontology Framework for Data Processing." International Journal of Embedded and Real-Time Communication Systems 12, no. 2 (April 2021): 37–56. http://dx.doi.org/10.4018/ijertcs.2021040103.

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Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.

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41

Verma, Bhavana, and Sona Malhotra. "An Enhanced AES based Secure Image Processing Framework." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 6 (June 30, 2017): 134–40. http://dx.doi.org/10.23956/ijarcsse/v7i6/0234.

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42

Ward, S. C., and C. B. Chapman. "Developing Competitive Bids: A Framework for Information Processing." Journal of the Operational Research Society 39, no. 2 (February 1988): 123. http://dx.doi.org/10.2307/2582375.

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43

Huang, Yuan-Ko, and Lien-Fa Lin. "A Framework for Processing K-Best Site Query." International Journal of Database Management Systems 5, no. 5 (October 31, 2013): 17–28. http://dx.doi.org/10.5121/ijdms.2013.5503.

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44

Duarte, João, and André Vasconcelos. "Evaluating Information Systems Constructing a Model Processing Framework." International Journal of Enterprise Information Systems 6, no. 3 (July 2010): 17–32. http://dx.doi.org/10.4018/jeis.2010070102.

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In the past decade, the rush to technology has created several flaws in terms of managing computers, applications, and middleware and information systems. Therefore, organizations struggle to understand how these elements behave. Even today, as Enterprise Architectures grow in significance and are acknowledged as advantageous artifacts to help manage change, their benefit to the organization has yet to be fully explored. In this paper, the authors focus on the challenge of real-time information systems evaluation, using the enterprise architecture as a boundary object and a base for communication. The solution proposed is comprised of five major steps: establishing a strong conceptual base on the evaluation of information systems, defining a high level language for this activity, extending an architecture creation pipeline, creating a framework that automates it, and the framework’s implementation. The conceptual framework proposed avoids imprecise definitions of quality and quality attributes, was materialized in a model-eval-display loop framework, and was implemented using Model Driven Software Development practices and tools. Finally, a prototype is applied to a real-world scenario to verify the conceptual solution in practice.

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45

Ekuni, Roberta, Leonardo José Vaz, and Orlando Francisco Amodeo Bueno. "Levels of processing: The evolution of a framework." Psychology & Neuroscience 4, no. 3 (July 2011): 333–39. http://dx.doi.org/10.3922/j.psns.2011.3.006.

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46

Vonikakis, Vassilios, and Stefan Winkler. "A center-surround framework for spatial image processing." Electronic Imaging 2016, no. 6 (February 14, 2016): 1–8. http://dx.doi.org/10.2352/issn.2470-1173.2016.6.retinex-020.

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47

Shekhar Gautam|1 Akhilesh A. Waoo, Chandra. "Speedup Query Processing in Hadoop Using Mapreduce Framework." Data Research 2, no. 1 (2018): 43. http://dx.doi.org/10.31058/j.data.2018.21004.

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48

Dannenberg, Valentin, Robert Schüler, and Achill Schürmann. "A Data Processing Framework for Polar Performance Diagrams." Applied Sciences 12, no. 6 (March 17, 2022): 3085. http://dx.doi.org/10.3390/app12063085.

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Polar performance diagrams are commonly used to predict the performance of a sailing vessel under given wind conditions. They are, in particular, an essential part of robotic sailing vessels and a basis for weather routing algorithms. In this paper we introduce a new framework for scientific work with such diagrams, which we make available as an open source Python package. It contains a model for the creation of polar performance diagrams from measurement data and supports different representations of polar performance diagrams for different tasks. The framework also includes several methods for the visualisation of polar performance diagrams, for example for scientific publications. Additionally, the presented framework solves basic tasks for the future development of weather-routing algorithms in a far more general manner than other methods did previously: it provides the calculation of costs of a sailing trip using custom cost functions, suggestions of optimal steering using convex hull calculations and a more flexible calculation of isochrone points, using custom weather models. Altogether, the presented framework allows future researchers to more easily handle polar performance diagrams. The corresponding Python package is compatible with various established file formats.

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49

Maclnnis, Deborah J., and Bernard J. Jaworski. "Information Processing from Advertisements: Toward an Integrative Framework." Journal of Marketing 53, no. 4 (October 1989): 1. http://dx.doi.org/10.2307/1251376.

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50

Sugiyama, Masashi, Takafumi Kanamori, Taiji Suzuki, Shohei Hido, Jun Sese, Ichiro Takeuchi, and Liwei Wang. "A Density-ratio Framework for Statistical Data Processing." IPSJ Transactions on Computer Vision and Applications 1 (2009): 183–208. http://dx.doi.org/10.2197/ipsjtcva.1.183.

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