Bibliografías temáticas / Data mining applications

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Literatura académica sobre el tema "Data mining applications"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 13 de junio de 2025

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Artículos de revistas sobre el tema "Data mining applications"

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Wang, Lidong, and Guanghui Wang. "Data Mining Applications in Big Data." Computer Engineering and Applications Journal 4, no. 3 (2015): 143–52. http://dx.doi.org/10.18495/comengapp.v4i3.155.

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Data mining is a process of extracting hidden, unknown, but potentially useful information from massive data. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in data mining and technologies in Big Data. Challenges of data mining and data mining with big data are discussed. Some technology progress of data mining and data mining with big data are also presented.
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Barai, Sudhir Kumar. "DATA MINING APPLICATIONS IN TRANSPORTATION ENGINEERING." TRANSPORT 18, no. 5 (2003): 216–23. http://dx.doi.org/10.3846/16483840.2003.10414100.

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Data mining is the extraction of implicit, previously unknown and potentially useful information from data. In recent time, data mining studies have been carried out in many engineering disciplines. In this paper the background of data mining and tools is introduced. Further applications of data mining to transportation engineering problems are reviewed. The application of data mining for typical example of ‘Vehicle Crash Study’ is demonstrated using commercially available data mining tool. The paper highlights the potential of data mining tool application in transportation engineering sector.
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Bathla, Gourav, Himanshu Aggarwal, and Rinkle Rani. "Migrating From Data Mining to Big Data Mining." International Journal of Engineering & Technology 7, no. 3.4 (2018): 13. http://dx.doi.org/10.14419/ijet.v7i3.4.14667.

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Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples. Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.
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Rahman, Nayem. "Data Mining Techniques and Applications." International Journal of Strategic Information Technology and Applications 9, no. 1 (2018): 78–97. http://dx.doi.org/10.4018/ijsita.2018010104.

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Data mining has been gaining attention with the complex business environments, as a rapid increase of data volume and the ubiquitous nature of data in this age of the internet and social media. Organizations are interested in making informed decisions with a complete set of data including structured and unstructured data that originate both internally and externally. Different data mining techniques have evolved over the last two decades. To solve a wide variety of business problems, different data mining techniques are developed. Practitioners and researchers in industry and academia continuously develop and experiment varieties of data mining techniques. This article provides an overview of data mining techniques that are widely used in different fields to discover knowledge and solve business problems. This article provides an update on data mining techniques based on extant literature as of 2018. That might help practitioners and researchers to have a holistic view of data mining techniques.
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Li, Jing Zhu, Qian Li, Tai Yu Liu, and Wei Hong Niu. "Data Mining: Modeling, Algorithms, Applications and Systems." Advanced Materials Research 926-930 (May 2014): 2786–89. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2786.

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Data mining is a multidisciplinary field of the 20th century gradually, this paper based on data mining modeling, algorithms, applications and software tools were reviewed, the definition of data mining, the scope and characteristics of the data sets and data mining various practical situations; summarizes the data mining in the practical application of the basic steps and processes; data mining tasks in a variety of applications and modeling issues were discussed; cited the current field of data mining is mainly popular algorithms, and algorithm design issues to consider briefly analyzed; overview of the current data mining algorithm in a number of areas; more comprehensive description of the current performance and data mining software tools developer circumstances; Finally, the development of data mining prospects and direction prospected.
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Tsuda, Koji. "Data Mining for Biologists." International Journal of Knowledge Discovery in Bioinformatics 3, no. 4 (2012): 1–14. http://dx.doi.org/10.4018/ijkdb.2012100101.

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In this tutorial article, the author reviews basics about frequent pattern mining algorithms, including itemset mining, association rule mining, and graph mining. These algorithms can find frequently appearing substructures in discrete data. They can discover structural motifs, for example, from mutation data, protein structures, and chemical compounds. As they have been primarily used for business data, biological applications are not so common yet, but their potential impact would be large. Recent advances in computers including multicore machines and ever increasing memory capacity support the application of such methods to larger datasets. The author explains technical aspects of the algorithms, but do not go into details. Current biological applications are summarized and possible future directions are given.
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Caby, Errol C. "Data Mining Using SAS Applications." Technometrics 46, no. 2 (2004): 260–61. http://dx.doi.org/10.1198/tech.2004.s805.

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Ghani, Rayid, and Carlos Soares. "Data mining for business applications." ACM SIGKDD Explorations Newsletter 8, no. 2 (2006): 79–81. http://dx.doi.org/10.1145/1233321.1233332.

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Washio, Takashi. "Applications eligible for data mining." Advanced Engineering Informatics 21, no. 3 (2007): 241–42. http://dx.doi.org/10.1016/j.aei.2007.01.001.

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Apte, Chidanand, Bing Liu, Edwin P. D. Pednault, and Padhraic Smyth. "Business applications of data mining." Communications of the ACM 45, no. 8 (2002): 49–53. http://dx.doi.org/10.1145/545151.545178.

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Tesis sobre el tema "Data mining applications"

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Eapen, Arun George. "Application of Data mining in Medical Applications." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/772.

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Abstract Data mining is a relatively new field of research whose major objective is to acquire knowledge from large amounts of data. In medical and health care areas, due to regulations and due to the availability of computers, a large amount of data is becoming available. On the one hand, practitioners are expected to use all this data in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make diagnosis, prognosis and treatment schedules. A major objective of this thesis is to evaluate data mining tools in medical and health care applications to develop a tool that can help make timely and accurate decisions. Two medical databases are considered, one for describing the various tools and the other as the case study. The first database is related to breast cancer and the second is related to the minimum data set for mental health (MDS-MH). The breast cancer database consists of 10 attributes and the MDS-MH dataset consists of 455 attributes. As there are a number of data mining algorithms and tools available we consider only a few tools to evaluate on these applications and develop classification rules that can be used in prediction. Our results indicate that for the major case study, namely the mental health problem, over 70 to 80% accurate results are possible. A further extension of this work is to make available classification rules in mobile devices such as PDAs. Patient information is directly inputted onto the PDA and the classification of these inputted values takes place based on the rules stored on the PDA to provide real time assistance to practitioners.
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Vithal, Kadam Omkar. "Novel applications of Association Rule Mining- Data Stream Mining." AUT University, 2009. http://hdl.handle.net/10292/826.

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From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.
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Du, Sang. "Data Mining Applications to Brain Energy Metabolism." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1323463827.

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Wang, Jie. "MATRIX DECOMPOSITION FOR DATA DISCLOSURE CONTROL AND DATA MINING APPLICATIONS." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_diss/677.

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Access to huge amounts of various data with private information brings out a dual demand for preservation of data privacy and correctness of knowledge discovery, which are two apparently contradictory tasks. Low-rank approximations generated by matrix decompositions are a fundamental element in this dissertation for the privacy preserving data mining (PPDM) applications. Two categories of PPDM are studied: data value hiding (DVH) and data pattern hiding (DPH). A matrix-decomposition-based framework is designed to incorporate matrix decomposition techniques into data preprocessing to distort original data sets. With respect to the challenge in the DVH, how to protect sensitive/confidential attribute values without jeopardizing underlying data patterns, we propose singular value decomposition (SVD)-based and nonnegative matrix factorization (NMF)-based models. Some discussion on data distortion and data utility metrics is presented. Our experimental results on benchmark data sets demonstrate that our proposed models have potential for outperforming standard data perturbation models regarding the balance between data privacy and data utility. Based on an equivalence between the NMF and K-means clustering, a simultaneous data value and pattern hiding strategy is developed for data mining activities using K-means clustering. Three schemes are designed to make a slight alteration on submatrices such that user-specified cluster properties of data subjects are hidden. Performance evaluation demonstrates the efficacy of the proposed strategy since some optimal solutions can be computed with zero side effects on nonconfidential memberships. Accordingly, the protection of privacy is simplified by one modified data set with enhanced performance by this dual privacy protection. In addition, an improved incremental SVD-updating algorithm is applied to speed up the real-time performance of the SVD-based model for frequent data updates. The performance and effectiveness of the improved algorithm have been examined on synthetic and real data sets. Experimental results indicate that the introduction of the incremental matrix decomposition produces a significant speedup. It also provides potential support for the use of the SVD technique in the On-Line Analytical Processing for business data analysis.
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Matyja, Dariusz. "Applications of data mining algorithms to analysis of medical data." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4253.

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Medical datasets have reached enormous capacities. This data may contain valuable information that awaits extraction. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making. The data which is analyzed comes from the Polish National Breast Cancer Prevention Program ran in Poland in 2006. The aim of this master's thesis is the evaluation of the analytical data from the Program to see if the domain can be a subject to data mining. The next step is to evaluate several data mining methods with respect to their applicability to the given data. This is to show which of the techniques are particularly usable for the given dataset. Finally, the research aims at extracting some tangible medical knowledge from the set. The research utilizes a data warehouse to store the data. The data is assessed via the ETL process. The performance of the data mining models is measured with the use of the lift charts and confusion (classification) matrices. The medical knowledge is extracted based on the indications of the majority of the models. The experiments are conducted in the Microsoft SQL Server 2005. The results of the analyses have shown that the Program did not deliver good-quality data. A lot of missing values and various discrepancies make it especially difficult to build good models and draw any medical conclusions. It is very hard to unequivocally decide which is particularly suitable for the given data. It is advisable to test a set of methods prior to their application in real systems. The data mining models were not unanimous about patterns in the data. Thus the medical knowledge is not certain and requires verification from the medical people. However, most of the models strongly associated patient's age, tissue type, hormonal therapies and disease in family with the malignancy of cancers. The next step of the research is to present the findings to the medical people for verification. In the future the outcomes may constitute a good background for development of a Medical Decision Support System.
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Qin, Xiao. "Sequential Data Mining and its Applications to Pharmacovigilance." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/515.

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With the phenomenal growth of digital devices coupled with their ever-increasing capabilities of data generation and storage, sequential data is becoming more and more ubiquitous in a wide spectrum of application scenarios. There are various embodiments of sequential data such as temporal database, time series and text (word sequence) where the first one is synchronous over time and the latter two often generated in an asynchronous fashion. In order to derive precious insights, it is critical to learn and understand the behavior dynamics as well as the causality relationships across sequences. Pharmacovigilance is defined as the science and activities relating to the detection, assessment, understanding and prevention of adverse drug reactions (ADR) or other drug-related problems. In the post-marketing phase, the effectiveness and the safety of drugs is monitored by regulatory agencies known as post-marketing surveillance. Spontaneous Reporting System (SRS), e.g., U.S. Food and Drug Administration Adverse Event Reporting System (FAERS), collects drug safety complaints over time providing the key evidence to support regularity actions towards the reported products. With the rapid growth of the reporting volume and velocity, data mining techniques promise to be effective to facilitating drug safety reviewers performing supervision tasks in a timely fashion. My dissertation studies the problem of exploring, analyzing and modeling various types of sequential data within a typical SRS: Temporal Correlations Discovery and Exploration. SRS can be seen as a temporal database where each transaction encodes the co-occurrence of some reported drugs and observed ADRs in a time frame. Temporal association rule learning (TARL) has been proven to be a prime candidate to derive associations among the objects from such temporal database. However, TARL is parameterized and computational expensive making it difficult to use for discovering interesting association among drugs and ADRs in a timely fashion. Worse yet, existing interestingness measures fail to capture the significance of certain types of association in the context of pharmacovigilance, e.g. drug-drug interaction (DDI) related ADR. To discover DDI related ADR using TARL, we propose an interestingness measure that aligns with the DDI semantics. We propose an interactive temporal association analytics framework that supports real-time temporal association derivation and exploration. Anomaly Detection in Time Series. Abnormal reports may reveal meaningful ADR case which is overlooked by frequency-based data mining approach such as association rule learning where patterns are derived from frequently occurred events. In addition, the sense of abnormal or rareness may vary in different contexts. For example, an ADR, normally occurs to adult population, may rarely happen to youth population but with life threatening outcomes. Local outlier factor (LOF) is identified as a suitable approach to capture such local abnormal phenomenon. However, existing LOF algorithms and its variations fail to cope with high velocity data streams due to its high algorithmic complexity. We propose new local outlier semantics that leverage kernel density estimation (KDE) to effectively detect local outliers from streaming data. A strategy to continuously detect top-N KDE-based local outliers over streams is also designed, called KELOS -- the first linear time complexity streaming local outlier detection approach. Text Modeling. Language modeling (LM) is a fundamental problem in many natural language processing (NLP) tasks. LM is the development of probabilistic models that are able to predict the next word in the sequence given the words that precede it. Recently, LM is advanced by the success of the recurrent neural networks (RNNs) which overcome the Markov assumption made in the traditional statistical language models. In theory, RNNs such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) can “remember� arbitrarily long span of history if provided with enough capacity. However, they do not perform well on very long sequences in practice as the gradient computation for RNNs becomes increasingly ill-behaved as the expected dependency becomes longer. One way of tackling this problem is to feed succinct information that encodes the semantic structure of the entire document such as latent topics as context to guide the modeling process. Clinical narratives that describe complex medical events are often accompanied by meta-information such as a patient's demographics, diagnoses and medications. This structured information implicitly relates to the logical and semantic structure of the entire narrative, and thus affects vocabulary choices for the narrative composition. To leverage this meta-information, we propose a supervised topic compositional neural language model, called MeTRNN, that integrates the strength of supervised topic modeling in capturing global semantics with the capacity of contextual recurrent neural networks (RNN) in modeling local word dependencies.
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Okafor, Anthony. "Entropy based techniques with applications in data mining." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0013113.

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You, Guangjing. "Two Data Mining Applications for Predicting Pre-Diabetes." Thesis, North Dakota State University, 2015. https://hdl.handle.net/10365/27638.

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Document incorrectly classified as a dissertation on title page (decision to classify as a thesis from NDSU Graduate School)<br>In this study, the performance of Logistic Regression and Decision Tree modeling is compared by using SAS Enterprise Miner for predicting pre-diabetes in US population by using several of the common factors from the type 2 diabetes screening criteria. From 17 variables of NHANES? three sets of dataset, a total of 13 risk factors were selected as predictors of pre-diabetes. A comparison of two data mining methodology showed that Decision Tree has a higher ROC index than Logistic Regression modeling. All ROC indexes for two models were greater than 77% indicating both methods present a good prediction for pre-diabetes. The predictive accuracy of the two models was greater than 72% on the whole dataset. Decision tree modeling also resulted in higher accuracy and sensitivity values than Logistic Regression modeling. Taken as a whole, the results of comparison indicated Decision Tree modeling is a better indicator to predict pre-diabetes.
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Better, Marco L. "Data mining techniques for prediction and classification in discrete data applications." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3273688.

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Varde, Aparna S. "Graphical data mining for computational estimation in materials science applications." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-081506-152633/.

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Libros sobre el tema "Data mining applications"

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A, Zanasi, Ebecken N. F. F, and Brebbia C. A, eds. Data mining V: Data mining, text mining, and their business applications. WIT, 2004.

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Soares, Carlos A. Mota, and Rayid Ghani. Data mining for business applications. IOS Press, 2010.

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S, Yu Philip, Zhang Chengqi, Zhang Huaifeng, and SpringerLink (Online service), eds. Data Mining for Business Applications. Springer US, 2009.

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Practical applications of data mining. Jones & Bartlett Learning, 2010.

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Innovative applications in data mining. Springer, 2009.

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Data mining using SAS applications. Chapman & Hall/CRC, 2003.

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Li, Jinyan, Xue Li, Shuliang Wang, Jianxin Li, and Quan Z. Sheng, eds. Advanced Data Mining and Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49586-6.

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Cao, Longbing, Jiang Zhong, and Yong Feng, eds. Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17313-4.

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Cao, Longbing, Yong Feng, and Jiang Zhong, eds. Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5.

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Li, Jianxin, Sen Wang, Shaowen Qin, Xue Li, and Shuliang Wang, eds. Advanced Data Mining and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35231-8.

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Capítulos de libros sobre el tema "Data mining applications"

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Shortland, R., and R. Scarfe. "Data Mining Applications." In Computer Aided Decision Support in Telecommunications. Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0081-3_2.

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Hadzic, Fedja, Henry Tan, and Tharam S. Dillon. "Tree Mining Applications." In Mining of Data with Complex Structures. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17557-2_9.

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Džeroski, Sašo. "Relational Data Mining Applications: An Overview." In Relational Data Mining. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04599-2_14.

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Grossmann, Wilfried, and Stefanie Rinderle-Ma. "Data Mining for Temporal Data." In Data-Centric Systems and Applications. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46531-8_6.

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Zaki, Mohammed. "Unified Approach to Rooted Tree Mining: Algorithms and Applications." In Mining Graph Data. John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/9780470073049.ch15.

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Chen, Qingfeng, Baoshan Chen, and Chengqi Zhang. "Data Resources and Applications." In Intelligent Strategies for Pathway Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04172-8_2.

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Grossmann, Wilfried, and Stefanie Rinderle-Ma. "Data Mining for Cross-Sectional Data." In Data-Centric Systems and Applications. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46531-8_5.

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Thuraisingham, Bhavani, Mohammad Mehedy Masud, Pallabi Parveen, and Latifur Khan. "Data Mining Techniques." In Big Data Analytics with Applications in Insider Threat Detection. Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781315119458-4.

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Ramakrishnan, Naren, and Ananth Y. Grama. "Data Mining Applications in Bioinformatics." In Data Mining for Scientific and Engineering Applications. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1733-7_8.

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Thuraisingham, Bhavani M. "Data Mining for Surveillance Applications." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731139_3.

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Actas de conferencias sobre el tema "Data mining applications"

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Holmes, Geoff. "Developing data mining applications." In the 18th ACM SIGKDD international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2339530.2339569.

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Haigh, Karen Zita, Wendy Foslien, and Valerie Guralnik. "Data Mining for Space Applications." In Space OPS 2004 Conference. American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-255-106.

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Thuraisingham, Bhavani, Latifur Khan, Mohammad M. Masud, and Kevin W. Hamlen. "Data Mining for Security Applications." In 2008 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2008. http://dx.doi.org/10.1109/euc.2008.62.

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Scarfe, R. T. "Data mining applications in BT." In IEE Colloquium on Knowledge Discovery in Databases. IEE, 1995. http://dx.doi.org/10.1049/ic:19950125.

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Wang, Xilei, Yongjun Ma, and Xin Li. "Data Mining in Inconsistent Data." In 2010 International Conference on Internet Technology and Applications (iTAP). IEEE, 2010. http://dx.doi.org/10.1109/itapp.2010.5566131.

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Liu, Miao, Hai-Feng Guo, and Zhengxin Chen. "On Multi-Relational Data Mining for Foundation of Data Mining." In 2007 IEEE/ACS International Conference on Computer Systems and Applications. IEEE, 2007. http://dx.doi.org/10.1109/aiccsa.2007.370911.

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Sobh, Karim M., Ahmed Rafea, and Amr El-Kadi. "Mining Cloud Environments Usage Data." In Artificial Intelligence and Applications. ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.795-031.

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Revesz, Peter Z. "Data Mining Citation Databases." In the 19th International Database Engineering & Applications Symposium. ACM Press, 2014. http://dx.doi.org/10.1145/2790755.2790763.

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Yuan, Hanning, and Shuliang Wang. "Data preprocessing of spatial data mining." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.650728.

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Ramakrishna, Mahesh Thylore, Latha Kolal Gowdar, Malatesh Somashekar Havanur, and Banur Puttappa Mallikarjuna Swamy. "Web Mining: Key Accomplishments, Applications and Future Directions." In 2010 International Conference on Data Storage and Data Engineering (DSDE). IEEE, 2010. http://dx.doi.org/10.1109/dsde.2010.53.

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Informes sobre el tema "Data mining applications"

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Bautista-Gomez, Leonardo, and Franck Cappello. Detecting Silent Data Corruption for Extreme-Scale Applications through Data Mining. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1177404.

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2

Yurica, Kevin, Rahul Pande, and Rajeev Motwani. Distributed Streams-Based Data-Mining for Application Intrusion Detection. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada424288.

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3

Liu, Baiyan, Bing Yan, Hailin Jiang, et al. The effectiveness of herbal acupoint application for functional diarrhea Protocol for a meta-analysis and data mining. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.7.0094.

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4

Liu, Yanze, Lin Yao, Fuchun Wang, et al. A Protocol for Effectiveness of Acupoint Application of Traditional Chinese Medicine in Treating Primary Dysmenorrhea : Meta-analysis and Data Mining. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.3.0011.

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5

Allende López, Marcos, Diego López, Sergio Cerón, et al. Quantum-Resistance in Blockchain Networks. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003313.

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Resumen
This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Cambridge Quantum Computing (CQC), and Tecnológico de Monterrey to identify and eliminate quantum threats in blockchain networks. The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms. When quantum computers become robust enough to run Shor's algorithm on a large scale, the most used asymmetric algorithms, utilized for digital signatures and message encryption, such as RSA, (EC)DSA, and (EC)DH, will be no longer secure. Quantum computers will be able to break them within a short period of time. Similarly, Grover's algorithm concedes a quadratic advantage for mining blocks in certain consensus protocols such as proof of work. Today, there are hundreds of billions of dollars denominated in cryptocurrencies that rely on blockchain ledgers as well as the thousands of blockchain-based applications storing value in blockchain networks. Cryptocurrencies and blockchain-based applications require solutions that guarantee quantum resistance in order to preserve the integrity of data and assets in their public and immutable ledgers. We have designed and developed a layer-two solution to secure the exchange of information between blockchain nodes over the internet and introduced a second signature in transactions using post-quantum keys. Our versatile solution can be applied to any blockchain network. In our implementation, quantum entropy was provided via the IronBridge Platform from CQC and we used LACChain Besu as the blockchain network.
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