Gotowe bibliografie tematyczne / Bug Processing

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Gotowa bibliografia na temat „Bug Processing”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Bug Processing"

Angel, T. S. Shiny, G. Senthil Kumar, V. Mohit Sehgal, and Gargi Nayak. "Effective Bug Processing and Tracking System." Journal of Computational and Theoretical Nanoscience 15, no. 8 (2018): 2604–6. http://dx.doi.org/10.1166/jctn.2018.7506.

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Swapnali, Sonawane *. Prof.D.S.Kulkarni. "CONSTRUCTION OF IMPROVED PROCESS MODELS BY CLUSTERING EVENT LOGS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 216–521. https://doi.org/10.5281/zenodo.829757.

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One main motive is to solve the problem that presently exist in process discovery, which includes unable to search out correct and understandable process models out of event logs stemming from exceptionally flexible environments. Programming analyst spend extra time in dealing with programming bugs. An unavoidable stride of fixing bugs is bug processing in a system, because of this to efficaciously relegate a designer to every other bug. To decrease the time cost in manual work, content classifications are linked to lead programmed bug processing. This system addresses the difficulty of statis

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Sutar, Manoj. "Software Bug Classification Using Machine Learning." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–9. https://doi.org/10.55041/isjem04233.

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Abstract This research presents a Software Bug Classification system that utilizes machine learning and natural language processing to automatically categorize bug reports. The system is trained on labeled datasets containing software issue descriptions and applies text classification techniques to identify the type of bug—such as functionality, performance, or user interface-related issues. Machine learning models like Support Vector Machines (SVM), Naive Bayes, and transformer-based models such as BERT are used to enhance classification accuracy. A user-friendly web interface developed using

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Kelin, A. R. Darshika, B. Nagarajan, Sasikumar Rajendran, and Muthumari S. "Automatic Bug Classification System to Improve the Software Organization Product Performance." International Journal of Sociotechnology and Knowledge Development 14, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/ijskd.310066.

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Consistently, many bugs are raised, which are not completely settled, and countless designers are utilizing open sources or outsider assets, which prompts security issues. Bug-triage is the impending mechanized bug report framework to appoint individual security teams for a more than adequate pace of bug reports submitted from various IDEs inside the association (on-premises). We can lessen the time and cost of bug following and allocate it to the fitting group by foreseeing which division it has a place in within an association. In this paper, the authors are executing an automatic bug tracki

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Chhabra, Deepshikha, and Raman Chadha. "Automatic Bug Triaging Process: An Enhanced Machine Learning Approach through Large Language Models." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18557–62. https://doi.org/10.48084/etasr.8829.

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Bug resolution and maintenance are the most critical phases of the software development life cycle. The traditional bug triaging concept refers to the manual assignment of bugs to the appropriate developer after reading the bug details from the bug tracker and further resolving it. The advent of machine learning algorithms provides various solutions for automated bug triaging. Machine learning algorithms can be used to classify bugs and assign each to a developer. Reducing manual efforts optimizes bug-triaging by utilizing manpower in other software development processes. Furthermore, machine

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Praveen, G., V. Sridhar Reddy, and Shaik Abdul Nabi. "A Novel Approach on Towards Effective Bug Triage and Improve the Quality of Bug Data." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 9 (2017): 1. http://dx.doi.org/10.23956/ijarcsse.v7i9.402.

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In order to diminution the time cost in physical work, text classification practices are pragmatic to conduct automatic bug triage. Presently, software companies spend over 45 percent of cost in dealing with software bugs. A foreseeable step of fixing bugs is bug triage, which ambitions to decorously consign a developer to a new bug. In this scheme, it is addressed the problem of data reduction for bug triage, to reduce the scale and progress the reputation of bug data. It is here combined with instance selection with feature selection to simultaneously reduce data scale on the bug dimension a

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Nhan, Phuc Minh, and Thien Hoang Duy Nguyen. "MULTI-FEATURE MODEL FOR DUPLICATE BUG REPORTS DETECTION SOLUTION." Scientific Journal of Tra Vinh University 1, no. 1 (2019): 29–36. http://dx.doi.org/10.35382/18594816.1.1.2019.84.

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For open source software such as Firefox, Eclipse, Subversion,. . . they usually have a system for bug management that sent by users. These bug reports help the system identify various software bugs which makes software maintenance better. However, a situation occurs that there are many error reports sent to the processing repository that these bug reports were previously reported by different users, this is called duplicate bug reports. In this paper, we introduce a multi-feature model combined with weighted improvements from CFC (ClassFeature-Centroid) to detect duplicate bug reportsautomati

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Kim, Kisub, Sankalp Ghatpande, Kui Liu, et al. "DigBug—Pre/post-processing operator selection for accurate bug localization." Journal of Systems and Software 189 (July 2022): 111300. http://dx.doi.org/10.1016/j.jss.2022.111300.

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Rucci, Michele, and Jonathan D. Victor. "The unsteady eye: an information-processing stage, not a bug." Trends in Neurosciences 38, no. 4 (2015): 195–206. http://dx.doi.org/10.1016/j.tins.2015.01.005.

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Luaphol, B., J. Polpinij, and M. Kaneampornpan. "Automatic dependent bug reports assembly for bug tracking systems by threshold-based similarity." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1620. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1620-1633.

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<p>Bug reports contain essential information for fixing problems that occur in software. Many studies have proposed methods for automatic analysis of bug reports. One such task could affect the completion of software bug fixing, known as “bug dependency”. Although this problem was mentioned by many researches, most of them discussed about the related bugs but not really dealt with dependency issue in bug reports. One possible solution used for addressing this issue is to assemble all relevant/dependent bug reports together before analysis of the next processing stages. This study present

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Rozprawy doktorskie na temat "Bug Processing"

Eriksson, Caroline, and Emilia Kallis. "NLP-Assisted Workflow Improving Bug Ticket Handling." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301248.

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Software companies spend a lot of resources on debugging, a process where previous solutions can help in solving current problems. The bug tickets, containing this information, are often time-consuming to read. To minimize the time spent on debugging and to make sure that the knowledge from prior solutions is kept in the company, an evaluation was made to see if summaries could make this process more efficient. Abstractive and extractive summarization models were tested for this task and fine-tuning of the bert-extractive-summarizer was performed. The model-generated summaries were compared in

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Ingvarsson, Sanne. "Using Machine Learning to Learn from Bug Reports : Towards Improved Testing Efficiency." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156711.

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The evolution of a software system originates from its changes, whether it comes from changed user needs or adaption to its current environment. These changes are as encouraged as they are inevitable, although every change to a software system comes with a risk of introducing an error or a bug. This thesis aimed to investigate the possibilities of using the description of bug reports as a decision basis for detecting the provenance of a bug by using machine learning. K-means and agglomerative clustering have been applied to free text documents by using Natural Language Processing to initially

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Bug, Daniel [Verfasser], Dorit [Akademischer Betreuer] Merhof, and Horst K. [Akademischer Betreuer] Hahn. "Digital histopathology : Image processing for histological analyses and immune response quantification / Daniel Bug ; Dorit Merhof, Horst K. Hahn." Aachen : Universitätsbibliothek der RWTH Aachen, 2020. http://d-nb.info/1240689543/34.

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Artchounin, Daniel. "Tuning of machine learning algorithms for automatic bug assignment." Thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139230.

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In software development projects, bug triage consists mainly of assigning bug reports to software developers or teams (depending on the project). The partial or total automation of this task would have a positive economic impact on many software projects. This thesis introduces a systematic four-step method to find some of the best configurations of several machine learning algorithms intending to solve the automatic bug assignment problem. These four steps are respectively used to select a combination of pre-processing techniques, a bug report representation, a potential feature selection tec

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Kasianenko, Stanislav. "Predicting Software Defectiveness by Mining Software Repositories." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78729.

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One of the important aims of the continuous software development process is to localize and remove all existing program bugs as fast as possible. Such goal is highly related to software engineering and defectiveness estimation. Many big companies started to store source code in software repositories as the later grew in popularity. These repositories usually include static source code as well as detailed data for defects in software units. This allows analyzing all the data without interrupting programing process. The main problem of large, complex software is impossibility to control everythi

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Nantes, Alfredo. "Computational approaches to the visual validation of 3D virtual environments." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48341/1/Alfredo_Nantes_Thesis.pdf.

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Virtual environments can provide, through digital games and online social interfaces, extremely exciting forms of interactive entertainment. Because of their capability in displaying and manipulating information in natural and intuitive ways, such environments have found extensive applications in decision support, education and training in the health and science domains amongst others. Currently, the burden of validating both the interactive functionality and visual consistency of a virtual environment content is entirely carried out by developers and play-testers. While considerable research

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Giordano, Manfredi. "Autonomic Big Data Processing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14837/.

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Apache Spark è un framework open source per la computazione distribuita su larga scala, caratterizzato da un engine in-memory che permette prestazioni superiori a soluzioni concorrenti nell’elaborazione di dati a riposo (batch) o in movimento (streaming). In questo lavoro presenteremo alcune tecniche progettate e implementate per migliorare l’elasticità e l’adattabilità del framework rispetto a modifiche dinamiche nell’ambiente di esecuzione o nel workload. Lo scopo primario di tali tecniche è di permettere ad applicazioni concorrenti di condividere le risorse fisiche disponibili nell’infrast

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Rupprecht, Lukas. "Network-aware big data processing." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/52455.

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The scale-out approach of modern data-parallel frameworks such as Apache Flink or Apache Spark has enabled them to deal with large amounts of data. These applications are often deployed in large-scale data centres with many resources. However, as deployments and data continue to grow, more network communication is incurred during a data processing query. At the same time, data centre networks (DCNs) are becoming increasingly more complex in terms of the physical network topology, the variety of applications that are sharing the network, and the different requirements of these applications on t

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Lei, Chuan. "Recurring Query Processing on Big Data." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-dissertations/550.

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The advances in hardware, software, and networks have enabled applications from business enterprises, scientific and engineering disciplines, to social networks, to generate data at unprecedented volume, variety, velocity, and varsity not possible before. Innovation in these domains is thus now hindered by their ability to analyze and discover knowledge from the collected data in a timely and scalable fashion. To facilitate such large-scale big data analytics, the MapReduce computing paradigm and its open-source implementation Hadoop is one of the most popular and widely used technologies. Had

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Nyström, Simon, and Joakim Lönnegren. "Processing data sources with big data frameworks." Thesis, KTH, Data- och elektroteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188204.

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Big data is a concept that is expanding rapidly. As more and more data is generatedand garnered, there is an increasing need for efficient solutions that can be utilized to process all this data in attempts to gain value from it. The purpose of this thesis is to find an efficient way to quickly process a large number of relatively small files. More specifically, the purpose is to test two frameworks that can be used for processing big data. The frameworks that are tested against each other are Apache NiFi and Apache Storm. A method is devised in order to, firstly, construct a data flow and sec

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Książki na temat "Bug Processing"

Legal and Accounting Management Seminars., ed. The millennium bug: Its impact on the legal & accounting professions. Legal and Accounting Management Seminars, 1998.

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Conning & Company., ed. Insurers and the Year 2000 computer bug: The Tower of Babel revisited : 1998. Conning & Co., 1998.

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Auditor-General, Zimbabwe Office of the Comptroller and. Special report of the Comptroller and Auditor-General on the preparedness for the millennium bug by the government and other state institutions. The Office, 1998.

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Problem, United States Congress Senate Special Committee on the Year 2000 Technology. Year 2000 global corporations: Will the bug bite big business : hearing before the Special Committee on the Year 2000 Technology Problem, United States Senate, One Hundred Sixth Congress, first session, on Y2K implications and problems internationally, July 22, 1999. U.S. G.P.O., 2000.

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Services, United States Congress House Committee on Banking and Financial. Millennium bug: Banking and the year 2000 computer problem : hearing before the Committee on Banking and Financial Services, House of Representatives, One Hundred Fifth Congress, first session, November 4, 1997. U.S. G.P.O., 1998.

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United States. Congress. House. Committee on Commerce. Subcommittee on Health and the Environment. Y2K and medical devices: Screening for the Y2K bug : joint hearing before the Subcommittees on Health and Environment and Oversight and Investigations of the Committee on Commerce, House of Representatives, One Hundred Sixth Congress, first session, May 25, 1999. U.S. G.P.O., 1999.

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Environment, United States Congress House Committee on Commerce Subcommittee on Health and the. Y2K and medical devices: Testing for the Y2K bug : joint hearing before the Subcommittee on Health and Environment and the Subcommittee on Oversight and Investigations of the Committee on Commerce, House of Representatives, One Hundred Sixth Congress, first session, October 21, 1999. U.S. G.P.O., 1999.

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Sakr, Sherif. Big Data 2.0 Processing Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44187-6.

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Sakr, Sherif. Big Data 2.0 Processing Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38776-5.

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Części książek na temat "Bug Processing"

Lin, Tao, Jianhua Gao, Xue Fu, and Yan Lin. "A Novel Bug Report Extraction Approach." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27161-3_70.

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Chmielowski, Lukasz, and Michal Kucharzak. "Impact of Software Bug Report Preprocessing and Vectorization on Bug Assignment Accuracy." In Progress in Image Processing, Pattern Recognition and Communication Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81523-3_15.

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Nnamoko, Nonso, Luis Adrián Cabrera-Diego, Daniel Campbell, and Yannis Korkontzelos. "Bug Severity Prediction Using a Hierarchical One-vs.-Remainder Approach." In Natural Language Processing and Information Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23281-8_20.

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Wu, Zhendong, Kai Lu, and Xiaoping Wang. "Identifying Repeated Interleavings to Improve the Efficiency of Concurrency Bug Detection." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27140-8_13.

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Pinel, Frédéric, Bernabé Dorronsoro, Pascal Bouvry, and Samee U. Khan. "It’s Not a Bug, It’s a Feature: Wait-Free Asynchronous Cellular Genetic Algorithm." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55224-3_34.

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Batista, Arthur, Fabricio D’Morison Marinho, Thiago Rocha, et al. "Automated Bug Triaging in a Global Software Development Environment: An Industry Experience." In Natural Language Processing and Information Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08473-7_15.

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Francalanci, Chiara, and Francesco Merlo. "Empirical Analysis of the Bug Fixing Process in Open Source Projects." In IFIP – The International Federation for Information Processing. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-09684-1_15.

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Datta, Subhajit, Proshanta Sarkar, Sutirtha Das, Sonu Sreshtha, Prasanth Lade, and Subhashis Majumder. "How Many Eyeballs Does a Bug Need? An Empirical Validation of Linus’ Law." In Lecture Notes in Business Information Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06862-6_17.

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Mohamed, Khaled Salah. "New Trends in SoC Verification: UVM, Bug Localization, Scan-C0068ain-Based Methodology, GA-Based Test Generation." In Analog Circuits and Signal Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-22035-2_6.

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Jangra, Monika, and Sandeep K. Singh. "Evaluating Topic Modeling as Pre-processing for Component Prediction in Bug Reports." In Advanced Computing and Communication Technologies. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1023-1_46.

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Streszczenia konferencji na temat "Bug Processing"

Sheng Xuan, Phang Justin, and Yunli Lee. "Automated Code Review and Bug Detection." In 2024 19th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). IEEE, 2024. https://doi.org/10.1109/isai-nlp64410.2024.10799479.

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Patil, Avinash, and Aryan Jadon. "Next-Generation Bug Reporting: Enhancing Development with AI Automation." In 2025 10th International Conference on Signal Processing and Communication (ICSC). IEEE, 2025. https://doi.org/10.1109/icsc64553.2025.10968932.

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Zhang, Xiangyu, Mengxiang Lin, and Kai Yu. "An integrated bug processing framework." In 2012 34th International Conference on Software Engineering (ICSE 2012). IEEE, 2012. http://dx.doi.org/10.1109/icse.2012.6227060.

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P, Gunalan, Jaiseenu V, and Madumidha S. "Natural Language Processing based Bug Categorization." In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, 2022. http://dx.doi.org/10.1109/icaaic53929.2022.9793205.

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Meddah, Farouk, and Lynda Dib. "E-Bug." In IPAC '15: International Conference on Intelligent Information Processing, Security and Advanced Communication. ACM, 2015. http://dx.doi.org/10.1145/2816839.2816864.

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Swe, Kyaw Ei Ei, and Hnin Min Oo. "Source Code Retrieval for Bug Localization using Bug Report." In 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2019. http://dx.doi.org/10.1109/iccp48234.2019.8959535.

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Öztürk, Ceyhun E., Eyup Halit Yilmaz, and Ömer KÖKSAL. "Transformer-Based Bug/Feature Classification." In 2023 31st Signal Processing and Communications Applications Conference (SIU). IEEE, 2023. http://dx.doi.org/10.1109/siu59756.2023.10223806.

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Patil, Avinash, and Aryan Jadon. "Auto-labelling of Bug Report using Natural Language Processing." In 2023 IEEE 8th International Conference for Convergence in Technology (I2CT). IEEE, 2023. http://dx.doi.org/10.1109/i2ct57861.2023.10126470.

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Jahangir, MD Ariyan, MD Abtahi Tajwar, Waythin Marma, and Md Siyamul Islam. "Intelligent Software Bug Prediction: An Empirical Approach." In 2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). IEEE, 2023. http://dx.doi.org/10.1109/icrest57604.2023.10070026.

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Öztürk, Ceyhun E., Eyüp Halit Yilmaz, Ömer Köksal, and Aykut Koç. "Software Module Classification for Commercial Bug Reports." In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2023. http://dx.doi.org/10.1109/icasspw59220.2023.10193706.

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Raporty organizacyjne na temat "Bug Processing"

Orozova, Daniela, and Krassimir Atanassov. Model of Big Data Map/Reduce Processing. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2019. http://dx.doi.org/10.7546/crabs.2019.11.11.

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Anumukonda, Bharath Chandr. Real World Scalable Big Data Processing for Smart Transportation Performance Assessment. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-1189.

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Schoening, Timm. OceanCV. GEOMAR, 2022. http://dx.doi.org/10.3289/sw_5_2022.

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OceanCV provides computer vision algorithms and tools for underwater image analysis. This includes image processing, pattern recognition, machine learning and geometric algorithms but also functionality for navigation data processing, data provenance etc.

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Chen, Yanpei, Sara Alspaugh, and Randy H. Katz. Interactive Query Processing in Big Data Systems: A Cross Industry Study of MapReduce Workloads. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada561769.

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Guicheney, William, Tinashe Zimani, Hope Kyarisiima, and Louisa Tomar. Big Data in the Public Sector: Selected Applications and Lessons Learned. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0007024.

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This paper analyzes different ways in which big data can be leveraged to improve the efficiency and effectiveness of government. It describes five cases where massive and diverse sets of information are gathered, processed, and analyzed in three different policy areas: smart cities, taxation, and citizen security. The cases, compiled from extensive desk research and interviews with leading academics and practitioners in the field of data analytics, have been analyzed from the perspective of public servants interested in big data and thus address both the technical and the institutional aspects

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Azar, Pablo, Sergio Olivas, and Nish D. Sinha. The Price of Processing: Information Frictions and Market Efficiency in DeFi. Federal Reserve Bank of New York, 2025. https://doi.org/10.59576/sr.1153.

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This paper investigates the speed of price discovery when information becomes publicly available but requires costly processing to become common knowledge. We exploit the unique institutional setting of hacks on decentralized finance (DeFi) protocols. Public blockchain data provides the precise time a hack’s transactions are recorded—becoming public information—while subsequent social media disclosures mark the transition to common knowledge. This empirical design allows us to isolate the price impact occurring during the interval characterized by information asymmetry driven purely by differe

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Cao, Larry. IV. Chatbot, Knowledge Graphs, and AI Infrastructure. CFA Institute Research Foundation, 2023. http://dx.doi.org/10.56227/23.1.10.

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Expert contributors discuss AI and big data applications that are being developed for financial services, such as AI-powered intelligent customer service systems; “factories” for data processing, AI, simulation, and visualization; and symbolic AI.

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Pueyo, Ana, Gisela Ngoo, Editruda Daulinge, and Adriana Fajardo. The Quest for Scalable Business Models for Mini-Grids in Africa: Implementing the Keymaker Model in Tanzania. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/ids.2022.071.

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Achieving universal electrification in sub-Saharan Africa requires creative solutions. Renewable mini-grids are a promising technology to electrify remote communities with a substantial productive demand, mainly from agro-processing. Mini-grids have experienced fast growth and there are now around 2,200 systems in the sub-Saharan Africa region. However, their economic case in the sub-continent is unclear. Most mini-grids are struggling not only to obtain a profit but also to recover costs. This Research Report describes the case of a private company in Tanzania implementing a business model fo

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Slone, Scott Michael, Marissa Torres, Nathan Lamie, Samantha Cook, and Lee Perren. Automated change detection in ground-penetrating radar using machine learning in R. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49442.

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Ground-penetrating radar (GPR) is a useful technique for subsurface change detection but is limited by the need for a subject matter expert to process and interpret coincident profiles. Use of a machine learning model can automate this process to reduce the need for subject matter expert processing and interpretation. Several machine learning models were investigated for the purpose of comparing coincident GPR profiles. Based on our literature review, a Siamese Twin model using a twinned convolutional network was identified as the optimum choice. Two neural networks were tested for the interna

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van der Sloot, Bart. The Quality of Life: Protecting Non-personal Interests and Non-personal Data in the Age of Big Data. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64579.

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Under the current legal paradigm, the rights to privacy and data protection provide natural persons with subjective rights to protect their private interests, such as related to human dignity, individual autonomy and personal freedom. In principle, when data processing is based on non-personal or aggregated data or when such data pro- cesses have an impact on societal, rather than individual interests, citizens cannot rely on these rights. Although this legal paradigm has worked well for decades, it is increasingly put under pressure because Big Data processes are typically based indis- crimin

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