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Artículos de revistas sobre el tema "Big geo data"

Autor: Grafiati

Publicado: 4 de junio de 2025

Última modificación: 1 de agosto de 2025

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1

Brovelli, M. A., P. Boccardo, G. Bordogna, et al. "URBAN GEO BIG DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W14 (August 23, 2019): 23–30. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w14-23-2019.

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<p><strong>Abstract.</strong> The paper deals with the general presentation of the Urban GEO BIG DATA, a collaborative acentric and distributed Free and Open Source (FOS) platform consisting of several components: local data nodes for data and related service Web deploy; a visualization node for data fruition; a catalog node for data discovery; a CityGML modeler; data-rich viewers based on virtual globes; an INSPIRE metadata management system enriched with quality indicators for each dataset.</p><p>Three use cases in five Italian cities (Turin, Milan, Padua, Rome,

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2

Arias Munoz, C., M. A. Brovelli, S. Corti, and G. Zamboni. "BIG GEO DATA MANAGEMENT: AN EXPLORATION WITH SOCIAL MEDIA AND TELECOMMUNICATIONS OPEN DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 595–601. http://dx.doi.org/10.5194/isprs-archives-xli-b2-595-2016.

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The term Big Data has been recently used to define big, highly varied, complex data sets, which are created and updated at a high speed and require faster processing, namely, a reduced time to filter and analyse relevant data. These data is also increasingly becoming Open Data (data that can be freely distributed) made public by the government, agencies, private enterprises and among others. There are at least two issues that can obstruct the availability and use of Open Big Datasets: Firstly, the gathering and geoprocessing of these datasets are very computationally intensive; hence, it is ne

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3

Arias Munoz, C., M. A. Brovelli, S. Corti, and G. Zamboni. "BIG GEO DATA MANAGEMENT: AN EXPLORATION WITH SOCIAL MEDIA AND TELECOMMUNICATIONS OPEN DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 595–601. http://dx.doi.org/10.5194/isprsarchives-xli-b2-595-2016.

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The term Big Data has been recently used to define big, highly varied, complex data sets, which are created and updated at a high speed and require faster processing, namely, a reduced time to filter and analyse relevant data. These data is also increasingly becoming Open Data (data that can be freely distributed) made public by the government, agencies, private enterprises and among others. There are at least two issues that can obstruct the availability and use of Open Big Datasets: Firstly, the gathering and geoprocessing of these datasets are very computationally intensive; hence, it is ne

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4

Jahn, M. W., P. E. Bradley, M. Al Doori, and M. Breunig. "TOPOLOGICALLY CONSISTENT MODELS FOR EFFICIENT BIG GEO-SPATIO-TEMPORAL DATA DISTRIBUTION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W5 (October 23, 2017): 65–72. http://dx.doi.org/10.5194/isprs-annals-iv-4-w5-65-2017.

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Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topolog

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5

Gutiérrez-Puebla, Javier, Juan Carlos García-Palomares, and María Henar Salas-Olmedo. "Big (Geo)Data en Ciencias Sociales: Retos y Oportunidades." Revista de Estudios Andaluces 33, no. 1 (2016): 1–23. http://dx.doi.org/10.12795/rea.2016.i33.01.

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6

Lu, Y. L., C. W. Liu, J. W. Li, and J. W. Jiang. "CONSTRUCTION METHOD OF “CELL-CUBE” SPATIO-TEMPORAL DATA MODEL FOR BIG DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 25–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-25-2020.

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Abstract. In recent years, with high accuracy, high frequency, considerable coverage of remote sensing images, map tiles, video surveillance, web crawlers, social networking platforms and other types of spatiotemporal data have exploded in geometric progression. Human society has come into the era of big data in time and space. In view of the characteristics of multi-attribute, multi-dimensional, multisource and heterogeneous spatiotemporal big data, how to make use of the emerging information technology means, combined with the geographic information data analysis means, the rapid mining and

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7

Goodchild, Michael F. "The quality of big (geo)data." Dialogues in Human Geography 3, no. 3 (2013): 280–84. http://dx.doi.org/10.1177/2043820613513392.

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8

Alkathiri, Mazin, Jhummarwala Abdul, and M. B. Potdar. "Geo-spatial Big Data Mining Techniques." International Journal of Computer Applications 135, no. 11 (2016): 28–36. http://dx.doi.org/10.5120/ijca2016908542.

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9

Petry, Frederick E. "Data Mining Approaches for Geo-Spatial Big Data." International Journal of Organizational and Collective Intelligence 3, no. 1 (2012): 52–71. http://dx.doi.org/10.4018/joci.2012010104.

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The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discovery from such volumes of data. An important consideration for this sort of data mining is to extend techniques to manage the inherent uncertainty involved in such spatial data. In this paper the authors fi

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10

Alciaturi, Giancarlo, María del Pilar García - Rodríguez, and Virginia Fernández. "A way for explaining Geo Big Data through its characteristics, sources, and central support technologies." Revista Geográfica de Chile Terra Australis 60, no. 1 (2024): 102–19. http://dx.doi.org/10.23854/07199562.2024601.alciaturi.

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Big Data has significantly impacted scientific research and everyday life, resulting in solutions that align with its principles. Therefore, it is essential to propose ways that enable a basic understanding of specific fields of knowledge, such as those related to spatial domain information. Through a narrative review, this document explains the fundamentals of Geo Big Data by contextualising the characteristics, sources, and central technologies of Big Data in relation to geographic information. One of the most significant findings is that Geo Big Data helps enhance the understanding of bioph

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11

Woo, Jongwook. "Big Data Geo Analysis for Ancient Historical Places." Barun Academy of History 13 (December 31, 2022): 73–84. http://dx.doi.org/10.55793/jkhc.2022.13.73.

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Information Technology (IT) has been moving quickly while computer and Internet technologies have been developed drastically and adopted in all areas such as business, science, communication, etc. History is one of the best areas to use IT because it has massive amounts of historical data, including text and media. The paper introduces the latest technologies that have been used or can be in the area of history. A tempo-spatial analysis is presented as an example to locate ancient historical places. Then, it shows the spots of Gija tomb using Baidu and Google, which is the location mentioned b

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12

Suryono, Hady, Arif Handoyo Marsuhandi, and Setia Pramana. "Klasifikasi Tutupan Lahan Berdasarkan Random Forest Algorithm Menggunakan Cloud Computing Platform." Jurnal Aplikasi Statistika & Komputasi Statistik 14, no. 1 (2022): 1–12. http://dx.doi.org/10.34123/jurnalasks.v14i1.383.

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Statistik pertanian merupakan salah satu data yang vital di dunia dan memiliki kontribusi besar terhadap pencapaian tujuan program Sustainable Development Goals (SDGs). Dalam SDGs, perhatian terhadap ketahanan pangan difokuskan pada indikator kunci kedua yaitu nol kelaparan (SDG 2). Ketersediaan data tutupan lahan yang akurat diperlukan sebagai data dasar untuk luasan baku sawah yang akan digunakan untuk mengukur tingkat ketahanan pangan. Pemetaan tanaman membutuhkan pemrosesan dan pengelolaan data citra satelit dengan volume yang sangat besar dan tidak terstruktur yang mengarah pada permasala

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13

Choi, Wonwook, Jongwook Ahn, and Dongbin Shin. "Text mining geo-visualization of patent documents on geo-spatial big-data industry." Spatial Information Research 27, no. 1 (2018): 109–20. http://dx.doi.org/10.1007/s41324-018-0201-3.

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14

Sowmya, T. Sai Raaga. "Cost Minimization for Big Data Processing in Geo-Distributed Data Centres." Asia-pacific Journal of Convergent Research Interchange 2, no. 4 (2016): 35–43. http://dx.doi.org/10.21742/apjcri.2016.12.05.

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15

Gu, Lin, Deze Zeng, Peng Li, and Song Guo. "Cost Minimization for Big Data Processing in Geo-Distributed Data Centers." IEEE Transactions on Emerging Topics in Computing 2, no. 3 (2014): 314–23. http://dx.doi.org/10.1109/tetc.2014.2310456.

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16

Xiao, Wenhua, Weidong Bao, Xiaomin Zhu, and Ling Liu. "Cost-Aware Big Data Processing Across Geo-Distributed Datacenters." IEEE Transactions on Parallel and Distributed Systems 28, no. 11 (2017): 3114–27. http://dx.doi.org/10.1109/tpds.2017.2708120.

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17

Dhamodaran, S., G. Mahalakshmi, P. Harika, J. Refonaa, and K. AshokKumar. "Big Data Analytics of Geo-Social Media on Planning and Real-Time Decisions." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3332–37. http://dx.doi.org/10.1166/jctn.2019.8188.

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The authorities are to be considered to make a real-time decision and future planning by various analyzing geo-social media posts in Geo-social Network. However, there are millions of Geo-social Network users who are producing overwhelming of data, called “Big Data” that is challenging to be analyzed and make the required real-time decisions. In our proposed system proposal of the efficient system for inquiring Geo-social Networks and harvesting the data as well as user’s location information (Dhamodaran, S. and Lakshmi, M., 2017. Design and Analysis of Spatial-Temporal Model Using Hydrologica

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18

Breunig, Martin, Patrick Erik Bradley, Markus Jahn, et al. "Geospatial Data Management Research: Progress and Future Directions." ISPRS International Journal of Geo-Information 9, no. 2 (2020): 95. http://dx.doi.org/10.3390/ijgi9020095.

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Without geospatial data management, today’s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were

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19

Bojkovic, Zoran, and Dragorad Milovanovic. "Mobile cloud analytics in Big data era." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 10 (March 22, 2022): 25–28. http://dx.doi.org/10.37394/232018.2022.10.3.

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Voluminous data are generated from a variety of users and devices and are to be stored and processed in powerful data center. As such, there is a strong demand for building a network infrastructure to gather distributed and rapidly generated data and move them to data center for knowledge discovery. Big data has received considerable attention, because it can mine new knowledge for economic growth and technical innovation. Many research efforts have been directed to big data processing due to its high volume, velocity and variety, referred to as 3V. This paper first describes challenges for bi

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20

Amani, Meisam, Arsalan Ghorbanian, Seyed Ali Ahmadi, et al. "Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (September 1, 2020): 5326–50. https://doi.org/10.1109/JSTARS.2020.3021052.

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Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applic

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21

Alin, Tomoiaga, Farelly John, and Nakamura Shintaro. "Genomic Big Data Regression Analysis." Genomic Big Data Regression Analysis 2, no. 1 (2018): 24–30. https://doi.org/10.5281/zenodo.1438968.

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DNA analysis is now a data intensive discipline.  New technology has transformed biomedical research by making a plethora of molecular data available at reduced costs and great speeds. Large consortiums and many individual laboratories have already generated vast datasets: as an example, one such database, the Gene Expression Omnibus (GEO contains more than 1.8 million samples. This data is readily, publicly available but analyzing it requires computational and statistical resources. A popular concern in biological research is to identify those genomic pathways that are related to the org

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22

Subbiah, Sankari, Perumal Varalakshmi, R. Prarthana, and C. Renuka Devi. "Energy Efficient Big Data Infrastructure Management in Geo-Federated Cloud Data Centers." Procedia Computer Science 58 (2015): 151–57. http://dx.doi.org/10.1016/j.procs.2015.08.043.

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23

M, Anoop. "Survival Study of Machine Learning Techniques for Big Data Clustering on Geo-social Networks." Journal of Advanced Research in Dynamical and Control Systems 12, no. 1 (2020): 254–62. http://dx.doi.org/10.5373/jardcs/v12i1/20201037.

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24

Adnan, T. M. Tariq, Md Mehrab Tanjim, and Muhammad Abdullah Adnan. "Fast, scalable and geo-distributed PCA for big data analytics." Information Systems 98 (May 2021): 101710. http://dx.doi.org/10.1016/j.is.2020.101710.

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25

Brovelli, M. A., P. Hogan, G. Prestifilippo, and G. Zamboni. "NASA WEBWORLDWIND: MULTIDIMENSIONAL VIRTUAL GLOBE FOR GEO BIG DATA VISUALIZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 563–66. http://dx.doi.org/10.5194/isprs-archives-xli-b2-563-2016.

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In this paper, we presented a web application created using the NASA WebWorldWind framework. The application is capable of visualizing n-dimensional data using a Voxel model. In this case study, we handled social media data and Call Detailed Records (CDR) of telecommunication networks. These were retrieved from the "BigData Challenge 2015" of Telecom Italia. We focused on the visualization process for a suitable way to show this geo-data in a 3D environment, incorporating more than three dimensions. This engenders an interactive way to browse the data in their real context and understand them

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Di Modica, Giuseppe, and Orazio Tomarchio. "A Hierarchical Hadoop Framework to Process Geo-Distributed Big Data." Big Data and Cognitive Computing 6, no. 1 (2022): 5. http://dx.doi.org/10.3390/bdcc6010005.

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In the past twenty years, we have witnessed an unprecedented production of data worldwide that has generated a growing demand for computing resources and has stimulated the design of computing paradigms and software tools to efficiently and quickly obtain insights on such a Big Data. State-of-the-art parallel computing techniques such as the MapReduce guarantee high performance in scenarios where involved computing nodes are equally sized and clustered via broadband network links, and the data are co-located with the cluster of nodes. Unfortunately, the mentioned techniques have proven ineffec

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27

Brovelli, M. A., P. Hogan, G. Prestifilippo, and G. Zamboni. "NASA WEBWORLDWIND: MULTIDIMENSIONAL VIRTUAL GLOBE FOR GEO BIG DATA VISUALIZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 563–66. http://dx.doi.org/10.5194/isprsarchives-xli-b2-563-2016.

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In this paper, we presented a web application created using the NASA WebWorldWind framework. The application is capable of visualizing n-dimensional data using a Voxel model. In this case study, we handled social media data and Call Detailed Records (CDR) of telecommunication networks. These were retrieved from the "BigData Challenge 2015" of Telecom Italia. We focused on the visualization process for a suitable way to show this geo-data in a 3D environment, incorporating more than three dimensions. This engenders an interactive way to browse the data in their real context and understand them

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28

Zhang, Hongli, Qiang Zhang, Zhigang Zhou, Xiaojiang Du, Wei Yu, and Mohsen Guizani. "Processing geo-dispersed big data in an advanced mapreduce framework." IEEE Network 29, no. 5 (2015): 24–30. http://dx.doi.org/10.1109/mnet.2015.7293301.

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29

Sinnott, Richard O., Christopher Bayliss, Andrew Bromage, et al. "Privacy Preserving Geo-Linkage in the Big Urban Data Era." Journal of Grid Computing 14, no. 4 (2016): 603–18. http://dx.doi.org/10.1007/s10723-016-9372-0.

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30

Gao, Song, Linna Li, Wenwen Li, Krzysztof Janowicz, and Yue Zhang. "Constructing gazetteers from volunteered Big Geo-Data based on Hadoop." Computers, Environment and Urban Systems 61 (January 2017): 172–86. http://dx.doi.org/10.1016/j.compenvurbsys.2014.02.004.

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31

Liakos, Leonidas, and Panos Panagos. "Challenges in the Geo-Processing of Big Soil Spatial Data." Land 11, no. 12 (2022): 2287. http://dx.doi.org/10.3390/land11122287.

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This study addressed a critical resource—soil—through the prism of processing big data at the continental scale. Rapid progress in technology and remote sensing has majorly improved data processing on extensive spatial and temporal scales. Here, the manuscript presents the results of a systematic effort to geo-process and analyze soil-relevant data. In addition, the main highlights include the difficulties associated with using data infrastructures, managing big geospatial data, decentralizing operations through remote access, mass processing, and automating the data-processing workflow using

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32

Metre, Kalpana Vivek, and Madan Kharat. "Efficient processing of continuous spatial-textual queries over geo-textual data stream." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1094–102. https://doi.org/10.11591/ijeecs.v25.i2.pp1094-1102.

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Due to the extensive use of social media and mobile devices, unbounded and massive data is generated continuously. The need to process this big data is increasing day by day. The traditional data processing algorithms fail to cater to the need of processing data generated by various applications such as digital geo-based advertising, and recommendation systems. There has been a high demand to process continuous spatial fuzzy textual queries over data stream of spatial-textual objects with high density by present locationbased and social network-based service applications. For the spatialkeywor

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P., K. Paul, S. Aithal P., Bhuimali A., S. Tiwary K., and C. Deka G. "Environmental Informatics Vis-à-Vis Big Data Analytics: The Geo-Spatial & Sustainable Solutions." International Journal of Applied Engineering and Management Letters (IJAEML) 4, no. 2 (2020): 31–40. https://doi.org/10.5281/zenodo.3977275.

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The merging of Environment and allied subjects such as Informatics has led to the development of Environmental Informatics. Environmental Informatics is a perfect solution for Environment related activities with different tools, techniques and sub-technologies of IT, Computing, Computer Science or allied branches. However, among the emerging technologies most prominent is the Internet of Things (IoT) and Cloud Computing (Big Data Analytics). This paper provides a comprehensive study on Environmental Informatics with special reference to the applications of Big Data Analytics. This paper descri

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Sakr, Sherif, Giuseppe Di Modica, and Orazio Tomarchio. "Editorial for Special Issue of Journal of Big Data Research on “Geo-distributed Big Data Processing and Management”." Big Data Research 16 (July 2019): 59. http://dx.doi.org/10.1016/j.bdr.2019.05.001.

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Metre, Kalpana Vivek, and Madan Kharat. "Efficient processing of continuous spatial-textual queries over geo-textual data stream." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1094. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1094-1102.

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<p>Due to the extensive use of social media and mobile devices, unbounded and massive data is generated continuously. The need to process this big data is increasing day by day. The traditional data processing algorithms fail to cater to the need of processing data generated by various applications such as digital geo-based advertising, and recommendation systems. There has been a high demand to process continuous spatial fuzzy textual queries over data stream of spatial-textual objects with high density by present locationbased and social network-based service applications. For the spat

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36

Al Jawarneh, Isam Mashhour, Luca Foschini, and Paolo Bellavista. "Efficient Integration of Heterogeneous Mobility-Pollution Big Data for Joint Analytics at Scale with QoS Guarantees." Future Internet 15, no. 8 (2023): 263. http://dx.doi.org/10.3390/fi15080263.

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Numerous real-life smart city application scenarios require joint analytics on unified views of georeferenced mobility data with environment contextual data including pollution and meteorological data. particularly, future urban planning requires restricting vehicle access to specific areas of a city to reduce the adverse effect of their engine combustion emissions on the health of dwellers and cyclers. Current editions of big spatial data management systems do not come with over-the-counter support for similar scenarios. To close this gap, in this paper, we show the design and prototyping of

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Athanasis, N., M. Themistocleous, K. Kalabokidis, A. Papakonstantinou, N. Soulakellis, and P. Palaiologou. "THE EMERGENCE OF SOCIAL MEDIA FOR NATURAL DISASTERS MANAGEMENT: A BIG DATA PERSPECTIVE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W4 (March 6, 2018): 75–82. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w4-75-2018.

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<p><strong>Abstract.</strong> Social media is rapidly emerging as a potential resource of information capable to support natural disasters management. Despite the growing research interest focused on using social media during natural disasters, many challenges may arise on how to handle the ‘big data’ problem: huge amounts of geo-social data are available, in different formats and varying quality that must be processed quickly. This article presents a state-of-the-art approach towards the enhancement of decision support tools for natural disaster management with information f

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Chen, Yu, Keyang Li, Qian Zhou, and Yuxin Zhang. "Can Population Mobility Make Cities More Resilient? Evidence from the Analysis of Baidu Migration Big Data in China." International Journal of Environmental Research and Public Health 20, no. 1 (2022): 36. http://dx.doi.org/10.3390/ijerph20010036.

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Knowledge spillover and capital agglomeration caused by population migration behavior are of great significance for improving the carrying capacity and adaptability of the urban economy and promoting high-quality economic development. Based on the big data collected on urban migration during the Spring Festival travel period, this paper constructs geographic, economic and geo-economic matrices, introduces two instrumental variables, and uses a spatial econometric model to investigate the mechanism between population mobility and urban economic resilience. The results show that (1) urban econom

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Anoop, M., and P. Sripriya. "Extended Jaccard Indexive Buffalo Optimized Clustering on Geo-social Networks with Big Data." Webology 18, no. 2 (2021): 166–82. http://dx.doi.org/10.14704/web/v18i2/web18314.

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Clustering is a general task of data mining where partitioning a large dataset into dissimilar groups is done. The enormous growth of Geo-Social Networks (GeoSNs) includes users, who create millions of heterogeneous data with a variety of information. Analyzing such volume of data is a challenging task. The clustering of large volume of data is used to identify the frequently visited location information of the users in Geo-Social Networks. In order to improve the clustering of a large volume of data, a novel technique called Extended Jaccard Indexive Buffalo Optimized Data Clustering (EJIBODC

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40

Goffi, Alessia, Gloria Bordogna, Daniela Stroppiana, Mirco Boschetti, and Pietro Alessandro Brivio. "A Scalable Synthesis of Multiple Models of Geo Big Data Interpretation." Journal of Software Engineering and Applications 13, no. 06 (2020): 104–28. http://dx.doi.org/10.4236/jsea.2020.136008.

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Jawarneh, Isam Mashhour Al, Luca Foschini, and Paolo Bellavista. "Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data." Sensors 23, no. 19 (2023): 8178. http://dx.doi.org/10.3390/s23198178.

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The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus countera

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Nikos, Giatrakos, Alevizos Elias, Artikis Alexander, Deligiannakis Antonios, and Garofalakis Minos. "Complex event recognition in the Big Data era: a survey." VLDB Journal 29, no. 1 (2019): 313–52. https://doi.org/10.1007/s00778-019-00557-w.

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The concept of event processing is established as a generic computational paradigm in various application fields. Events report on state changes of a system and its environment. Complex event recognition (CER) refers to the identification of composite events of interest, which are collections of simple, derived events that satisfy some pattern, thereby providing the opportunity for reactive and proactive measures. Examples include the recognition of anomalies in maritime surveillance, electronic fraud, cardiac arrhythmias and epidemic spread. This survey elaborates on the whole pipeline from t

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43

Xu, Chenhan, Kun Wang, Peng Li, Rui Xia, Song Guo, and Minyi Guo. "Renewable Energy-Aware Big Data Analytics in Geo-Distributed Data Centers with Reinforcement Learning." IEEE Transactions on Network Science and Engineering 7, no. 1 (2020): 205–15. http://dx.doi.org/10.1109/tnse.2018.2813333.

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Yassine, Abdulsalam, Ali Asghar Nazari Shirehjini, and Shervin Shirmohammadi. "Bandwidth On-Demand for Multimedia Big Data Transfer Across Geo-Distributed Cloud Data Centers." IEEE Transactions on Cloud Computing 8, no. 4 (2020): 1189–98. http://dx.doi.org/10.1109/tcc.2016.2617369.

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45

Tomarchio, Orazio, Giuseppe Di Modica, Marco Cavallo, and Carmelo Polito. "A Hierarchical Hadoop Framework to Handle Big Data in Geo-Distributed Computing Environments." International Journal of Information Technologies and Systems Approach 11, no. 1 (2018): 16–47. http://dx.doi.org/10.4018/ijitsa.2018010102.

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Advances in the communication technologies, along with the birth of new communication paradigms leveraging on the power of the social, has fostered the production of huge amounts of data. Old-fashioned computing paradigms are unfit to handle the dimensions of the data daily produced by the countless, worldwide distributed sources of information. So far, the MapReduce has been able to keep the promise of speeding up the computation over Big Data within a cluster. This article focuses on scenarios of worldwide distributed Big Data. While stigmatizing the poor performance of the Hadoop framework

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Mesquitela, Joao, Luis B. Elvas, Joao C. Ferreira, and Luis Nunes. "Data Analytics Process over Road Accidents Data—A Case Study of Lisbon City." ISPRS International Journal of Geo-Information 11, no. 2 (2022): 143. http://dx.doi.org/10.3390/ijgi11020143.

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Traffic accidents in urban areas lead to reduced quality of life and added pressure in the cities’ infra-structures. In the context of smart city data is becoming available that allows a deeper analysis of the phenomenon. We propose a data fusion process from different information sources like road accidents, weather conditions, local authority reports tools, traffic, fire brigade. These big data analytics allow the creation of knowledge for local municipalities using local data. Data visualizations allow big picture overview. This paper presents an approach to the geo-referenced accident-hots

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Lutsai, Kateryna, and Christoph Lampert. "Predicting the geolocation of tweets using transformer models on customized data." Journal of Spatial Information Science, no. 29 (December 26, 2024): 69–99. https://doi.org/10.5311/josis.2024.29.295.

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This research is aimed to solve the tweet/user geolocation prediction task and provide a flexible methodology for the geo-tagging of textual big data. The suggested approach implements neural networks for natural language processing (NLP) to estimate the location as coordinate pairs (longitude, latitude) and two-dimensional Gaussian Mixture Models (GMMs). The scope of proposed models has been finetuned on a Twitter dataset using pretrained Bidirectional Encoder Representations from Transformers (BERT) as base models. Performance metrics show a median error of fewer than 30 km on a worldwide-le

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Denneau, Larry. "ATLAS: Big Data in a Small Package?" Proceedings of the International Astronomical Union 10, S318 (2015): 299–305. http://dx.doi.org/10.1017/s174392131500722x.

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AbstractFor even small astronomy projects, the petabyte scale is now upon us. The Asteroid Terrestrial-impact Last Alert System (Tonry 2011) will survey the entire visible sky from Hawaii multiple times per night to search for near-Earth asteroids on impact trajectories. While the ATLAS optical system is modest by modern astronomical standards — two 0.5 m F/2.0 telescopes — each night the ATLAS system will measure nearly 109 astronomical sources to a photometric accuracy of <5%, totaling 1012 individual observations over its initial 3-year mission. This ever-growing dataset must be searched

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Biradar, C. M., S. Ghosh, F. Löw, et al. "GEO BIG DATA AND DIGITAL AUGMENTATION FOR ACCELERATING AGROECOLOGICAL INTENSIFICATION IN DRYLANDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 545–48. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-545-2019.

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<p><strong>Abstract.</strong> Integrated system approach becomes key pillar for sustainable intensification of agri-food systems while ensuring ecological functions under changing climate, diet and demography. The digitization of the agroecosystems become most essential entry point for any sustainable developmental entities whether it is plant genetics for breeding better varieties, crop diversification and intensification, efficient use of farm inputs, agronomic practices, stable economic return, to ecosystem services management. The functional production systems become more

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Abesinghe, Sandulika, Nayomi Kankanamge, Tan Yigitcanlar, and Surabhi Pancholi. "Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data." Future Internet 15, no. 1 (2023): 32. http://dx.doi.org/10.3390/fi15010032.

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The image of a city represents the sum of beliefs, ideas, and impressions that people have of that city. Mostly, city images are assessed through direct or indirect interviews and cognitive mapping exercises. Such methods consume more time and effort and are limited to a small number of people. However, recently, people tend to use social media to express their thoughts and experiences of a place. Taking this into consideration, this paper attempts to explore city images through social media big data, considering Colombo, Sri Lanka, as the testbed. The aim of the study is to examine the image

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