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Relevant bibliographies by topics / Data stream mining

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Academic literature on the topic 'Data stream mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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Journal articles on the topic "Data stream mining"

1

Ramzan, Faisal, and Muawaz Ayyaz. "A COMPREHENSIVE REVIEW ON DATA STREAM MINING TECHNIQUES FOR DATA CLASSIFICATION; AND FUTURE TRENDS." EPH - International Journal of Science And Engineering 9, no. 3 (2023): 1–29. http://dx.doi.org/10.53555/ephijse.v9i3.201.

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Data Mining is a developing interdisciplinary control managing Data Reclamation and Data Stream Mining techniques, whose subject is gathering, overseeing, processing, breaking down, and visualizing the huge volume of organized or unstructured data. Data stream mining indicates how to look at Unknown patterns from a massive amount of data over algorithms. It has experienced quick improvement with significant progress in math, statistics, data science, and computer science domains. Data streams are commonly generated by various sources such as sensor networks, social media feeds, financial transactions, online retail, network traffic, and many other applications. The gathered data could be additionally utilized for various purposes, for example, execution assessment, irregularity discovery, change identification, or issue finding of the operating systems. This data stream analysis is done using different data stream mining techniques. This paper provides a broad overview of the distinct approaches used for data stream mining. Initially, we studied the different techniques of data stream mining. Next, we discuss the different clustering and classification techniques and their benefits. Then we examine the evaluation of different data stream mining techniques results that some techniques are feasible for real-time data streams and some of not. This study provides a complete understanding of techniques and their benefits. The studies done so far need to be sufficiently exhaustive for data mining techniques, so future work is needed to assess which technique is feasible for real-time data streams.

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A Ravi Kishore, Dr Gururaj Murtugudde. "Secure And Adaptive Data Stream Mining For New Generation Big Data." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 2055–61. http://dx.doi.org/10.52783/tjjpt.v44.i4.1180.

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As the era of new-generation big data applications unfolds, the need for secure and adaptive data stream mining has become increasingly paramount. Evolving databases, characterized by ever-changing data streams and dynamic data distributions, present unique challenges and opportunities. This paper addresses the crucial intersection of security and adaptability in the context of data stream mining for new-generation big data. First, we delve into the evolving landscape of big data, where real-time data streams from diverse sources drive decision-making processes. Ensuring the privacy and security of sensitive information within these data streams is a fundamental concern. We explore cryptographic techniques, anonymization methods, and access control mechanisms that safeguard data while allowing for meaningful analysis. We present novel adaptive algorithms and model update strategies that can continuously learn and adjust to changing data distributions. These approaches enable data stream mining to remain effective and accurate over time. This paper offers insights into the fusion of security and adaptability in data stream mining, providing a foundation for the development of robust and privacy-conscious solutions for the evolving landscape of new-generation big data applications.

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Mansour, ManaL, and Manal Abdullah. "Mining Techniques for Streaming Data." International Journal of Data Mining & Knowledge Management Process 12, no. 2 (2022): 1–14. http://dx.doi.org/10.5121/ijdkp.2022.12201.

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The huge explosion in using real time technology leads to infinite flow of data which known as data streams. The characteristics of streaming data require different techniques for processing due its volume, velocity and volatility, beside issues related to the limited storage capabilities. Hence, this research highlights the significant aspects to consider when building a framework for mining data streams. It reviews the methods for data stream summarizing and creating synopsis, and the approaches of processing these data synopses. The goal is to present a model for mining the streaming data which describes the main phases of data stream manipulation.

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CHEN, HUI. "EFFICIENTLY MINING RECENT FREQUENT PATTERNS OVER ONLINE TRANSACTIONAL DATA STREAMS." International Journal of Software Engineering and Knowledge Engineering 19, no. 05 (2009): 707–25. http://dx.doi.org/10.1142/s0218194009004325.

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Recent emerging applications, such as network traffic analysis, web click stream mining, power consumption measurement, sensor network data analysis, and dynamic tracing of stock fluctuation, call for study of a new kind of data, stream data. Many data stream management systems, prototype systems and software components have been developed to manage the streams or extract knowledge from stream data. Mining frequent patterns is a foundational job for the methods of data mining and knowledge discovery. This paper proposes an algorithm for mining the recent frequent patterns over an online data stream. This method uses RFP-tree to store compactly the recent frequent patterns of a stream. The content of each transaction is incrementally updated into the pattern tree upon its arrival by scanning the stream only once. Moreover, the strategy of conservative computation and time decaying model are used to ensure the correctness of the mining results. Finally, the performance results of extensive simulation show that our work can reduce the average processing time of stream data element and it is superior to other analogous algorithms.

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Eiman, Alothali, Alashwal Hany, and Harous Saad. "Data stream mining techniques: a review." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 2 (2019): 728–37. https://doi.org/10.12928/TELKOMNIKA.v17i2.11752.

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A plethora of infinite data is generated from the Internet and other information sources. Analyzing this massive data in real-time and extracting valuable knowledge using different mining applications platforms have been an area for research and industry as well. However, data stream mining has different challenges making it different from traditional data mining. Recently, many studies have addressed the concerns on massive data mining problems and proposed several techniques that produce impressive results. In this paper, we review real time clustering and classification mining techniques for data stream. We analyze the characteristics of data stream mining and discuss the challenges and research issues of data steam mining. Finally, we present some of the platforms for data stream mining.

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Grzenda, Maciej, Heitor Murilo Gomes, and Albert Bifet. "Delayed labelling evaluation for data streams." Data Mining and Knowledge Discovery 34, no. 5 (2019): 1237–66. http://dx.doi.org/10.1007/s10618-019-00654-y.

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AbstractA large portion of the stream mining studies on classification rely on the availability of true labels immediately after making predictions. This approach is well exemplified by the test-then-train evaluation, where predictions immediately precede true label arrival. However, in many real scenarios, labels arrive with non-negligible latency. This raises the question of how to evaluate classifiers trained in such circumstances. This question is of particular importance when stream mining models are expected to refine their predictions between acquiring instance data and receiving its true label. In this work, we propose a novel evaluation methodology for data streams when verification latency takes place, namely continuous re-evaluation. It is applied to reference data streams and it is used to differentiate between stream mining techniques in terms of their ability to refine predictions based on newly arriving instances. Our study points out, discusses and shows empirically the importance of considering the delay of instance labels when evaluating classifiers for data streams.

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Homayoun, Sajad, and Marzieh Ahmadzadeh. "A review on data stream classification approaches." Journal of Advanced Computer Science & Technology 5, no. 1 (2016): 8. http://dx.doi.org/10.14419/jacst.v5i1.5225.

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<p>Stream data is usually in vast volume, changing dynamically, possibly infinite, and containing multi-dimensional features. The attention towards data stream mining is increasing as regards to its presence in wide range of real-world applications, such as e-commerce, banking, sensor data and telecommunication records. Similar to data mining, data stream mining includes classification, clustering, frequent pattern mining etc. techniques; the special focus of this paper is on classification methods invented to handle data streams. Early methods of data stream classification needed all instances to be labeled for creating classifier models, but there are some methods (Semi-Supervised Learning and Active Learning) in which unlabeled data is employed as well as labeled data. In this paper, by focusing on ensemble methods, semi-supervised and active learning, a review on some state of the art researches is given.</p>

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Yi, Wenquan, Fei Teng, and Jianfeng Xu. "Noval Stream Data Mining Framework under the Background of Big Data." Cybernetics and Information Technologies 16, no. 5 (2016): 69–77. http://dx.doi.org/10.1515/cait-2016-0053.

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Abstract Stream data mining has been a hot topic for research in the data mining research area in recent years, as it has an extensive application prospect in big data ages. Research on stream data mining mainly focuses on frequent item sets mining, clustering and classification. However, traditional steam data mining methods are not effective enough for handling high dimensional data set because these methods are not fit for the characteristics of stream data. So, these traditional stream data mining methods need to be enhanced for big data applications. To resolve this issue, a hybrid framework is proposed for big steam data mining. In this framework, online and offline model are organized for different tasks, the interior of each model is rationally organized according to different mining tasks. This framework provides a new research idea and macro perspective for stream data mining under the background of big data.

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Anita A. Parmar. "Privacy Preserving Data Stream Classification: Recent Approaches and Open Challenges." Journal of Electrical Systems 20, no. 3 (2024): 2545–49. http://dx.doi.org/10.52783/jes.4235.

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With the relevant growth of big data stream, the research industry has great attention to data stream mining which has a wide range of applications like banking, education, networking, telecommunication, weather forecasting, a stock market, and so on. Because of this, privacy preserving in data stream mining is having more attention from researchers. In this paper, we mainly focus on review of privacy preserving classification methods for data streams, which applies classification algorithms to big data streams while ensuring the privacy of data. Recently, the emerging big data analytics context has conferred a new light to this exciting research area.

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Raviteja, Mellacheruvu, Vanguru Venkata Varun Kumar Reddy, Shaik Aqibuddin, and Hareendra Sri Nag Nerusu. "Real-Time Event Detection in Social Media Streams using Stream Data Mining." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 1676–82. http://dx.doi.org/10.22214/ijraset.2023.55328.

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Abstract: The need for real-time event detection has grown rapidly in the modern world of instantaneous information transmission through social media. In order to implement real-time event detection inside the dynamic environment of social media streams, this research article provides a ground-breaking framework that harnesses the power of stream data mining techniques. The combination of three different stream data mining algorithms—Sliding Window Analysis, Burst Detection using K-Means, and Agglomerative Hierarchical Clustering—allows us to tackle this problem. Together, these algorithms make it possible to extract important patterns, shedding light on how events arise inside social media streams. Utilizing cutting-edge stream data mining techniques, this study introduces a novel framework for real-time event detection within social media streams. The quick identification and monitoring of real-world events take on critical importance in the modern environment of rapid information dissemination through social media channels. The dynamic and high-velocity characteristics of social media data streams present difficulties for conventional event detection methodologies.

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Dissertations / Theses on the topic "Data stream mining"

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Tong, Suk-man Ivy. "Techniques in data stream mining." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B34737376.

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Tong, Suk-man Ivy, and 湯淑敏. "Techniques in data stream mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B34737376.

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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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Kranen, Philipp [Verfasser]. "Anytime algorithms for stream data mining / Philipp Kranen." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2011. http://d-nb.info/1018257942/34.

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Boedihardjo, Arnold Priguna. "Efficient Algorithms for Mining Data Streams." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28686.

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Data streams are ordered sets of values that are fast, continuous, mutable, and potentially unbounded. Examples of data streams include the pervasive time series which span domains such as finance, medicine, and transportation. Mining data streams require approaches that are efficient, adaptive, and scalable. For several stream mining tasks, knowledge of the data's probability density function (PDF) is essential to deriving usable results. Providing an accurate model for the PDF benefits a variety of stream mining applications and its successful development can have far-reaching impact to the general discipline of stream analysis. Therefore, this research focuses on the construction of efficient and effective approaches for estimating the PDF of data streams. In this work, kernel density estimators (KDEs) are developed that satisfy the stringent computational stipulations of data streams, model unknown and dynamic distributions, and enhance the estimation quality of complex structures. Contributions of this work include: (1) theoretical development of the local region based KDE; (2) construction of a local region based estimation algorithm; (3) design of a generalized local region approach that can be applied to any global bandwidth KDE to enhance estimation accuracy; and (4) application extension of the local region based KDE to multi-scale outlier detection. Theoretical development includes the formulation of the local region concept to effectively approximate the computationally intensive adaptive KDE. This work also analyzes key theoretical properties of the local region based approach which include (amongst others) its expected performance, an alternative local region construction criterion, and its robustness under evolving distributions. Algorithmic design includes the development of a specific estimation technique that reduces the time/space complexities of the adaptive KDE. In order to accelerate mining tasks such as outlier detection, an integrated set of optimizations are proposed for estimating multiple density queries. Additionally, the local region concept is extended to an efficient algorithmic framework which can be applied to any global bandwidth KDEs. The combined solution can significantly improve estimation accuracy while retaining overall linear time/space costs. As an application extension, an outlier detection framework is designed which can effectively detect outliers within multiple data scale representations.<br>Ph. D.

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Meng, Yu. "Extensible Markov model an efficient data mining framework for spatiotemporal stream data /." Ann Arbor, Mich. : ProQuest, 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:3258040.

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Thesis (Ph.D. in Computer Science)--S.M.U., 2007.<br>Title from PDF title page (viewed Mar. 18, 2008). Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1732. Adviser: Margaret H. Dunham. Includes bibliographical references.

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Wang, Dan Tong. "Outlier detection with data stream mining approach in high-dimenional time series data." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691091.

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Mumtaz, Ali. "Window-based stream data mining for classification of Internet traffic." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/27601.

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Accurate classification of Internet applications is a fundamental requirement for network provisioning, network security, maintaining quality of services and network management. Increasingly, new applications are being introduced on the Internet. The traffic volume and patterns of some of the new applications such as Peer-to-Peer (P2P) file sharing put pressure on service providers' networks in terms of congestion and delay, to the point that maintaining Quality of Services (QoS) planned in the access network requires the provisioning of additional bandwidth sooner than planned. Peer-to-Peer applications enable users to communicate directly over the Internet, thus bypassing central server control implemented by service providers and poses threats in terms of network congestion, and creating an environment for malicious attacks on networks. One key challenge in this area is to adapt to the dynamic nature of Internet traffic. With the growth in Internet traffic, in terms of number and type of applications, traditional classification techniques such as port matching, protocol decoding or packet payload analysis are no longer effective For instance, P2P applications may use randomly selected non-standard ports to communicate which makes it difficult to distinguish from other types of traffic only by inspecting port number. The present research introduces two new techniques to classify stream (online) data using K-means clustering and Fast Decision Tree (FDT). In the first technique, we first generate micro-clusters using k-means clustering with different values of k. Micro clusters are then merged into two clusters based on weighted averages of P2P and NonP2P population. This technique generates two merged clusters, each representing P2P or NonP2P traffic. The simulation results confirm that the two final clusters represent P2P and NonP2P traffic each with a good accuracy. The second technique employs a two-stage architecture for classification of P2P traffic, where in the first stage, the traffic is filtered using standard port numbers and layer 4 port matching to label well-known P2P and NonP2P traffics, leaving the rest of the traffic as "Unknown". The labeled traffic generated in the first stage is used to train a Fast Decision Tree (FDT) classifier with high accuracy. The Unknown traffic is then applied to the FDT model which classifies the traffic into P2P and NonP2P with high accuracy. The two-stage architecture, therefore, not only classifies well-known P2P applications, it also classifies applications that use random or private (non standard) port numbers and can not be classified otherwise. We performed various experiments where we captured Internet traffic at a main gateway router, pre-processed the data and selected three most significant attributes, namely Packet Length, Source IP address and Destination IP address. We then applied the proposed technique to three different windows of records. Accuracy, Specificity and Sensitivity of the model are calculated. Our simulation results confirm that the predicted output represents P2P and NonP2P traffic with accuracy higher than 90%.

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Silvestri, Claudio <1974&gt. "Distributed and stream data mining algorithms for frequent pattern discovery." Doctoral thesis, Università Ca' Foscari Venezia, 2006. http://hdl.handle.net/10579/143.

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Hung, Yee-shing Regant, and 洪宜成. "The complexities of tracking quantiles and frequent items in a data stream." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B41758183.

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Books on the topic "Data stream mining"

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Bifet, Albert. Adaptive stream mining: Pattern learning and mining from evolving data streams. IOS Press, 2010.

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Babichev, Sergii, Dmytro Peleshko, and Olena Vynokurova, eds. Data Stream Mining & Processing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61656-4.

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Rutkowski, Leszek, Maciej Jaworski, and Piotr Duda. Stream Data Mining: Algorithms and Their Probabilistic Properties. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-13962-9.

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G, Eppinger R., and Geological Survey (U.S.), eds. Geochemical data for environmental studies at Nabesna and Kennecott, Alaska: Water, leachates, stream-sediments, heavy-mineral-concentrates, and rocks. U.S. Geological Survey, 1995.

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United States. Bureau of Land Management, Colorado. Mined Land Reclamation Division, and Geological Survey (U.S.), eds. Indexes of hydrologic data from selected coal-mining areas in northwestern Colorado. Dept. of the Interior, U.S. Geological Survey, 1989.

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Ann, Alf Lee, and Geological Survey (U.S.), eds. Hydrologic data for selected streams in the coal area of southeastern Oklahoma, July 1978 to September 1982. Dept. of the Interior, U.S. Geological Survey, 1987.

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Arihood, Leslie D. Description of sediment data collected by the U.S. Geological Survey in small watersheds in coal-mining areas of the eastern United States, 1980-84. U.S. Dept. of the Interior, Geological Survey, 1986.

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Arihood, Leslie D. Description of sediment data collected by the U.S. Geological Survey in small watersheds in coal-mining areas of the eastern United States, 1980-84. U.S. Dept. of the Interior, Geological Survey, 1986.

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E, Kilburn James, and Geological Survey (U.S.), eds. Geochemical data and sample locality maps for stream-sediment, heavy-mineral-concentrate, mill tailing, water, and precipitate samples collected in and around the Holden mine, Chelan County, Washington. U.S. Geological Survey, 1994.

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1937-, Holmes Charles Ward, and Geological Survey (U.S.), eds. Geochemical and lead-isotope data from stream and lake sediments, and cores from the upper Arkansas River drainage: Effects of mining at Leadville Colorado on heavy-metal concentrations in the Arkansas River. U.S. Dept. of the Interior, U.S. Geological Survey, 1993.

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Book chapters on the topic "Data stream mining"

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Shekhar, Shashi, and Hui Xiong. "Stream Data Mining." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1357.

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Gaber, Mohamed Medhat, Arkady Zaslavsky, and Shonali Krishnaswamy. "Data Stream Mining." In Data Mining and Knowledge Discovery Handbook. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09823-4_39.

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Spiliopoulou, Myra, Eirini Ntoutsi, and Max Zimmermann. "Opinion Stream Mining." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_905-1.

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Spiliopoulou, Myra, Eirini Ntoutsi, and Max Zimmermann. "Opinion Stream Mining." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_905.

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Papadimitriou, Spiros, Anthony Brockwell, and Christos Faloutsos. "Adaptive, Automatic Stream Mining." In Data-Centric Systems and Applications. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-540-28608-0_24.

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Udommanetanakit, Komkrit, Thanawin Rakthanmanon, and Kitsana Waiyamai. "E-Stream: Evolution-Based Technique for Stream Clustering." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73871-8_58.

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Gama, João. "Trends in Data Stream Mining." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_15.

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AbstractLearning from data streams is a hot topic in machine learning and data mining. This article presents our recent work on the topic of learning from data streams. We focus on emerging topics, including fraud detection and hyper-parameter tuning for streaming data. The first study is a case study on interconnected by-pass fraud. This is a real-world problem from high-speed telecommunications data that clearly illustrates the need for online data stream processing. In the second study, we present an optimization algorithm for online hyper-parameter tuning from nonstationary data streams.

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Cherfi, Anis, and Kaouther Nouira. "Data Discretization for Data Stream Mining." In Agents and Multi-agent Systems: Technologies and Applications 2023. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3068-5_5.

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Xu, Yabo, Ke Wang, Ada Wai-Chee Fu, Rong She, and Jian Pei. "Privacy-Preserving Data Stream Classification." In Privacy-Preserving Data Mining. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-70992-5_20.

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Stefanowski, Jerzy, and Dariusz Brzezinski. "Stream Classification." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_908-1.

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Conference papers on the topic "Data stream mining"

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Han, Shujie, Zirui Ou, Qun Huang, and Patrick P. C. Lee. "Scaling Disk Failure Prediction via Multi-Source Stream Mining." In 2024 IEEE International Conference on Data Mining (ICDM). IEEE, 2024. https://doi.org/10.1109/icdm59182.2024.00020.

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Zhang, Shengyuan, Baiwei Sun, and Taiying Chen. "Research on Data Mining Technology Based on Data Stream Management Systems." In 2024 International Conference on Electronics and Devices, Computational Science (ICEDCS). IEEE, 2024. https://doi.org/10.1109/icedcs64328.2024.00209.

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"Session C: Dynamic data mining & data stream mining." In 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2016. http://dx.doi.org/10.1109/dsmp.2016.7583553.

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"Topic 3: Dynamic Data Mining & Data Stream Mining." In 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2020. http://dx.doi.org/10.1109/dsmp47368.2020.9204139.

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"Topic #2: Dynamic Data Mining & Data Stream Mining." In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2018. http://dx.doi.org/10.1109/dsmp.2018.8478573.

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Thakkar, Hetal, Barzan Mozafari, and Carlo Zaniolo. "A Data Stream Mining System." In 2008 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2008. http://dx.doi.org/10.1109/icdmw.2008.133.

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De Francisci Morales, Gianmarco, Albert Bifet, Latifur Khan, Joao Gama, and Wei Fan. "IoT Big Data Stream Mining." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016. http://dx.doi.org/10.1145/2939672.2945385.

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Michael, P. A., and D. Stott Parker. "Real-time spatio-temporal data mining with the “streamonas” data stream management system." In DATA MINING 2009. WIT Press, 2009. http://dx.doi.org/10.2495/data090121.

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Hu, Hanqing, and Mehmed Kantardzic. "Smart Preprocessing Improves Data Stream Mining." In 2016 49th Hawaii International Conference on System Sciences (HICSS). IEEE, 2016. http://dx.doi.org/10.1109/hicss.2016.220.

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Martín, Eva García, Niklas Lavesson, and Håkan Grahn. "Energy Efficiency in Data Stream Mining." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. ACM, 2015. http://dx.doi.org/10.1145/2808797.2808863.

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Reports on the topic "Data stream mining"

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Han, Keesook, Tao Zhang, and Qi Liao. Data Stream Mining Based Dynamic Link Anomaly Analysis Using Paired Sliding Time Window Data. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada613504.

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Wiltse, M. A., P. A. Metz, M. S. Robinson, and D. S. Pinney. Geochemical trace-element data for stream sediment samples collected in the Circle mining district, 1983. Alaska Division of Geological & Geophysical Surveys, 1994. http://dx.doi.org/10.14509/1655.

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Wiltse, M. A., P. A. Metz, M. S. Robinson, and D. S. Pinney. Electronic file of geochemical trace-element data for stream sediment samples collected in the Circle mining district, 1983. Alaska Division of Geological & Geophysical Surveys, 1994. http://dx.doi.org/10.14509/1656.

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Noll, R. S., and Jim Vohden. Investigation of stream sediment loads related to placer mining in the Goldstream Creek Basin: Preliminary TMDL Data Collection. Alaska Division of Geological & Geophysical Surveys, 1994. http://dx.doi.org/10.14509/1669.

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Szumigala, D. J., C. C. Puchner, and R. E. Myers. Geochemical data from reanalysis of stream-sediment samples collected in 1982 from the Livengood area, Tolovana mining district, Alaska. Alaska Division of Geological & Geophysical Surveys, 2005. http://dx.doi.org/10.14509/7063.

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Jozwik, Diana. Trace element geochemical data from reanalysis of stream-sediment samples collected in 1981 from the Fairbanks mining district, Alaska. Alaska Division of Geological & Geophysical Surveys, 2007. http://dx.doi.org/10.14509/15759.

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McCurdy, M. W., and R. J. McNeil. Geochemical data from stream silts and surface waters in the Pine Point Mining District, Northwest Territories (NTS 85-B). Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2014. http://dx.doi.org/10.4095/293913.

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Ray, S. R., and William Morgan. Investigation of stream sediment loads related to placer mining in the upper Birch Creek Basin, Alaska: preliminary TMDL data collection. Alaska Division of Geological & Geophysical Surveys, 1993. http://dx.doi.org/10.14509/1599.

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Kidder, J. A., M. B. McClenaghan, M I Leybourne, et al. Geochemical data for stream and groundwaters around the Casino Cu-Au-Mo porphyry deposit, Yukon (NTS 115 J/10 and 115 J/15). Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328862.

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Abstract:

This open file reports geochemical data for stream and groundwater samples collected around the Casino porphyry Cu-Au-Mo deposit, one of the largest and highest-grade deposits of its kind in Canada. The calc-alkaline porphyry is hosted in a Late Cretaceous quartz monzonite and associated breccias in the unglaciated region of west central Yukon. Water chemistry around the deposit was investigated because: (i) the deposit has not yet been disturbed by mining; (ii) the deposit was known to have metal-rich waters in local streams; and (iii) the deposit has atypically preserved ore zones. Stream water samples were collected at 22 sites and groundwater samples were collected from eight sites. Surface and groundwaters around the Casino deposit are anomalous with respect to Cd (up to 5.4 µg/L), Co (up to 64 µg/L), Cu (up to 1657 µg/L), Mo (up to 25 µg/L), As (up to 17 µg/L), Re (up to 0.7 µg/L), and Zn (up to 354 µg/L) concentrations. The stable isotopes of O and H of the groundwaters are essentially identical to the surface waters and plot close to the local and global meteoric water lines, indicating that the waters represent modern recharge, consistent with the generally low salinities of all the waters (total dissolved solids range from 98 to 1320 mg/L). Sulfur and Sr isotopes are consistent with proximal waters interacting with the Casino rocks and mineralization; a sulfide-rich bedrock sample from the deposit has delta-34S = -1.2 permille and proximal groundwaters are only slightly heavier (-0.3 to 3.1 permille). These geochemical and isotopic results indicate that surface water geochemistry is a suitable medium for mineral exploration for porphyry-style mineralization in the Yukon, and similar unglaciated regions in Canada. The atypical geochemical signature (Mo, Se, Re, As, Cu) of these types of deposits are typically reflected in the water chemistry and S isotopes provide a more local vectoring tool.

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Athey, J. E., L. K. Freeman, M. B. Werdon, et al. Major-oxide, minor-oxide, and trace-element geochemical data from rocks and stream sediments collected in the northern Fairbanks mining district, Circle Quadrangle, Alaska in 2007. Alaska Division of Geological & Geophysical Surveys, 2008. http://dx.doi.org/10.14509/15901.

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