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Journal articles on the topic 'Big data storage'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Thota, Subash. "Big Data Storage Analytics." International Journal of Computer Trends and Technology 51, no. 2 (2017): 68–74. http://dx.doi.org/10.14445/22312803/ijctt-v51p111.

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Kodabagi, M. M., Savita Rathod, and Vilas Naik. "Distributed Data Storage Technique for Big Data using Hadoop." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (2016): 43–48. http://dx.doi.org/10.9756/bijsesc.8240.

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Zasuhina, Ol'ga, Egor Ershov, Leonid Golovatiukov, and Grigory Shitenkov. "BIG DATA PROCESSING TECHNOLOGY." Bulletin of the Angarsk State Technical University 1, no. 16 (2022): 98–100. http://dx.doi.org/10.36629/2686-777x-2022-1-16-98-100.

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Kaisler, Stephen H., William H. Money, Frank Armour, and J. Alberto Espinosa. "Big Data." International Journal of Systems and Service-Oriented Engineering 7, no. 2 (2017): 1–23. http://dx.doi.org/10.4018/ijssoe.2017040101.

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Big Data refers to data volumes in the range of exabytes (1018th) requiring processing from distributed on-line storage systems with thousands of processors, mainframes or supercomputers where processing speed is measured in GFLOPS. The rate at which data are being collected are accelerating and will approach the zettabyte/year range. Other attributes of Bi Data are also concurrently expanding including variety/variability, velocity, value, and vital concerns for veracity. Storage and data transport technology issues may be solvable in the near-term. However, these communication, quantity mana

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Cherubini, Giovanni, Jens Jelitto, and Vinodh Venkatesan. "Cognitive Storage for Big Data." Computer 49, no. 4 (2016): 43–51. http://dx.doi.org/10.1109/mc.2016.117.

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Dr., Vishnu M. Pawar. "Application of Big Data in Libraries." International Journal of Advance and Applied Research 4, no. 32 (2023): 32–34. https://doi.org/10.5281/zenodo.8434023.

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<strong>Abstract:</strong> Nowadays Big Data is an emerging field that presents several Information Technology challenges regarding the capture, storage search, structure, and visualization of the data.  Big Data provides the answer to several valuable questions related to patterns, trends, and user behavior. Big data plays a major role in helping libraries to know the changing user demands, accordingly helping to reshape and restructure library services and procedures. This paper provides an overview of the big data concept and its application in libraries. The present paper aims to crea

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Singh, Manpreet, Jatinder Singh Bhatia, and Devansh Malhotra. "Big Data: The Future of Data Storage." IOSR Journal of Computer Engineering 16, no. 5 (2014): 130–36. http://dx.doi.org/10.9790/0661-1654130136.

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Patel, Jigna, Priyanka Sharma, and Jitali Patel. "Big Data Harmonization-Data Loading and Data Storage." Journal of Engineering and Applied Sciences 14, no. 20 (2019): 7731–35. http://dx.doi.org/10.36478/jeasci.2019.7731.7735.

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Youssra, Riahi, and Riahi Sara. "Big Data and Big Data Analytics: Concepts, Types and Technologies." International Journal of Research and Engineering 5, no. 9 (2018): 524–28. https://doi.org/10.21276/ijre.2018.5.9.5.

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Nowadays, companies are starting to realize the importance of data availability in large amounts in order to make the right decisions and support their strategies. With the development of new technologies, the Internet and social networks, the production of digital data is constantly growing. The term "Big Data" refers to the heterogeneous mass of digital data produced by companies and individuals whose characteristics (large volume, different forms, speed of processing ) require specific and increasingly sophisticated computer storage and analysis tools. This article intends to defi

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Bravo, Giangiacomo, Mikko Laitinen, Magnus Levin, Welf Löwe, and Göran Petersson. "Big Data in Cross-Disciplinary Research." JUCS - Journal of Universal Computer Science 23, no. (11) (2017): 1035–37. https://doi.org/10.3217/jucs-023-11-1035.

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The ubiquity of sensor, computing, communication, and storage technologies provides us with access to previously unknown amounts of data - Big Data. Big Data has revolutionized research communities and their scientific methodologies. It has, for instance, innovated the approaches to knowledge and theory building, validation, and exploitation taken in the engineering sciences. The humanities and social sciences even face a paradigm shift away from data-scarce, static, coarse-grained and simple studies towards data-rich, dynamic, high resolution, and complex observations and simulations. The pre

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Siddiqa, Aisha, Ahmad Karim, and Abdullah Gani. "Big data storage technologies: a survey." Frontiers of Information Technology & Electronic Engineering 18, no. 8 (2017): 1040–70. http://dx.doi.org/10.1631/fitee.1500441.

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Moscoso Zea, Oswaldo. "Megastore: structured storage for Big Data." Enfoque UTE 3, no. 2 (2012): 01–12. http://dx.doi.org/10.29019/enfoqueute.v3n2.1.

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Megastore es uno de los componentes principales de la infraestructura de datos de Google, elcual ha permitido el procesamiento y almacenamiento de grandes volúmenes de datos (BigData) con alta escalabilidad, confiabilidad y seguridad. Las compañías e individuos que usanestá tecnología se están beneficiando al mismo tiempo de un servicio estable y de altadisponibilidad. En este artículo se realiza un análisis de la infraestructura de datos de Google,comenzando por una revisión de los componentes principales que se han implementado en losúltimos años hasta la creación de Megastore. Se presenta t

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Lv, Zhihan, Xiaoming Li, Haibin Lv, and Wenqun Xiu. "BIM Big Data Storage in WebVRGIS." IEEE Transactions on Industrial Informatics 16, no. 4 (2020): 2566–73. http://dx.doi.org/10.1109/tii.2019.2916689.

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Weichen, Wang. "Survey of Big Data Storage Technology." Internet of Things and Cloud Computing 4, no. 3 (2016): 28. http://dx.doi.org/10.11648/j.iotcc.20160403.13.

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Sharma, Shyna, Shruti Pathak, Taranjot Singh Kathuria, Tarun Sharma, and Malvinder Singh Bali. "Big Data Storage using Synthetic DNA." International Journal of Computer Trends & Technology 67, no. 4 (2019): 128–30. http://dx.doi.org/10.14445/22312803/ijctt-v67i4p125.

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Nawaz, Hasnain, and Tariq Rahim. "Private Cloud Storage in Big Data." International Journal of Computer Applications 142, no. 4 (2016): 33–37. http://dx.doi.org/10.5120/ijca2016909756.

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Rossi, Rogério, and Kechi Hirama. "Characterizing Big Data Management." Issues in Informing Science and Information Technology 12 (2015): 165–80. http://dx.doi.org/10.28945/2204.

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Big data management is a reality for an increasing number of organizations in many areas and represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial to facilitate the management of big data in any kind of organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management can be supported by these three dimensions: technology, people and processes. Hence, this article discusses these dimensions: the technological dime

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Rawish Siddiqui, Muhammad. "Big Data vs. Traditional Data, Data Warehousing, AI, and Beyond." Chemistry Research and Practice 1, no. 2 (2024): 01–06. https://doi.org/10.64030/3065-906x.01.02.04.

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In the age of digital transformation, the rise of Big Data has fundamentally altered how organizations store, process, and utilize information. This whitepaper provides a comprehensive analysis comparing Big Data with traditional data systems, data warehousing, business intelligence (BI), artificial intelligence (AI), data science, and NoSQL databases. By exploring key differentiators such as volume, variety, velocity, and processing capabilities, this paper aims to shed light on how Big Data has reshaped modern technology infrastructures and its role in advancing analytics, decision-making, a

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Himanshu, Dehariya Amit Kumar Sharma &. Chandrakant Tiwari. "AN INITIAL IMPERATIVE STUDY ON BIG DATA." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES [FRTSSDS-18] (June 13, 2018): 90–94. https://doi.org/10.5281/zenodo.1288447.

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Big data has become possible due to low cost storage, high performance servers, high-speed networking. Big data is a word for data sets that are so huge or intricate that conventional data processing application software is not enough to deal with them. This paper presents big data Techniques, different big data Sources and Hadoop Distributed file System.

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Tan Xiaodi, 谭小地. "Optical data storage technologies for big data era." Infrared and Laser Engineering 45, no. 9 (2016): 0935001. http://dx.doi.org/10.3788/irla201645.0935001.

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Tan Xiaodi, 谭小地. "Optical data storage technologies for big data era." Infrared and Laser Engineering 45, no. 9 (2016): 935001. http://dx.doi.org/10.3788/irla20164509.935001.

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22

Chen, Xingyue, Tao Shang, Feng Zhang, Jianwei Liu, and Zhenyu Guan. "Dynamic data auditing scheme for big data storage." Frontiers of Computer Science 14, no. 1 (2019): 219–29. http://dx.doi.org/10.1007/s11704-018-8117-6.

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23

Bhat, Wasim Ahmad. "Bridging data-capacity gap in big data storage." Future Generation Computer Systems 87 (October 2018): 538–48. http://dx.doi.org/10.1016/j.future.2017.12.066.

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24

Zhou, Jiahao. "Comparison Of Cloud Storage in The Field of Personal Data and Big Data." Highlights in Science, Engineering and Technology 81 (January 26, 2024): 547–52. http://dx.doi.org/10.54097/wqsgs575.

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Background: With the development of information technology and the popularization of the Internet, a large amount of data has been generated and collected. These data typically have the characteristics of massive, diverse, and high-speed growth, and traditional data storage devices and technologies are no longer able to meet the storage and analysis needs of data. And people need more and more storage space to store personal files, photos, videos, and other data. Traditional personal computer hard drives have limited capacity and cannot meet the growing data demand. The emergence of cloud stor

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Babu, K. R. Remesh, and K. P. Madhu. "Intelligent Secure Storage Mechanism for Big Data." Webology 18, Special Issue 01 (2021): 246–61. http://dx.doi.org/10.14704/web/v18si01/web18057.

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The management of big data became more important due to the wide spread adoption of internet of things in various fields. The developments in technology, science, human habits, etc., generates massive amount of data, so it is increasingly important to store and protect these data from attacks. Big data analytics is now a hot topic. The data storage facility provided by the cloud computing enabled business organizations to overcome the burden of huge data storage and maintenance. Also, several distributed cloud applications supports them to analyze this data for taking appropriate decisions. Th

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Rizal, Saiful. "Development of Big Data Analytics Model." ITEJ (Information Technology Engineering Journals) 4, no. 1 (2019): 14–25. http://dx.doi.org/10.24235/itej.v4i1.47.

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The development of information technology produces very large data sizes, with various variations in data and complex data structures. Traditional data storage techniques are not sufficient for storage and analysis with very large volumes of data. Many researchers conducted their research in analyzing big data with various analytics models in big data. Therefore, the purpose of the survey paper is to provide an understanding of analytics models in big data for various uses using algorithms in data mining. Preprocessing big data is the key to turning big data into big value.

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Manikandan, D., C. Valliyammai, and RN Karthika. "Blockchain-based Secure Big Data Storage on Cloud." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 4 (2020): 37–45. https://doi.org/10.35940/ijrte.D4744.119420.

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In the cryptocurrency era, Blockchain is one of the expeditiously growing information technologies that help in providing security to the data. Data tampering and authentication problems generally occur in centralized servers while sharing and storing the data. Blockchain provides the platform for big data and cloud storage in enhancing the security by evading from pernicious users. In this paper, we have discussed the exhaustive description of blockchain and its need, features and applications. Analysis of blockchain is done for different domains such as big data, cloud, internet of things an

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O., Reznichenko, Liashenko O., and Arkhypova V. "Database models for storing big data." System technologies 6, no. 155 (2025): 155–95. https://doi.org/10.34185/1562-9945-6-155-2024-18.

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The issues related to the formation of large data collections are not fully resolved. The amount of information in the world is constantly increasing, which has resulted in the problem of its storage. The term "big data" created to define this data includes the following characteristics such as quantity, processing speed, variety, reliability, variability and value. This type of information includes environmental characteristics; the data determine the distribution of relevant indicators on the Earth and make it possible to make a forecast for the future regarding their changes in time and spa

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S., Muthuraj Kumar. "DATA STORING AND RETRIEVAL METHOD IN BIG DATA USING FUZZY BASED SCALABLE CLUSTERING ALGORITHMS." International Journal of Engineering Technologies and Management Research 9, no. 17 (2019): 9–17. https://doi.org/10.5281/zenodo.3245179.

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A massive volume of digital data holding valuable information, called Big Data, is produced each generation. To process and excavate such valuable information, clustering is commonly used as a data investigation technique. A huge amount of Big Data diagnostics contexts have been established to measure the clustering procedures used for big data analysis. There exists one and only framework called Fuzzy based mechanism which actually fits in for iterative method by associate in storage divisions and accessible. The proposed algorithm is motivated towards the design and implementation of fuzzy b

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Elvin Jafarov, Elvin Jafarov. "DATA CLEANING BEFORE UPLOADING TO STORAGE." ETM - Equipment, Technologies, Materials 13, no. 01 (2023): 117–27. http://dx.doi.org/10.36962/etm13012023-117.

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The article considered the issue of cleaning big data before uploading it to storage. At this time, the errors made and the methods of eliminating these errors have been clarified. The technology of creating a big data storage and analysis system is reviewed, as well as solutions for the implementation of the first stages of the Data Science process: data acquisition, cleaning and loading are described. The results of the research allow us to move towards the realization of future steps in the field of big data processing. It was noted that Data cleansing is an essential step in working with b

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Liang, Ye. "Big Data Storage Method in Wireless Communication Environment." Advanced Materials Research 756-759 (September 2013): 899–904. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.899.

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Big data phenomenon refers to the practice of collection and processing of very large data sets and associated systems and algorithms used to analyze these massive data sets. Big data service is very attractive in the field of wireless communication environment, especially when we face the spatial applications, which are typical applications of big data. Because of the complexity to ingest, store and analyze geographical information data, this paper reflects on a few of the technical problems presented by the exploration of big data, and puts forward an effective storage method in wireless com

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Gu, Min, Xiangping Li, and Yaoyu Cao. "Optical storage arrays: a perspective for future big data storage." Light: Science & Applications 3, no. 5 (2014): e177-e177. http://dx.doi.org/10.1038/lsa.2014.58.

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Binoy, Kurikaparambil Revi. "Data Storage Using Mathematical Model." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 6, no. 4 (2020): 1–4. https://doi.org/10.5281/zenodo.14203741.

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Data is precious and critical in today’s world of IOT and Big data. When it comes to data storage it has become even more expensive with the new technologies. Data Storage using Mathematical Models is a scientific technique that can help a fairly predictable system to store the data in a very compact form. This technique can also combine with the traditional data storage mechanism to provide a Hybrid Data Storage using Mathematical Model. The technique is very useful in storing data such as data for IOT and big data applications where the data can be considered as a continuous time model

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Hu, Bo, Nuno Carvalho, and Takahide Matsutsuka. "Towards Big Linked Data." International Journal of Data Warehousing and Mining 9, no. 4 (2013): 19–43. http://dx.doi.org/10.4018/ijdwm.2013100102.

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In light of the challenges of effectively managing Big Data, the authors are witnessing a gradual shift towards the increasingly popular Linked Open Data (LOD) paradigm. LOD aims to impose a machine-readable semantic layer over structured as well as unstructured data and hence automate some data analysis tasks that are not designed for computers. The convergence of Big Data and LOD is, however, not straightforward: the semantic layer of LOD and the Big Data large scale storage do not get along easily. Meanwhile, the sheer data size envisioned by Big Data denies certain computationally expensiv

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Belov, Vladimir, Andrey Tatarintsev, and Evgeny Nikulchev. "Comparative Characteristics of Big Data Storage Formats." Journal of Physics: Conference Series 1727 (January 2021): 012005. http://dx.doi.org/10.1088/1742-6596/1727/1/012005.

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Papageorgiou, Louis, Picasi Eleni, Sofia Raftopoulou, Meropi Mantaiou, Vasileios Megalooikonomou, and Dimitrios Vlachakis. "Genomic big data hitting the storage bottleneck." EMBnet.journal 24 (April 18, 2018): e910. http://dx.doi.org/10.14806/ej.24.0.910.

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During the last decades, there is a vast data explosion in bioinformatics. Big data centres are trying to face this data crisis, reaching high storage capacity levels. Although several scientific giants examine how to handle the enormous pile of information in their cupboards, the problem remains unsolved. On a daily basis, there is a massive quantity of permanent loss of extensive information due to infrastructure and storage space problems. The motivation for sequencing has fallen behind. Sometimes, the time that is spent to solve storage space problems is longer than the one dedicated to co

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Al-Shomrani, Abdullah, Fathy Eassa, and Kamal Jambi. "A Framework to Secure Big Data Storage." Journal of Computational and Theoretical Nanoscience 14, no. 11 (2017): 5600–5605. http://dx.doi.org/10.1166/jctn.2017.6990.

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Tanigawa, Hitoshi. "Storage for Big Data^|^frasl;Cloud Era." JAPAN TAPPI JOURNAL 68, no. 3 (2014): 307–10. http://dx.doi.org/10.2524/jtappij.68.307.

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Lei, Lei, and Yangxia Shu. "Research on Big Data efficient hybrid cloud storage model and algorithm based on 5G network." Journal of Intelligent Systems and Internet of Things 14, no. 2 (2025): 103–14. http://dx.doi.org/10.54216/jisiot.140209.

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Due to the large demand for big data storage capacity, the storage intensity index is not calculated in the current big data cloud storage process, resulting in a high storage space usage. This paper proposes a big data efficient hybrid cloud storage model and algorithm under 5G network. The model is based on the 5G network performance framework and consists of three parts: users, private cloud and public cloud storage service providers. The purpose of efficient hybrid cloud storage of big data is achieved by using consistent hash algorithm. The simulation results show that the above algorithm

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Khalid, Z. M., and S. R. M. Zeebaree. "Big Data Analysis for Data Visualization: A Review." International Journal of Science and Business 5, no. 2 (2021): 64–75. https://doi.org/10.5281/zenodo.4481357.

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One of the main characteristics of scaling data is complexity. Heterogeneous data contributes to data integration and the process of big data problems. Both of them are essential and difficult to visualize and interpret large-scale databases since they require considerable data processing and storage capacity. The data age, where data grows exponentially, is a significant struggle to extract data in a manner that the human mind can grasp. This paper reviews and provides data visualization and the Heterogeneous Distributed Storage description and their challenges using different methods through

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Chang, Xiangli, and Hailang Cui. "Distributed Storage Strategy and Visual Analysis for Economic Big Data." Journal of Mathematics 2021 (November 27, 2021): 1–13. http://dx.doi.org/10.1155/2021/3224190.

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With the increasing popularity of a large number of Internet-based services and a large number of services hosted on cloud platforms, a more powerful back-end storage system is needed to support these services. At present, it is very difficult or impossible to implement a distributed storage to meet all the above assumptions. Therefore, the focus of research is to limit different characteristics to design different distributed storage solutions to meet different usage scenarios. Economic big data should have the basic requirements of high storage efficiency and fast retrieval speed. The large

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Alsulbi, Khalil Ahmad, Maher Ali Khemakhem, Abdullah Ahamd Basuhail, Fathy Eassa Eassa, Kamal Mansur Jambi, and Khalid Ali Almarhabi. "A Proposed Framework for Secure Data Storage in a Big Data Environment Based on Blockchain and Mobile Agent." Symmetry 13, no. 11 (2021): 1990. http://dx.doi.org/10.3390/sym13111990.

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The sum of Big Data generated from different sources is increasing significantly with each passing day to extent that it is becoming challenging for traditional storage methods to store this massive amount of data. For this reason, most organizations have resolved to use third-party cloud storage to store data. Cloud storage has advanced in recent times, but it still faces numerous challenges with regard to security and privacy. This paper discusses Big Data security and privacy challenges and the minimum requirements that must be provided by future solutions. The main objective of this paper

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Nazia, Tazeen, and Rani K.Sandhya. "A Survey on Some Big Data Applications Tools and Technologies." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 6 (2021): 239–42. https://doi.org/10.35940/ijrte.F5575.039621.

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<strong>Abstract</strong>: Big Data is a broad area that deals with enormous chunks of data sets. It is a word for enormous data sets having huge volume, more diverse structures of data originating from diverse sources are growing rapidly. Many data being generated because of fast data transmission between devices concerning different sectors like healthcare, science, media, business, entertainment and engineering. Data collection capacity and its storage is big concern. Apache Hadoop software is a store of accessible source programs to store big data and perform analytics and various other op

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Lucivero, Federica. "Big Data, Big Waste? A Reflection on the Environmental Sustainability of Big Data Initiatives." Science and Engineering Ethics 26, no. 2 (2019): 1009–30. http://dx.doi.org/10.1007/s11948-019-00171-7.

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AbstractThis paper addresses a problem that has so far been neglected by scholars investigating the ethics of Big Data and policy makers: that is the ethical implications of Big Data initiatives’ environmental impact. Building on literature in environmental studies, cultural studies and Science and Technology Studies, the article draws attention to the physical presence of data, the material configuration of digital service, and the space occupied by data. It then explains how this material and situated character of data raises questions concerning the ethics of the increasingly fashionable Bi

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Sharma, Seemu, and Seema Bawa. "CBDR: An efficient storage repository for cultural big data." Digital Scholarship in the Humanities 35, no. 4 (2019): 893–903. http://dx.doi.org/10.1093/llc/fqz083.

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Abstract Cultural data and information on the web are continuously increasing, evolving, and reshaping in the form of big data due to globalization, digitization, and its vast exploration, with common people realizing the importance of ancient values. Therefore, before it becomes unwieldy and too complex to manage, its integration in the form of big data repositories is essential. This article analyzes the complexity of the growing cultural data and presents a Cultural Big Data Repository as an efficient way to store and retrieve cultural big data. The repository is highly scalable and provide

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Orike, Sunny, and Daboso Brown. "Big Data Management." International Journal of Interdisciplinary Telecommunications and Networking 8, no. 4 (2016): 34–50. http://dx.doi.org/10.4018/ijitn.2016100104.

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Organizations and governments leverage on the potentials in data to plan and compete globally. Data from various sources are continually mined, stored in databases and utilized in a manner that improves processes, products and ensure steady profitability. The traditional relational database management systems are unable to cope with these new forms of data. The velocity, volume and variety in which data are generated qualify them as “Big Data”. This scales up the storage and processing needs of client organizations, allowing them to focus on their core areas of expertise. This paper investigat

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Panwar, Arvind, and Vishal Bhatnagar. "Data Lake Architecture." International Journal of Organizational and Collective Intelligence 10, no. 1 (2020): 63–75. http://dx.doi.org/10.4018/ijoci.2020010104.

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Data is the biggest asset after people for businesses, and it is a new driver of the world economy. The volume of data that enterprises gather every day is growing rapidly. This kind of rapid growth of data in terms of volume, variety, and velocity is known as Big Data. Big Data is a challenge for enterprises, and the biggest challenge is how to store Big Data. In the past and some organizations currently, data warehouses are used to store Big Data. Enterprise data warehouses work on the concept of schema-on-write but Big Data analytics want data storage which works on the schema-on-read conce

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Vistro, Daniel Mago. "IoT based Big Data Analytics for Cloud Storage Using Edge Computing." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (2020): 1594–98. http://dx.doi.org/10.5373/jardcs/v12sp7/20202262.

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Blessing, E. James. "HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENT." International Journal of Computer Science, Engineering and Applications (IJCSEA) 7, no. 3/4 (2020): 15–27. https://doi.org/10.5281/zenodo.3674667.

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Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that t

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Holmes, J. H., J. Sun, and N. Peek. "Technical Challenges for Big Data in Biomedicine and Health: Data Sources, Infrastructure, and Analytics." Yearbook of Medical Informatics 23, no. 01 (2014): 42–47. http://dx.doi.org/10.15265/iy-2014-0018.

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Abstract:

Summary Objectives: To review technical and methodological challenges for big data research in biomedicine and health. Methods: We discuss sources of big datasets, survey infrastructures for big data storage and big data processing, and describe the main challenges that arise when analyzing big data. Results: The life and biomedical sciences are massively contributing to the big data revolution through secondary use of data that were collected during routine care and through new data sources such as social media. Efficient processing of big datasets is typically achieved by distributing comput

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