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Journal articles on the topic 'Data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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1

Martha, Ranjith. "Real-Time Data Ingestion for Big Data Processing." International Journal of Science and Research (IJSR) 14, no. 2 (February 27, 2025): 570–72. https://doi.org/10.21275/sr25209075243.

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Mehraj, Nadiya, and Harveen Kour. "Data Processing Through Image Processing using Gaussian Minimum Shift Keying." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 977–81. http://dx.doi.org/10.31142/ijtsrd18819.

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3

Rossmann, Michael G., and Cornelis G. van Beek. "Data processing." Acta Crystallographica Section D Biological Crystallography 55, no. 10 (October 1, 1999): 1631–40. http://dx.doi.org/10.1107/s0907444999008379.

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X-ray diffraction data processing proceeds through indexing, pre-refinement of camera parameters and crystal orientation, intensity integration, post-refinement and scaling. TheDENZOprogram has set new standards for autoindexing, but no publication has appeared which describes the algorithm. In the development of the newData Processing Suite(DPS), one of the first aims has been the development of an autoindexing procedure at least as powerful as that used byDENZO. The resultant algorithm will be described. Another major problem which has arisen in recent years is scaling and post-refinement of data from different images when there are few, if any, full reflections. This occurs when the mosaic spread approaches or exceeds the angle of oscillation, as is usually the case for frozen crystals. A procedure which is able to obtain satisfactory results for such a situation will be described.

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Zasuhina, Ol'ga, Egor Ershov, Leonid Golovatiukov, and Grigory Shitenkov. "BIG DATA PROCESSING TECHNOLOGY." Bulletin of the Angarsk State Technical University 1, no. 16 (December 27, 2022): 98–100. http://dx.doi.org/10.36629/2686-777x-2022-1-16-98-100.

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Volkova, T., E. Furta, O. Dmitrieva, and I. Shabalina. "Pattern Building Methods in Genetic Data Processing." Journal on Selected Topics in Nano Electronics and Computing 1, no. 2 (June 2014): 2–6. http://dx.doi.org/10.15393/j8.art.2014.3041.

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Dayalan, Muthu. "MapReduce: Simplified Data Processing on Large Cluster." International Journal of Research and Engineering 5, no. 5 (April 2018): 399–403. http://dx.doi.org/10.21276/ijre.2018.5.5.4.

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Starukhin, Yaroslav, and Vladimir Diukarev. "AUTOMATION OF TEXT DATA PROCESSING USING NLP." American Journal of Engineering and Technology 6, no. 7 (July 1, 2024): 24–39. http://dx.doi.org/10.37547/tajet/volume06issue07-04.

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This study aims to develop an automated system for processing scientific texts using advanced NLP techniques. The methodology integrates classical NLP methods with deep learning approaches, employing SciBERT for text classification, LDA for topic modeling, and a modified TextRank algorithm for keyword extraction. Results demonstrate high accuracy in document classification (F1-score of 0.92), effective topic identification, and precise keyword extraction. The developed web interface showcases the system's practical applicability. This research contributes to the field by presenting a comprehensive solution for scientific text analysis, combining state-of-the-art language models with established NLP techniques. The study's novelty lies in its tailored approach to scientific literature, addressing the unique challenges of domain-specific language and complex content structure in academic texts.

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Seenivasan, Dhamotharan. "Real-Time Data Processing with Streaming ETL." International Journal of Science and Research (IJSR) 12, no. 11 (November 27, 2023): 2185–92. https://doi.org/10.21275/sr24619000026.

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Patrick Bell, Denis, Eliasu Tambominyi, and Yang Chunting. "Real-Time Stream Processing of Big Data." International Journal of Science and Research (IJSR) 10, no. 3 (March 27, 2021): 1247–52. https://doi.org/10.21275/sr21320045639.

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Karan, Patel, Sakaria Yash, and Bhadane Chetashri. "Real Time Data Processing Frameworks." International Journal of Data Mining & Knowledge Management Process (IJDKP) 5, no. 5 (September 12, 2019): 49–63. https://doi.org/10.5281/zenodo.3406010.

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On a business level, everyone wants to get hold of the business value and other organizational advantages that big data has to offer. Analytics has arisen as the primitive path to business value from big data. Hadoop is not just a storage platform for big data; it’s also a computational and processing platform for business analytics. Hadoop is, however, unsuccessful in fulfilling business requirements when it comes to live data streaming. The initial architecture of Apache Hadoop did not solve the problem of live stream data mining. In summary, the traditional approach of big data being co-relational to Hadoop is false; focus needs to be given on business value as well. Data Warehousing, Hadoop and stream processing complement each other very well. In this paper, we have tried reviewing a few frameworks and products which use real time data streaming by providing modifications to Hadoop.

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Gnip, P., and S. Kafka. "Using technology of data collection and data processing in precision farming." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (March 2, 2012): 419–26. http://dx.doi.org/10.17221/5426-agricecon.

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Data collection, data processing, data presentation and data application in the System of Precision farming guarantee a success of this system in the market. Difficulties of technologies, which are currently and continually involved in this system, argue against its practical using by farmers. In this case, service company wants to create a suitable environment not only for data collection, but also for the high quality of the information distribution to customers. One of such tools is the MapServer placed on Internet web sites.

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Sharopova, Muxayyo Muxtor qizi. "PROCESSING TECHNOLOGIES BIG DATA." Multidisciplinary Journal of Science and Technology 4, no. 3 (March 25, 2024): 390–95. https://doi.org/10.5281/zenodo.10870056.

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13

Stefanowicz, Bogdan, and Marek Cierpiał-Wolan. "Data processing errors." Wiadomości Statystyczne. The Polish Statistician 60, no. 9 (September 28, 2015): 23–29. http://dx.doi.org/10.5604/01.3001.0014.8296.

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The article highlights the need to broaden the analysis of the quality of the survey results, taking into account the negative impact of certain operations of so-called editing input data, such as checking their accuracy and correction of errors. In the conclusions it underlines the need to extend the programs for academic lectures in statistics for analysis of the impact of processing operations on the quality of the results.

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14

Jaworski, John, and Elizabeth Bliss. "Data Processing Mathematics." Mathematical Gazette 71, no. 458 (December 1987): 334. http://dx.doi.org/10.2307/3617092.

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OHE, Shuzo. "Statistical Data Processing." Journal of the Japan Society of Colour Material 67, no. 9 (1994): 590–95. http://dx.doi.org/10.4011/shikizai1937.67.590.

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Sarychev, Dmitriy S. "Lidar data processing." SAPR i GIS avtomobilnykh dorog, no. 1(2) (2014): 16–19. http://dx.doi.org/10.17273/cadgis.2014.1.4.

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17

Pu, Wenjing. "Standardized Data Processing." Transportation Research Record: Journal of the Transportation Research Board 2338, no. 1 (January 2013): 44–57. http://dx.doi.org/10.3141/2338-06.

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18

Ahlswede, R., and P. Lober. "Quantum data processing." IEEE Transactions on Information Theory 47, no. 1 (2001): 474–78. http://dx.doi.org/10.1109/18.904565.

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19

Ahituv, Niv, Yeheskel Lapid, and Seev Neumann. "Processing encrypted data." Communications of the ACM 30, no. 9 (September 1987): 777–80. http://dx.doi.org/10.1145/30401.30404.

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20

Hagaman, Edward W., Jeffrey C. Hoch, and Alan S. Stern. "NMR Data Processing." Radiation Research 147, no. 2 (February 1997): 272. http://dx.doi.org/10.2307/3579432.

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21

Scherr, A. L. "Distributed data processing." IBM Systems Journal 38, no. 2.3 (1999): 354–74. http://dx.doi.org/10.1147/sj.382.0354.

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22

Satoh, Ichiro. "Pervasive Data Processing." Procedia Computer Science 63 (2015): 16–23. http://dx.doi.org/10.1016/j.procs.2015.08.307.

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23

Bouchachia, Abdelhamid. "Online data processing." Neurocomputing 126 (February 2014): 116–17. http://dx.doi.org/10.1016/j.neucom.2013.05.008.

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24

Cameron, David G. "Advanced data processing." Mikrochimica Acta 93, no. 1-6 (January 1987): 229–39. http://dx.doi.org/10.1007/bf01201692.

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25

Gough, T. G. "Data Processing Methods." Data Processing 27, no. 5 (June 1985): 51. http://dx.doi.org/10.1016/0011-684x(85)90145-5.

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26

Richards, B. "Data processing mathematics." Data Processing 28, no. 3 (April 1986): 162. http://dx.doi.org/10.1016/0011-684x(86)90015-8.

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27

Lepper, AM. "Data Processing Budgets." Data Processing 28, no. 2 (March 1986): 103. http://dx.doi.org/10.1016/0011-684x(86)90114-0.

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28

Campbell-Kelly, Martin. "Victorian data processing." Communications of the ACM 53, no. 10 (October 2010): 19–21. http://dx.doi.org/10.1145/1831407.1831417.

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29

Starck, J. L., A. Abergel, H. Aussel, M. Sauvage, R. Gastaud, A. Claret, X. Desert, C. Delattre, and E. Pantin. "ISOCAM data processing." Astronomy and Astrophysics Supplement Series 134, no. 1 (January 1999): 135–48. http://dx.doi.org/10.1051/aas:1999129.

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30

McIntyre, D. J. O. "NMR data processing." NMR in Biomedicine 12, no. 6 (October 1999): 405–6. http://dx.doi.org/10.1002/(sici)1099-1492(199910)12:6<405::aid-nbm590>3.0.co;2-c.

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31

Görlitz, L., B. H. Menze, B. M. Kelm, and F. A. Hamprecht. "Processing spectral data." Surface and Interface Analysis 41, no. 8 (August 2009): 636–44. http://dx.doi.org/10.1002/sia.3066.

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32

KRYVENCHUK, Yurii, and Mykhailo-Yurii KHANAS. "ALGORITHM OF DATA MINING AND PROCESSING OF RELATED DATA IN SOCIAL NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 115–18. http://dx.doi.org/10.31891/2307-5732-2022-311-4-115-118.

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We live in a time of rapid growth of information technology, which is firmly entrenched in our daily lives. It is simply impossible to imagine a modern person without social networks, because they perform a communicative and informational function, namely: communication, information retrieval, news exchange, etc. Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. So, there is a lot of information available to the user, which is difficult to identify something specific and necessary in the usual way viewing. Accordingly, there is a need for technologies that can quickly process large amounts of data and highlight only the information that is useful to a particular user. This technology called recommender systems. It automatically suggest items to users that might be interesting for them. Due to the desire to unite people with common interests, it is relevant to develop a recommendation system based on social networks that help in personification of the user and compilation of his psychotype using his profile. The paper has description and results of the creation of recommendation system. The basis of this work is one of the algorithms used in recommendation systems – the recommendation system is based on content filtering. It analyzes users’ Twitter posts and calculates their interests. If we consider all the words, our model will not have good results and do not pay attention to what is important to use. Therefore, the most important step is always filtering data, so the number one task is to speed up the time of filtering text and retrieving data from the social network for further processing. The feature of this system is that this algorithm uses parallel calculations and frequency analysis of the text.

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33

Nikolova, Evgeniya, Mariya Monova-Zheleva, and Yanislav Zhelev. "Personal Data Processing in a Digital Educational Environment." Mathematics and Informatics LXV, no. 4 (August 30, 2022): 365–78. http://dx.doi.org/10.53656/math2022-4-4-per.

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New technologies provide innovative spaces for cooperation and communication between employers and employees, citizens and structures, educators, and learners. Data protection issues have always been key to education providers, but the proliferation of online learning forms and formats poses new and unique challenges in this regard. When introducing a new technology that involves the collection of sensitive data, the General Data Protection Regulation (GDPR) of the European Parliament and the Council of the European Union requires the identification and mitigation of all risks that could lead to the misuse of personal data. The article discusses some critical points regarding the application of GDPR in online learning. The goal of this article is to investigate the vulnerabilities to personal data security during online learning and to identify methods that schools and universities may apply to ensure that personal data are kept private while students utilize online platforms to learn. For the purposes of the research, the published privacy, and data protection policies of all Bulgarian universities as well as papers on how universities could adapt to the new EU General Data Protection Regulation were revised and analysed. Best practices of some foreign universities in this regard were studied as well.

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34

Ganachari, Girish. "Event-Driven Data Processing for Business Intelligence Reporting." International Journal of Science and Research (IJSR) 8, no. 12 (December 5, 2019): 2077–80. http://dx.doi.org/10.21275/sr24801085403.

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35

MARTYNIUK, Tatiana, Andrii KOZHEMIAKO, Bohdan KRUKIVSKYI, and Antonina BUDA. "ASSOCIATIVE OPERATIONS BASED ON DIFFERENCE-SLICE DATA PROCESSING." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 159–63. http://dx.doi.org/10.31891/2307-5732-2022-311-4-159-163.

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Associative operations are effectively used to solve such application problems as sorting, searching for certain features, and identifying extreme (maximum/minimum) elements in data sets. Thus, determining the maximum number as a result of sorting a numerical array is an acceptable operation in implementing the competition mechanism in neural networks. In addition, determining the average number in a numerical series by sorting significantly speeds up the process of median filtering of images and signals. In this case, the implementation of median filtering requires the use of sorting with the ranking of the elements of the number array. This paper analyses the possibilities of associative operations implementing the elements of a vector (one-dimensional) array of numbers based on processing by difference slices (DS). A simplified description of DS processing with a selection of the common part of the elements of the vector and the difference slice formed from its elements is given. In addition, elements of the binary mask matrix are used as an example of a topological feature matrix. The proposed approach allows for the formation of the ranks of the elements of the initial vector, as a result of sorting in ascending order of their numerical values. The paper shows a schematic representation of the process of DS processing, as well as an example of DS processing of a number vector in the form of a table, which shows the formation sequence of numbers of the sorted array and the ranks of numbers of the initial array. Therefore, the proposed use of topological features allows to determine the comparative relations between the elements of the numerical array in the process of spatially distributed DS processing, as well as to confirm the versatility of this approach.

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Johnson, Sarah L. "Quantum Machine Learning Algorithms for Big Data Processing." International Journal of Innovative Computer Science and IT Research 1, no. 02 (January 1, 2025): 1–11. https://doi.org/10.63665/ijicsitr.v1i02.04.

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Quantum Machine Learning (QML) is a new discipline that unites artificial intelligence and quantum computing and can address computational problems of big data analysis. Traditional machine learning algorithms may be pushed to their limits in dealing with the increased complexity and scale of today's data sets and thus are unable to find useful insights within a reasonable time frame. Quantum computing, capable of tapping quantum mechanical processes like superposition and entanglement, is capable of turning this field upside down. In this paper, the concepts behind quantum computing are discussed and how machine learning could be used using the assistance of quantum algorithms in order to better deal with big data. It explains the most optimal quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Principal Component Analysis (QPCA), and Quantum k-Means Clustering, and why they are better and faster compared to their classical counterparts. It also explores actual applications in medicine, finance, and artificial intelligence. It also addresses the limits and disadvantages of existing quantum technology like hardware limitations, noise, and complexity of algorithms. Last but not least, it also considers the future direction of trends within the field, with emphasis placed on hybrid quantum-classical systems and quantum machine learning application within the construction of big data analysis.

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37

Kalyan Uppala, Venkat. "Architecting a Cloud Data Platform: Bridging Storage and Compute for Enhanced Data Processing." International Journal of Science and Research (IJSR) 8, no. 1 (January 5, 2019): 2281–89. http://dx.doi.org/10.21275/sr24810090304.

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38

Rostek, Katarzyna. "Data Analytical Processing in Data Warehouses." Foundations of Management 2, no. 1 (January 1, 2010): 99–116. http://dx.doi.org/10.2478/v10238-012-0023-x.

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Data Analytical Processing in Data Warehouses The article presents issues connected with processing information from data warehouses (the analytical enterprise databases) and two basic types of analytical data processing in data warehouse. The genesis, main definitions, scope of application and real examples from business implementations will be described for each type of analysis. There will be presented copyrighted method of knowledge discovering in databases, together with practical guidelines for its proper and effective use in the enterprise.

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39

K., Dharani* &. Dr. G. Abel Thangaraja**. "BIG DATA PREPROCESSING USING ENHANCED DATA QUALITY RULES DISCOVERY MODEL (EDQRM)." International Journal of Engineering Research and Modern Education (IJERME) 8, no. 2 (October 10, 2023): 33–41. https://doi.org/10.5281/zenodo.8428545.

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In the Big Data Era, data is the center for any governmental, institutional, and private organization. Endeavors were equipped towards extricating profoundly important bits of knowledge that can't occur assuming data is of low quality. Hence, data quality (DQ) is considered as a vital component in big data processing. In this stage, bad quality data isn't entered to the Big Data value chain. This paper, proposed the Enhanced data quality Rules discovery model (EDQRM) for assessment of quality and Big Data pre-processing. EDQRM discovery model to improve and precisely focus on the pre-processing exercises in view of quality requirements. Characterized, a bunch of pre-processing exercises related with data quality dimensions (DQD's) to automatize the EDQRM process. Rules improvement is applied on approved rules to stay away from multi-passes pre-processing exercises and disposes of copy rules. Directed tests showed an expanded quality scores in the wake of applying the found and optimized EDQRM's on data.

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Hwa Choi, Hyun, Kangho Kim, and Seung Jo Bae. "A Remote Memory System for High Performance Data Processing." International Journal of Future Computer and Communication 4, no. 1 (February 2015): 50–54. http://dx.doi.org/10.7763/ijfcc.2015.v4.354.

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41

Osborn, Wendy. "Unbounded Spatial Data Stream Query Processing using Spatial Semijoins." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 02 (March 1, 2021): 33–41. http://dx.doi.org/10.5383/juspn.15.02.005.

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In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified.

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Proshin, A. A., A. M. Matveev, A. V. Kashnitskiy, and M. A. Burtsev. "Satellite data efficient processing with dynamic block archive access." Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 17, no. 6 (2020): 56–60. http://dx.doi.org/10.21046/2070-7401-2020-17-6-56-60.

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43

Penížek, V., and L. Borůvka. "Processing of conventional soil survey data using geostatistical methods." Plant, Soil and Environment 50, No. 8 (December 10, 2011): 352–57. http://dx.doi.org/10.17221/4043-pse.

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The aim of this study is to find a suitable treatment of conventional soil survey data for geostatistical exploitation. Different aims and methods of a conventional soil survey and the geostatistics can cause some problems. The spatial variability of clay content and pH for an area of 543 km<sup>2</sup> was described by variograms. First the original untreated data were used. Then the original data were treated to overcome the problems that arise from different aims of conventional soil survey and geostatistical approaches. Variograms calculated from the original data, both for clay content and pH, showed a big portion of nugget variability caused by a few extreme values. Simple exclusion of data representing some specific soil units (local extremes, non-zonal soils) did not bring almost any improvement. Exclusion of outlying values from the first three lag classes that were the most influenced due to a relatively big portion of these extreme values provided much better results. The nugget decreased from pure nugget to 50% of the sill variability for clay content and from 81 to 23% for pH.

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MILOSAN, Ioan. "STATISTICAL PROCESSING OF EXPERIMENTAL DATA USING ANALYSIS OF VARIANCE." SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 18, no. 1 (June 24, 2016): 489–96. http://dx.doi.org/10.19062/2247-3173.2016.18.1.67.

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More, Prof Vijay, Ms Ankita Shetty, and Ms Aishwarya Mapara Mr Rahul Ghuge Mr Rohit Sharma. "Employee Data Mining Based on Text and Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 379–81. http://dx.doi.org/10.31142/ijtsrd10791.

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Ch, Bilal Hussain. "Securing Cloud Data with the Application of Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 297–301. http://dx.doi.org/10.31142/ijtsrd18454.

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Maruddani, Baso, and Efri Sandi. "The Development of Ground Penetrating Radar (GPR) Data Processing." International Journal of Machine Learning and Computing 9, no. 6 (December 2019): 768–73. http://dx.doi.org/10.18178/ijmlc.2019.9.6.871.

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48

Ummatovich, Eshonqulov Sherzod. "DATA FILTERING IN THE IMAGE PROCESSING TOOLBOX(IPT) ENVIRONMENT." American Journal of Applied Science and Technology 4, no. 3 (March 1, 2024): 24–28. http://dx.doi.org/10.37547/ajast/volume04issue03-05.

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Analysis of the Aydar-Arnasoy lake system in the environment of IPT. A brief hydrogeological description of the Aydar-Arnasoy lake system. Digital filtering of the Aydar-Arnasoy lake system image. Development of a digital model of the image of the Aydar-Arnasoy lake system using discrete Fourier transformation.

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Skoropad, Pylyp, and Andrii Yuras. "MACHINE LEARNING METHODS IN THERMOMETERS’ DATA EXTRACTION AND PROCESSING." Measuring Equipment and Metrology 85, no. 2 (2024): 40–45. http://dx.doi.org/10.23939/istcmtm2024.02.040.

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Research focuses on developing an all-encompassing algorithm for efficiently extracting, processing, and analyz- ing data about thermometers. The examination involves the application of a branch of artificial intelligence, in particular machine learning (ML) methods, as a means of automating processes. Such methods facilitate the identification and aggregation of pertinent data, the detection of gaps, and the conversion of unstructured text into an easily analyzable structured format. The paper details the employment of reinforcement learning for the automatic extraction of information from diverse resources, natural language pro- cessing for analysis of textual values, and the decision tree method for discerning patterns within the data.

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Waller, John, Nikolay Volik, Federico Mendez, and Andrea Hahn. "GBIF Data Processing and Validation." Biodiversity Information Science and Standards 5 (September 27, 2021): e75686. https://doi.org/10.3897/biss.5.75686.

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GBIF (Global Biodiversity Information Facility) is the largest data aggregator of biological occurrences in the world. GBIF was officially established in 2001 and has since aggregated 1.8 billion occurrence records from almost 2000 publishers. GBIF relies heavily on Darwin Core (DwC) for organising the data it receives. GBIF Data Processing PipelinesEvery single occurrence record that gets published to GBIF goes through a series of three processing steps until it becomes available on GBIF.org.source downloadingparsing into verbatim occurrences interpreting verbatim valuesOnce all records are available in the standard verbatim form, they go through a set of interpretations.In 2018, GBIF processing underwent a significant rewrite in order to improve speed and maintainablility. One of the main goals of this rewrite was to improve the consistency between GBIF's processing and that of the Living Atlases. In connection with this, GBIF's current data validator fell out of sync with GBIF pipelines processing.New GBIF Data ValidatorThe current GBIF data validator is a service that allows anyone with a GBIF-relevant dataset to receive a report on the syntactical correctness and the validity of the content contained within the dataset. By submitting a dataset to the validator, users can go through the validation and interpretation procedures usually associated with publishing in GBIF and quickly determine potential issues in data, without having to publish it. GBIF is planning to rework the current validator because the current validator does not exactly match current GBIF pipelines processing.Planned Changes The new validator will match the processing of the GBIF pipelines project.Validations will be saved and show up on user pages similar to the way downloads and derived datasets appear now (no more bookmarking validations!)A downloadable report of issues found will be produced.Suggested Changes/Ideas One of the main guiding philosophies for the new validator user interface will be avoiding information overload. The current validator is often quite verbose in its feedback, highlighting data issues that may or may not be fixable or particularly important. The new validator will:generate a map of record geolocations;give users issues by order of importance;give "What", "Where", "When" flags priority;give some possible solutions or suggested fixes for flagged records.We see the hosted portal environment as a way to quickly implement a pre-publication validation environment that is interactive and visual. Potential New Data Quality Flags The GBIF team has been compiling a list of new data quality flags. Not all of the suggested flags are easy to implement, so GBIF cannot promise the flags will get implemented, even if they are a great idea. The advantage of the new processing pipelines is that almost any new data quality flag or processing step in pipelines will be available for the data validator. Easy new potential flags:country centroid flag: Country/province centroids are a known data quality problem.any zero coordinate flag: Sometimes publishers leave either the latitude or longitude field as zero when it should have been left blank or NULL.default coordinate uncertainty in meters flag: Sometimes a default value or code is used for dwc:coordinateUncertaintyInMeters, which might indicate that it is incorrect. This is especially the case for values 301, 3036, 999, 9999.no higher taxonomy flag: Often publishers will leave out the higher taxonomy of a record. This can cause problems for matching to the GBIF backbone taxonomy..null coordinate uncertainty in meters flag: There has been some discussion that GBIF should encourage publishers more to fill in dwc:coordinateUncertaintyInMeters. This is because every record, even ones taken from a Global Positioning System (GPS) reading, have an associated dwc:coordinateUncertaintyInMetersIt is also nice when a data quality flag has an escape hatch, such that a data publisher can get rid of false positives or remove a flag through filling in a value.Batch-type validations that are doable for pipelines, but probably not in the validator include:outlier: Outliers are a known data quality problem. There are generally two types of outliers: environmental outliers and distance outliers. Currently GBIF does not flag either type of outlier.record is sensitive species: A sensitive species would be a record where the species is considered vulnerable in some way. Usually this is due to poaching threat or the species is only found in one area.gridded dataset: Rasterized or gridded datasets are common on GBIF. These are datasets where location information is pinned to a low-resolution grid. This is already available with an experimental API (Application Programming Interface).ConclusionData quality and data processing are moving targets. Variable source data will always be an issue when aggregating large amounts of data. With GBIF's new processing architecture, we hope that new features and data quality flags can be added more easily. Time and staffing resources are always in short supply, so we plan to prioritise the feedback we give to publishers, in order for them to work on correcting the most important and fixable issues. With new GBIF projects like the vocabulary server, we also hope that GBIF data processing can have more community participation.

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