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Relevant bibliographies by topics / Streaming Data Analysis

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Academic literature on the topic 'Streaming Data Analysis'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Streaming Data Analysis"

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Chowdhury, Sugnik Roy. "Twitter Data Analysis by Live Streaming." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3178–82. http://dx.doi.org/10.1166/jctn.2019.8156.

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Streaming now a days have been of great use when comes to Social Media. Streaming of data have made it easy for Companies to understand the pros and cons of their product. Streaming acts as a survey now a days which a few years ago were done by a team of individual using pen and papers. In order to collect and process the streaming data from various streaming sites to produce an analytical report that helps to get a clear pictorial representation of events, the assets of streaming process generates a huge volume of real time data mainly referred to as “Big Data.” In order to aggregate, store and analyses the streaming data that are being generated Day-By-Day we get into the concept of Hadoop and Flume Technologies, API that helps to collect data from Twitter/other streaming sites by using “#” tag/Keywords. Tweets by the News channel and retweets by the public are being collected.

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Ibrahim, Omar A., Yiqing Wang, and James M. Keller. "Analysis of Incremental Cluster Validity for Big Data Applications." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, Suppl. 2 (2018): 47–62. http://dx.doi.org/10.1142/s0218488518400111.

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Online clustering has attracted attention due to the explosion of ubiquitous continuous sensing. Streaming clustering algorithms need to look for new structures and adapt as the data evolves, such that outliers are detected, and that new emerging clusters are automatically formed. The performance of a streaming clustering algorithm needs to be monitored over time to understand the behavior of the streaming data in terms of new emerging clusters and number of outlier data points. Small datasets with 2 or 3 dimensions can be monitored by plotting the clustering results as data evolves. However, as the size and dimensions of streaming data increase, plotting the clustering result becomes unfeasible. Therefore, incremental internal Validity Indices (iCVIs) could be applied for monitoring the performance of an online clustering algorithm. In this paper, we study the internal incremental Davies-Bouldin (iDB) cluster validity index in the context of big streaming data analysis. Also, we study the effect of large number of samples on the values of the iCVI (iDB). Finally, we propose a way to project streaming data into a lower space for cases where the distance measure does not perform as expected in the high dimensional space.

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Kim, Kyeongjoo. "Real-time Streaming Data Analysis using Spark." International Journal of Emerging Trends in Engineering Research 6, no. 1 (2018): 1–5. http://dx.doi.org/10.30534/ijeter/2018/01612018.

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Guo, Shu-Hui, and Xin Lu. "Live streaming: Data mining and behavior analysis." Acta Physica Sinica 69, no. 8 (2020): 088908. http://dx.doi.org/10.7498/aps.69.20191776.

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Bagui, Sikha, and Katie Jin. "A Survey of Challenges Facing Streaming Data." Transactions on Machine Learning and Artificial Intelligence 8, no. 4 (2020): 63–73. http://dx.doi.org/10.14738/tmlai.84.8579.

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This survey performs a thorough enumeration and analysis of existing methods for data stream processing. It is a survey of the challenges facing streaming data. The challenges addressed are preprocessing of streaming data, detection and dealing with concept drifts in streaming data, data reduction in the face of data streams, approximate queries and blocking operations in streaming data.

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López-Lagunas, Abelardo, and Sek Chai. "Streaming Data Movement for Real-Time Image Analysis." Journal of Signal Processing Systems 62, no. 1 (2009): 29–42. http://dx.doi.org/10.1007/s11265-008-0336-x.

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Matteussi, Kassiano J., Julio C. S. dos Anjos, Valderi R. Q. Leithardt, and Claudio F. R. Geyer. "Performance Evaluation Analysis of Spark Streaming Backpressure for Data-Intensive Pipelines." Sensors 22, no. 13 (2022): 4756. http://dx.doi.org/10.3390/s22134756.

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A significant rise in the adoption of streaming applications has changed the decision-making processes in the last decade. This movement has led to the emergence of several Big Data technologies for in-memory processing, such as the systems Apache Storm, Spark, Heron, Samza, Flink, and others. Spark Streaming, a widespread open-source implementation, processes data-intensive applications that often require large amounts of memory. However, Spark Unified Memory Manager cannot properly manage sudden or intensive data surges and their related in-memory caching needs, resulting in performance and throughput degradation, high latency, a large number of garbage collection operations, out-of-memory issues, and data loss. This work presents a comprehensive performance evaluation of Spark Streaming backpressure to investigate the hypothesis that it could support data-intensive pipelines under specific pressure requirements. The results reveal that backpressure is suitable only for small and medium pipelines for stateless and stateful applications. Furthermore, it points out the Spark Streaming limitations that lead to in-memory-based issues for data-intensive pipelines and stateful applications. In addition, the work indicates potential solutions.

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Okan, Emmanuel Tettey. "Forensic Analysis on Streaming Multimedia." Advances in Multidisciplinary and scientific Research Journal Publication 1, no. 1 (2022): 221–26. http://dx.doi.org/10.22624/aims/crp-bk3-p36.

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Since the advent of technology and digitalization of multimedia, there has been a massive increase in cybercrime. During streaming, with the availability of a network or internet source, multimedia; audio and visual can easily be accessed whiles being aired live. This technology dates as far back as 1990s. Similar to still videos and images, the user is able to download, pause, reverse or forward the show. The ability to stream multimedia has made it easier for users to partake or retrieve multimedia from the comfort of their homes, offices or personal spaces without necessarily being present. However, there are several challenges that affect the functionality of this technology, slow network connection and cybercrime. The issue of slow network may easily be handled by network providers, but cybercrimes has become rampant over the years. These attackers, also known as cyber criminals, use various activities to attack data. Some of their activities include phishing, data breach, identity theft and harassment. The paper has been written to assess forensic analysis of streaming multimedia. While exploring existing studies, it was realized that despite the rich availability of digital image forensics, video forensics hasn’t been explored much. This is because of the difficulty involved in analyzing the video data. Video data is always presented in a compressed form, unlike still images that are obtained in their original state. The compressed data often cancels or totally compromises the existing fingerprints, hence making it difficult to monitor or recover data. It was also revealed that, much has not been done so far as the research area is concerned. Keywords: Mobile Forensics, Cybersecurity, Streaming, Media, Video, Networks BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security & Forensics. Open Access. Distributed Free Citation: Emmanuel Tettey Okan (2022): Forensic Analysis On Streaming Multimedia Book Chapter Series on Research Nexus in IT, Law, Cyber Security & Forensics. Pp 221-226 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P36

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Adewole, Kayode S., Taofeekat T. Salau-Ibrahim, Agbotiname Lucky Imoize, et al. "Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection." Electronics 11, no. 19 (2022): 3109. http://dx.doi.org/10.3390/electronics11193109.

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Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA (82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended.

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Lee, Heung Ki, Jaehee Jung, Kyung Jin Ahn, Hwa-Young Jeong, and Gangman Yi. "Numeric Analysis for Relationship-Aware Scalable Streaming Scheme." Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/195781.

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Frequent packet loss of media data is a critical problem that degrades the quality of streaming services over mobile networks. Packet loss invalidates frames containing lost packets and other related frames at the same time. Indirect loss caused by losing packets decreases the quality of streaming. A scalable streaming service can decrease the amount of dropped multimedia resulting from a single packet loss. Content providers typically divide one large media stream into several layers through a scalable streaming service and then provide each scalable layer to the user depending on the mobile network. Also, a scalable streaming service makes it possible to decode partial multimedia data depending on the relationship between frames and layers. Therefore, a scalable streaming service provides a way to decrease the wasted multimedia data when one packet is lost. However, the hierarchical structure between frames and layers of scalable streams determines the service quality of the scalable streaming service. Even if whole packets of layers are transmitted successfully, they cannot be decoded as a result of the absence of reference frames and layers. Therefore, the complicated relationship between frames and layers in a scalable stream increases the volume of abandoned layers. For providing a high-quality scalable streaming service, we choose a proper relationship between scalable layers as well as the amount of transmitted multimedia data depending on the network situation. We prove that a simple scalable scheme outperforms a complicated scheme in an error-prone network. We suggest an adaptive set-top box (AdaptiveSTB) to lower the dependency between scalable layers in a scalable stream. Also, we provide a numerical model to obtain the indirect loss of multimedia data and apply it to various multimedia streams. Our AdaptiveSTB enhances the quality of a scalable streaming service by removing indirect loss.

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Dissertations / Theses on the topic "Streaming Data Analysis"

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Anagnostopoulos, Christoforos. "A Statistical Framework for Streaming Data Analysis." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520838.

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Patni, Harshal Kamlesh. "Real Time Semantic Analysis of Streaming Sensor Data." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1324181415.

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Fairbanks, James Paul. "Graph analysis combining numerical, statistical, and streaming techniques." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54972.

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Graph analysis uses graph data collected on a physical, biological, or social phenomena to shed light on the underlying dynamics and behavior of the agents in that system. Many fields contribute to this topic including graph theory, algorithms, statistics, machine learning, and linear algebra. This dissertation advances a novel framework for dynamic graph analysis that combines numerical, statistical, and streaming algorithms to provide deep understanding into evolving networks. For example, one can be interested in the changing influence structure over time. These disparate techniques each contribute a fragment to understanding the graph; however, their combination allows us to understand dynamic behavior and graph structure. Spectral partitioning methods rely on eigenvectors for solving data analysis problems such as clustering. Eigenvectors of large sparse systems must be approximated with iterative methods. This dissertation analyzes how data analysis accuracy depends on the numerical accuracy of the eigensolver. This leads to new bounds on the residual tolerance necessary to guarantee correct partitioning. We present a novel stopping criterion for spectral partitioning guaranteed to satisfy the Cheeger inequality along with an empirical study of the performance on real world networks such as web, social, and e-commerce networks. This work bridges the gap between numerical analysis and computational data analysis.

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Menglei, Min. "Anomaly detection based on multiple streaming sensor data." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36275.

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Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the application of anomaly detection in the field of data mining is very extensive. This paper proposes an anomaly detection method based on multiple streaming sensor data and performs anomaly detection on three data sets which are from the real company. First, this project proposes the state transition detection algorithm, state classification algorithm, and the correlation analysis method based on frequency. Then two algorithms were implemented in Python, and then make the correlation analysis using the results from the system to find some possible meaningful relations which can be used in the anomaly detection. Finally, calculate the accuracy and time complexity of the system, and then evaluated its feasibility and scalability. From the evaluation result, it is concluded that the method

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Giannini, Andrea. "Social Network Analysis: Architettura Streaming Big Data di Raccolta e Analisi Dati da Twitter." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25378/.

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Negli ultimi anni i social media, come ad esempio Facebook, Twitter, WhatsApp, YouTube, si sono diffusi a macchia d'olio. Ormai quasi tutti accedono giornalmente su almeno uno di questi per informarsi, esprimere opinioni e interagire con altri utenti. Per questa ragione sono diventati fondamentali per i reparti marketing delle aziende essendo non solo un ottimo canale di comunicazione, ma anche una fonte di informazioni sui clienti e potenziali tali. La tesi si focalizza proprio su quest'ultimo aspetto. Il progetto Social Network Analysis (SNA) vuole essere infatti uno strumento attraverso il quale è possibile visionare e analizzare per intero le reti di interazione tra utenti. Ci si è posti l'obiettivo di realizzare SNA in modo che raccogliesse e si aggiornasse in tempo reale, così da essere sempre al passo con le ultime novità, data la dinamicità delle informazioni all'interno dei social media. Un progetto come SNA comporta dover affrontare diversi ostacoli. Oltre a quello di riuscire a realizzare un'architettura che accolga un flusso continuo di informazioni, uno degli ostacoli più importanti è quello di gestire la grande mole di dati. Per farlo ci si è affidati ad un'architettura distribuita e facilmente scalabile che comprende l'uso di elaborazioni in cluster, di funzioni serverless e di database NoSQL approvvigionati attraverso il servizio cloud di Microsoft, Azure. In questa tesi SNA è stato progettato e implementato basandosi su Twitter, ma è possibile sfruttare la stessa idea su tanti altri social media.

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Kühn, Eileen [Verfasser], and A. [Akademischer Betreuer] Streit. "Online Analysis of Dynamic Streaming Data / Eileen Kühn ; Betreuer: A. Streit." Karlsruhe : KIT-Bibliothek, 2018. http://d-nb.info/1161008721/34.

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Moitra, Anindya. "Computation and Application of Persistent Homology on Streaming Data." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613686214764863.

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Zubeir, Abdulghani Ismail. "OAP: An efficient online principal component analysis algorithm for streaming EEG data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392403.

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Data processing on streaming data poses computational as well as statistical challenges. Streaming data requires that data processing algorithms are able to process a new data point within micro-seconds. This is especially challenging on dimension reduction, where traditional methods as Principal Component Analysis (PCA) require eigenvectors decomposition of a matrix based on the complete dataset. So a proper online version of PCA should avoid this computational involved step in favor for a more efficient update rule. This is implemented by an algorithm named Online Angle Preservation (OAP), which is able to handle large dimensions in the required time limitations. This project presents an application of OAP in the case of Electroencephalography (EEG). For this, an interface was coded from an openBCI EEG device, through a Java API to a streaming environment called Stream Analyzer (sa.engine). The performance of this solution was compared to a standard Windowised PCA solution, indicating its competitive performance. This report details this setup and details the results.

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Vigraham, Sushrutha. "Design and Analysis of a Real-time Data Monitoring Prototype for the LWA Radio Telescope." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/31306.

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Increasing computing power has been helping researchers understand many complex scientific problems. Scientific computing helps to model and visualize complex processes such as molecular modelling, medical imaging, astrophysics and space exploration by processing large set of data streams collected through sensors or cameras. This produces a massive amount of data which consume a large amount of processing and storage resources. Monitoring the data streams and filtering unwanted information will enable efficient use of the available resources. This thesis proposes a data-centric system that can monitor high-speed data streams in real-time. The proposed system provides a flexible environment where users can plug-in application-specific data monitoring algorithms. The Long Wavelength Array telescope (LWA) is an astronomical apparatus that works with high speed data streams, and the proposed data-centric platform is developed to evaluate FPGAs to implement data monitoring algorithms in LWA. The throughput of the data-centric system has been modeled and it is observed that the developed data-centric system can deliver a maximum throughput of 164 MB/s.<br>Master of Science

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Landford, Jordan. "Event Detection Using Correlation within Arrays of Streaming PMU Data." PDXScholar, 2016. http://pdxscholar.library.pdx.edu/open_access_etds/3031.

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This thesis provides a synchrophasor data analysis methodology that leverages both statistical correlation techniques and a statistical distribution in order to identify data inconsistencies, as well as power system contingencies. This research utilizes archived Phasor Measurement Unit (PMU) data obtained from the Bonneville Power Administration in order to show that this methodology is not only feasible, but extremely useful for power systems monitoring, decision support, and planning purposes. By analyzing positive sequence voltage angles between a pair of PMUs at two different substation locations, an historic record of correlation is established. From this record, a Rayleigh distribution of correlation coefficients is calculated. The statistical parameters of this Rayleigh distribution are used to infer occurrences of power system and data events. To monitor an entire system, a simple solution would be observing each of these parameters for every PMU combination. One issue with this approach is that correlation of some PMU pairs may be redundant or yield little value to monitoring capabilities. Additionally, this approach quickly encounters scalability issues as each additional PMU adds considerably to computation - for example, if the system contains n PMUs the amount of computations will be n(n-1)/2. System-wide monitoring of these parameters in this fashion is cumbersome and inefficient. To address these issues, an alternative scheme is proposed which involves monitoring only a subset of PMUs characterized by electrically coupled zones, or clusters, of PMUs. These clusters include both electrically-distant and electrically-near PMU sites. When monitored over an event, these yield statistical parameters sufficient for detecting event occurrences. This clustering scheme can be utilized to significantly decrease computation time and allocation of resources while maintaining optimal system observability. Results from the statistical methods are presented for a select few case studies for both data and power system event detection. In addition, determination of cluster size and content is discussed in detail. Lastly, the viability of monitoring pertinent statistical parameters over various clustering schemes is demonstrated.

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Books on the topic "Streaming Data Analysis"

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Roumeliotis, Rachel, ed. Visualizing Streaming Data: Interactive Analysis Beyond Static Limits. O’Reilly Media, 2018.

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Shumilina, Vera, Galina Krohicheva, Nataliya Izvarina, et al. Application of accounting, analysis and audit in enterprise management. AUS PUBLISHERS, 2021. http://dx.doi.org/10.26526/monography_618ba6f2989171.05397055.

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It is impractical to plan the further work of the company without taking into account analytical data for previous production periods. Analytical accounting data allows managers to build a company's strategy or make changes to an existing development plan. The importance of accounting for the enterprise plays a large role at the planning stage of the further business strategy. A competent manager studies accounting data before making the next decision regarding the company's finances. The easiest way to streamline accounting documents and eliminate errors in it is to conduct an internal audit. Such an audit will protect the company from possible fines and problems with tax audits. It will help optimize accounting and document flow, and simplify relations with banks and counterparties. Economic analysis aims to turn economic and non-economic information into useful information for decision making. Logical processing, study, generalization of facts, their systematization, conclusions, proposals, search for reserves - all these tasks are solved within the framework of economic analysis, which is designed to ensure the validity of management decisions and increase its effectiveness. This monograph is a collective work of teachers and students of the Department of Economic Security, Accounting and Law of the Don State Technical University. It is devoted to the consideration of certain issues of accounting, audit and economic analysis at the enterprise in modern conditions.

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Books, Worth. Summary and Analysis of Streaming, Sharing, Stealing : Big Data and the Future of Entertainment: Based on the Book by Michael D. Smith and Rahul Telang. Worth Books, 2017.

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Jaeckle, Jeff, and Susan Ryan, eds. ReFocus: The Films of Barbara Kopple. Edinburgh University Press, 2019. http://dx.doi.org/10.3366/edinburgh/9781474439947.001.0001.

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Building on existing interviews, journal articles, and archival research, The Films of Barbara Kopple assesses Kopple’s entire career to date, paying particular attention historical contexts, technique, critical reception, and ongoing influence. Each chapter blends close analyses of the films with insights drawn from film history and documentary studies to demonstrate that Kopple has consistently and often doggedly pursued projects that document the experiences of the victimized, the voiceless, and those in crisis. The contributors treat the entire scope of Kopple’s career, from her work in the early 1970s as an intern for David and Albert Maysles, to her mid-career experiments with commercial television and fictional projects, and finally to her recent forays into digital streaming platforms such as YouTube. The book also provides cultural contexts for Kopple’s films, including representations of class, gender, sexuality, and race. Finally, it assesses the contours of Kopple’s critical reputation and popularity, including her influence on contemporary filmmakers. In all, the book aims to stir interest in the life and films of Barbara Kopple, reminding readers why her films continue to be culturally significant.

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Book chapters on the topic "Streaming Data Analysis"

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Loia, Vincenzo, Francesco Orciuoli, and Angelo Gaeta. "Data Streaming Scenarios." In Computational Techniques for Intelligence Analysis. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20851-5_10.

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Cormode, Graham. "Streaming Methods in Data Analysis." In Data Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20424-6_1.

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Bezdek, James C. "Structural Assessment in Streaming Data." In Elementary Cluster Analysis. River Publishers, 2022. http://dx.doi.org/10.1201/9781003338086-14.

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Daruki, Samira, Justin Thaler, and Suresh Venkatasubramanian. "Streaming Verification in Data Analysis." In Algorithms and Computation. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48971-0_60.

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Chinthala, Santhi, Ramesh Mande, Suneetha Manne, and Sindhura Vemuri. "Sentiment Analysis on Twitter Streaming Data." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13728-5_18.

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Sarmento, Rui, Márcia Oliveira, Mário Cordeiro, Shazia Tabassum, and João Gama. "Social Network Analysis in Streaming Call Graphs." In Studies in Big Data. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26989-4_10.

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Zhang, Xudong, Zhongwen Qian, Siqi Shen, Jia Shi, and Shujun Wang. "Streaming Massive Electric Power Data Analysis Based on Spark Streaming." In Database Systems for Advanced Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18590-9_14.

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Chen, Jianzong, Hanlu Li, Qing Xie, Lin Li, and Yongjian Liu. "Streaming Recommendation Algorithm with User Interest Drift Analysis." In Web and Big Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26075-0_10.

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Kurte, Kuldeep, Neena Imam, S. M. Shamimul Hasan, and Ramakrishnan Kannan. "Phoenix: A Scalable Streaming Hypergraph Analysis Framework." In Advances in Data Science and Information Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71704-9_1.

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Carmona-Cejudo, José M., Manuel Baena-García, José del Campo-Ávila, Albert Bifet, João Gama, and Rafael Morales-Bueno. "Online Evaluation of Email Streaming Classifiers Using GNUsmail." In Advances in Intelligent Data Analysis X. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24800-9_11.

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Conference papers on the topic "Streaming Data Analysis"

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Ferdman, Michael, and Babak Falsafi. "Last-Touch Correlated Data Streaming." In 2007 IEEE International Symposium on Performance Analysis of Systems & Software. IEEE, 2007. http://dx.doi.org/10.1109/ispass.2007.363741.

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Balduini, Marco, Sivam Pasupathipillai, and Emanuele Delia Valle. "Cost-Aware Streaming Data Analysis." In DEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems. ACM, 2018. http://dx.doi.org/10.1145/3210284.3210294.

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Kang, Sungjoo, Kyung I. Ku, Sung Jin Hur, and Wan Choi. "Analysis of Software Streaming Data." In The 9th International Conference on Advanced Communication Technology. IEEE, 2007. http://dx.doi.org/10.1109/icact.2007.358576.

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Katramatos, Dimitrios, Meng Yue, Shinjae Yoo, Kerstin Kleese van Dam, Jin Xu, and Jiayao Zhang. "Streaming data analysis on the wire." In 2016 New York Scientific Data Summit (NYSDS). IEEE, 2016. http://dx.doi.org/10.1109/nysds.2016.7747816.

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Hoque, Sheik, and Andriy Miranskyy. "Architecture for Analysis of Streaming Data." In 2018 IEEE International Conference on Cloud Engineering (IC2E). IEEE, 2018. http://dx.doi.org/10.1109/ic2e.2018.00053.

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Wylie, Brian, Daniel Dunlavy, Warren Davis, and Jeff Baumes. "Using NoSQL databases for streaming network analysis." In 2012 IEEE Symposium on Large Data Analysis and Visualization (LDAV). IEEE, 2012. http://dx.doi.org/10.1109/ldav.2012.6378986.

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Kim, Hwi-Gang, Seongjoo Lee, and Sunghyon Kyeong. "Discovering hot topics using Twitter streaming data." In ASONAM '13: Advances in Social Networks Analysis and Mining 2013. ACM, 2013. http://dx.doi.org/10.1145/2492517.2500286.

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Lin, Yuewei, Dmitri Zakharov, Remi Megret, Shinjae Yoo, and Eric Stach. "Near real time ETEM streaming video analysis." In 2017 New York Scientific Data Summit (NYSDS). IEEE, 2017. http://dx.doi.org/10.1109/nysds.2017.8085054.

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Zhang, Hai, Zhuxu Yang, and Wenming Guo. "Threshold Sampling for Network Streaming Data Analysis." In 2008 International Conference on Advanced Computer Theory and Engineering (ICACTE). IEEE, 2008. http://dx.doi.org/10.1109/icacte.2008.109.

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Dijkman, Remco, Sander Peters, and Arthur ter Hofstede. "A Toolkit for Streaming Process Data Analysis." In 2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW). IEEE, 2016. http://dx.doi.org/10.1109/edocw.2016.7584341.

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Reports on the topic "Streaming Data Analysis"

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Sadot, Einat, Christopher Staiger, and Mohamad Abu-Abied. Studies of Novel Cytoskeletal Regulatory Proteins that are Involved in Abiotic Stress Signaling. United States Department of Agriculture, 2011. http://dx.doi.org/10.32747/2011.7592652.bard.

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In the original proposal we planned to focus on two proteins related to the actin cytoskeleton: TCH2, a touch-induced calmodulin-like protein which was found by us to interact with the IQ domain of myosin VIII, ATM1; and ERD10, a dehydrin which was found to associate with actin filaments. As reported previously, no other dehydrins were found to interact with actin filaments. In addition so far we were unsuccessful in confirming the interaction of TCH2 with myosin VIII using other methods. In addition, no other myosin light chain candidates were found in a yeast two hybrid survey. Nevertheless we have made a significant progress in our studies of the role of myosins in plant cells. Plant myosins have been implicated in various cellular activities, such as cytoplasmic streaming (1, 2), plasmodesmata function (3-5), organelle movement (6-10), cytokinesis (4, 11, 12), endocytosis (4, 5, 13-15) and targeted RNA transport (16). Plant myosins belong to two main groups of unconventional myosins: myosin XI and myosin VIII, both closely related to myosin V (17-19). The Arabidopsis myosin family contains 17 members: 13 myosin XI and four myosin VIII (19, 20). The data obtained from our research of myosins was published in two papers acknowledging BARD funding. To address whether specific myosins are involved with the motility of specific organelles, we cloned the cDNAs from neck to tail of all 17 Arabidopsis myosins. These were fused to GFP and used as dominant negative mutants that interact with their cargo but are unable to walk along actin filaments. Therefore arrested organelle movement in the presence of such a construct shows that a particular myosin is involved with the movement of that particular organelle. While no mutually exclusive connections between specific myosins and organelles were found, based on overexpression of dominant negative tail constructs, a group of six myosins (XIC, XIE, XIK, XI-I, MYA1 and MYA2) were found to be more important for the motility of Golgi bodies and mitochondria in Nicotiana benthamiana and Nicotiana tabacum (8). Further deep and thorough analysis of myosin XIK revealed a potential regulation by head and tail interaction (Avisar et al., 2011). A similar regulatory mechanism has been reported for animal myosin V and VIIa (21, 22). In was shown that myosin V in the inhibited state is in a folded conformation such that the tail domain interacts with the head domain, inhibiting its ATPase and actinbinding activities. Cargo binding, high Ca2+, and/or phosphorylation may reduce the interaction between the head and tail domains, thus restoring its activity (23). Our collaborative work focuses on the characterization of the head tail interaction of myosin XIK. For this purpose the Israeli group built yeast expression vectors encoding the myosin XIK head. In addition, GST fusions of the wild-type tail as well as a tail mutated in the amino acids that mediate head to tail interaction. These were sent to the US group who is working on the isolation of recombinant proteins and performing the in vitro assays. While stress signals involve changes in Ca2+ levels in plants cells, the cytoplasmic streaming is sensitive to Ca2+. Therefore plant myosin activity is possibly regulated by stress. This finding is directly related to the goal of the original proposal.

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Visser, R., H. Kao, R. M. H. Dokht, A. B. Mahani, and S. Venables. A comprehensive earthquake catalogue for northeastern British Columbia: the northern Montney trend from 2017 to 2020 and the Kiskatinaw Seismic Monitoring and Mitigation Area from 2019 to 2020. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329078.

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To increase our understanding of induced seismicity, we develop and implement methods to enhance seismic monitoring capabilities in northeastern British Columbia (NE BC). We deploy two different machine learning models to identify earthquake phases using waveform data from regional seismic stations and utilize an earthquake database management system to streamline the construction and maintenance of an up-to-date earthquake catalogue. The completion of this study allows for a comprehensive catalogue in NE BC from 2014 to 2020 by building upon our previous 2014-2016 and 2017-2018 catalogues. The bounds of the area where earthquakes were located were between 55.5°N-60.0°N and 119.8°W-123.5°W. The earthquakes in the catalogue were initially detected by machine learning models, then reviewed by an analyst to confirm correct identification, and finally located using the Non-Linear Location (NonLinLoc) algorithm. Two distinct sub-areas within the bounds consider different periods to supplement what was not covered in previously published reports - the Northern Montney Trend (NMT) is covered from 2017 to 2020 while the Kiskatinaw Seismic Monitoring and Mitigation Area (KSMMA) is covered from 2019 to 2020. The two sub-areas are distinguished by the BC Oil &amp; Gas Commission (BCOGC) due to differences in their geographic location and geology. The catalogue was produced by picking arrival phases on continuous seismic waveforms from 51 stations operated by various organizations in the region. A total of 17,908 events passed our quality control criteria and are included in the final catalogue. Comparably, the routine Canadian National Seismograph Network (CNSN) catalogue reports 207 seismic events - all events in the CNSN catalogue are present in our catalogue. Our catalogue benefits from the use of enhanced station coverage and improved methodology. The total number of events in our catalogue in 2017, 2018, 2019, and 2020 were 62, 47, 9579 and 8220, respectively. The first two years correspond to seismicity in the NMT where poor station coverage makes it difficult to detect small magnitude events. The magnitude of completeness within the KSMMA (ML = ~0.7) is significantly smaller than that obtained for the NMT (ML = ~1.4). The new catalogue is released with separate files for origins, arrivals, and magnitudes which can be joined using the unique ID assigned to each event.

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