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Artículos de revistas sobre el tema "Text Data Streams"

Autor: Grafiati

Publicado: 28 de diciembre de 2024

Última modificación: 31 de julio de 2025

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1

Liu, Yu-Bao, Jia-Rong Cai, Jian Yin, and Ada Wai-Chee Fu. "Clustering Text Data Streams." Journal of Computer Science and Technology 23, no. 1 (2008): 112–28. http://dx.doi.org/10.1007/s11390-008-9115-1.

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2

Aggarwal, Charu C., and Philip S. Yu. "On clustering massive text and categorical data streams." Knowledge and Information Systems 24, no. 2 (2009): 171–96. http://dx.doi.org/10.1007/s10115-009-0241-z.

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3

FRAHLING, GEREON, PIOTR INDYK, and CHRISTIAN SOHLER. "SAMPLING IN DYNAMIC DATA STREAMS AND APPLICATIONS." International Journal of Computational Geometry & Applications 18, no. 01n02 (2008): 3–28. http://dx.doi.org/10.1142/s0218195908002520.

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A dynamic geometric data stream is a sequence of m ADD/REMOVE operations of points from a discrete geometric space {1,…, Δ} d ?. ADD (p) inserts a point p from {1,…, Δ} d into the current point set P , REMOVE(p) deletes p from P . We develop low-storage data structures to (i) maintain ε-nets and ε-approximations of range spaces of P with small VC-dimension and (ii) maintain a (1 + ε)-approximation of the weight of the Euclidean minimum spanning tree of P . Our data structure for ε-nets uses [Formula: see text] bits of memory and returns with probability 1 – δ a set of [Formula: see text] point

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4

Zhang, Yuhong, Guang Chu, Peipei Li, Xuegang Hu, and Xindong Wu. "Three-layer concept drifting detection in text data streams." Neurocomputing 260 (October 2017): 393–403. http://dx.doi.org/10.1016/j.neucom.2017.04.047.

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5

Russo, Matthew, Tatsunori Hashimoto, Daniel Kang, Yi Sun, and Matei Zaharia. "Accelerating Aggregation Queries on Unstructured Streams of Data." Proceedings of the VLDB Endowment 16, no. 11 (2023): 2897–910. http://dx.doi.org/10.14778/3611479.3611496.

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Analysts and scientists are interested in querying streams of video, audio, and text to extract quantitative insights. For example, an urban planner may wish to measure congestion by querying the live feed from a traffic camera. Prior work has used deep neural networks (DNNs) to answer such queries in the batch setting. However, much of this work is not suited for the streaming setting because it requires access to the entire dataset before a query can be submitted or is specific to video. Thus, to the best of our knowledge, no prior work addresses the problem of efficiently answering queries

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6

Devendra, Kumar Mishra*1. "CHALLENGES IN TEXT MINING FOR BUSINESS INTELLIGENCE." International Journal of Engineering Technologies and Management Research 5, no. 2 (SE) (2018): 301–4. https://doi.org/10.5281/zenodo.1247479.

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Today is the era of internet; the internet represents a big space where large amounts of data are added every day. This huge amount of digital data and interconnection exploding data. Big Data mining have the capability to retrieving useful information in large datasets or streams of data. Analysis can also be done in a distributed environment. The framework needed for analysis to this large amount of data must support statistical analysis and data mining. The framework should be design in such a way so that big data and traditional data can be combined, so results that come analyzing new data

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7

Belov, Boris, and Peter Panfilov. "Generative AI-based Approach to Concept Drift Generation in Streaming Text Data." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 22 (October 21, 2024): 11–20. https://doi.org/10.37394/23209.2025.22.2.

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Real-time analysis of text streams is crucial for industrial and business processes and scenarios. It is expected to be one of the important future research topics in the text processing and understanding domain. Analysis of text data is based on the use of pre-trained machine learning/data mining (ML/DM) models that may demonstrate performance degradation over time due to the drift in text data. The problem of tracking drift in data and quickly retraining a model in response to changes in the operational environment represents a great challenge in product model environments. We discuss and ev

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8

Petrasova, Svitlana, Nina Khairova, and Anastasiia Kolesnyk. "TECHNOLOGY FOR IDENTIFICATION OF INFORMATION AGENDA IN NEWS DATA STREAMS." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 1 (5) (July 12, 2021): 86–90. http://dx.doi.org/10.20998/2079-0023.2021.01.14.

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Currently, the volume of news data streams is growing that contributes to increasing interest in systems that allow automating the big data streams processing. Based on intelligent data processing tools, the semantic similarity identification of text information will make it possible to select common information spaces of news. The article analyzes up-to-date statistical metrics for identifying coherent fragments, in particular, from news texts displaying the agenda, identifies the main advantages and disadvantages as well. The information technology is proposed for identifying the common info

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9

AL-Dyani, Wafa Zubair, Farzana Kabir Ahmad, and Siti Sakira Kamaruddin. "A Survey on Event Detection Models for Text Data Streams." Journal of Computer Science 16, no. 7 (2020): 916–35. http://dx.doi.org/10.3844/jcssp.2020.916.935.

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10

Hasan, Maryam, Elke Rundensteiner, and Emmanuel Agu. "Automatic emotion detection in text streams by analyzing Twitter data." International Journal of Data Science and Analytics 7, no. 1 (2018): 35–51. http://dx.doi.org/10.1007/s41060-018-0096-z.

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11

Rekik, Amal, and Salma Jamoussi. "Incremental autoencoders for text streams clustering in social networks." JUCS - Journal of Universal Computer Science 27, no. (11) (2021): 1203–21. https://doi.org/10.3897/jucs.76770.

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Clustering data streams in order to detect trending topic on social networks is a chal- lenging task that interests the researchers in the big data field. In fact, analyzing such data needs several requirements to be addressed due to their large amount and evolving nature. For this purpose, we propose, in this paper, a new evolving clustering method which can take into account the incremental nature of the data and meet with its principal requirements. Our method explores a deep learning technique to learn incrementally from unlabelled examples generated at high speed which need to be clustere

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12

Zhao, Xuezhuan, Ziheng Zhou, Lingling Li, Lishen Pei, and Zhaoyi Ye. "Scene Text Detection Based On Fusion Network." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 10 (2021): 2153005. http://dx.doi.org/10.1142/s0218001421530050.

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Due to the robustness resulted from scale transformation and unbalanced distribution of training samples in scene text detection task, a new fusion framework TSFnet is proposed in this paper. This framework is composed of Detection Stream, Judge Stream and Fusion Stream. In the Detection Stream, loss balance factor (LBF) is raised to improve the region proposal network (RPN). To predict the global text segmentation map, the algorithm combines regression strategy and case segmentation method. In the Judge Stream, a classification of the samples is proposed based on the Judge Map and the corresp

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13

Liu, Rui Fang, Bao Jin Yu, Jiang Xue, and Li Xin Xu. "The Design of kNN Text Categorization on Storm Cluster." Applied Mechanics and Materials 427-429 (September 2013): 2701–6. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2701.

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Try to handle a big data stream for information retrieval is just a beginning from TREC KBA2012. Storm is a free and open source distributed real-time computation system, which makes it easy to reliably process unbounded streams of data. For the issue of KBA2012, the combination of k-nearest neighbor (kNN) algorithm and Storm cluster will be an effective solution. kNN classification technique stands out for its simplicity and capability of being implemented on distributed platform. In addition, the entities (categories) in the issue of KBA2012 are settled down, which corresponds to the situati

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14

Azkan, Can, Markus Spiekermann, and Henry Goecke. "Uncovering Research Streams in the Data Economy Using Text Mining Algorithms." Technology Innovation Management Review 9, no. 11 (2019): 62–74. http://dx.doi.org/10.22215/timreview/1284.

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15

Mochamad, Nizar Palefi Ma'ady, Yang Chuan-Kai, Pradina Kusumawardani Renny, and Suryotrisongko Hatma. "Temporal Exploration in 2D Visualization of Emotions on Twitter Stream." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 1 (2018): 376–84. https://doi.org/10.12928/TELKOMNIKA.v16i1.6591.

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As people freely express their opinions toward a product on Twitter streams without being bound by time, visualizing time pattern of customers emotional behavior can play a crucial role in decisionmaking. We analyze how emotions are fluctuated in pattern and demonstrate how we can explore it into useful visualizations with an appropriate framework. We manually customized the current framework in order to improve a state-of-the-art of crawling and visualizing Twitter data. The data, post or update on status on the Twitter website about iPhone, was collected from U.S.A, Japan, Indonesia, and Tai

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16

Rekik, Amal, and Salma Jamoussi. "Incremental autoencoders for text streams clustering in social networks." JUCS - Journal of Universal Computer Science 27, no. 11 (2021): 1203–21. http://dx.doi.org/10.3897/jucs.76770.

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Clustering data streams in order to detect trending topic on social networks is a chal- lenging task that interests the researchers in the big data field. In fact, analyzing such data needs several requirements to be addressed due to their large amount and evolving nature. For this purpose, we propose, in this paper, a new evolving clustering method which can take into account the incremental nature of the data and meet with its principal requirements. Our method explores a deep learning technique to learn incrementally from unlabelled examples generated at high speed which need to be clustere

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17

Ranganathan, Gunasundari, Clara Barathi Priyadharshini Ganesan, and Balakumar Chellamuthu. "Emotional Tendency Analysis of Twitter Data Streams." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 116–26. http://dx.doi.org/10.17762/ijritcc.v11i11s.8077.

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The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed

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18

Krstajić, Miloš, Mohammad Najm-Araghi, Florian Mansmann, and Daniel A. Keim. "Story Tracker: Incremental visual text analytics of news story development." Information Visualization 12, no. 3-4 (2013): 308–23. http://dx.doi.org/10.1177/1473871613493996.

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Online news sources produce thousands of news articles every day, reporting on local and global real-world events. New information quickly replaces the old, making it difficult for readers to put current events in the context of the past. The stories about these events have complex relationships and characteristics that are difficult to model: they can be weakly or strongly related or they can merge or split over time. In this article, we present a visual analytics system for temporal analysis of news stories in dynamic information streams, which combines interactive visualization and text min

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19

Krzywicki, Alfred, Wayne Wobcke, Michael Bain, John Calvo Martinez, and Paul Compton. "Data mining for building knowledge bases: techniques, architectures and applications." Knowledge Engineering Review 31, no. 2 (2016): 97–123. http://dx.doi.org/10.1017/s0269888916000047.

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AbstractData mining techniques for extracting knowledge from text have been applied extensively to applications including question answering, document summarisation, event extraction and trend monitoring. However, current methods have mainly been tested on small-scale customised data sets for specific purposes. The availability of large volumes of data and high-velocity data streams (such as social media feeds) motivates the need to automatically extract knowledge from such data sources and to generalise existing approaches to more practical applications. Recently, several architectures have b

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20

Netolický, Pavel, Jonáš Petrovský, and František Dařena. "Text‑Mining in Streams of Textual Data Using Time Series Applied to Stock Market." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 66, no. 6 (2018): 1573–80. http://dx.doi.org/10.11118/actaun201866061573.

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Each day, a lot of text data is generated. This data comes from various sources and may contain valuable information. In this article, we use text mining methods to discover if there is a connection between news articles and changes of the S&P 500 stock index. The index values and documents were divided into time windows according to the direction of the index value changes. We achieved a classification accuracy of 65–74 %.

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21

Feng, Long, Haojie Ren, and Changliang Zou. "A setwise EWMA scheme for monitoring high-dimensional datastreams." Random Matrices: Theory and Applications 09, no. 02 (2019): 2050004. http://dx.doi.org/10.1142/s2010326320500045.

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The monitoring of high-dimensional data streams has become increasingly important for real-time detection of abnormal activities in many statistical process control (SPC) applications. Although the multivariate SPC has been extensively studied in the literature, the challenges associated with designing a practical monitoring scheme for high-dimensional processes when between-streams correlation exists are yet to be addressed well. Classical [Formula: see text]-test-based schemes do not work well because the contamination bias in estimating the covariance matrix grows rapidly with the increase

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22

Chen, Lisi, and Shuo Shang. "Region-Based Message Exploration over Spatio-Temporal Data Streams." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 873–80. http://dx.doi.org/10.1609/aaai.v33i01.3301873.

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Massive amount of spatio-temporal data that contain location and text content are being generated by location-based social media. These spatio-temporal messages cover a wide range of topics. It is of great significance to discover local trending topics based on users’ location-based and topicbased requirements. We develop a region-based message exploration mechanism that retrieve spatio-temporal message clusters from a stream of spatio-temporal messages based on users’ preferences on message topic and message spatial distribution. Additionally, we propose a region summarization algorithm that

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23

Padovani, José. "Pandemics, Delays, and Pure Data: on ‘afterlives’ (2020), for Flute and Live Electronics and Visuals." Revista Vórtex 9, no. 2 (2021): 1–14. http://dx.doi.org/10.33871/23179937.2021.9.2.17.

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The essay addresses creative and technical aspects of the piece ‘afterlives’ (2020), for flute and live electronics and visuals. Composed and premiered in the context of the COVID-19 pandemic, the composition employs audiovisual processes based on different audiovisual techniques: phase-vocoders, buffer-based granulations, Ambisonics spatialization, and variable delay of video streams. The resulting sounds and images allude to typical situations of social interaction via video conferencing applications. ‘Afterlives’ relies on an interplay between current, almost-current, and past moments of th

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24

Mishra, Devendra Kumar. "CHALLENGES IN TEXT MINING FOR BUSINESS INTELLIGENCE." International Journal of Engineering Technologies and Management Research 5, no. 2 (2020): 301–4. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.660.

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Today is the era of internet; the internet represents a big space where large amounts of data are added every day. This huge amount of digital data and interconnection exploding data. Big Data mining have the capability to retrieving useful information in large datasets or streams of data. Analysis can also be done in a distributed environment. The framework needed for analysis to this large amount of data must support statistical analysis and data mining. The framework should be design in such a way so that big data and traditional data can be combined, so results that come analyzing new data

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25

Perkins, Mark. "Aspects of Discourse Stream Analysis." Global Language Review IV, no. II (2019): 1–6. http://dx.doi.org/10.31703/glr.2019(iv-ii).01.

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The huge proliferation of textual (and other data) in digital and organisational sources has led to new techniques of text analysis. The potential thereby unleashed may be underpinned by further theoretical developments to the theory of Discourse Stream Analysis (DSA) as presented here. These include the notion of change in the discourse stream in terms of discourse stream fronts, linguistic elements evolving in real time, and notions of time itself in terms of relative speed, subject orientation and perception. Big data has also given rise to fake news, the manipulation of messages on a large

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26

Korostin, O. "Optimising Machine Learning Integration in Real-Time Text Analytics Platforms: Technical Approaches and Performance Criteria." COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, no. 58 (March 26, 2025): 38–45. https://doi.org/10.36910/6775-2524-0560-2025-58-05.

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The article investigates the integration of machine learning into real-time platforms for analysing text streams. The relevance of the topic is driven by the growing volume of unstructured textual data and the need for its prompt and accurate processing to support decision-making in such fields as media monitoring, cybersecurity, finance, and healthcare. The effectiveness of such platforms is shown to depend on the adaptability of algorithms, analysis accuracy, scalability, and transparency of results. Special attention is paid to the technical aspects of implementation, including distributed

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27

Kapenieks, Jānis. "A WEB-BASED FAST AND RELIABLE TEXT CLASSIFICATION TOOL." SOCIETY. TECHNOLOGY. SOLUTIONS. Proceedings of the International Scientific Conference 1 (April 17, 2019): 24. http://dx.doi.org/10.35363/via.sts.2019.21.

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INTRODUCTION Opinion analysis in the big data analysis context has been a hot topic in science and the business world recently. Social media has become a key data source for opinions generating a large amount of data every day providing content for further analysis. In the Big data age, unstructured data classification is one of the key tools for fast and reliable content analysis. I expect significant growth in the demand for content classification services in the nearest future. There are many online text classification tools available providing limited functionality -such

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28

Karim, Farah, Ioanna Lytra, Christian Mader, Sören Auer, and Maria-Esther Vidal. "DESERT: A Continuous SPARQL Query Engine for On-Demand Query Answering." International Journal of Semantic Computing 12, no. 03 (2018): 373–97. http://dx.doi.org/10.1142/s1793351x18400172.

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The Internet of Things (IoT) has been rapidly adopted in many domains ranging from household appliances e.g. ventilation, lighting, and heating, to industrial manufacturing and transport networks. Despite the, enormous benefits of optimization, monitoring, and maintenance rendered by IoT devices, an ample amount of data is generated continuously. Semantically describing IoT generated data using ontologies enables a precise interpretation of this data. However, ontology-based descriptions tremendously increase the size of IoT data and in presence of repeated sensor measurements, a large amount

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29

Kim, Hajin, Myeong-Seon Gil, Yang-Sae Moon, and Mi-Jung Choi. "Variable size sampling to support high uniformity confidence in sensor data streams." International Journal of Distributed Sensor Networks 14, no. 4 (2018): 155014771877399. http://dx.doi.org/10.1177/1550147718773999.

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In order to rapidly process large amounts of sensor stream data, it is effective to extract and use samples that reflect the characteristics and patterns of the data stream well. In this article, we focus on improving the uniformity confidence of KSample, which has the characteristics of random sampling in the stream environment. For this, we first analyze the uniformity confidence of KSample and then derive two uniformity confidence degradation problems: (1) initial degradation, which rapidly decreases the uniformity confidence in the initial stage, and (2) continuous degradation, which gradu

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30

Butakova, M. A., and G. S. Miziukov. "Measure and conditions for determining the information proximity of text information streams." Informatization and communication, no. 2 (April 30, 2020): 114–18. http://dx.doi.org/10.34219/2078-8320-2020-11-2-114-118.

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The aim of the article is to present a new approach for determining the informational proximity between textual information flows. To achieve this goal, the article considers the existing approach for determining the similarity between poorly structured data. The basic conditions for the results are described, the stages of validation and the coefficients for determining information proximity are highlighted. Examples of calculations on test data are given. In conclusion, the results of the verification of the approach are described, a brief description of the results is given.

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31

PhridviRaj, Chintakindi Srinivas, and C. V. GuruRao. "Clustering Text Data Streams – A Tree based Approach with Ternary Function and Ternary Feature Vector." Procedia Computer Science 31 (2014): 976–84. http://dx.doi.org/10.1016/j.procs.2014.05.350.

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32

Krukovets, Dmytro. "Data Science Opportunities at Central Banks: Overview." Visnyk of the National Bank of Ukraine, no. 249 (June 30, 2020): 13–24. http://dx.doi.org/10.26531/vnbu2020.249.02.

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This paper reviews the main streams of Data Science algorithm usage at central banks and shows their rising popularity over time. It contains an overview of use cases for macroeconomic and financial forecasting, text analysis (newspapers, social networks, and various types of reports), and other techniques based on or connected to large amounts of data. The author also pays attention to the recent achievements of the National Bank of Ukraine in this area. This study contributes to the building of the vector for research the role of Data Science for central banking.

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33

Masegosa, Andrés R., Darío Ramos-López, Antonio Salmerón, Helge Langseth, and Thomas D. Nielsen. "Variational Inference over Nonstationary Data Streams for Exponential Family Models." Mathematics 8, no. 11 (2020): 1942. http://dx.doi.org/10.3390/math8111942.

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In many modern data analysis problems, the available data is not static but, instead, comes in a streaming fashion. Performing Bayesian inference on a data stream is challenging for several reasons. First, it requires continuous model updating and the ability to handle a posterior distribution conditioned on an unbounded data set. Secondly, the underlying data distribution may drift from one time step to another, and the classic i.i.d. (independent and identically distributed), or data exchangeability assumption does not hold anymore. In this paper, we present an approximate Bayesian inference

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34

VALENCIA, MARIA, CODRINA LAUTH, and ERNESTINA MENASALVAS. "EMERGING USER INTENTIONS: MATCHING USER QUERIES WITH TOPIC EVOLUTION IN NEWS TEXT STREAMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, supp01 (2009): 59–80. http://dx.doi.org/10.1142/s0218488509006030.

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Trend detection analysis from unstructured data poses a huge challenge to current advanced, web-enabled knowledge-based systems (KBS). Consolidated studies in topic and trend detection from text streams have concentrated so far mainly on identifying and visualizing dynamically evolving text patterns. From the knowledge modeling perspective identifying and defining new, relevant features that are able to synchronize the emergent user intentions to the dynamicity of the system's structure is a need. Additionally the advanced KBS have to remain highly sensitive to the content change, marked by ev

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35

Tran, Van, Dosam Hwang, and Jason Jung. "TwiSNER: Semi-supervised Method for Named Entity Recognition from Text Streams on Twitter." JUCS - Journal of Universal Computer Science 22, no. (6) (2016): 782–801. https://doi.org/10.3217/jucs-022-06-0782.

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The data on Social Network Services (SNSs) has recently become an interesting source for researchers conducting different Natural Language Processing (NLP) experiments, such as sentiment analysis, information extraction, Named Entity Recognition (NER), and so on. The characteristics of SNS data are usually described as short, noisy, with insufficient supplemental information. They often contain grammatical errors, misspellings, and unreliable capitalization. Thus, standard NLP tools (e.g., NER systems) have difficulty obtaining good results when they are applied on these data, even if they per

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Prof. Dr. Husam Abulrazzak and Fatma Hassan Al-Rubbiay. "Analyzing Time series by using Data mining." Journal of Administration and Economics 48, no. 139 (2024): 279–81. http://dx.doi.org/10.31272/jae.i139.1099.

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Algorithms and complex data analysis techniques are used in multiple fields that are expanding daily, and with it the challenges in facing multiple and more complex data types, and the directions of exploration research vary according to the diversity of these fields, and their use is increasing in the modern era in the field of artificial intelligence, which aims to facilitate human life in various fields. Mining of complex data types includes mining of time series, symbolic chains, and biological chains, in addition to mining of graphs, computer networks, mobile data, text mining, and data s

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37

Sun, Yuchang, Xinran Li, Tao Lin, and Jun Zhang. "Learn How to Query from Unlabeled Data Streams in Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 19 (2025): 20752–60. https://doi.org/10.1609/aaai.v39i19.34287.

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Federated learning (FL) enables collaborative learning among decentralized clients while safeguarding the privacy of their local data. Existing studies on FL typically assume offline labeled data available at each client when the training starts. Nevertheless, the training data in practice often arrive at clients in a streaming fashion without ground-truth labels. Given the expensive annotation cost, it is critical to identify a subset of informative samples for labeling on clients. However, selecting samples locally while accommodating the global training objective presents a challenge unique

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38

Yadav, Piyush, Dhaval Salwala, Dibya Prakash Das, and Edward Curry. "Knowledge Graph Driven Approach to Represent Video Streams for Spatiotemporal Event Pattern Matching in Complex Event Processing." International Journal of Semantic Computing 14, no. 03 (2020): 423–55. http://dx.doi.org/10.1142/s1793351x20500051.

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Complex Event Processing (CEP) is an event processing paradigm to perform real-time analytics over streaming data and match high-level event patterns. Presently, CEP is limited to process structured data stream. Video streams are complicated due to their unstructured data model and limit CEP systems to perform matching over them. This work introduces a graph-based structure for continuous evolving video streams, which enables the CEP system to query complex video event patterns. We propose the Video Event Knowledge Graph (VEKG), a graph-driven representation of video data. VEKG models video ob

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39

Naldi, Giovanni, Andrea Mattana, Sandro Pastore, et al. "The Digital Signal Processing Platform for the Low Frequency Aperture Array: Preliminary Results on the Data Acquisition Unit." Journal of Astronomical Instrumentation 06, no. 01 (2017): 1641014. http://dx.doi.org/10.1142/s2251171716410142.

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A signal processing hardware platform has been developed for the Low Frequency Aperture Array component of the Square Kilometre Array (SKA). The processing board, called an Analog Digital Unit (ADU), is able to acquire and digitize broadband (up to 500[Formula: see text]MHz bandwidth) radio-frequency streams from 16 dual polarized antennas, channel the data streams and then combine them flexibly as part of a larger beamforming system. It is envisaged that there will be more than 8000 of these signal processing platforms in the first phase of the SKA, so particular attention has been devoted to

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Elkamchouchi, Hassan, Rosemarie Anton, and Yasmine Abouelseoud. "Multimedia Data Secure Transmission: A Review." International Journal of Scientific Research and Management 10, no. 12 (2022): 949–71. http://dx.doi.org/10.18535/ijsrm/v10i12.ec01.

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Encryption is a technique of encoding data so that they can only be recognized by authorized receivers. More interactive media information is communicated in the medical, business, and military fields because of the rapid advances in various multimedia transmission and networking technologies, which may contain sensitive information that must be kept hidden from public users. Advanced encryption standards (AES) and data encryption standards (DES) are widely used encryption algorithms for text data. However, they are not appropriate for video data. To ensure that this information cannot be acce

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Zubair Al-Dyani, Wafa, Adnan Hussein Yahya, and Farzana Kabir Ahmad. "Challenges of event detection from social media streams." International Journal of Engineering & Technology 7, no. 2.15 (2018): 72. http://dx.doi.org/10.14419/ijet.v7i2.15.11217.

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The area of Event Detection (ED) has attracted researchers' attention over the last few years because of the wide use of social media. Many studies have examined the problem of ED in various social media platforms, like Twitter, Facebook, YouTube, etc. The ED task for social networks involves many issues, including the processing of huge volumes of data with a high level of noise, data collection and privacy issues, etc. Hence, this article discusses and presents the wide range of challenges encountered in the ED process from unstructured text data for the most popular Social Networks (SNs), s

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Asha Priyadarshini.M. "Integrating Diverse Data Streams for Enhanced Emotional Intelligence in Mental Health Care." Communications on Applied Nonlinear Analysis 32, no. 1s (2024): 182–95. http://dx.doi.org/10.52783/cana.v32.2147.

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This research presents a comprehensive system for real-time emotion recognition and analysis using multimodal data, including image, video, audio, and text. The system employs deep learning models to extract features and classify emotions from each modality. By integrating these predictions, we aim to provide a holistic understanding of a user's emotional state and assess potential risks. The system further analyzes the collected emotion data to identify trends, patterns, and indicators of emotional well-being and suicide risk. Visualizations such as time-series plots, distribution charts, and

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Li, Shuqi, Weiheng Liao, Yuhan Chen, and Rui Yan. "PEN: Prediction-Explanation Network to Forecast Stock Price Movement with Better Explainability." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 5187–94. http://dx.doi.org/10.1609/aaai.v37i4.25648.

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Nowadays explainability in stock price movement prediction is attracting increasing attention in banks, hedge funds and asset managers, primarily due to audit or regulatory reasons. Text data such as financial news and social media posts can be part of the reasons for stock price movement. To this end, we propose a novel framework of Prediction-Explanation Network (PEN) jointly modeling text streams and price streams with alignment. The key component of the PEN model is an shared representation learning module that learns which texts are possibly associated with the stock price movement by mod

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Chudaev, D. A. "Materials to diatom flora of Moscow Region: naviculoid diatoms of Meleevsky Stream (Zvenigorod Biological Station)." Novosti sistematiki nizshikh rastenii 50 (2016): 142–59. http://dx.doi.org/10.31111/nsnr/2016.50.142.

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The article contains a brief review of investigation of diatom flora of S. N. Skadovsky Zvenigorod Biological Station (Moscow Region). Until present the shallow streams of Moscow Region and European Russia did not attract a proper attention of the diatomologists that makes this study important. To date studies of algae of Meleevsky Stream have not been conducted. The stream flows a in spruce forest, its valley is swampy and water is characterized by circumneutral pH values and medium electrolyte content. The list of 98 species, varietes and morphotypes of naviculoid diatoms belonging to 18 gen

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Wang, Y. D., T. Wang, X. Y. Ye, J. Q. Zhu, and J. Lee. "Using social media for disaster emergency management." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 579–81. http://dx.doi.org/10.5194/isprs-archives-xli-b2-579-2016.

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Social media have become a universal phenomenon in our society (Wang et al., 2012). As a new data source, social media have been widely used in knowledge discovery in fields related to health (Jackson et al., 2014), human behaviour (Lee, 2014), social influence (Hong, 2013), and market analysis (Hanna et al., 2011). <br><br> In this paper, we report a case study of the 2012 Beijing Rainstorm to investigate how emergency information was timely distributed using social media during emergency events. We present a classification and location model for social media text streams during e

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Wang, Y. D., T. Wang, X. Y. Ye, J. Q. Zhu, and J. Lee. "Using social media for disaster emergency management." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 579–81. http://dx.doi.org/10.5194/isprsarchives-xli-b2-579-2016.

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Social media have become a universal phenomenon in our society (Wang et al., 2012). As a new data source, social media have been widely used in knowledge discovery in fields related to health (Jackson et al., 2014), human behaviour (Lee, 2014), social influence (Hong, 2013), and market analysis (Hanna et al., 2011). &lt;br&gt;&lt;br&gt; In this paper, we report a case study of the 2012 Beijing Rainstorm to investigate how emergency information was timely distributed using social media during emergency events. We present a classification and location model for social media text

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Гибадуллин, Р. Ф., Д. А. Гашигуллин, and И. С. Вершинин. "Development of StegoStream decorator for associative protection of byte stream." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 11, no. 2(41) (2023): 23–24. http://dx.doi.org/10.26102/2310-6018/2023.41.2.023.

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Потоковая архитектура .NET основана на трех концепциях: опорные хранилища, декораторы и адаптеры. Опорное хранилище представляет собой конечную точку, такую как файл на накопителе, массив в оперативной памяти или сетевое подключение. Опорное хранилище не может использоваться, если программисту не открыт к нему доступ. Стандартным классом .NET, который предназначен для такой цели, является Stream (поток); он предоставляет стандартный набор методов, позволяющих выполнять побайтовое чтение, запись и позиционирование. Потоки делятся на две категории: потоки с опорными хранилищами и потоки с декора

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Al-alshaqi, Mohammed, Danda B. Rawat, and Chunmei Liu. "A BERT-Based Multimodal Framework for Enhanced Fake News Detection Using Text and Image Data Fusion." Computers 14, no. 6 (2025): 237. https://doi.org/10.3390/computers14060237.

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The spread of fake news on social media is complicated by the fact that fake information spreads extremely fast in both textual and visual formats. Traditional approaches to the detection of fake news focus mainly on text and image features, thereby missing valuable information contained within images and texts. In response to this, we propose a multimodal fake news detection method based on BERT, with an extension to text combined with the extracted text from images through Optical Character Recognition (OCR). Here, we consider extending feature analysis with BERT_base_uncased to process inpu

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Zhang, Congle, Stephen Soderland, and Daniel S. Weld. "Exploiting Parallel News Streams for Unsupervised Event Extraction." Transactions of the Association for Computational Linguistics 3 (December 2015): 117–29. http://dx.doi.org/10.1162/tacl_a_00127.

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Most approaches to relation extraction, the task of extracting ground facts from natural language text, are based on machine learning and thus starved by scarce training data. Manual annotation is too expensive to scale to a comprehensive set of relations. Distant supervision, which automatically creates training data, only works with relations that already populate a knowledge base (KB). Unfortunately, KBs such as FreeBase rarely cover event relations ( e.g. “person travels to location”). Thus, the problem of extracting a wide range of events — e.g., from news streams — is an important, open

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Tonkin, Emma, Alison Burrows, Przemysław Woznowski, et al. "Talk, Text, Tag? Understanding Self-Annotation of Smart Home Data from a User’s Perspective." Sensors 18, no. 7 (2018): 2365. http://dx.doi.org/10.3390/s18072365.

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Delivering effortless interactions and appropriate interventions through pervasive systems requires making sense of multiple streams of sensor data. This is particularly challenging when these concern people’s natural behaviours in the real world. This paper takes a multidisciplinary perspective of annotation and draws on an exploratory study of 12 people, who were encouraged to use a multi-modal annotation app while living in a prototype smart home. Analysis of the app usage data and of semi-structured interviews with the participants revealed strengths and limitations regarding self-annotati

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