Relevant bibliographies by topics / Big Data analytics applications

Academic literature on the topic 'Big Data analytics applications'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Big Data analytics applications.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Big Data analytics applications"

1

K., Sangeetha, Poongothai T., Anguraj S., and Nithya Kalyani S. "An Overview of Applications of Big Data Analytics." Bonfring International Journal of Software Engineering and Soft Computing 8, no. 1 (March 30, 2018): 06–11. http://dx.doi.org/10.9756/bijsesc.8381.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Bi, Zhuming, and David Cochran. "Big data analytics with applications." Journal of Management Analytics 1, no. 4 (October 2, 2014): 249–65. http://dx.doi.org/10.1080/23270012.2014.992985.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Memon, Mashooque A., Safeeullah Soomro, Awais K. Jumani, and Muneer A. Kartio. "Big Data Analytics and Its Applications." Annals of Emerging Technologies in Computing 1, no. 1 (October 1, 2017): 45–54. http://dx.doi.org/10.33166/aetic.2017.01.006.

Full text
Abstract:
The term, Big Data, has been authored to refer to the extensive heave of data that can't be managed by traditional data handling methods or techniques. The field of Big Data plays an indispensable role in various fields, such as agriculture, banking, data mining, education, chemistry, finance, cloud computing, marketing, health care stocks. Big data analytics is the method for looking at big data to reveal hidden patterns, incomprehensible relationship and other important data that can be utilize to resolve on enhanced decisions. There has been a perpetually expanding interest for big data because of its fast development and since it covers different areas of applications. Apache Hadoop open source technology created in Java and keeps running on Linux working framework was used. The primary commitment of this exploration is to display an effective and free solution for big data application in a distributed environment, with its advantages and indicating its easy use. Later on, there emerge to be a required for an analytical review of new developments in the big data technology. Healthcare is one of the best concerns of the world. Big data in healthcare imply to electronic health data sets that are identified with patient healthcare and prosperity. Data in the healthcare area is developing past managing limit of the healthcare associations and is relied upon to increment fundamentally in the coming years.
APA, Harvard, Vancouver, ISO, and other styles
4

Das, Nivedita, Leena Das, Siddharth Swarup Rautaray, and Manjusha Pandey. "Big Data Analytics for Medical Applications." International Journal of Modern Education and Computer Science 10, no. 2 (February 8, 2018): 35–42. http://dx.doi.org/10.5815/ijmecs.2018.02.04.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Al-Sai, Zaher Ali, Mohd Heikal Husin, Sharifah Mashita Syed-Mohamad, Rasha Moh’d Sadeq Abdin, Nour Damer, Laith Abualigah, and Amir H. Gandomi. "Explore Big Data Analytics Applications and Opportunities: A Review." Big Data and Cognitive Computing 6, no. 4 (December 14, 2022): 157. http://dx.doi.org/10.3390/bdcc6040157.

Full text
Abstract:
Big data applications and analytics are vital in proposing ultimate strategic decisions. The existing literature emphasizes that big data applications and analytics can empower those who apply Big Data Analytics during the COVID-19 pandemic. This paper reviews the existing literature specializing in big data applications pre and peri-COVID-19. A comparison between Pre and Peri of the pandemic for using Big Data applications is presented. The comparison is expanded to four highly recognized industry fields: Healthcare, Education, Transportation, and Banking. A discussion on the effectiveness of the four major types of data analytics across the mentioned industries is highlighted. Hence, this paper provides an illustrative description of the importance of big data applications in the era of COVID-19, as well as aligning the applications to their relevant big data analytics models. This review paper concludes that applying the ultimate big data applications and their associated data analytics models can harness the significant limitations faced by organizations during one of the most fateful pandemics worldwide. Future work will conduct a systematic literature review and a comparative analysis of the existing Big Data Systems and models. Moreover, future work will investigate the critical challenges of Big Data Analytics and applications during the COVID-19 pandemic.
APA, Harvard, Vancouver, ISO, and other styles
6

Ravada, Siva. "Big data spatial analytics for enterprise applications." SIGSPATIAL Special 6, no. 2 (March 10, 2015): 34–41. http://dx.doi.org/10.1145/2744700.2744705.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Islam, Akinul. "Applications of Real-Time Big Data Analytics." International Journal of Computer Applications 144, no. 5 (June 17, 2016): 1–5. http://dx.doi.org/10.5120/ijca2016910208.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Akinnagbe, Akindele, K. Dharini Amitha Peiris, and Oluyemi Akinloye. "Prospects of Big Data Analytics in Africa Healthcare System." Global Journal of Health Science 10, no. 6 (May 8, 2018): 114. http://dx.doi.org/10.5539/gjhs.v10n6p114.

Full text
Abstract:
Big data is having a positive impact in almost every sphere of life, such as in military intelligence, space science, aviation, banking, and health. Big data is a growing force in healthcare. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa. Healthcare systems in Africa are, in relative terms, behind the rest of the world. Platforms and technologies used to amass big data such as the Internet and mobile phones are already in use in Africa, thereby making big data applications to be emerging. Hence, the key research question we address is whether big data applications can improve healthcare in Africa especially during epidemics and through the public health system. In this study, a literature review is carried out, firstly to present cases of big data applications in healthcare in Africa, and secondly, to explore potential ethical challenges of such applications. This review will provide an update on the application of big data in the health sector in Africa that can be useful for future researchers and health care practitioners in Africa.
APA, Harvard, Vancouver, ISO, and other styles
9

Woodard, Joshua. "Big data and Ag-Analytics." Agricultural Finance Review 76, no. 1 (May 3, 2016): 15–26. http://dx.doi.org/10.1108/afr-03-2016-0018.

Full text
Abstract:
Purpose – The purpose of this paper is to provide a brief and necessarily partial overview of the design, motivation, and use of the Ag-Analytics platform (ag-analytics.org), focussing on integration and warehousing of publicly available research data for broad communities of researchers, including those in the area of agricultural finance. Design/methodology/approach – The paper walks the reader through an overview of the layout and utilization of the Ag-Analytics platform, including a few example applications of some of the tools and web API’s. Findings – Much of the data researchers routinely use in agricultural and environmental finance and related fields are often – strictly speaking – publicly available; however the form in which they are distributed leads to great inefficiencies in data sourcing and processing which can be greatly improved. The goal of the Ag-Analytics open data/open source platform is to help researchers centralize and share in such efforts. Development of systems for disseminating, documenting, and automating the processing of such data can lead to more transparency in research, better routes for validation, and a more robust research community. Practical implications – Some of the tools and methods are discussed, as well as practical issues in data sourcing and automation for research. A few high level introductory examples and applications are illustrated. Originality/value – Development and adoption of such systems and data resources remains seriously lacking in social science research, particularly in the economics, natural resource, environmental, and agricultural finance spheres. This brief provides an overview of one such system which should be of value to researchers in this field and many others.
APA, Harvard, Vancouver, ISO, and other styles
10

Hassani, Hossein, Christina Beneki, Stephan Unger, Maedeh Taj Mazinani, and Mohammad Reza Yeganegi. "Text Mining in Big Data Analytics." Big Data and Cognitive Computing 4, no. 1 (January 16, 2020): 1. http://dx.doi.org/10.3390/bdcc4010001.

Full text
Abstract:
Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Big Data analytics applications"

1

Al-Shiakhli, Sarah. "Big Data Analytics: A Literature Review Perspective." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74173.

Full text
Abstract:
Big data is currently a buzzword in both academia and industry, with the term being used todescribe a broad domain of concepts, ranging from extracting data from outside sources, storingand managing it, to processing such data with analytical techniques and tools.This thesis work thus aims to provide a review of current big data analytics concepts in an attemptto highlight big data analytics’ importance to decision making.Due to the rapid increase in interest in big data and its importance to academia, industry, andsociety, solutions to handling data and extracting knowledge from datasets need to be developedand provided with some urgency to allow decision makers to gain valuable insights from the variedand rapidly changing data they now have access to. Many companies are using big data analyticsto analyse the massive quantities of data they have, with the results influencing their decisionmaking. Many studies have shown the benefits of using big data in various sectors, and in thisthesis work, various big data analytical techniques and tools are discussed to allow analysis of theapplication of big data analytics in several different domains.
APA, Harvard, Vancouver, ISO, and other styles
2

Talevi, Iacopo. "Big Data Analytics and Application Deployment on Cloud Infrastructure." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14408/.

Full text
Abstract:
This dissertation describes a project began in October 2016. It was born from the collaboration between Mr.Alessandro Bandini and me, and has been developed under the supervision of professor Gianluigi Zavattaro. The main objective was to study, and in particular to experiment with, the cloud computing in general and its potentiality in the data elaboration field. Cloud computing is a utility-oriented and Internet-centric way of delivering IT services on demand. The first chapter is a theoretical introduction on cloud computing, analyzing the main aspects, the keywords, and the technologies behind clouds, as well as the reasons for the success of this technology and its problems. After the introduction section, I will briefly describe the three main cloud platforms in the market. During this project we developed a simple Social Network. Consequently in the third chapter I will analyze the social network development, with the initial solution realized through Amazon Web Services and the steps we took to obtain the final version using Google Cloud Platform with its charateristics. To conclude, the last section is specific for the data elaboration and contains a initial theoretical part that describes MapReduce and Hadoop followed by a description of our analysis. We used Google App Engine to execute these elaborations on a large dataset. I will explain the basic idea, the code and the problems encountered.
APA, Harvard, Vancouver, ISO, and other styles
3

Abounia, Omran Behzad. "Application of Data Mining and Big Data Analytics in the Construction Industry." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu148069742849934.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zhang, Liangwei. "Big Data Analytics for Fault Detection and its Application in Maintenance." Doctoral thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60423.

Full text
Abstract:
Big Data analytics has attracted intense interest recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional, streaming, and nonlinear data are being collected and curated to support decision-making. The detection of faults in these data is an important application in eMaintenance solutions, as it can facilitate maintenance decision-making. Early discovery of system faults may ensure the reliability and safety of industrial systems and reduce the risk of unplanned breakdowns. Complexities in the data, including high dimensionality, fast-flowing data streams, and high nonlinearity, impose stringent challenges on fault detection applications. From the data modelling perspective, high dimensionality may cause the notorious “curse of dimensionality” and lead to deterioration in the accuracy of fault detection algorithms. Fast-flowing data streams require algorithms to give real-time or near real-time responses upon the arrival of new samples. High nonlinearity requires fault detection approaches to have sufficiently expressive power and to avoid overfitting or underfitting problems. Most existing fault detection approaches work in relatively low-dimensional spaces. Theoretical studies on high-dimensional fault detection mainly focus on detecting anomalies on subspace projections. However, these models are either arbitrary in selecting subspaces or computationally intensive. To meet the requirements of fast-flowing data streams, several strategies have been proposed to adapt existing models to an online mode to make them applicable in stream data mining. But few studies have simultaneously tackled the challenges associated with high dimensionality and data streams. Existing nonlinear fault detection approaches cannot provide satisfactory performance in terms of smoothness, effectiveness, robustness and interpretability. New approaches are needed to address this issue. This research develops an Angle-based Subspace Anomaly Detection (ABSAD) approach to fault detection in high-dimensional data. The efficacy of the approach is demonstrated in analytical studies and numerical illustrations. Based on the sliding window strategy, the approach is extended to an online mode to detect faults in high-dimensional data streams. Experiments on synthetic datasets show the online extension can adapt to the time-varying behaviour of the monitored system and, hence, is applicable to dynamic fault detection. To deal with highly nonlinear data, the research proposes an Adaptive Kernel Density-based (Adaptive-KD) anomaly detection approach. Numerical illustrations show the approach’s superiority in terms of smoothness, effectiveness and robustness.
APA, Harvard, Vancouver, ISO, and other styles
5

Green, Oded. "High performance computing for irregular algorithms and applications with an emphasis on big data analytics." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51860.

Full text
Abstract:
Irregular algorithms such as graph algorithms, sorting, and sparse matrix multiplication, present numerous programming challenges, including scalability, load balancing, and efficient memory utilization. In this age of Big Data we face additional challenges since the data is often streaming at a high velocity and we wish to make near real-time decisions for real-world events. For instance, we may wish to track Twitter for the pandemic spread of a virus. Analyzing such data sets requires combing algorithmic optimizations and utilization of massively multithreaded architectures, accelerator such as GPUs, and distributed systems. My research focuses upon designing new analytics and algorithms for the continuous monitoring of dynamic social networks. Achieving high performance computing for irregular algorithms such as Social Network Analysis (SNA) is challenging as the instruction flow is highly data dependent and requires domain expertise. The rapid changes in the underlying network necessitates understanding real-world graph properties such as the small world property, shrinking network diameter, power law distribution of edges, and the rate at which updates occur. These properties, with respect to a given analytic, can help design load-balancing techniques, avoid wasteful (redundant) computations, and create streaming algorithms. In the course of my research I have considered several parallel programming paradigms for a wide range systems of multithreaded platforms: x86, NVIDIA's CUDA, Cray XMT2, SSE-SIMD, and Plurality's HyperCore. These unique programming models require examination of the parallel programming at multiple levels: algorithmic design, cache efficiency, fine-grain parallelism, memory bandwidths, data management, load balancing, scheduling, control flow models and more. This thesis deals with these issues and more.
APA, Harvard, Vancouver, ISO, and other styles
6

Svenningsson, Philip, and Maximilian Drubba. "How to capture that business value everyone talks about? : An exploratory case study on business value in agile big data analytics organizations." Thesis, Internationella Handelshögskolan, Jönköping University, IHH, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48882.

Full text
Abstract:
Background: Big data analytics has been referred to as a hype the past decade, making manyorganizations adopt data-driven processes to stay competitive in their industries. Many of theorganizations adopting big data analytics use agile methodologies where the most importantoutcome is to maximize business value. Multiple scholars argue that big data analytics lead toincreased business value, however, there is a theoretical gap within the literature about how agileorganizations can capture this business value in a practically relevant way. Purpose: Building on a combined definition that capturing business value means being able todefine-, communicate- and measure it, the purpose of this thesis is to explore how agileorganizations capture business value from big data analytics, as well as find out what aspects ofvalue are relevant when defining it. Method: This study follows an abductive research approach by having a foundation in theorythrough the use of a qualitative research design. A single case study of Nike Inc. was conducted togenerate the primary data for this thesis where nine participants from different domains within theorganization were interviewed and the results were analysed with a thematic content analysis. Findings: The findings indicate that, in order for agile organizations to capture business valuegenerated from big data analytics, they need to (1) define the value through a synthezised valuemap, (2) establish a common language with the help of a business translator and agile methods,and (3), measure the business value before-, during- and after the development by usingindividually idenified KPIs derived from the business value definition.
APA, Harvard, Vancouver, ISO, and other styles
7

Zubar, Tymofiy, Тимофій Андрійович Зубар, Olena Volovyk, and Олена Іванівна Воловик. "Big data in logistics: last mile application." Thesis, National Aviation University, 2021. https://er.nau.edu.ua/handle/NAU/50494.

Full text
Abstract:
1. 3PL Survey - https://www.3plstudy.com/ 2. 5 Examples of How Big Data in Logistics Can Transform The Supply Chain - https://www.datapine.com/blog/how-big-data-logistics-transform-supply-chain
Big data is revolutionizing many business areas, including logistics and business processes in it. The complexity and dynamics of logistics, coupled with the reliance on many movable parts, can cause bottlenecks at any point in the supply chain, making big data application a vital element of effectiveness in logistical processes design and management. For example, big data logistics can be used to optimize routing, simplify factory functions and give transparency to the entire supply chain, from which both logistics companies and shipping companies may benefit. The third-party logistical company and a transportation company may agree on this issue. Though big data require a large number of high-quality information sources to work effectively.
Великі дані зробили революцію в багатьох сферах бізнесу, включаючи логістику та бізнес-процеси в ній. Складність та динаміка логістики в поєднанні з опорою на багато рухомих частин можуть спричинити вузькі місця у будь-якій точці ланцюга поставок, роблячи застосування великих даних важливим елементом ефективності у проектуванні та управлінні логістичними процесами. Наприклад, логістика великих даних може бути використана для оптимізації маршрутизації, спрощення заводських функцій та надання прозорості усьому ланцюжку поставок, від чого можуть виграти як логістичні компанії, так і судноплавні компанії. Стороння логістична компанія та транспортна компанія можуть домовитись щодо цього питання. Хоча великі дані вимагають великої кількості високоякісних джерел інформації для ефективної роботи.
APA, Harvard, Vancouver, ISO, and other styles
8

Cui, Henggang. "Exploiting Application Characteristics for Efficient System Support of Data-Parallel Machine Learning." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/908.

Full text
Abstract:
Large scale machine learning has many characteristics that can be exploited in the system designs to improve its efficiency. This dissertation demonstrates that the characteristics of the ML computations can be exploited in the design and implementation of parameter server systems, to greatly improve the efficiency by an order of magnitude or more. We support this thesis statement with three case study systems, IterStore, GeePS, and MLtuner. IterStore is an optimized parameter server system design that exploits the repeated data access pattern characteristic of ML computations. The designed optimizations allow IterStore to reduce the total run time of our ML benchmarks by up to 50×. GeePS is a parameter server that is specialized for deep learning on distributed GPUs. By exploiting the layer-by-layer data access and computation pattern of deep learning, GeePS provides almost linear scalability from single-machine baselines (13× more training throughput with 16 machines), and also supports neural networks that do not fit in GPU memory. MLtuner is a system for automatically tuning the training tunables of ML tasks. It exploits the characteristic that the best tunable settings can often be decided quickly with just a short trial time. By making use of optimization-guided online trial-and-error, MLtuner can robustly find and re-tune tunable settings for a variety of machine learning applications, including image classification, video classification, and matrix factorization, and is over an order of magnitude faster than traditional hyperparameter tuning approaches.
APA, Harvard, Vancouver, ISO, and other styles
9

Sharma, Rahil. "Shared and distributed memory parallel algorithms to solve big data problems in biological, social network and spatial domain applications." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2277.

Full text
Abstract:
Big data refers to information which cannot be processed and analyzed using traditional approaches and tools, due to 4 V's - sheer Volume, Velocity at which data is received and processed, and data Variety and Veracity. Today massive volumes of data originate in domains such as geospatial analysis, biological and social networks, etc. Hence, scalable algorithms for effcient processing of this massive data is a signicant challenge in the field of computer science. One way to achieve such effcient and scalable algorithms is by using shared & distributed memory parallel programming models. In this thesis, we present a variety of such algorithms to solve problems in various above mentioned domains. We solve five problems that fall into two categories. The first group of problems deals with the issue of community detection. Detecting communities in real world networks is of great importance because they consist of patterns that can be viewed as independent components, each of which has distinct features and can be detected based upon network structure. For example, communities in social networks can help target users for marketing purposes, provide user recommendations to connect with and join communities or forums, etc. We develop a novel sequential algorithm to accurately detect community structures in biological protein-protein interaction networks, where a community corresponds with a functional module of proteins. Generally, such sequential algorithms are computationally expensive, which makes them impractical to use for large real world networks. To address this limitation, we develop a new highly scalable Symmetric Multiprocessing (SMP) based parallel algorithm to detect high quality communities in large subsections of social networks like Facebook and Amazon. Due to the SMP architecture, however, our algorithm cannot process networks whose size is greater than the size of the RAM of a single machine. With the increasing size of social networks, community detection has become even more difficult, since network size can reach up to hundreds of millions of vertices and edges. Processing such massive networks requires several hundred gigabytes of RAM, which is only possible by adopting distributed infrastructure. To address this, we develop a novel hybrid (shared + distributed memory) parallel algorithm to efficiently detect high quality communities in massive Twitter and .uk domain networks. The second group of problems deals with the issue of effciently processing spatial Light Detection and Ranging (LiDAR) data. LiDAR data is widely used in forest and agricultural crop studies, landscape classification, 3D urban modeling, etc. Technological advancements in building LiDAR sensors have enabled highly accurate and dense LiDAR point clouds resulting in massive data volumes, which pose computing issues with processing and storage. We develop the first published landscape driven data reduction algorithm, which uses the slope-map of the terrain as a filter to reduce the data without sacrificing its accuracy. Our algorithm is highly scalable and adopts shared memory based parallel architecture. We also develop a parallel interpolation technique that is used to generate highly accurate continuous terrains, i.e. Digital Elevation Models (DEMs), from discrete LiDAR point clouds.
APA, Harvard, Vancouver, ISO, and other styles
10

Matteuzzi, Tommaso. "Network diffusion methods for omics big bio data analytics and interpretation with application to cancer datasets." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13660/.

Full text
Abstract:
Nella attuale ricerca biomedica un passo fondamentale verso una comprensione dei meccanismi alla radice di una malattia è costituito dalla identificazione dei disease modules, cioè quei sottonetwork dell'interattoma, il network delle interazioni tra proteine, con un alto numero di alterazioni geniche. Tuttavia, l'incompletezza del network e l'elevata variabilità dei geni alterati rendono la soluzione di questo problema non banale. I metodi fisici che sfruttano le proprietà dei processi diffusivi su network, dei quali mi sono occupato in questo lavoro di tesi, sono quelli che consentono di ottenere le migliori prestazioni. Nella prima parte del mio lavoro, ho indagato la teoria relativa alla diffusione ed ai random walk su network, trovando interessanti relazioni con le tecniche di clustering e con altri modelli fisici la cui dinamica è descritta dalla matrice laplaciana. Ho poi implementato un tecnica basata sulla diffusione su rete applicandola a dati di espressione genica e mutazioni somatiche di tre diverse tipologie di cancro. Il metodo è organizzato in due parti. Dopo aver selezionato un sottoinsieme dei nodi dell'interattoma, associamo ad ognuno di essi un'informazione iniziale che riflette il "grado" di alterazione del gene. L'algoritmo di diffusione propaga l'informazione iniziale nel network raggiungendo, dopo un transiente, lo stato stazionario. A questo punto, la quantità di fluido in ciascun nodo è utilizzata per costruire un ranking dei geni. Nella seconda parte, i disease modules sono identificati mediante una procedura di network resampling. L'analisi condotta ci ha permesso di identificare un numero consistente di geni già noti nella letteratura relativa ai tipi di cancro studiati, nonché un insieme di altri geni correlati a questi che potrebbero essere interessanti candidati per ulteriori approfondimenti.Attraverso una procedura di Gene Set Enrichment abbiamo infine testato la correlazione dei moduli identificati con pathway biologici noti.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Big Data analytics applications"

1

Alani, Mohammed M., Hissam Tawfik, Mohammed Saeed, and Obinna Anya, eds. Applications of Big Data Analytics. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76472-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Prabhu, C. S. R., Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghosh, and L. M. Jenila Livingston. Big Data Analytics: Systems, Algorithms, Applications. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0094-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Big data: Algorithms, analytics, and applications. Boca Raton: CRC Press, Taylor & Francis Group, 2015.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Yaseen, Saad G., ed. Digital Economy, Business Analytics, and Big Data Analytics Applications. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05258-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Seng, Kah Phooi, Li-minn Ang, Alan Wee-Chung Liew, and Junbin Gao, eds. Multimodal Analytics for Next-Generation Big Data Technologies and Applications. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-97598-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Pani, Subhendu Kumar, Somanath Tripathy, Talal Ashraf Butt, Sumit Kundu, and George Jandieri. Applications of Machine Learning in Big-Data Analytics and Cloud Computing. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003337218.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hassanien, Aboul Ella, and Ashraf Darwish, eds. Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-59338-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Pehcevski, Jovan. Big Data Analytics - Methods and Applications. Arcler Education Inc, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Yang, Laurence T., Kuan-Ching Li, Alfredo Cuzzocrea, and Hai Jiang. Big Data: Algorithms, Analytics, and Applications. Taylor & Francis Group, 2015.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Livingston, L. M. Jenila, C. S. R. Prabhu, Aneesh Sreevallabh Chivukula, Aditya Mogadala, and Rohit Ghosh. Big Data Analytics: Systems, Algorithms, Applications. Springer Singapore Pte. Limited, 2020.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Big Data analytics applications"

1

Tsai, Chun-Wei, Chin-Feng Lai, Han-Chieh Chao, and Athanasios V. Vasilakos. "Big Data Analytics." In Big Data Technologies and Applications, 13–52. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44550-2_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Prabhu, C. S. R., Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghosh, and L. M. Jenila Livingston. "Big Data Analytics." In Big Data Analytics: Systems, Algorithms, Applications, 1–23. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0094-7_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Abhijit, Suprem. "Big Data Analysis Technology and Applications." In Big Data Analytics, 249–316. Boca Raton, FL : Taylor & Francis Group, [2018] | “A science publishers book.”: CRC Press, 2018. http://dx.doi.org/10.1201/9781315112626-12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Pusala, Murali K., Mohsen Amini Salehi, Jayasimha R. Katukuri, Ying Xie, and Vijay Raghavan. "Massive Data Analysis: Tasks, Tools, Applications, and Challenges." In Big Data Analytics, 11–40. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-3628-3_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Hasan, Nabeela, and Mansaf Alam. "Applications of Big Data Analytics in Supply-Chain Management." In Big Data Analytics, 173–99. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9781003175711-10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Kaushik, Prachi, and Suraiya Jabin. "Advancements and Challenges in Business Applications of SAR Images." In Big Data Analytics, 87–111. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9781003175711-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Singh, Dhananjay Kumar, Pijush Kanti Dutta Pramanik, and Prasenjit Choudhury. "Big Graph Analytics: Techniques, Tools, Challenges, and Applications." In Data Analytics, 171–97. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429446177-7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Paruchuri, Praveen, and Sujit Gujar. "Fusion of Game Theory and Big Data for AI Applications." In Big Data Analytics, 55–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04780-1_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Simmhan, Yogesh, and Srinath Perera. "Big Data Analytics Platforms for Real-Time Applications in IoT." In Big Data Analytics, 115–35. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-3628-3_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Priyadarshini, Rojalina, Rabindra K. Barik, and Brojo Kishore Mishra. "Analysis of Deep Learning Tools and Applications in e-Healthcare." In Big Data Analytics, 68–90. Boca Raton, FL : Taylor & Francis Group, [2018] | “A science publishers book.”: CRC Press, 2018. http://dx.doi.org/10.1201/9781315112626-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Big Data analytics applications"

1

Chandarana, Parth, and M. Vijayalakshmi. "Big Data analytics frameworks." In 2014 International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA). IEEE, 2014. http://dx.doi.org/10.1109/cscita.2014.6839299.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Shatnawi, Mohammed Q., Muneer Bani Yassein, Qusai Abuein, and Lujain Nsuir. "Big data analytics tools and applications." In the Second International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3368691.3368741.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Neshatpour, Katayoun, Maria Malik, Mohammad Ali Ghodrat, Avesta Sasan, and Houman Homayoun. "Energy-efficient acceleration of big data analytics applications using FPGAs." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363748.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

"Big Data Management and Analytics for Supporting Smart Healthcare Applications." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020839.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Tamez, Giovanni, Danytza Castillo, Aaron Colmenero, Jorge A. Ayala, and Colleen Bailey. "Machine learning application to hydraulic fracturing." In Big Data: Learning, Analytics, and Applications, edited by Fauzia Ahmad. SPIE, 2019. http://dx.doi.org/10.1117/12.2518996.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Tahmassebi, Amirhessam, Anahid Ehtemami, Behshad Mohebali, Amir H. Gandomi, Katja Pinker, and Anke Meyer-Baese. "Big data analytics in medical imaging using deep learning." In Big Data: Learning, Analytics, and Applications, edited by Fauzia Ahmad. SPIE, 2019. http://dx.doi.org/10.1117/12.2516014.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Amin, Moeness G., Arun Ravisankar, and Ronny G. Guendel. "RF sensing for continuous monitoring of human activities for home consumer applications." In Big Data: Learning, Analytics, and Applications, edited by Fauzia Ahmad. SPIE, 2019. http://dx.doi.org/10.1117/12.2519984.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Liu, Ying, Zach Bellay, Payton Bradsky, Glen Chandler, and Brandon Craig. "Edge-to-fog computing for color-assisted moving object detection." In Big Data: Learning, Analytics, and Applications, edited by Fauzia Ahmad. SPIE, 2019. http://dx.doi.org/10.1117/12.2516023.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Mohebali, Behshad, Amirhessam Tahmassebi, Amir H. Gandomi, and Anke Meyer-Bäse. "A big data inspired preprocessing scheme for bandwidth use optimization in smart cities applications using Raspberry Pi." In Big Data: Learning, Analytics, and Applications, edited by Fauzia Ahmad. SPIE, 2019. http://dx.doi.org/10.1117/12.2517440.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Bekit, Adam, Charles J. Della Porta, Bernard H. Lampe, Bai Xue, and Chein-I. Chang. "Unsupervised automatic target generation process via compressive sensing." In Big Data: Learning, Analytics, and Applications, edited by Fauzia Ahmad. SPIE, 2019. http://dx.doi.org/10.1117/12.2518359.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Big Data analytics applications"

1

Mazorchuk, Mariia S., Tetyana S. Vakulenko, Anna O. Bychko, Olena H. Kuzminska, and Oleksandr V. Prokhorov. Cloud technologies and learning analytics: web application for PISA results analysis and visualization. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4451.

Full text
Abstract:
This article analyzes the ways to apply Learning Analytics, Cloud Technologies, and Big Data in the field of education on the international level. This paper provides examples of international analytical researches and cloud technologies used to process the results of those researches. It considers the PISA research methodology and related tools, including the IDB Analyzer application, free R intsvy environment for processing statistical data, and cloud-based web application PISA Data Explorer. The paper justifies the necessity of creating a stand-alone web application that supports Ukrainian localization and provides Ukrainian researchers with rapid access to well-structured PISA data. In particular, such an application should provide for data across the factorial features and indicators applied at the country level and demonstrate the Ukrainian indicators compared to the other countries’ results. This paper includes a description of the application core functionalities, architecture, and technologies used for development. The proposed solution leverages the shiny package available with R environment that allows implementing both the UI and server sides of the application. The technical implementation is a proven solution that allows for simplifying the access to PISA data for Ukrainian researchers and helping them utilize the calculation results on the key features without having to apply tools for processing statistical data.
APA, Harvard, Vancouver, ISO, and other styles
2

Ansari, A., S. Mohaghegh, M. Shahnam, J. F. Dietiker, and T. Li. Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level. Office of Scientific and Technical Information (OSTI), April 2018. http://dx.doi.org/10.2172/1431303.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ansari, A., S. Mohaghegh, M. Shahnam, J. F. Dietiker, T. Li, and A. Gel. Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Part Three: Model Building at the Layer Level. Office of Scientific and Technical Information (OSTI), May 2018. http://dx.doi.org/10.2172/1463895.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Doucet, Rachel A., Deyan M. Dontchev, Javon S. Burden, and Thomas L. Skoff. Big Data Analytics Test Bed. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada589903.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Ruch, Marc Lavi. Data Analytics for Nonproliferation Applications. Office of Scientific and Technical Information (OSTI), June 2019. http://dx.doi.org/10.2172/1529508.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Ansari, A., S. Mohaghegh, M. Shahnam, J. F. Dietiker, A. Takbiri Borujeni, and E. Fathi. Data Driven Smart Proxy for CFD: Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Part One: Proof of Concept; NETL-PUB-21574; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2017. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1417305.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Alexandrov, Boian, Velimir Valentinov Vesselinov, and Hristo Nikolov Djidjev. Non-negative Tensor Factorization for Robust Exploratory Big-Data Analytics. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1417803.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Yin, Rongxin, Girish Ghatikar, and Mary Piette. Big-Data Analytics for Electric Grid and Demand-Side Management. Office of Scientific and Technical Information (OSTI), May 2019. http://dx.doi.org/10.2172/1773709.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Kezunovic, Mladen, Tatjana Djokic, Rashid Baembitov, Taif Mohamed, Zoran Obradovic, Ameen Hai, Mohammad Alqudah, et al. Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART). Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1887273.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Herzer, John A., and Pengchu Zhang. The Bird project: Using Big Data tools to support Search Analytics. Office of Scientific and Technical Information (OSTI), January 2017. http://dx.doi.org/10.2172/1505412.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Big Data analytics applications' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography