Relevant bibliographies by topics / Implicazioni bigdata e real time data

Academic literature on the topic 'Implicazioni bigdata e real time data'

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Published: 14 March 2023

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Journal articles on the topic "Implicazioni bigdata e real time data"

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Ramasubramanian and Hariharan Shanmugasundaram. "A Review on Classification of Data Imbalance using BigData." International Journal of Managing Information Technology 13, no. 03 (August 30, 2021): 09–22. http://dx.doi.org/10.5121/ijmit.2021.13302.

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Classification is one among the data mining function that assigns items in a collection to target categories or collection of data to provide more accurate predictions and analysis. Classification using supervised learning method aims to identify the category of the class to which a new data will fall under. With the advancement of technology and increase in the generation of real-time data from various sources like Internet, IoT and Social media it needs more processing and challenging. One such challenge in processing is data imbalance. In the imbalanced dataset, majority classes dominate over minority classes causing the machine learning classifiers to be more biased towards majority classes and also most classification algorithm predicts all the test data with majority classes. In this paper, the author analysis the data imbalance models using big data and classification algorithm.
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Sujatha, V., S. Prasanna Devi, S. Vinu Kiran, and S. Manivannan. "Bigdata Analytics on Diabetic Retinopathy Study (DRS) on Real-time Data Set Identifying Survival Time and Length of Stay." Procedia Computer Science 87 (2016): 227–32. http://dx.doi.org/10.1016/j.procs.2016.05.153.

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El-Moamen, Soha Abd Mohamed, Marghany Hassan Mohamed, and Mohammed F. Farghally. "Constructive Learning of Deep Neural Networks for Bigdata Analysis." International Journal of Computer Applications Technology and Research 9, no. 12 (December 1, 2020): 311–22. http://dx.doi.org/10.7753/ijcatr0912.1001.

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The need for tracking and evaluation of patients in real-time has contributed to an increase in knowing people’s actions to enhance care facilities. Deep learning is good at both a rapid pace in collecting frameworks of big data healthcare and good predictions for detection the lung cancer early. In this paper, we proposed a constructive deep neural network with Apache Spark to classify images and levels of lung cancer. We developed a binary classification model using threshold technique classifying nodules to benign or malignant. At the proposed framework, the neural network models training, defined using the Keras API, is performed using BigDL in a distributed Spark clusters. The proposed algorithm has metrics AUC-0.9810, a misclassifying rate from which it has been shown that our suggested classifiers perform better than other classifiers.
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Rytsarev, I. A., A. V. Kupriyanov, D. V. Kirsh, and R. A. Paringer. "Research and analysis of messages of users of social networks using BigData technology." Information Technology and Nanotechnology, no. 2416 (2019): 504–9. http://dx.doi.org/10.18287/1613-0073-2019-2416-504-509.

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In this paper is dedicated to the World Cup held in the city of Samara from June 15 to July 15, 2018. As part of the work, a multithreaded collection in real time was organized, filtering and processing messages from users of the social network Twitter within the host city and its surroundings from May 15 to August 15, 2018. Then, a study was conducted of the texts of user messages on the subject of the popularity of topics and the construction of a “word cloud”. The second study was the construction of a diagram of the dynamics of the number of messages in different languages. As part of the work, modules for collecting, filtering and processing data using BigData technology were implemented.
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G.N, Basavaraj, and Jaidhar C.D. "Low latency and energy efficient cluster based routing design for wireless sensor network." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 2 (February 1, 2019): 615. http://dx.doi.org/10.11591/ijeecs.v13.i2.pp615-625.

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<span>Wireless sensor network (WSN) has attained wide adoption across various sectors and is considered to be key component of future real-time application such as BigData, Internet of things (IoT) etc. The modern application requires low latency and scalable real-time data access considering heterogeneous network. However, provisioning low latency real-time data access incurs energy overhead among sensor device. Clustering technique aided in providing scalability and minimizing energy consumption among sensor device. However, it incurs energy overhead among cluster head and sensor device closer to sink. To address, many optimization technique is been presented in recent time for optimal cluster selection. However, these technique are designed considering homogenous network. To address, this work presented Low Latency and Energy Efficient Routing (LLEER) design for heterogeneous WSN. The LLEER adopts multi-objective function such as</span><span>connectivity, connection time, radio signal strength, coverage time, and network traffic for cluster head and hop node selection. Experiment are conducted to evaluate LLEER design shows significant performance improvement over state-of-art model in terms of network lifetime considering total node death, first node death, and loss of connectivity, communication overhead, and packet transmission latency. Proposed LLEER brings a good trade-off between energy efficiency, and latency requirement of future real-time application.<span> </span></span>
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Chyzhevska, Lyudmyla, Lidiia Voloschuk, Liubov Shatskova, and Liudmyla Sokolenko. "Digitalization as a Vector of Information Systems Development and Accounting System Modernization." Studia Universitatis „Vasile Goldis” Arad – Economics Series 31, no. 4 (October 11, 2021): 18–39. http://dx.doi.org/10.2478/sues-2021-0017.

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Abstract Digitalization causes structural changes in the economic sectors and influences business activity and processes, leading to the companies’ increased productivity, competitive advantages and effective development creation and consolidation. This article is purposed to substantiate that the economy digitalization entails the need for companies’ system and mechanisms modernization to ensure their competitiveness and to improve management systems. The research is conducted using methods of theoretical generalization, analysis, synthesis, comparison and grouping. This study resulted in defining both global and Ukrainian economies’ trends for digitalization with a description of its impact on the business activity and business processes, assessing the economy digitalization effects to the requirements for the company’s information system that therefore serves to establishing directions for its modernization through the introduction of digital technologies, such as Electronic Data Interchange, Extensible Business Reporting Language, BigData, Internet of Things, Robotic Process Automation, Artificial intelligence, Real-time Adherence, cloud technologies, blockchain. The directions of companies’ accounting digitalization implementation and development are as flows: changes in the accounting system in terms of its method elements; application of Assets, Liabilities and Capital new digital forms; advanced training in compliance with the requirements for the accounting personnel digital competencies.
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Barquin, Miguel, Francisco Garcia-Garcia, Atocha Romero, Enric Carcereny, Delvys Rodriguez-Abreu, Rafael Lopez Castro, Maria Guirado, et al. "Sex is a strong prognostic factor for overall survival in advanced non small cell lung cancer patients and should be considered for survival rates estimations." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e20580-e20580. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e20580.

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e20580 Background: Biological differences between sexes have a major impact on disease and treatment outcome. In this paper, we evaluate the prognostic value of sex in advance NSCLC in the context of real world data. Methods: Clinical data from stage IV non-small cell lung cancer (NSCLC) patients from Hospital Puerta de Hierro (HPH) was retrieved from Electronical records using BigData Analytics (N = 387). In addition, data from Spanish Lung Cancer Group (GECP) Tumor Registry (N = 1382) and from a published study through cBioPortal (MSK) (N = 601) was analyzed. Survival curves were estimated using Kaplan-Meier analysis. Cox proportional hazards regression model was used to assess the prognostic factor of sex. Results: The median overall survival (OS) time was 12 months for men and 19 months for women (P < 0.001). Similarly, women with stage IV NSCLC harbouring an EGFR sensitizing mutation lived longer than men (median overall survival was 19 months for men and 32 months for women). Gender effect was still significant after adjustment by Cox regression for other potential confounding factors. The adjusted hazard ratios for sex were 0.65 (95% CI, 0.51-0.83), 0.84 (95% CI, 0.66-1.1) and 0.76 (95% CI, 0.65-0.88) for HPH, MSK and GECP data sets respectively. Similarly, in EGFR positive population adjusted hazard ratios for sex were 0.53 (95% CI, 0.25-1.1), 0.59 (95% CI, 0.35-0.98) and 0.60 (95% CI, 0.45-0.86) for HPH, MSK and GECP data sets respectively. Conclusions: Using real world data we confirm previous finding that among stage IV NSCLC patients, women live almost twice longer than men. This effect persisted after adjusting for several factors highlighting the fact that survival rates estimations which are usually performed grouping men and women together might not be accurate enough for prognosis assessments
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Selvaraj, P., Venkatesh Kannan, and Bruno Voisin. "Modified Data Storage and Replication Mechanism with Frequent Use-Case Based Indexing." Journal of Computational and Theoretical Nanoscience 17, no. 12 (December 1, 2020): 5229–37. http://dx.doi.org/10.1166/jctn.2020.9413.

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The real time applications demands high speed and reliable data access from the remote database. An effective logical data management strategy that handles simultaneous connections with better performance negotiation is inevitable. This work considers an e-health care application that proposes MongoDB based modified indexing and performance tuning methods. To cope with certain high frequency use case and its performance mandates, a flexible and efficient logical data management may be preferred. By analysing the data dependency, data decomposition concerns and the performance requirements of the specific use case of the medical application, a logical schema may be customized on an ala-carte basis. This work focused on the flexible logical data modeling schemes and its performance factors of the NoSql DB. The efficiency of unstructured data base management in storing and retrieving the e-health care data was analysed with a web based tool. To enable faster data retrieval and query processing over the distributed nodes, a Spark based storage engine was built on top of the MongoDB based data storage management. With Spark tool, the database has been made distributed as master–slave structures with suitable data replication mechanisms. In such distributed database the fail-over also implemented with the suitable replication mechanism. This work considered MongoDB based flexible schema modeling and Spark based distributed computation with multiple chunks of data. The flexible data modeling scheme with MongoDB with the on-demand Spark based computation framework was proposed. To facilitate the eventual consistency, scalability aspects of the e-health care applications, use case based indexing was proposed. With the effective data management, faster query processing the horizontal scalability has been increased. The overall efficiency and scalability of the proposed logical data management approach was analysed. Through the simulation studies, the proposed approach has been claimed to boost the performance of the bigdata based application to a considerable extent.
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Spiga, Daniele, Marica Antonacci, Tommaso Boccali, Andrea Ceccanti, Diego Ciangottini, Riccardo Di Maria, Giacinto Donvito, et al. "Exploiting private and commercial clouds to generate on-demand CMS computing facilities with DODAS." EPJ Web of Conferences 214 (2019): 07027. http://dx.doi.org/10.1051/epjconf/201921407027.

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Minimising time and cost is key to exploit private or commercial clouds. This can be achieved by increasing setup and operational efficiencies. The success and sustainability are thus obtained reducing the learning curve, as well as the operational cost of managing community-specific services running on distributed environments. The greater beneficiaries of this approach are communities willing to exploit opportunistic cloud resources. DODAS builds on several EOSC-hub services developed by the INDIGO-DataCloud project and allows to instantiate on-demand container-based clusters. These execute software applications to benefit of potentially “any cloud provider”, generating sites on demand with almost zero effort. DODAS provides ready-to-use solutions to implement a “Batch System as a Service” as well as a BigData platform for a “Machine Learning as a Service”, offering a high level of customization to integrate specific scenarios. A description of the DODAS architecture will be given, including the CMS integration strategy adopted to connect it with the experiment’s HTCondor Global Pool. Performance and scalability results of DODAS-generated tiers processing real CMS analysis jobs will be presented. The Instituto de Física de Cantabria and Imperial College London use cases will be sketched. Finally a high level strategy overview for optimizing data ingestion in DODAS will be described.
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Ekler, Péter, and Dániel Pásztor. "Alkalmazott mesterséges intelligencia felhasználási területei és biztonsági kérdései – Mesterséges intelligencia a gyakorlatban." Scientia et Securitas 1, no. 1 (December 17, 2020): 35–42. http://dx.doi.org/10.1556/112.2020.00006.

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Összefoglalás. A mesterséges intelligencia az elmúlt években hatalmas fejlődésen ment keresztül, melynek köszönhetően ma már rengeteg különböző szakterületen megtalálható valamilyen formában, rengeteg kutatás szerves részévé vált. Ez leginkább az egyre inkább fejlődő tanulóalgoritmusoknak, illetve a Big Data környezetnek köszönhető, mely óriási mennyiségű tanítóadatot képes szolgáltatni. A cikk célja, hogy összefoglalja a technológia jelenlegi állapotát. Ismertetésre kerül a mesterséges intelligencia történelme, az alkalmazási területek egy nagyobb része, melyek központi eleme a mesterséges intelligencia. Ezek mellett rámutat a mesterséges intelligencia különböző biztonsági réseire, illetve a kiberbiztonság területén való felhasználhatóságra. A cikk a jelenlegi mesterséges intelligencia alkalmazások egy szeletét mutatja be, melyek jól illusztrálják a széles felhasználási területet. Summary. In the past years artificial intelligence has seen several improvements, which drove its usage to grow in various different areas and became the focus of many researches. This can be attributed to improvements made in the learning algorithms and Big Data techniques, which can provide tremendous amount of training. The goal of this paper is to summarize the current state of artificial intelligence. We present its history, introduce the terminology used, and show technological areas using artificial intelligence as a core part of their applications. The paper also introduces the security concerns related to artificial intelligence solutions but also highlights how the technology can be used to enhance security in different applications. Finally, we present future opportunities and possible improvements. The paper shows some general artificial intelligence applications that demonstrate the wide range usage of the technology. Many applications are built around artificial intelligence technologies and there are many services that a developer can use to achieve intelligent behavior. The foundation of different approaches is a well-designed learning algorithm, while the key to every learning algorithm is the quality of the data set that is used during the learning phase. There are applications that focus on image processing like face detection or other gesture detection to identify a person. Other solutions compare signatures while others are for object or plate number detection (for example the automatic parking system of an office building). Artificial intelligence and accurate data handling can be also used for anomaly detection in a real time system. For example, there are ongoing researches for anomaly detection at the ZalaZone autonomous car test field based on the collected sensor data. There are also more general applications like user profiling and automatic content recommendation by using behavior analysis techniques. However, the artificial intelligence technology also has security risks needed to be eliminated before applying an application publicly. One concern is the generation of fake contents. These must be detected with other algorithms that focus on small but noticeable differences. It is also essential to protect the data which is used by the learning algorithm and protect the logic flow of the solution. Network security can help to protect these applications. Artificial intelligence can also help strengthen the security of a solution as it is able to detect network anomalies and signs of a security issue. Therefore, the technology is widely used in IT security to prevent different type of attacks. As different BigData technologies, computational power, and storage capacity increase over time, there is space for improved artificial intelligence solution that can learn from large and real time data sets. The advancements in sensors can also help to give more precise data for different solutions. Finally, advanced natural language processing can help with communication between humans and computer based solutions.
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Dissertations / Theses on the topic "Implicazioni bigdata e real time data"

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Monrat, Ahmed Afif. "A BELIEF RULE BASED FLOOD RISK ASSESSMENT EXPERT SYSTEM USING REAL TIME SENSOR DATA STREAMING." Thesis, Luleå tekniska universitet, Datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71081.

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Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socio-economic lifeline of a country. The Assessment of flood risks facilitates taking appropriate measures to reduce the consequences of flooding. The flood risk assessment requires Big data which are coming from different sources, such as sensors, social media, and organizations. However, these data sources contain various types of uncertainties because of the presence of incomplete and inaccurate information. This paper presents a Belief rule-based expert system (BRBES) which is developed in Big data platform to assess flood risk in real time. The system processes extremely large dataset by integrating BRBES with Apache Spark while a web-based interface has developed allowing the visualization of flood risk in real time. Since the integrated BRBES employs knowledge driven learning mechanism, it has been compared with other data-driven learning mechanisms to determine the reliability in assessing flood risk. Integrated BRBES produces reliable results comparing from the other data-driven approaches. Data for the expert system has been collected targeting different case study areas from Bangladesh to validate the integrated system.
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Book chapters on the topic "Implicazioni bigdata e real time data"

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Qin, Lu, Xiaowei Xu, and Jiao Li. "A Real-Time Professional Content Recommendation System for Healthcare Providers’ Knowledge Acquisition." In Big Data – BigData 2018, 367–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94301-5_31.

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Yoon, John, and Michael DeBiase. "Real-Time Analysis of Big Network Packet Streams by Learning the Likelihood of Trusted Sequences." In Big Data – BigData 2018, 43–56. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94301-5_4.

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Cuzzocrea, Alfredo, Rim Moussa, and Gianni Vercelli. "An Innovative Lambda-Architecture-Based Data Warehouse Maintenance Framework for Effective and Efficient Near-Real-Time OLAP over Big Data." In Big Data – BigData 2018, 149–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94301-5_12.

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Tidke, Bharat, Rupa G. Mehta, and Jenish Dhanani. "Real-Time Bigdata Analytics: A Stream Data Mining Approach." In Advances in Intelligent Systems and Computing, 345–51. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8636-6_36.

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Conference papers on the topic "Implicazioni bigdata e real time data"

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Garzo, Andras, Andras A. Benczur, Csaba Istvan Sidlo, Daniel Tahara, and Erik Francis Wyatt. "Real-time streaming mobility analytics." In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691639.

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Kranjc, Janez, Vid Podpecan, and Nada Lavrac. "Real-time data analysis in ClowdFlows." In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691682.

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Marascu, Alice, Pascal Pompey, Eric Bouillet, Michael Wurst, Olivier Verscheure, Martin Grund, and Philippe Cudre-Mauroux. "TRISTAN: Real-time analytics on massive time series using sparse dictionary compression." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004244.

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Svingos, Christoforos, Theofilos Mailis, Herald Kllapi, Lefteris Stamatogiannakis, Yannis Kotidis, and Yannis Ioannidis. "Real time processing of streaming and static information." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840631.

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Katragadda, Satya, Raju Gottumukkala, Murali Pusala, Vijay Raghavan, and Jessica Wojtkiewicz. "Distributed Real Time Link Prediction on Graph Streams." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8621934.

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Anjos, Diogo, Paulo Carreira, and Alexandre P. Francisco. "Real-Time Integration of Building Energy Data." In 2014 IEEE International Congress on Big Data (BigData Congress). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.congress.2014.44.

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Diaz-Aviles, Ernesto, Fabio Pinelli, Karol Lynch, Zubair Nabi, Yiannis Gkoufas, Eric Bouillet, Francesco Calabrese, Eoin Coughlan, Peter Holland, and Jason Salzwedel. "Towards real-time customer experience prediction for telecommunication operators." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363860.

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Wang, Yida, Bryn Keller, Mihai Capota, Michael J. Anderson, Narayanan Sundaram, Jonathan D. Cohen, Kai Li, Nicholas B. Turk-Browne, and Theodore L. Willke. "Real-time full correlation matrix analysis of fMRI data." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840728.

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Sun, Bo Beth, Eric Zielonka, Aleksandr Fritz, Matthew Schofield, Brennan Ringel, Brendan Armstrong, Shen-Shyang Ho, et al. "Visual Analytics for Real-Time Flight Behavior Threat Assessment." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622086.

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Zhang, Jiawei, Philip S. Yu, Charu C. Aggarwal, and Limeng Cui. "Real Time Social Attitude Expression Prediction." In 2017 IEEE International Congress on Big Data (BigData Congress). IEEE, 2017. http://dx.doi.org/10.1109/bigdatacongress.2017.69.

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