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Relevant bibliographies by topics / Cleaning of data

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Academic literature on the topic 'Cleaning of data'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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Journal articles on the topic "Cleaning of data"

1

Pahwa, Payal, and Rashmi Chhabra. "BST Algorithm for Duplicate Elimination in Data Warehouse." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 4, no. 1 (2013): 190–97. http://dx.doi.org/10.24297/ijmit.v4i1.4636.

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Data warehousing is an emerging technology and has proved to be very important for an organization. Today every business organization needs accurate and large amount of information to make proper decisions. For taking the business decisions the data should be of good quality. To improve the data quality data cleansing is needed. Data cleansing is fundamental to warehouse data reliability, and to data warehousing success. There are various methods for datacleansing. This paper addresses issues related data cleaning. We focus on the detection of duplicate records. Also anefficient algorithm for

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Chu, Xu, and Ihab F. Ilyas. "Qualitative data cleaning." Proceedings of the VLDB Endowment 9, no. 13 (2016): 1605–8. http://dx.doi.org/10.14778/3007263.3007320.

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Broman, Karl W. "Cleaning genotype data." Genetic Epidemiology 17, S1 (1999): S79—S83. http://dx.doi.org/10.1002/gepi.1370170714.

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Singh, Mohini. "Cleaning Up Company Data." CFA Institute Magazine 27, no. 1 (2016): 53. http://dx.doi.org/10.2469/cfm.v27.n1.18.

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Geerts, Floris, Giansalvatore Mecca, Paolo Papotti, and Donatello Santoro. "Cleaning data with Llunatic." VLDB Journal 29, no. 4 (2019): 867–92. http://dx.doi.org/10.1007/s00778-019-00586-5.

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Zhang, Aoqian, Shaoxu Song, Jianmin Wang, and Philip S. Yu. "Time series data cleaning." Proceedings of the VLDB Endowment 10, no. 10 (2017): 1046–57. http://dx.doi.org/10.14778/3115404.3115410.

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Holstad, Mark S. "Data Driven Interceptor Cleaning." Proceedings of the Water Environment Federation 2010, no. 8 (2010): 7636–64. http://dx.doi.org/10.2175/193864710798207792.

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Karr, Alan F. "Exploratory Data Mining and Data Cleaning." Journal of the American Statistical Association 101, no. 473 (2006): 399. http://dx.doi.org/10.1198/jasa.2006.s81.

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Rahul, Kumar, and Rohitash Kumar Banyal. "Detection and Correction of Abnormal Data with Optimized Dirty Data: A New Data Cleaning Model." International Journal of Information Technology & Decision Making 20, no. 02 (2021): 809–41. http://dx.doi.org/10.1142/s0219622021500188.

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Each and every business enterprises require noise-free and clean data. There is a chance of an increase in dirty data as the data warehouse loads and refreshes a large quantity of data continuously from the various sources. Hence, in order to avoid the wrong conclusions, the data cleaning process becomes a vital one in various data-connected projects. This paper made an effort to introduce a novel data cleaning technique for the effective removal of dirty data. This process involves the following two steps: (i) dirty data detection and (ii) dirty data cleaning. The dirty data detection process

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D. Pandya, Sohil, and Paresh V. Virparia. "Context Free Data Cleaning and its Application in Mechanism for Suggestive Data Cleaning." International Journal of Information Science 1, no. 1 (2012): 32–35. http://dx.doi.org/10.5923/j.ijis.20110101.05.

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Dissertations / Theses on the topic "Cleaning of data"

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Li, Lin. "Data quality and data cleaning in database applications." Thesis, Edinburgh Napier University, 2012. http://researchrepository.napier.ac.uk/Output/5788.

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Today, data plays an important role in people's daily activities. With the help of some database applications such as decision support systems and customer relationship management systems (CRM), useful information or knowledge could be derived from large quantities of data. However, investigations show that many such applications fail to work successfully. There are many reasons to cause the failure, such as poor system infrastructure design or query performance. But nothing is more certain to yield failure than lack of concern for the issue of data quality. High quality of data is a key to to

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2

Liebchen, Gernot Armin. "Data cleaning techniques for software engineering data sets." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/5951.

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Data quality is an important issue which has been addressed and recognised in research communities such as data warehousing, data mining and information systems. It has been agreed that poor data quality will impact the quality of results of analyses and that it will therefore impact on decisions made on the basis of these results. Empirical software engineering has neglected the issue of data quality to some extent. This fact poses the question of how researchers in empirical software engineering can trust their results without addressing the quality of the analysed data. One widely accepted

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Iyer, Vasanth. "Ensemble Stream Model for Data-Cleaning in Sensor Networks." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/973.

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Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the

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4

Kokkonen, H. (Henna). "Effects of data cleaning on machine learning model performance." Bachelor's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201911133081.

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Abstract. This thesis is focused on the preprocessing and challenges of a university student data set and how different levels of data preprocessing affect the performance of a prediction model both in general and in selected groups of interest. The data set comprises the students at the University of Oulu who were admitted to the Faculty of Information Technology and Electrical Engineering during years 2006–2015. This data set was cleaned at three different levels, which resulted in three differently processed data sets: one set is the original data set with only basic cleaning, the second ha

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Jia, Xibei. "From relations to XML : cleaning, integrating and securing data." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3161.

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While relational databases are still the preferred approach for storing data, XML is emerging as the primary standard for representing and exchanging data. Consequently, it has been increasingly important to provide a uniform XML interface to various data sources— integration; and critical to protect sensitive and confidential information in XML data — access control. Moreover, it is preferable to first detect and repair the inconsistencies in the data to avoid the propagation of errors to other data processing steps. In response to these challenges, this thesis presents an integrated framewor

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Bischof, Stefan, Benedikt Kämpgen, Andreas Harth, Axel Polleres, and Patrik Schneider. "Open City Data Pipeline." Department für Informationsverarbeitung und Prozessmanagement, WU Vienna University of Economics and Business, 2017. http://epub.wu.ac.at/5438/1/city%2Dqb%2Dpaper.pdf.

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Statistical data about cities, regions and at country level is collected for various purposes and from various institutions. Yet, while access to high quality and recent such data is crucial both for decision makers as well as for the public, all to often such collections of data remain isolated and not re-usable, let alone properly integrated. In this paper we present the Open City Data Pipeline, a focused attempt to collect, integrate, and enrich statistical data collected at city level worldwide, and republish this data in a reusable manner as Linked Data. The main feature of the Open C

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Pumpichet, Sitthapon. "Novel Online Data Cleaning Protocols for Data Streams in Trajectory, Wireless Sensor Networks." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/1004.

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The promise of Wireless Sensor Networks (WSNs) is the autonomous collaboration of a collection of sensors to accomplish some specific goals which a single sensor cannot offer. Basically, sensor networking serves a range of applications by providing the raw data as fundamentals for further analyses and actions. The imprecision of the collected data could tremendously mislead the decision-making process of sensor-based applications, resulting in an ineffectiveness or failure of the application objectives. Due to inherent WSN characteristics normally spoiling the raw sensor readings, many researc

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Artilheiro, Fernando Manuel Freitas. "Analysis and procedures of multibeam data cleaning for bathymetric charting." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq23776.pdf.

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Ramakrishnan, Ranjani. "A data cleaning and annotation framework for genome-wide studies." Full text open access at:, 2007. http://content.ohsu.edu/u?/etd,263.

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Hallström, Fredrik, and David Adolfsson. "Data Cleaning Extension on IoT Gateway : An Extended ThingsBoard Gateway." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-84376.

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Machine learning algorithms that run on Internet of Things sensory data requires high data quality to produce relevant output. By providing data cleaning at the edge, cloud infrastructures performing AI computations is relieved by not having to perform preprocessing. The main problem connected with edge cleaning is the dependency on unsupervised pre-processing as it leaves no guarantee of high quality output data. In this thesis an IoT gateway is extended to provide cleaning and live configuration of cleaning parameters before forwarding the data to a server cluster. Live configuration is impl

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Books on the topic "Cleaning of data"

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Exploratory data mining and data cleaning. John Wiley & Sons, 2004.

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Best practices in data cleaning. SAGE, 2013.

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Institute, SAS, ed. Cody's data cleaning techniques using SAS. 2nd ed. SAS Institute Inc., 2008.

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Institute, SAS, ed. Cody's data cleaning techniques using SAS software. SAS Institute Inc., 1999.

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de Jonge, Edwin, and Mark van der Loo. Statistical Data Cleaning with Applications in R. John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781118897126.

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Buttrey, Samuel. A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R. John Wiley & Sons Ltd, 2017. http://dx.doi.org/10.1002/9781119080053.

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7

Gibbs, Roger. A review of the data available on cleaning services. Department of Trade and Industry?], 1987.

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8

Kimball, Ralph. The data warehouse ETL toolkit: Practical techniques for extracting, cleaning, conforming, and delivering data. Wiley, 2004.

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9

Waschbusch, Robert J. Data and methods of a 1999-2000 street sweeping study on an urban freeway in Milwaukee County, Wisconsin. U.S. Dept. of the Interior, U.S. Geological Survey, 2003.

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H, Long Stephen, Marquis M. Susan, Robert Wood Johnson Foundation, and Rand Corporation, eds. Data cleaning procedures for the 1993 Robert Wood Johnson Foundation family health insurance survey. Rand, 1997.

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Book chapters on the topic "Cleaning of data"

1

Van den Broeck, Jan, and Lars Thore Fadnes. "Data Cleaning." In Epidemiology: Principles and Practical Guidelines. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5989-3_20.

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Chu, Xu. "Data Cleaning." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_3.

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Chu, Xu. "Data Cleaning." In Encyclopedia of Big Data Technologies. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_3-1.

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Whitmore, Nathan. "Data cleaning." In R for Conservation and Development Projects. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429262180-ch10.

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Ganti, Venkatesh. "Data Cleaning." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_592.

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Ganti, Venkatesh. "Data Cleaning." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_592.

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Winson-Geideman, Kimberly, Andy Krause, Clifford A. Lipscomb, and Nicholas Evangelopoulos. "Data cleaning." In Real Estate Analysis in the Information Age. Routledge, 2017. http://dx.doi.org/10.4324/9781315311135-9.

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Ganti, Venkatesh. "Data Cleaning." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_592-2.

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Costello, Tim, and Lori Blackshear. "Cleaning." In Prepare Your Data for Tableau. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5497-4_7.

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Li, Deren, Shuliang Wang, and Deyi Li. "Spatial Data Cleaning." In Spatial Data Mining. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48538-5_4.

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Conference papers on the topic "Cleaning of data"

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Chu, Xu, Ihab F. Ilyas, Sanjay Krishnan, and Jiannan Wang. "Data Cleaning." In SIGMOD/PODS'16: International Conference on Management of Data. ACM, 2016. http://dx.doi.org/10.1145/2882903.2912574.

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Volkovs, Maksims, Fei Chiang, Jaroslaw Szlichta, and Renee J. Miller. "Continuous data cleaning." In 2014 IEEE 30th International Conference on Data Engineering (ICDE). IEEE, 2014. http://dx.doi.org/10.1109/icde.2014.6816655.

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Alipour-Langouri, Morteza, Zheng Zheng, Fei Chiang, Lukasz Golab, and Jaroslaw Szlichta. "Contextual Data Cleaning." In 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW). IEEE, 2018. http://dx.doi.org/10.1109/icdew.2018.00010.

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Tang, Jie, Hang Li, Yunbo Cao, and Zhaohui Tang. "Email data cleaning." In Proceeding of the eleventh ACM SIGKDD international conference. ACM Press, 2005. http://dx.doi.org/10.1145/1081870.1081926.

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Zhang, Aoqian, Shaoxu Song, and Jianmin Wang. "Sequential Data Cleaning." In SIGMOD/PODS'16: International Conference on Management of Data. ACM, 2016. http://dx.doi.org/10.1145/2882903.2915233.

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Johnson, Theodore, and Tamraparni Dasu. "Data quality and data cleaning." In the 2003 ACM SIGMOD international conference on. ACM Press, 2003. http://dx.doi.org/10.1145/872757.872875.

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Parulian, Nikolaus N., and Bertram Ludascher. "Towards Transparent Data Cleaning: The Data Cleaning Model Explorer (DCM/X)." In 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL). IEEE, 2021. http://dx.doi.org/10.1109/jcdl52503.2021.00054.

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Hua, Ming, and Jian Pei. "Cleaning disguised missing data." In the 13th ACM SIGKDD international conference. ACM Press, 2007. http://dx.doi.org/10.1145/1281192.1281294.

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Tang, Nan. "Big RDF data cleaning." In 2015 31st IEEE International Conference on Data Engineering Workshops (ICDEW). IEEE, 2015. http://dx.doi.org/10.1109/icdew.2015.7129549.

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Musleh, Mashaal, Mourad Ouzzani, Nan Tang, and AnHai Doan. "CoClean: Collaborative Data Cleaning." In SIGMOD/PODS '20: International Conference on Management of Data. ACM, 2020. http://dx.doi.org/10.1145/3318464.3384698.

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Reports on the topic "Cleaning of data"

1

Research Institute (IFPRI), International Food Policy. A guide to data cleaning using Stata. International Food Policy Research Institute, 2018. http://dx.doi.org/10.2499/1024320680.

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Bollinger, Christopher, and Amitabh Chandra. Iatrogenic Specification Error: A Cautionary Tale of Cleaning Data. National Bureau of Economic Research, 2003. http://dx.doi.org/10.3386/t0289.

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Marinshaw, Richard J., and Hazem Qawasmeh. Characterizing Water Use at Mosques in Abu Dhabi. RTI Press, 2020. http://dx.doi.org/10.3768/rtipress.2020.mr.0042.2004.

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In areas where Muslims constitute much of the population, mosques can account for a significant portion of overall water consumption. Among the various uses of water at mosques, ablution (i.e., ritual cleansing) is generally assumed to be the largest, by far. As part of an initiative to reduce water consumption at mosques in Abu Dhabi, we collected data on ablution and other end uses for water from hundreds of mosques in and around Abu Dhabi City. This paper takes a closer look at how water is used at mosques in Abu Dhabi and presents a set of water use profiles that provide a breakdown of mos

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Martin, Mark, Lance Vowell, Ian King, and Chris Augustus. Automated Data Cleansing in Data Harvesting and Data Migration. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/949761.

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Adjaye-Gbewonyo, Dzifa, and Lindsey Back. Dental Care Utilization Among Children Aged 1–17 Years: United States, 2019 and 2020. National Center for Health Statistics (U.S.), 2021. http://dx.doi.org/10.15620/cdc:111175.

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This report uses data from the 2019 and 2020 National Health Interview Survey (NHIS) to describe recent changes in the prevalence of dental examinations or cleanings in the past 12 months among children aged 1–17 years by selected sociodemographic characteristics.

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