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Journal articles on the topic 'Multiple data sources'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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1

JAMES, Daniel, Raymond LEADBETTER, James LEE, Brendan BURKETT, and David THIEL. "B23 Integration of multiple data sources for swimming biomechanics." Proceedings of the Symposium on sports and human dynamics 2011 (2011): 364–66. http://dx.doi.org/10.1299/jsmeshd.2011.364.

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2

COSTAGLIOLA, GENNARO, and ERLAND JUNGERT. "Towards Querying Multiple Data Sources." Journal of Visual Languages & Computing 12, no. 1 (2001): 1–2. http://dx.doi.org/10.1006/jvlc.2000.0196.

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3

Saunders, Robert C., and Craig Anne Heflinger. "Integrating Data from Multiple Public Sources." Evaluation 10, no. 3 (2004): 349–65. http://dx.doi.org/10.1177/1356389004048282.

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4

Wang, Ruili, Wanting Ji, Mingzhe Liu, et al. "Review on mining data from multiple data sources." Pattern Recognition Letters 109 (July 2018): 120–28. http://dx.doi.org/10.1016/j.patrec.2018.01.013.

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5

Gross, Richard D., and Bruce A. Barton. "P74 Insuring data completeness from multiple data sources." Controlled Clinical Trials 16, no. 3 (1995): 114S. http://dx.doi.org/10.1016/0197-2456(95)90554-i.

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6

Sridevi, P. "Informative Knowledge Discovery using Multiple Data Sources, Multiple Features and Multiple Data Mining Techniques." IOSR Journal of Engineering 3, no. 01 (2013): 20–25. http://dx.doi.org/10.9790/3021-03142025.

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7

Nguyen, Khanh, and Jinli Cao. "Top-K data source selection for keyword queries over multiple XML data sources." Journal of Information Science 38, no. 2 (2012): 156–75. http://dx.doi.org/10.1177/0165551511435875.

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8

Saegusa, Takumi. "Semiparametric inference for merged data from multiple data sources." Journal of Statistical Planning and Inference 216 (January 2022): 1–14. http://dx.doi.org/10.1016/j.jspi.2021.05.002.

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9

Luaces, Miguel R., Jesús A. Fisteus, Luis Sánchez-Fernández, et al. "Accessible Routes Integrating Data from Multiple Sources." ISPRS International Journal of Geo-Information 10, no. 1 (2020): 7. http://dx.doi.org/10.3390/ijgi10010007.

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Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

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Sperhac, Arlene M., and Laura D. Goodwin. "Using multiple data sources for curriculum revision." Journal of Pediatric Health Care 17, no. 4 (2003): 169–75. http://dx.doi.org/10.1067/mph.2003.21.

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11

Baliga, Ganesh, Sanjay Jain, and Arun Sharma. "Learning from Multiple Sources of Inaccurate Data." SIAM Journal on Computing 26, no. 4 (1997): 961–90. http://dx.doi.org/10.1137/s0097539792239461.

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12

Horwitz, Ralph I. "Comparison of epidemiologic data from multiple sources." Journal of Chronic Diseases 39, no. 11 (1986): 889–96. http://dx.doi.org/10.1016/0021-9681(86)90037-8.

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13

Ling, Charles X., and Qiang Yang. "Discovering Classification from Data of Multiple Sources." Data Mining and Knowledge Discovery 12, no. 2-3 (2006): 181–201. http://dx.doi.org/10.1007/s10618-005-0013-7.

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14

Zhang, Shichao, and Mohammed J. Zaki. "Mining Multiple Data Sources: Local Pattern Analysis." Data Mining and Knowledge Discovery 12, no. 2-3 (2006): 121–25. http://dx.doi.org/10.1007/s10618-006-0041-y.

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15

Wheeler, Matthew W., and A. John Bailer. "Benchmark Dose Estimation Incorporating Multiple Data Sources." Risk Analysis 29, no. 2 (2009): 249–56. http://dx.doi.org/10.1111/j.1539-6924.2008.01144.x.

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16

Tu, Doan Quang, A. S. M. Kayes, Wenny Rahayu, and Kinh Nguyen. "IoT streaming data integration from multiple sources." Computing 102, no. 10 (2020): 2299–329. http://dx.doi.org/10.1007/s00607-020-00830-9.

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17

Ramkumar, T., S. Hariharan, and S. Selvamuthukumaran. "A survey on mining multiple data sources." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, no. 1 (2012): 1–11. http://dx.doi.org/10.1002/widm.1077.

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18

Seedah, Dan P. K., Bharathwaj Sankaran, and William J. O'Brien. "Approach to Classifying Freight Data Elements across Multiple Data Sources." Transportation Research Record: Journal of the Transportation Research Board 2529, no. 1 (2015): 56–65. http://dx.doi.org/10.3141/2529-06.

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19

ARENS, YIGAL, CHIN Y. CHEE, CHUN-NAN HSU, and CRAIG A. KNOBLOCK. "RETRIEVING AND INTEGRATING DATA FROM MULTIPLE INFORMATION SOURCES." International Journal of Cooperative Information Systems 02, no. 02 (1993): 127–58. http://dx.doi.org/10.1142/s0218215793000071.

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With the current explosion of data, retrieving and integrating information from various sources is a critical problem. Work in multidatabase systems has begun to address this problem, but it has primarily focused on methods for communicating between databases and requires significant effort for each new database added to the system. This paper describes a more general approach that exploits a semantic model of a problem domain to integrate the information from various information sources. The information sources handled include both databases and knowledge bases, and other information sources (e.g. programs) could potentially be incorporated into the system. This paper describes how both the domain and the information sources are modeled, shows how a query at the domain level is mapped into a set of queries to individual information sources, and presents algorithms for automatically improving the efficiency of queries using knowledge about both the domain and the information sources. This work is implemented in a system called SIMS and has been tested in a transportation planning domain using nine Oracle databases and a Loom knowledge base.

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20

Marques, Nubia, Carla Danielle de Melo Soares, Daniel de Melo Casali, et al. "Retrieving biodiversity data from multiple sources: making secondary data standardised and accessible." Biodiversity Data Journal 12 (September 20, 2024): e133775. https://doi.org/10.3897/BDJ.12.e133775.

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Biodiversity data, particularly species occurrence and abundance, are indispensable for testing empirical hypothesis in natural sciences. However, datasets built for research programmes do not often meet FAIR (findable, accessible, interoperable and reusable) principles, which raises questions about data quality, accuracy and availability. The 21st century has markedly been a new era for data science and analytics and every effort to aggregate, standardise, filter and share biodiversity data from multiple sources have become increasingly necessary. In this study, we propose a framework for refining and conforming secondary biodiversity data to FAIR standards to make them available for use such as macroecological modelling and other studies. We relied on a Darwin Core base model to standardise and further facilitate the curation and validation of data related including the occurrence and abundance of multiple taxa of a region that encompasses estuarine ecosystems in an ecotonal area bordering the easternmost Amazonia. We further discuss the significance of feeding standardised public data repositories to advance scientific progress and highlight their role in contributing to the biodiversity management and conservation.

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21

Gao, Feng. "Fitting smoothing splines to data from multiple sources." Communications in Statistics - Theory and Methods 23, no. 6 (1994): 1665–98. http://dx.doi.org/10.1080/03610929408831346.

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22

Chen, Bolin, Jianxin Wang, Min Li, and Fang-Xiang Wu. "Identifying disease genes by integrating multiple data sources." BMC Medical Genomics 7, Suppl 2 (2014): S2. http://dx.doi.org/10.1186/1755-8794-7-s2-s2.

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23

SPERHAC, A., and L. GOODWIN. "Using multiple data sources for curriculum revision*1." Journal of Pediatric Health Care 17, no. 4 (2003): 169–75. http://dx.doi.org/10.1016/s0891-5245(02)88321-3.

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24

Huang, Pipei, Gang Wang, and Shiyin Qin. "Boosting for transfer learning from multiple data sources." Pattern Recognition Letters 33, no. 5 (2012): 568–79. http://dx.doi.org/10.1016/j.patrec.2011.11.023.

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25

Wang, Yuqi, Jiannong Cao, Wengen Li, Tao Gu, and Wenzhong Shi. "Exploring traffic congestion correlation from multiple data sources." Pervasive and Mobile Computing 41 (October 2017): 470–83. http://dx.doi.org/10.1016/j.pmcj.2017.03.015.

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26

Kong, Chao, Ming Gao, Chen Xu, Yunbin Fu, Weining Qian, and Aoying Zhou. "EnAli: entity alignment across multiple heterogeneous data sources." Frontiers of Computer Science 13, no. 1 (2018): 157–69. http://dx.doi.org/10.1007/s11704-017-6561-3.

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27

Koch-Henriksen, Nils. "Alternative data sources for estimating multiple sclerosis prevalence." Multiple Sclerosis Journal 19, no. 13 (2013): 1682–83. http://dx.doi.org/10.1177/1352458513511879.

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28

Pirc, Jure, Goran Turk, and Marijan Žura. "Highway travel time estimation using multiple data sources." IET Intelligent Transport Systems 10, no. 10 (2016): 649–57. http://dx.doi.org/10.1049/iet-its.2015.0211.

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29

Casstevens, T. M., and E. S. Buckler. "GDPC: connecting researchers with multiple integrated data sources." Bioinformatics 20, no. 16 (2004): 2839–40. http://dx.doi.org/10.1093/bioinformatics/bth277.

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30

Bellier, Edwige, Philipp Neubauer, Pascal Monestiez, et al. "Marine reserve spillover: Modelling from multiple data sources." Ecological Informatics 18 (November 2013): 188–93. http://dx.doi.org/10.1016/j.ecoinf.2013.09.004.

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31

Li, Chenwei, Zedao Liu, and Zemin Zhang. "Integrating human single-cell data from multiple sources." Quantitative Biology 10, no. 3 (2022): 299. http://dx.doi.org/10.15302/j-qb-022-0304.

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32

Caulkins, Jonathan P., Greg Midgette, and Peter Reuter. "Improving Opioid Use Estimates Through Multiple Data Sources." JAMA Health Forum 6, no. 5 (2025): e250806. https://doi.org/10.1001/jamahealthforum.2025.0806.

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33

Geys, Lotte, Tina Parciak, Ashkan Pirmani, et al. "The Multiple Sclerosis Data Alliance Catalogue." International Journal of MS Care 23, no. 6 (2021): 261–68. http://dx.doi.org/10.7224/1537-2073.2021-006.

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Abstract Background: One of the major objectives of the Multiple Sclerosis Data Alliance (MSDA) is to enable better discovery of multiple sclerosis (MS) real-world data (RWD). Methods: We implemented the MSDA Catalogue, which is available worldwide. The current version of the MSDA Catalogue collects descriptive information on governance, purpose, inclusion criteria, procedures for data quality control, and how and which data are collected, including the use of e-health technologies and data on collection of COVID-19 variables. The current cataloguing procedure is performed in several manual steps, securing an effective catalogue. Results: Herein we summarize the status of the MSDA Catalogue as of January 6, 2021. To date, 38 data sources across five continents are included in the MSDA Catalogue. These data sources differ in purpose, maturity, and variables collected, but this landscaping effort shows that there is substantial alignment on some domains. The MSDA Catalogue shows that personal data and basic disease data are the most collected categories of variables, whereas data on fatigue measurements and cognition scales are the least collected in MS registries/cohorts. Conclusions: The Web-based MSDA Catalogue provides strategic overview and allows authorized end users to browse metadata profiles of data cohorts and data sources. There are many existing and arising RWD sources in MS. Detailed cataloguing of MS RWD is a first and useful step toward reducing the time needed to discover MS RWD sets and promoting collaboration.

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34

Zeng, Erliang, Chengyong Yang, Tao Li, and Giri Narasimhan. "Clustering Genes Using Heterogeneous Data Sources." International Journal of Knowledge Discovery in Bioinformatics 1, no. 2 (2010): 12–28. http://dx.doi.org/10.4018/jkdb.2010040102.

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Clustering of gene expression data is a standard exploratory technique used to identify closely related genes. Many other sources of data are also likely to be of great assistance in the analysis of gene expression data. This data provides a mean to begin elucidating the large-scale modular organization of the cell. The authors consider the challenging task of developing exploratory analytical techniques to deal with multiple complete and incomplete information sources. The Multi-Source Clustering (MSC) algorithm developed performs clustering with multiple, but complete, sources of data. To deal with incomplete data sources, the authors adopted the MPCK-means clustering algorithms to perform exploratory analysis on one complete source and other potentially incomplete sources provided in the form of constraints. This paper presents a new clustering algorithm MSC to perform exploratory analysis using two or more diverse but complete data sources, studies the effectiveness of constraints sets and robustness of the constrained clustering algorithm using multiple sources of incomplete biological data, and incorporates such incomplete data into constrained clustering algorithm in form of constraints sets.

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35

WANG, Xiao-Qing. "An Efficient Multiple Data Sources Selection Algorithm in Data-Sharing Environments." Journal of Software 19, no. 2 (2008): 314–22. http://dx.doi.org/10.3724/sp.j.1001.2008.00314.

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36

Robinson, Chris, and Claire E. Terhune. "Error in geometric morphometric data collection: Combining data from multiple sources." American Journal of Physical Anthropology 164, no. 1 (2017): 62–75. http://dx.doi.org/10.1002/ajpa.23257.

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37

Fasihuddin, Mirza. "Integrating with Various Data Sources and Formats, Including Structured, Semi-Structured, and Unstructured Data." Journal of Scientific and Engineering Research 8, no. 2 (2021): 263–68. https://doi.org/10.5281/zenodo.11216190.

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The increasing availability and importance of data in various formats have led to the necessity for efficient integration methods to extract meaningful insights. This academic journal explores the challenges and solutions associated with integrating data from multiple sources, including structured, semi-structured, and unstructured data. The study aims to provide an overview of the techniques and tools available to businesses and researchers for effectively integrating diverse data types, enabling better decision-making and improving overall data-driven processes.

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38

B, L. Shilpa, and R. Shambhavi B. "Structuring of Unstructured Data from Heterogeneous Sources." Indian Journal of Science and Technology 15, no. 41 (2022): 2188–93. https://doi.org/10.17485/IJST/v15i41.1566.

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Abstract <strong>Objectives:</strong> To develop a new data gathering processing under Big Data Perspectives. To convert unstructured text data into structured format by not missing out any text data available.<strong> Methods:</strong> The unstructured data is preprocessed using modified stemming and tokenization. From the stemming output, the proposed Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram features are derived. Unstructured data is considered from multiple sources like twitter, consumer complaints and news blog. <strong>Findings:</strong> The proposed model with extant TF-IDF features has exposed relatively high Mean Average Error (MAE) value which is 1.4325 when compared to the proposed model without optimization to be 0.5197. <strong>Novelty:</strong> The novelty of the research work is of the stemming process where dictionary checking process is added and the improved feature extraction, interclass dispersion coefficient is computed in TF-IDF features. <strong>Keywords:</strong> Natural language processing; Structured data; Unstructured data; Big data; Feature extraction

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39

Burkom, Howard S. "Biosurveillance applying scan statistics with multiple, disparate data sources." Journal of Urban Health 80, S1 (2003): i57—i65. http://dx.doi.org/10.1007/pl00022316.

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40

Shah, Muhammad Kashif Ali, Supranee Lisawadi, and S. Ejaz Ahmed. "Merging data from multiple sources: pretest and shrinkage perspectives." Journal of Statistical Computation and Simulation 87, no. 8 (2017): 1577–92. http://dx.doi.org/10.1080/00949655.2016.1277427.

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41

Bove, V. Michael. "Probabilistic method for integrating multiple sources of range data." Journal of the Optical Society of America A 7, no. 12 (1990): 2193. http://dx.doi.org/10.1364/josaa.7.002193.

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42

Hosseini, Masoud, Jonathan Meade, Jamie Schnitzius, and Brian E. Dixon. "Consolidating CCDs from multiple data sources: a modular approach." Journal of the American Medical Informatics Association 23, no. 2 (2015): 317–23. http://dx.doi.org/10.1093/jamia/ocv084.

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Abstract Background Healthcare providers sometimes receive multiple continuity of care documents (CCDs) for a single patient encompassing the patient’s various encounters and medical history recorded in different information systems. It is cumbersome for providers to explore different pages of CCDs to find specific data which can be duplicated or even conflicted. This study describes initial steps toward a modular system that integrates and de-duplicates multiple CCDs into one consolidated document for viewing or processing patient-level data. Materials and Methods The authors developed a prototype system to consolidate and de-duplicate CCDs. The system is engineered to be scalable, extensible, and open source. Using a corpus of 150 de-identified CCDs synthetically generated from a single data source with a common vocabulary to represent 50 unique patients, the authors tested the system’s performance and output. Performance was measured based on document throughput and reduction in file size and volume of data. The authors further compared the output of the system with manual consolidation and de-duplication. Testing across multiple vendor systems or implementations was not performed. Results All of the input CCDs was successfully consolidated, and no data were lost. De-duplication significantly reduced the number of entries in different sections (49% in Problems, 60.6% in Medications, and 79% in Allergies) and reduced the size of the documents (57.5%) as well as the number of lines in each document (58%). The system executed at a rate of approximately 0.009–0.03 s per rule depending on the complexity of the rule. Discussion and Conclusion Given increasing adoption and use of health information exchange (HIE) to share data and information across the care continuum, duplication of information is inevitable. A novel system designed to support automated consolidation and de-duplication of information across clinical documents as they are exchanged shows promise. Future work is needed to expand the capabilities of the system and further test it using heterogeneous vocabularies across multiple HIE scenarios.

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43

Harris, James M., and Cynthia Z. F. Clark. "Strengthening methodological architecture with multiple frames and data sources." Statistical Journal of the IAOS: Journal of the International Association for Official Statistics 30, no. 4 (2014): 381–84. https://doi.org/10.3233/sji-140853.

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The United States Department of Agriculture, National Agriculture Statistics Service (NASS) has long conducted statistical surveys and published official statistics about almost every aspect of agriculture. Survey frames include both a list of farm establishments and an area frame, which are often combined to produce multiple-frame estimates. The survey results have also been compared to administrative records on the disposition of commodities. The disposition of commodities, or other intermittent administrative records, which are often received sometime after the survey results are published, must align with published results. For example, hog and pig inventories are published quarterly, while complete slaughter administrative information that matches the inventories is available six to seven months later. NASS has utilized balance sheets and other analysis tools in an expert review process to align the published results to the administrative information. However, the tools lack the statistical rigor that is required by the U.S. Office of Management and Budget (OMB) statistical standards. They are also deficient in the area of statistical measures of error, and are somewhat dependent upon the analyst. This paper will describe efforts to combine the multiple-frame survey results and other data sources using rigorous, statistically defensible methodologies that strengthen the overall results and meet OMB guidelines.

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44

Brown, Jeanne M., and Cory Tucker. "Using Multiple Data Sources to Develop a Liaison Agenda." College & Undergraduate Libraries 17, no. 4 (2010): 365–85. http://dx.doi.org/10.1080/10691316.2010.525424.

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45

Saegusa, Takumi. "Large sample theory for merged data from multiple sources." Annals of Statistics 47, no. 3 (2019): 1585–615. http://dx.doi.org/10.1214/18-aos1727.

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46

Zhang, Duo, Benjamin I. P. Rubinstein, and Jim Gemmell. "Principled Graph Matching Algorithms for Integrating Multiple Data Sources." IEEE Transactions on Knowledge and Data Engineering 27, no. 10 (2015): 2784–96. http://dx.doi.org/10.1109/tkde.2015.2426714.

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47

Zhang, Huichu, Yu Zheng, and Yong Yu. "Detecting Urban Anomalies Using Multiple Spatio-Temporal Data Sources." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, no. 1 (2018): 1–18. http://dx.doi.org/10.1145/3191786.

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48

Martin, P. A. J., A. R. Cameron, K. Barfod, E. S. G. Sergeant, and M. Greiner. "Demonstrating freedom from disease using multiple complex data sources." Preventive Veterinary Medicine 79, no. 2-4 (2007): 98–115. http://dx.doi.org/10.1016/j.prevetmed.2006.09.007.

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Martin, P. A. J., A. R. Cameron, and M. Greiner. "Demonstrating freedom from disease using multiple complex data sources." Preventive Veterinary Medicine 79, no. 2-4 (2007): 71–97. http://dx.doi.org/10.1016/j.prevetmed.2006.09.008.

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50

White, Gary C., and Bruce C. Lubow. "Fitting Population Models to Multiple Sources of Observed Data." Journal of Wildlife Management 66, no. 2 (2002): 300. http://dx.doi.org/10.2307/3803162.

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