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Journal articles on the topic 'Discovery from data'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

Mak, H. Craig. "Discovery from data repositories." Nature Biotechnology 29, no. 1 (2011): 46–47. http://dx.doi.org/10.1038/nbt0111-46.

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2

Pazzani, M. J. "Knowledge discovery from data?" IEEE Intelligent Systems 15, no. 2 (2000): 10–12. http://dx.doi.org/10.1109/5254.850821.

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3

Gama, João, and Jesus Aguilar-Ruiz. "Knowledge discovery from data streams." Intelligent Data Analysis 11, no. 1 (2007): 1–2. http://dx.doi.org/10.3233/ida-2007-11101.

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4

Gama, João, Jesus Aguilar-Ruiz, and Ralf Klinkenberg. "Knowledge discovery from data streams." Intelligent Data Analysis 12, no. 3 (2008): 251–52. http://dx.doi.org/10.3233/ida-2008-12301.

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5

Gama, João, Auroop Ganguly, Olufemi Omitaomu, Raju Vatsavai, and Mohamed Gaber. "Knowledge discovery from data streams." Intelligent Data Analysis 13, no. 3 (2009): 403–4. http://dx.doi.org/10.3233/ida-2009-0372.

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6

Morita, Chie, and Hiroshi Tsukimoto. "Knowledge discovery from numerical data." Knowledge-Based Systems 10, no. 7 (1998): 413–19. http://dx.doi.org/10.1016/s0950-7051(98)00040-9.

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7

Cook, Diane J., Lawrence B. Holder, and Surnjani Djoko. "Knowledge discovery from structural data." Journal of Intelligent Information Systems 5, no. 3 (1995): 229–48. http://dx.doi.org/10.1007/bf00962235.

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8

Kalenkova, Anna, Andrea Burattin, Massimiliano de Leoni, Wil van der Aalst, and Alessandro Sperduti. "Discovering high-level BPMN process models from event data." Business Process Management Journal 25, no. 5 (2019): 995–1019. http://dx.doi.org/10.1108/bpmj-02-2018-0051.

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Purpose The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias. Design/methodology/approach The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of step

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9

Gottlob, Georg, and Pierre Senellart. "Schema mapping discovery from data instances." Journal of the ACM 57, no. 2 (2010): 1–37. http://dx.doi.org/10.1145/1667053.1667055.

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Vatsavai, Ranga Raju, Olufemi A. Omitaomu, Joao Gama, Nitesh V. Chawla, Mohamed Medhat Gaber, and Auroop R. Ganguly. "Knowledge discovery from sensor data (SensorKDD)." ACM SIGKDD Explorations Newsletter 10, no. 2 (2008): 68–73. http://dx.doi.org/10.1145/1540276.1540297.

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Kazolis, Dimitrios Th, Jacob G. Fantidis, and Nikolaos Roumeliotis. "Knowledge discovery from energy consumption data." E3S Web of Conferences 551 (2024): 02002. http://dx.doi.org/10.1051/e3sconf/202455102002.

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The acquisition of information and thus, the knowledgeextraction from large databases, is a constantly developing modernscientific field, and a particularly important aspect of InformationTechnology. Different techniques and methodologies have been applied incombination with different types of data for obtaining the optimal result.This paper is a continuation of the effort to discover knowledge, in theform of correlations, from data concerning electricity consumption. Theinnovative part of this attempt is, the way that data was associated withtime, and moreover, the combination of the used met

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Omitaomu, Olufemi A., Ranga Raju Vatsavai, Auroop R. Ganguly, Nitesh V. Chawla, Joao Gama, and Mohamed Medhat Gaber. "Knowledge discovery from sensor data (SensorKDD)." ACM SIGKDD Explorations Newsletter 11, no. 2 (2010): 84–87. http://dx.doi.org/10.1145/1809400.1809417.

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13

Braun, Peter, Alfredo Cuzzocrea, Carson K. Leung, Adam G. M. Pazdor, and Kimberly Tran. "Knowledge Discovery from Social Graph Data." Procedia Computer Science 96 (2016): 682–91. http://dx.doi.org/10.1016/j.procs.2016.08.250.

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14

Zhu, Xiaofeng, Jie Shao, and Jilian Zhang. "Pattern discovery from multi-source data." Pattern Recognition Letters 109 (July 2018): 1–3. http://dx.doi.org/10.1016/j.patrec.2018.03.012.

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15

Chandola, Varun, Olufemi A. Omitaomu, Auroop R. Ganguly, et al. "Knowledge discovery from sensor data (SensorKDD)." ACM SIGKDD Explorations Newsletter 12, no. 2 (2011): 50–53. http://dx.doi.org/10.1145/1964897.1964911.

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16

Im, Seunghyun, Zbigniew Raś, and Hanna Wasyluk. "Action rule discovery from incomplete data." Knowledge and Information Systems 25, no. 1 (2009): 21–33. http://dx.doi.org/10.1007/s10115-009-0221-3.

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17

Yang, Yongliang, S. James Adelstein, and Amin I. Kassis. "Target discovery from data mining approaches." Drug Discovery Today 14, no. 3-4 (2009): 147–54. http://dx.doi.org/10.1016/j.drudis.2008.12.005.

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Yang, Yongliang, S. James Adelstein, and Amin I. Kassis. "Target discovery from data mining approaches." Drug Discovery Today 17 (February 2012): S16—S23. http://dx.doi.org/10.1016/j.drudis.2011.12.006.

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19

Xing, Junjie, Xinyu Wang, and H. V. Jagadish. "Data-Driven Insight Synthesis for Multi-Dimensional Data." Proceedings of the VLDB Endowment 17, no. 5 (2024): 1007–19. http://dx.doi.org/10.14778/3641204.3641211.

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Exploratory data analysis can uncover interesting data insights from data. Current methods utilize "interestingness measures" designed based on system designers' perspectives, thus inherently restricting the insights to their defined scope. These systems, consequently, may not adequately represent a broader range of user interests. Furthermore, most existing approaches that formulate "interestingness measure" are rule-based, which makes them inevitably brittle and often requires holistic re-design when new user needs are discovered. This paper presents a data-driven technique for deriving an "

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20

Mrs., Shweta A. Dubey* Prof. Kemal. Koche. "A SURVEY PAPER ON HIGH UTILITY ITEMSETS MINING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 852–57. https://doi.org/10.5281/zenodo.52492.

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An important data mining task that has received considerable research attention in recent years is the discovery of association rules from the transactional databases. Recently, Utility mining plays a vital role in data mining. To discover high utility itemset from transactional database means discovering item sets with high profits. In this survey paper, we discuss about various methods and algorithms which were used for recovering high utility itemsets from a large database without losing large amount of information.We present different kind of algorithm such as CHUD(Closed High Utility Item

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21

Mahoto, Naeem Ahmed, Asadullah Shaikh, Mana Saleh Al Reshan, Muhammad Ali Memon, and Adel Sulaiman. "Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment." Sustainability 13, no. 16 (2021): 8900. http://dx.doi.org/10.3390/su13168900.

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The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transform

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22

Wang, Zhaochao, Juanjuan Yu, Chenjie Wang, Yi Hua, Hong Wang, and Jianwei Chen. "The Deep Mining Era: Genomic, Metabolomic, and Integrative Approaches to Microbial Natural Products from 2018 to 2024." Marine Drugs 23, no. 7 (2025): 261. https://doi.org/10.3390/md23070261.

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Over the past decade, microbial natural products research has witnessed a transformative “deep-mining era” driven by key technological advances such as high-throughput sequencing (e.g., PacBio HiFi), ultra-sensitive HRMS (resolution ≥ 100,000), and multi-omics synergy. These innovations have shifted discovery from serendipitous isolation to data-driven, targeted mining. These innovations have transitioned discovery from serendipitous isolation to data-driven targeted mining. Genome mining pipelines (e.g., antiSMASH 7.0 and DeepBGC) can now systematically discover hidden biosynthetic gene clust

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23

Volodymyr, Leno, Dumas Marlon, Maria Maggia Fabrizio, La Rosa Marcello, and Polyvyanyy Artem. "Automated discovery of declarative process models with correlated data conditions." Information Systems 89 (March 1, 2020): 101482. https://doi.org/10.1016/j.is.2019.101482.

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Automated process discovery techniques enable users to generate business process models from event logs extracted from enterprise information systems. Traditional techniques in this field generate procedural process models (e.g., in the BPMN notation). When dealing with highly variable processes, the resulting procedural models are often too complex to be practically usable. An alternative approach is to discover declarative process models, which represent the behavior of the process as a set of constraints. Declarative process discovery techniques have been shown to produce simpler models tha

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24

OTSUKI, Akira, and Masayoshi KAWAMURA. "Knowledge Discovery from Cadastral Information Big Data." Joho Chishiki Gakkaishi 23, no. 2 (2013): 327–32. http://dx.doi.org/10.2964/jsik.23_327.

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25

Holder, L. B., and D. J. Cook. "Discovery of inexact concepts from structural data." IEEE Transactions on Knowledge and Data Engineering 5, no. 6 (1993): 992–94. http://dx.doi.org/10.1109/69.250085.

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26

YAMANISHI, Kenji. "Discovery of Deep Knowledge from Complex Data." Journal of the Society of Mechanical Engineers 118, no. 1163 (2015): 616–19. http://dx.doi.org/10.1299/jsmemag.118.1163_616.

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27

Ao Kong, Chinmaya Gupta, Mauro Ferrari, et al. "Biomarker Signature Discovery from Mass Spectrometry Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 11, no. 4 (2014): 766–72. http://dx.doi.org/10.1109/tcbb.2014.2318718.

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28

Afify, Ashraf A. "Discovery of association rules from manufacturing data." International Journal of Computer Aided Engineering and Technology 3, no. 3/4 (2011): 360. http://dx.doi.org/10.1504/ijcaet.2011.040053.

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29

Cholleti, Sharath R., Sanjay Agravat, Tim Morris, et al. "Automated Motif Discovery from Glycan Array Data." OMICS: A Journal of Integrative Biology 16, no. 10 (2012): 497–512. http://dx.doi.org/10.1089/omi.2012.0013.

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30

Rani, K. Swarupa. "Tree Representation: Knowledge Discovery from Uncertain Data." Procedia Computer Science 78 (2016): 683–90. http://dx.doi.org/10.1016/j.procs.2016.02.117.

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31

Shettar, Rajashree. "Sequential Pattern Discovery from Web Log Data." International Journal of Computer Applications 42, no. 8 (2012): 8–11. http://dx.doi.org/10.5120/5710-7766.

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32

Baurley, James W., David V. Conti, W. James Gauderman, and Duncan C. Thomas. "Discovery of complex pathways from observational data." Statistics in Medicine 29, no. 19 (2010): 1998–2011. http://dx.doi.org/10.1002/sim.3962.

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33

Van Hulse, Jason, and Taghi Khoshgoftaar. "Knowledge discovery from imbalanced and noisy data." Data & Knowledge Engineering 68, no. 12 (2009): 1513–42. http://dx.doi.org/10.1016/j.datak.2009.08.005.

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34

Liu, Jixue, Feiyue Ye, Jiuyong Li, and Junhu Wang. "On discovery of functional dependencies from data." Data & Knowledge Engineering 86 (July 2013): 146–59. http://dx.doi.org/10.1016/j.datak.2013.01.008.

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35

Kléma, Jiří, Sylvain Blachon, Arnaud Soulet, Bruno Crémilleux, and Olivier Gandrillon. "Constraint-Based Knowledge Discovery from SAGE Data." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 8, no. 2 (2008): 157–75. https://doi.org/10.3233/isb-00351.

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Current analyses of co-expressed genes are often based on global approaches such as clustering or bi-clustering. An alternative way is to employ local methods and search for patterns – sets of genes displaying specific expression properties in a set of situations. The main bottleneck of this type of analysis is twofold – computational costs and an overwhelming number of candidate patterns which can hardly be further exploited. A timely application of background knowledge available in literature databases, biological ontologies and other sources can help to focus on the most plausible patterns

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36

Wu, Wanqing, and Wenyu Mao. "An Efficient and Scalable Algorithm to Mine Functional Dependencies from Distributed Big Data." Sensors 22, no. 10 (2022): 3856. http://dx.doi.org/10.3390/s22103856.

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A crucial step in improving data quality is to discover semantic relationships between data. Functional dependencies are rules that describe semantic relationships between data in relational databases and have been applied to improve data quality recently. However, traditional functional discovery algorithms applied to distributed data may lead to errors and the inability to scale to large-scale data. To solve the above problems, we propose a novel distributed functional dependency discovery algorithm based on Apache Spark, which can effectively discover functional dependencies in large-scale

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37

Huang, Ying, Liyun Zhong, and Yan Chen. "Filtering Infrequent Behavior in Business Process Discovery by Using the Minimum Expectation." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (2020): 1–15. http://dx.doi.org/10.4018/ijcini.2020040101.

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The aim of process discovery is to discover process models from the process execution data stored in event logs. In the era of “Big Data,” one of the key challenges is to analyze the large amounts of collected data in meaningful and scalable ways. Most process discovery algorithms assume that all the data in an event log fully comply with the process execution specification, and the process event logs are no exception. However, real event logs contain large amounts of noise and data from irrelevant infrequent behavior. The infrequent behavior or noise has a negative influence on the process di

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38

Ding, Jun, Haiyan Hu, and Xiaoman Li. "SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data." Nucleic Acids Research 42, no. 5 (2013): e35-e35. http://dx.doi.org/10.1093/nar/gkt1288.

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Abstract The identification of transcription factor binding motifs is important for the study of gene transcriptional regulation. The chromatin immunoprecipitation (ChIP), followed by massive parallel sequencing (ChIP-seq) experiments, provides an unprecedented opportunity to discover binding motifs. Computational methods have been developed to identify motifs from ChIP-seq data, while at the same time encountering several problems. For example, existing methods are often not scalable to the large number of sequences obtained from ChIP-seq peak regions. Some methods heavily rely on well-annota

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39

Wilson, Lee. "Exploring the Canadian Federated Research Data Repository Service." Biodiversity Information Science and Standards 1 (August 11, 2017): e20185. https://doi.org/10.3897/tdwgproceedings.1.20185.

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Good data management requires support for researchers at all stages of the data lifecycle, from policy and planning development to infrastructure that ensures data is findable, accessible, interoperable, and reusable (FAIR). While several excellent institutional, domain-specific, and general repositories currently exist both within Canada and abroad, Canada lacks nationally coordinated solutions for managing research data, and the question of where to deposit data for discovery, reuse, and preservation remains pervasive. Developed through a partnership between the Canadian Association of Resea

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40

Lauber, Chris, and Stefan Seitz. "Opportunities and Challenges of Data-Driven Virus Discovery." Biomolecules 12, no. 8 (2022): 1073. http://dx.doi.org/10.3390/biom12081073.

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Virus discovery has been fueled by new technologies ever since the first viruses were discovered at the end of the 19th century. Starting with mechanical devices that provided evidence for virus presence in sick hosts, virus discovery gradually transitioned into a sequence-based scientific discipline, which, nowadays, can characterize virus identity and explore viral diversity at an unprecedented resolution and depth. Sequencing technologies are now being used routinely and at ever-increasing scales, producing an avalanche of novel viral sequences found in a multitude of organisms and environm

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41

Zhang, Hao, Yewei Xia, Yixin Ren, Jihong Guan, and Shuigeng Zhou. "Differentially Private Nonlinear Causal Discovery from Numerical Data." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 12321–28. http://dx.doi.org/10.1609/aaai.v37i10.26452.

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Recently, several methods such as private ANM, EM-PC and Priv-PC have been proposed to perform differentially private causal discovery in various scenarios including bivariate, multivariate Gaussian and categorical cases. However, there is little effort on how to conduct private nonlinear causal discovery from numerical data. This work tries to challenge this problem. To this end, we propose a method to infer nonlinear causal relations from observed numerical data by using regression-based conditional independence test (RCIT) that consists of kernel ridge regression (KRR) and Hilbert-Schmidt i

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42

Suzuki, Einoshin. "Data Mining Methods for Discovering Interesting Exceptions from an Unsupervised Table." JUCS - Journal of Universal Computer Science 12, no. (6) (2006): 627–53. https://doi.org/10.3217/jucs-012-06-0627.

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In this paper, we survey efforts devoted to discovering interesting exceptions from data in data mining. An exception differs from the rest of data and thus is interesting and can be a clue for further discoveries. We classify methods into exception instance discovery, exception rule discovery, and exception structured-rules discovery and give a condensed and comprehensive introduction.

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43

Han, Henry, and Xiaoqian Jiang. "Disease Biomarker Query from RNA-Seq Data." Cancer Informatics 13s1 (January 2014): CIN.S13876. http://dx.doi.org/10.4137/cin.s13876.

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As a revolutionary way to unveil transcription, RNA-Seq technologies are challenging bioinformatics for its large data volumes and complexities. A large number of computational models have been proposed for differential expression (DE) analysis and normalization from different standing points. However, there were no studies available yet to conduct disease biomarker discovery for this type of high-resolution digital gene expression data, which will actually be essential to explore its potential in clinical bioinformatics. Although there were many biomarker discovery algorithms available in tra

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44

Wong, A. K. C., and Yang Wang. "High-order pattern discovery from discrete-valued data." IEEE Transactions on Knowledge and Data Engineering 9, no. 6 (1997): 877–93. http://dx.doi.org/10.1109/69.649314.

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45

Chen, Min, Anne Trefethen, Rene Banares-Alcantara, et al. "From Data Analysis and Visualization to Causality Discovery." Computer 44, no. 10 (2011): 84–87. http://dx.doi.org/10.1109/mc.2011.313.

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46

Tanaka, Isao. "Data-Driven Materials Discovery from Large Chemistry Spaces." Matter 3, no. 2 (2020): 327–28. http://dx.doi.org/10.1016/j.matt.2020.07.010.

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47

Amaro, Rommie E. "Drug Discovery Gets a Boost from Data Science." Structure 24, no. 8 (2016): 1225–26. http://dx.doi.org/10.1016/j.str.2016.07.003.

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48

Hońko, Piotr. "Association discovery from relational data via granular computing." Information Sciences 234 (June 2013): 136–49. http://dx.doi.org/10.1016/j.ins.2013.01.004.

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49

Hand, David J. "Knowledge Discovery from Data Streams by João Gama." International Statistical Review 80, no. 1 (2012): 181–82. http://dx.doi.org/10.1111/j.1751-5823.2012.00179_5.x.

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50

Shein, Thi Thi, Sutheera Puntheeranurak, and Makoto Imamura. "Discovery of evolving companion from trajectory data streams." Knowledge and Information Systems 62, no. 9 (2020): 3509–33. http://dx.doi.org/10.1007/s10115-020-01471-2.

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