Relevant bibliographies by topics / Big data predictive analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Big data predictive analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Big data predictive analysis"

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Sharma, Mansi. "Predictive Analysis: Rolein Big Data." Indian Journal of Science and Technology 12, no. 38 (2019): 1–8. http://dx.doi.org/10.17485/ijst/2019/v12i38/145564.

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kumar, N. M. Saravana, T. Eswari, P. Sampath, and S. Lavanya. "Predictive Methodology for Diabetic Data Analysis in Big Data." Procedia Computer Science 50 (2015): 203–8. http://dx.doi.org/10.1016/j.procs.2015.04.069.

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Maurya, Neha, Anirudh Tripathi, Pankaj Pratap Singh, and Amit Kishor. "Big Data Analysis for Predictive Healthcare Information System." International Journal of Computer Sciences and Engineering 7, no. 6 (2019): 47–51. http://dx.doi.org/10.26438/ijcse/v7i6.4751.

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A.S, Prakaash, and Sivakumar K. "Data Analytics and Predictive Modelling In the Application of Big Data: A Systematic Review." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (2019): 395–99. http://dx.doi.org/10.5373/jardcs/v11sp11/20193047.

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Elshendy, Mohammed, and Andrea Fronzetti Colladon. "Big data analysis of economic news." International Journal of Engineering Business Management 9 (January 1, 2017): 184797901772004. http://dx.doi.org/10.1177/1847979017720040.

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We propose a novel method to improve the forecast of macroeconomic indicators based on social network and semantic analysis techniques. In particular, we explore variables extracted from the Global Database of Events, Language, and Tone, which monitors the world’s broadcast, print and web news. We investigate the locations and the countries involved in economic events (such as business or economic agreements), as well as the tone and the Goldstein scale of the news where the events are reported. We connect these elements to build three different social networks and to extract new network metrics, which prove their value in extending the predictive power of models only based on the inclusion of other economic or demographic indices. We find that the number of news, their tone, the network constraint of nations and their betweenness centrality oscillations are important predictors of the Gross Domestic Product per Capita and of the Business and Consumer Confidence indices.
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Bag, Surajit. "Big Data and Predictive Analysis is Key to Superior Supply Chain Performance." International Journal of Information Systems and Supply Chain Management 10, no. 2 (2017): 66–84. http://dx.doi.org/10.4018/ijisscm.2017040104.

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The study considers samples from the South African engineering companies who are strategic suppliers to mining and minerals industry and further explores the uncertainties persisting in the supply chain network. Further investigation was done to understand the role of big data and predictive analysis (BDPA) in managing the supply uncertainties. The paper finally uses partial least square regression analysis to study the relationship among buyer-supplier relationship, big data and predictive analysis and supply chain performance. The analysis supported the second and third hypothesis. Therefore, it is established that firstly, there is a positive relationship between big data, predictive analysis and supply chain performance and secondly, there is a positive relationship between and big data, predictive analysis and buyer-supplier relationship. The study is a unique contribution to the current literature by shedding light on the practical problems persisting in the South African context.
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Bāliņa, Signe, Rita Žuka, and Juris Krasts. "Opportunities for the Use of Business Data Analysis Technologies." Economics and Business 28, no. 1 (2016): 20–25. http://dx.doi.org/10.1515/eb-2016-0003.

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Abstract The paper analyses the business data analysis technologies, provides their classification and considers relevant terminology. The feasibility of business data analysis technologies handling big data sources is overviewed. The paper shows the results of examination of the online big data source analytics technologies, data mining and predictive modelling technologies and their trends.
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D K, Thara, and Veena A. "Predictive Analysis on Big data for a Febrile Viral Syndrome." International Journal of Engineering Trends and Technology 12, no. 8 (2014): 388–92. http://dx.doi.org/10.14445/22315381/ijett-v12p275.

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Fényes, Dániel, Balázs Németh, and Péter Gáspar. "A predictive control for autonomous vehicles using big data analysis." IFAC-PapersOnLine 52, no. 5 (2019): 191–96. http://dx.doi.org/10.1016/j.ifacol.2019.09.031.

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López, Victoria, Diego Urgelés, Óscar Sánchez, and Gabriel Valverde. "Big Data in Healthcare and Social Sciences." International Journal of Information Systems and Social Change 8, no. 3 (2017): 1–16. http://dx.doi.org/10.4018/ijissc.2017070101.

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Healthcare providers and payers are increasingly turning to Big Data and analytics, to help them understand their patients and the context of their illnesses in more detail. Industry leaders are exploring/using Big Data to reduce costs, increase efficiency and improve patient care. The next future is an innovative approach to improving patient access using lean methods and predictive analytics. Social sciences are very much related to healthcare and both areas develop in a parallel way. In this article, we introduce one example of application: Bip4cast (a bipolar disorder CAD system). This paper shows how Bip4cast deals with different data sources to enrich the knowledge and improve predictive analysis.
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Dissertations / Theses on the topic "Big data predictive analysis"

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Islam, Md Zahidul. "A Cloud Based Platform for Big Data Science." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-103700.

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With the advent of cloud computing, resizable scalable infrastructures for data processing is now available to everyone. Software platforms and frameworks that support data intensive distributed applications such as Amazon Web Services and Apache Hadoop enable users to the necessary tools and infrastructure to work with thousands of scalable computers and process terabytes of data. However writing scalable applications that are run on top of these distributed frameworks is still a demanding and challenging task. The thesis aimed to advance the core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large data sets, collectively known as “big data”. The term “big-data” in this thesis refers to large, diverse, complex, longitudinal and/or distributed data sets generated from instruments, sensors, internet transactions, email, social networks, twitter streams, and/or all digital sources available today and in the future. We introduced architectures and concepts for implementing a cloud-based infrastructure for analyzing large volume of semi-structured and unstructured data. We built and evaluated an application prototype for collecting, organizing, processing, visualizing and analyzing data from the retail industry gathered from indoor navigation systems and social networks (Twitter, Facebook etc). Our finding was that developing large scale data analysis platform is often quite complex when there is an expectation that the processed data will grow continuously in future. The architecture varies depend on requirements. If we want to make a data warehouse and analyze the data afterwards (batch processing) the best choices will be Hadoop clusters and Pig or Hive. This architecture has been proven in Facebook and Yahoo for years. On the other hand, if the application involves real-time data analytics then the recommendation will be Hadoop clusters with Storm which has been successfully used in Twitter. After evaluating the developed prototype we introduced a new architecture which will be able to handle large scale batch and real-time data. We also proposed an upgrade of the existing prototype to handle real-time indoor navigation data.
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Rossi, Tisbeni Simone. "Big data analytics towards predictive maintenance at the INFN-CNAF computing centre." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18430/.

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La Fisica delle Alte Energie (HEP) è da lungo tra i precursori nel gestire e processare enormi dataset scientifici e nell'operare alcuni tra i più grandi data centre per applicazioni scientifiche. HEP ha sviluppato una griglia computazionale (Grid) per il calcolo al Large Hadron Collider (LHC) del CERN di Ginevra, che attualmente coordina giornalmente le operazioni di calcolo su oltre 800k processori in 170 centri di calcolo e gestendo mezzo Exabyte di dati su disco distribuito in 5 continenti. Nelle prossime fasi di LHC, soprattutto in vista di Run-4, il quantitativo di dati gestiti dai centri di calcolo aumenterà notevolmente. In questo contesto, la HEP Software Foundation ha redatto un Community White Paper (CWP) che indica il percorso da seguire nell'evoluzione del software moderno e dei modelli di calcolo in preparazione alla fase cosiddetta di High Luminosity di LHC. Questo lavoro ha individuato in tecniche di Big Data Analytics un enorme potenziale per affrontare le sfide future di HEP. Uno degli sviluppi riguarda la cosiddetta Operation Intelligence, ovvero la ricerca di un aumento nel livello di automazione all'interno dei workflow. Questo genere di approcci potrebbe portare al passaggio da un sistema di manutenzione reattiva ad uno, più evoluto, di manutenzione predittiva o addirittura prescrittiva. La tesi presenta il lavoro fatto in collaborazione con il centro di calcolo dell'INFN-CNAF per introdurre un sistema di ingestione, organizzazione e processing dei log del centro su una piattaforma di Big Data Analytics unificata, al fine di prototipizzare un modello di manutenzione predittiva per il centro. Questa tesi contribuisce a tale progetto con lo sviluppo di un algoritmo di clustering dei messaggi di log basato su misure di similarità tra campi testuali, per superare il limite connesso alla verbosità ed eterogeneità dei log raccolti dai vari servizi operativi 24/7 al centro.
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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.

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Zapata, Gianpierre, Javier Murga, Carlos Raymundo, Jose Alvarez, and Francisco Dominguez. "Predictive model based on sentiment analysis for peruvian smes in the sustainable tourist sector." SciTePress, 2017. http://hdl.handle.net/10757/656350.

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In the sustainable tourist sector today, there is a wide margin of loss in small and medium-sized enterprise (SMEs) because of a poor control in logistical expenses. In other words, acquired goods are note being sold, a scenario which is very common in tourism SMEs. These SMEs buy a number of travel packages to big companies and because of the lack of demand of said packages, they expire and they become an expense, not the investment it was meant to be. To solve this problem, we propose a Predictive model based on sentiment analysis of a social networks that will help the sales decision making. Once the data of the social network is analyzed, we also propose a prediction model of tourist destinations, using this information as data source it will be able to predict the tourist interest. In addition, a case study was applied to a real Peruvian tourist enterprise showing their data before and after using the proposed model in order to validate the feasibility of proposed model.
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Rodriguez, Pellière Lineth Arelys. "A qualitative analysis to investigate the enablers of big data analytics that impacts sustainable supply chain." Thesis, Ecole centrale de Nantes, 2019. http://www.theses.fr/2019ECDN0019/document.

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Les académiques et les professionnels ont déjà montré que le Big Data et l'analyse prédictive, également connus dans la littérature sous le nom de BDPA, peuvent jouer un rôle fondamental dans la transformation et l'amélioration des fonctions de l'analyse de la chaîne d'approvisionnement durable (SSCA). Cependant, les connaissances sur la meilleure manière d'utiliser la BDPA pour augmenter simultanément les performances sociales, environnementale et financière. Par conséquent, avec les connaissances tirées de la littérature sur la SSCA, il semble que les entreprises peinent encore à mettre en oeuvre les pratiques de la SSCA. Les chercheursconviennent qu'il est encore nécessaire de comprendre les techniques, outils et facteurs des concepts de base de la SSCA pour adoption. C’est encore plus important d’intégrer BDPA en tant qu’atout stratégique dans les activités commerciales. Par conséquent, cette étude examine, par exemple, quels sont les facteurs de SSCA et quels sont les outils et techniques de BDPA qui permettent de mettre en évidence le 3BL (pour ses abréviations en anglais : "triple bottom line") des rendements de durabilité (environnementale, sociale et financière) via SCA.La thèse a adopté un constructionniste modéré, car elle comprend l’impact des facteurs Big Data sur les applications et les indicateurs de performance de la chaîne logistique analytique et durable. La thèse a également adopté un questionnaire et une étude de cas en tant que stratégie de recherche permettant de saisir les différentes perceptions des personnes et des entreprises dans l'application des mégadonnées sur la chaîne d'approvisionnement analytique et durable. La thèse a révélé une meilleure vision des facteurs pouvant influencer l'adoption du Big Data dans la chaîne d'approvisionnement analytique et durable. Cette recherche a permis de déterminer les facteurs en fonction des variables ayant une incidence sur l'adoption de BDPA pour SSCA, des outils et techniques permettant la prise de décision via SSCA et du coefficient de chaque facteur pour faciliter ou retarder l'adoption de la durabilité. Il n'a pas été étudié avant. Les résultats de la thèse suggèrent que les outils actuels utilisés par les entreprises ne peuvent pas analyser de grandes quantités de données par eux-mêmes. Les entreprises ont besoin d'outils plus appropriés pour effectuer ce travail<br>Scholars and practitioners already shown that Big Data and Predictive Analytics also known in the literature as BDPA can play a pivotal role in transforming and improving the functions of sustainable supply chain analytics (SSCA). However, there is limited knowledge about how BDPA can be best leveraged to grow social, environmental and financial performance simultaneously. Therefore, with the knowledge coming from literature around SSCA, it seems that companies still struggled to implement SSCA practices. Researchers agree that is still a need to understand the techniques, tools, and enablers of the basics SSCA for its adoption; this is even more important to integrate BDPA as a strategic asset across business activities. Hence, this study investigates, for instance, what are the enablers of SSCA, and what are the tools and techniques of BDPA that enable the triple bottom line (3BL) of sustainability performances through SCA. The thesis adopted moderate constructionism since understanding of how the enablers of big data impacts sustainable supply chain analytics applications and performances. The thesis also adopted a questionnaire and a case study as a research strategy in order to capture the different perceptions of the people and the company on big data application on sustainable supply chain analytics. The thesis revealed a better insight of the factors that can affect in the adoption of big data on sustainable supply chain analytics. This research was capable to find the factors depending on the variable loadings that impact in the adoption of BDPA for SSCA, tools and techniques that enable decision making through SSCA, and the coefficient of each factor for facilitating or delaying sustainability adoption that wasn’t investigated before. The findings of the thesis suggest that the current tools that companies are using by itself can’t analyses data. The companies need more appropriate tools for the data analysis
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Singh, Shailendra. "Smart Meters Big Data : Behavioral Analytics via Incremental Data Mining and Visualization." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35244.

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The big data framework applied to smart meters offers an exception platform for data-driven forecasting and decision making to achieve sustainable energy efficiency. Buying-in consumer confidence through respecting occupants' energy consumption behavior and preferences towards improved participation in various energy programs is imperative but difficult to obtain. The key elements for understanding and predicting household energy consumption are activities occupants perform, appliances and the times that appliances are used, and inter-appliance dependencies. This information can be extracted from the context rich big data from smart meters, although this is challenging because: (1) it is not trivial to mine complex interdependencies between appliances from multiple concurrent data streams; (2) it is difficult to derive accurate relationships between interval based events, where multiple appliance usage persist; (3) continuous generation of the energy consumption data can trigger changes in appliance associations with time and appliances. To overcome these challenges, we propose an unsupervised progressive incremental data mining technique using frequent pattern mining (appliance-appliance associations) and cluster analysis (appliance-time associations) coupled with a Bayesian network based prediction model. The proposed technique addresses the need to analyze temporal energy consumption patterns at the appliance level, which directly reflect consumers' behaviors and provide a basis for generalizing household energy models. Extensive experiments were performed on the model with real-world datasets and strong associations were discovered. The accuracy of the proposed model for predicting multiple appliances usage outperformed support vector machine during every stage while attaining accuracy of 81.65\%, 85.90\%, 89.58\% for 25\%, 50\% and 75\% of the training dataset size respectively. Moreover, accuracy results of 81.89\%, 75.88\%, 79.23\%, 74.74\%, and 72.81\% were obtained for short-term (hours), and long-term (day, week, month, and season) energy consumption forecasts, respectively.
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Ramanayaka, Mudiyanselage Asanga. "Data Engineering and Failure Prediction for Hard Drive S.M.A.R.T. Data." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1594957948648404.

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Olli, Oscar. "Big Data in Small Tunnels : Turning Alarms Into Intelligence." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292946.

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In this thesis we examine methods for evaluating a traffic alarm system. Nuisance alarms can quickly increase the volume of alarms experienced by the alarm operator and obstruct their work. We propose two methods for removing a number of these nuisance alarms, so that events of higher priority can be targeted. A parallel correlation analysis demonstrated significant correlation between single and clusters of alarms, presenting a strong cause for causality. While a serial correlation was performed, it could not conclude evidence of consequential alarms. In order to assist Trafikverket with maintenance scheduling, a long short-term model (LSTM) model, to predict univariate time-series of discretely binned alarm sequences. Experiments conclude that the LSTM model provides higher precision for alarm sequences with higher repeatability and recurring patterns. For other, randomly occurring alarms, the model performs unsatisfactory.<br>Den här examensuppsatsen granskar olika metoder för att utvärdera ett larmsystem med inriktning mot trafiksäkerhet. Störande larm kan skapa stora mängder larm som försvårar arbetet för larmoperatörer. Vi föreslår två metoder för att avlägsna störande larm, så att uppmärksamhet kan riktas mot varningar med högre prioritet. En parallell korrelationsanalys som demonstrerade hög korrelation mellan både enskilda och kluster av larm. Detta presenterar ett starkt orsakssamband. En korskorrelation utfördes även, men denna kunde inte fastställa existens av s.k. följdlarm. För att assistera Trafikverket med schemaläggning av underhåll har en long short-term memory (LSTM) modell implementerats för att förutspå univariata tidsserier av diskretiserade larmsekvenser. Utförda experiment sammanfattar att LSTM modellen presterar bättre för larmsekvenser med återkommande mönster. För mera slumpmässigt genererade larmsekvenser, presterar modellen med lägre precision.
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Lindström, Maja. "Food Industry Sales Prediction : A Big Data Analysis & Sales Forecast of Bake-off Products." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184184.

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In this thesis, the sales of bread and coffee bread at Coop Värmland AB have been studied. The aim was to find what factors that are important for the sales and then make predictions of how the sales will look like in the future to reduce waste and increase profits. Big data analysis and data exploration was used to get to know the data and find the factors that affect the sales the most. Time series forecasting and supervised machine learning models were used to predict future sales. The main focus was five different models that were compared and analysed, they were; Decision tree regression, Random forest regression, Artificial neural networks, Recurrent neural networks and a time series model called Prophet. Comparing the observed values to the predictions made by the models indicated that using a model based on the time series is to be preferred, that is, Prophet and Recurrent neural network. These two models gave the lowest errors and by that, the most accurate results. Prophet yielded mean absolute percentage errors of 8.295% for bread and 9.156% for coffee bread. The Recurrent neural network gave mean absolute percentage errors of 7.938% for bread and 13.12% for coffee bread. That is about twice as good as the models they are using today at Coop which are based on the mean value of the previous sales.<br>I denna avhandling har försäljningen av matbröd och fikabröd på Coop Värmland AB studerats. Målet var att hitta vilka faktorer som är viktiga för försäljningen och sedan förutsäga hur försäljningen kommer att se ut i framtiden för att minska svinn och öka vin- ster. Big data- analys och explorativ dataanalys har använts för att lära känna datat och hitta de faktorer som påverkar försäljningen mest. Tidsserieprediktion och olika mask- ininlärningsmodeller användes för att förutspå den framtida försäljningen. Huvudfokus var fem olika modeller som jämfördes och analyserades. De var Decision tree regression, Random forest regression, Artificial neural networks, Recurrent neural networks och en tidsseriemodell som kallas Prophet. Jämförelse mellan de observerade värdena och de värden som predicerats med modellerna indikerade att de modeller som är baserade på tidsserierna är att föredra, det vill säga Prophet och Recurrent neural networks. Dessa två modeller gav de lägsta felen och därmed de mest exakta resultaten. Prophet gav genomsnittliga absoluta procentuella fel på 8.295% för matbröd och 9.156% för fikabröd. Recurrent neural network gav genomsnittliga absoluta procentuella fel på 7.938% för matbröd och 13.12% för fikabröd. Det är ungefär dubbelt så korrekt som de modeller de använder idag på Coop som baseras på medelvärdet av tidigare försäljning.
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Tadisetty, Srikanth. "Prediction of Psychosis Using Big Web Data in the United States." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1532962079970169.

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Books on the topic "Big data predictive analysis"

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Statistical and machine-learning data mining: Techniques for better predictive modeling and analysis of big data. 2nd ed. Taylor & Francis, 2012.

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Kreinovich, Vladik, Songsak Sriboonchitta, and Nopasit Chakpitak, eds. Predictive Econometrics and Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-70942-0.

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Neagu, Daniel, and Andrea-Nicole Richarz, eds. Big Data in Predictive Toxicology. Royal Society of Chemistry, 2019. http://dx.doi.org/10.1039/9781782623656.

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Finlay, Steven. Predictive Analytics, Data Mining and Big Data. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137379283.

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Chen, Chen, Yuzhuo Ren, and C. C. Jay Kuo. Big Visual Data Analysis. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0631-9.

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John, MacGregor. Predictive analysis with SAP: The comprehensive guide. Galileo Press, 2014.

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McCue, Colleen. Data mining and predictive analysis: Intelligence gathering and crime analysis. Butterworth-Heinemann, 2007.

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McCue, Colleen. Data mining and predictive analysis: Intelligence gathering and crime analysis. Elsevier/Butterworth-Heinemann, 2006.

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Ahmed, S. Ejaz, ed. Big and Complex Data Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41573-4.

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Ahmed, S., ed. Perspectives on Big Data Analysis. American Mathematical Society, 2014. http://dx.doi.org/10.1090/conm/622.

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Book chapters on the topic "Big data predictive analysis"

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Dinov, Ivo D. "Big Longitudinal Data Analysis." In Data Science and Predictive Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72347-1_19.

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Vashisht, Priyanka, Vijay Kumar, and Meghna Sharma. "IoT, Big Data, and Analytics." In Predictive Analytics. CRC Press, 2020. http://dx.doi.org/10.1201/9781003083177-10.

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Nair, Mydhili K., Arjun Rao, and Mipsa Patel. "Big Data Predictive Modeling and Analytics." In Big Data Analytics. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b21822-6.

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Ramírez, Margarita Ramírez, Esperanza Manrique Rojas, Sergio Octavio Vázquez Núñez, and María de los Angeles Quezada. "Big Data and Predictive Health Analysis." In Innovation in Medicine and Healthcare Systems, and Multimedia. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8566-7_27.

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Finlay, Steven. "Text Mining and Social Network Analysis." In Predictive Analytics, Data Mining and Big Data. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137379283_9.

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Osathanunkul, Rossarin, Natthaphat Kingnetr, and Songsak Sriboonchitta. "Emissions, Trade Openness, Urbanisation, and Income in Thailand: An Empirical Analysis." In Predictive Econometrics and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70942-0_37.

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Poggi, Nicolas, Josep Ll Berral, and David Carrera. "ALOJA: A Benchmarking and Predictive Platform for Big Data Performance Analysis." In Big Data Benchmarking. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49748-8_4.

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Ma, Ji, Jianxu Liu, and Songsak Sriboonchitta. "Technical Efficiency Analysis of China’s Agricultural Industry: A Stochastic Frontier Model with Panel Data." In Predictive Econometrics and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70942-0_33.

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Suwannajak, Jirawan, Woraphon Yamaka, Songsak Sriboonchitta, and Roengchai Tansuchat. "The Analysis of the Effect of Monetary Policy on Consumption and Investment in Thailand." In Predictive Econometrics and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70942-0_46.

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Finlay, Steven. "Using Predictive Models." In Predictive Analytics, Data Mining and Big Data. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137379283_2.

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Conference papers on the topic "Big data predictive analysis"

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Sattar, Naw Safrin, Shaikh Arifuzzaman, Minhaz F. Zibran, and Md Mohiuddin Sakib. "Detecting Web Spam in Webgraphs with Predictive Model Analysis." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006282.

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Prasad, S. Thanga, S. Sangavi, A. Deepa, F. Sairabanu, and R. Ragasudha. "Diabetic data analysis in big data with predictive method." In 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET). IEEE, 2017. http://dx.doi.org/10.1109/icammaet.2017.8186738.

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Shinde, Priyanka P., Kavita S. Oza, and R. K. Kamat. "Big data predictive analysis: Using R analytical tool." In 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2017. http://dx.doi.org/10.1109/i-smac.2017.8058297.

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Ravindran, Nambiar Jyothi, and Prakash Gopalakrishnan. "Predictive Analysis for Healthcare Sector Using Big data Technology." In 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE, 2018. http://dx.doi.org/10.1109/icgciot.2018.8753090.

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Xu, Yichuan, and Vlado Keselj. "Stock Prediction using Deep Learning and Sentiment Analysis." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006342.

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Yang, Wanshan, Ting Huang, Junlin Zeng, et al. "Purchase Prediction in Free Online Games via Survival Analysis." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006031.

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Sibony, Eric, Stephan Clemencon, and Jeremie Jakubowicz. "Multiresolution analysis of incomplete rankings with applications to prediction." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004361.

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Perera, Ian, Jena Hwang, Kevin Bayas, Bonnie Dorr, and Yorick Wilks. "Cyberattack Prediction Through Public Text Analysis and Mini-Theories." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622106.

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Putra, Hafid Yoza, and Masayu Leylia Khodra. "Descriptive And Predictive Analysis Of Mail Order Pharmacy." In 2018 International Workshop on Big Data and Information Security (IWBIS). IEEE, 2018. http://dx.doi.org/10.1109/iwbis.2018.8471714.

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Sharma, Parul, and Teng-Sheng Moh. "Prediction of Indian election using sentiment analysis on Hindi Twitter." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840818.

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Reports on the topic "Big data predictive analysis"

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Bauer, Stephen J. Analysis of Subsidence Data for the Big Hill Site, Texas. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/8849.

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De, Kaushik. Next Generation Workload Management and Analysis System for Big Data. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1352908.

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Hardy, B., A. D'Entremont, B. Garcia-Diaz, et al. PROCESS IMAGE ANALYSIS USING BIG DATA, MACHINE LEARNING, AND COMPUTER VISION. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1568782.

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HARDY, BRUCE, ANNA D'ENTREMONT, MICHAEL MARTINEZ-RODRIGUEZ, et al. PROCESS IMAGE ANALYSIS USING BIG DATA, MACHINE LEARNING, AND COMPUTER VISION. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1676412.

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Gamwo, I. K., A. Miller, and D. Gidaspow. Spectral analysis of CFB data: Predictive models of Circulating Fluidized Bed combustors. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/5098191.

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Gamwo, I. K., A. Miller, and D. Gidaspow. Spectral analysis of CFB data: Predictive models of Circulating Fluidized Bed combustors. 11th technical progress report. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10156489.

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Rathinam, Francis, Sayak Khatua, Zeba Siddiqui, et al. Using big data for evaluating development outcomes: a systematic map. Centre of Excellence for Development Impact and Learning (CEDIL), 2020. http://dx.doi.org/10.51744/cmwp2.

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Abstract:
This paper discusses the methodological, ethical and practical constraints relating to the use of big data for measuring and evaluating development outcomes. The paper presents the analysis of a systematic gap map developed by 3ie. The map included 437 studies, comprising impact evaluations, systematic reviews and big data measurement studies.
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Kang, Ji Hye, and Sungha Jang. How Do Consumers Evaluate the Identical Product on Competing Online Retailers? A Big Data Analysis Approach Using Consumer Reviews. Iowa State University, Digital Repository, 2016. http://dx.doi.org/10.31274/itaa_proceedings-180814-1508.

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Wang, Yifeng. Big-Data-Driven Geo-Spatiotemporal Correlation Analysis between Precursor Pollen and Influenza and its Implication to Novel Coronavirus Outbreak. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1668134.

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Salter, R., Quyen Dong, Cody Coleman, et al. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40203.

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The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.
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