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Journal articles on the topic 'BigData'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 June 2024

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1

Zhang, Jinson, Mao Huang, and Zhao-Peng Meng. "Visual analytics for BigData variety and its behaviours." Computer Science and Information Systems 12, no. 4 (2015): 1171–91. http://dx.doi.org/10.2298/csis141122050z.

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BigData, defined as structured and unstructured data containing images, videos, texts, audio and other forms of data collected from multiple datasets, is too big, too complex and moves too fast to analyze using traditional methods. This has given rise to a few issues that must be addressed; 1) how to analyze BigData across multiple datasets, 2) how to classify the different data forms, 3) how to identify BigData patterns based on its behaviours, 4) how to visualize BigData attributes in order to gain a better understanding of data. It is therefore necessary to establish a new framework for BigData analysis and visualization. In this paper, we have extended our previous works for classifying the BigData attributes into the "5Ws" dimensions based on different data behaviours. Our approach not only classifies BigData attributes for different data forms across multiple datasets, but also establishes the "5Ws" densities to represent the characteristics of data flow patterns. We use additional non-dimensional parallel axes in parallel coordinates to display the ?5Ws? sending and receiving densities, which provide more analytic features for BigData analysis. The experiment shows that our approach with parallel coordinate visualization can be efficiently used for BigData analysis and visualization.

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Fromm, Davida, and Brian MacWhinney. "AphasiaBank as BigData." Seminars in Speech and Language 37, no. 01 (February 16, 2016): 010–22. http://dx.doi.org/10.1055/s-0036-1571357.

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Ибрагимов, И. Р., and М. С. У. Халиев. "Большие данные и их структура." ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ 92, no. 10 (2022): 87–89. http://dx.doi.org/10.18411/trnio-12-2022-486.

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4

Et. al., Govindaraju G. N,. "Big Data Analytics Performance Enhancement For Covid-19 Data Using Machine Learning And Cloud." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (April 28, 2021): 5608–14. http://dx.doi.org/10.17762/turcomat.v12i10.5371.

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The exponential rise in software computing, internet and web-services has broadened the horizon for BigData that demands robust and highly efficient analytics system to serve timely and accurate distributed data support. The distributed frameworks with parallelized computing have been found key driving force behind the contemporary BigData analytics systems; however, the lack of optimal data pre-processing, feature sensitive computation and more importantly feature learning makes major at-hand solutions inferior, especially in terms of time and accuracy. Unlike major at hand methods employing machine learning for BigData analytics, in this paper the key emphasis was made on improving pre-processing, low-dimensional semantic feature extraction and lightweight improved machine learning based feature learning for BigData analytics. Noticeably, the proposed model hypothesizes that an analytics solution with BigData characteristics must have the potential to process humongous, heterogenous, unstructured and multi-dimensional features to yield time-efficient and accuracy analytical outputs. In this reference, we proposed a state-of-art new and robust BigData analytics model, specially designed for Spark distributed framework. To process analytical task our proposed model at first employs tokenization, followed by Word2Vec based semantic feature extraction using CBOW and N-Skip-Gram methods. Our proposed model was found more effective with Skip-Gram Word2Vec feature extraction. Simulation results with a publicly available COVID-19 data exhibited better performance than existing K-Means based MapReduce distributed data frameworks.

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Kaur, Pankaj Deep, Amneet Kaur, and Sandeep Kaur. "Performance Analysis in Bigdata." International Journal of Information Technology and Computer Science 7, no. 11 (October 8, 2015): 55–61. http://dx.doi.org/10.5815/ijitcs.2015.11.07.

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6

Zolotov, Oleg, Yulia Romanovskaya, and Varvara Rzhannikova. "On Definition of BigData." EPJ Web of Conferences 224 (2019): 04011. http://dx.doi.org/10.1051/epjconf/201922404011.

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The term Big Data (or BigData) is widely used in scientific, educational, and business literature; however, there does not exist a single definition that can be unreservedly called “canonical”. A careless use of Big Data term to promote commercial software further emphasizes the importance of this issue. In this paper, we have performed a review of definitions of Big Data and highlighted the principal features that are attributed to Big Data. We compared all these principal features with features of databases compiled using Edgar F. Codd’s publications, and showed that they are not unique and can also be attributed to the databases. Having studied C. Lynch original work, we proposed the definition of Big Data based on the so-called conservation institution. The key point of this definition is a shift from purely technical attitude towards public institutions. Since the current use of the Big Data term may lead to a loss of meaning. There is a need not only to spread out best practices but also to eliminate or minimize the use of dubious or misleading ones.

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Ranjan, Rajiv, Saurabh Garg, Ali Reza Khoskbar, Ellis Solaiman, Philip James, and Dimitrios Georgakopoulos. "Orchestrating BigData Analysis Workflows." IEEE Cloud Computing 4, no. 3 (2017): 20–28. http://dx.doi.org/10.1109/mcc.2017.55.

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Raikhlin, Vadim A., and Roman K. Klassen. "Clusterix-Like BigData DBMS." Data Science and Engineering 5, no. 1 (February 20, 2020): 80–93. http://dx.doi.org/10.1007/s41019-020-00116-2.

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9

Ridho, Farid, and Arya Aji Kusuma. "Deteksi Intrusi Jaringan dengan K-Means Clustering pada Akses Log dengan Teknik Pengolahan Big Data." Jurnal Aplikasi Statistika & Komputasi Statistik 10, no. 1 (August 15, 2019): 53. http://dx.doi.org/10.34123/jurnalasks.v10i1.202.

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Keamanan jaringan, adalah salah satu aspek penting dalam terciptanya proses komunikasi data yang baik dan aman. Namun, masih adanya serangan yang efektif membuktikan bahwa sistem keamanan yang berlaku belum cukup efektif untuk mencegah dan mendeteksi serangan. Salah satu metode yang dapat digunakan untuk mendeteksi serangan ini adalah dengan dengan Intrusion Detection System (IDS). Besarnya data (volume), cepatnya perubahan data (velocity), serta variasi data (variety) merupakan ciri-ciri dari Big data. Akses log, secara teori termasuk dalam kategori ini sehingga dapat dilakukan pemrosesan menggunakan teknologi bigdata dengan Hadoop. Hal ini mendorong penulis untuk dapat menerapkan metode pengolahan baru yang dapat mengatasi perkembangan data tersebut, yaitu Big data. Penelitian ini dilakukan dengan menganalisis akses log dengan K-Means Clustering menggunakan metode pengolahan bigdata. Penelitian menghasilkan satu model yang dapat digunakan untuk mendeteksi sebuah serangan dengan probabilitas deteksi sebesar 99.68%. Serta dari hasil perbandingan kedua metode pengolahan bigdata menggunakan pyspark dan metode tradisional menggunakan python standar, metode bigdata memiliki perbedaan yang signifikan dalam waktu yang dibutuhkan dalam eksekusi program.

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Chahal, Ayushi, Preeti Gulia, and Nasib Singh Gill. "Different analytical frameworks and bigdata model for internet of things." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (February 1, 2022): 1159. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1159-1166.

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Sensor devices used in internet of things (IoT) enabled environment produce large amount of data. This data plays a major role in bigdata landscape. In recent years, correlation, and implementation of bigdata and IoT is being extrapolated. Nowadays, predictive analytics is gaining attention of many researchers for big IoT data analytics. This paper summarizes different sort of IoT analytical platforms which consist in-built features for further use in machine learning, MATLAB, and data security. It emphasizes on different machine learning algorithms that plays important role in big IoT data analytics. Besides different analytical frameworks, this paper highlights the proposed model for bigdata in IoT domain and elaborates different forms of data analytical methods. Proposed model comprises different phases i.e., data storing, data cleaning, data analytics, and data visualization. These phases cover the basic characteristics of bigdata V’s model and most important phase is data analytics or big IoT analytics. This model is implemented using an IoT dataset and results are presented in graphical and tabular form using different machine learning techniques. This study enhances researchers’ knowledge about various IoT analytical platforms and usability of these platforms in their respective problem domains.

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BUCEA-MANEA-TONIS, Radu. "Deductive systems for BigData integration." Journal of Economic Development, Environment and People 7, no. 1 (March 30, 2018): 49. http://dx.doi.org/10.26458/jedep.v7i1.578.

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The globalization is associated with an increased data to be processed from E-commerce transactions. The specialists are looking for different solutions, such as BigData, Hadoop, Datawarehoues, but it seems that the future is the predicative logic implemented through deductive database technology. It has to be done the swift from imperative languages, to not declaratively languages used for the application development. The deductive databases are very useful in the student teaching programs, too. Thus, the article makes a consistent literature review in the field and shows practical examples of using predicative logic in deductive systems, in order to integrate different kind of data types.

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Kim, Jeong-Joon, Kwang-Jin Kwak, Don-Hee Lee, and Yong-Soo Lee. "Study of Trust Bigdata Platform." Journal of the Institute of Internet Broadcasting and Communication 16, no. 6 (December 31, 2016): 225–30. http://dx.doi.org/10.7236/jiibc.2016.16.6.225.

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13

Horváth, Richárd. "A BigData lehetőségei a közlekedésben." Közlekedéstudományi Szemle 67, no. 4 (2017): 46–51. http://dx.doi.org/10.24228/ktsz.2017.4.4.

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Kim, Hyoungrae, Do-hong Jeon, and Sunghyun Jee. "Bigdata Analysis Project Development Methodology." Journal of the Korea Society of Computer and Information 19, no. 3 (March 31, 2014): 73–85. http://dx.doi.org/10.9708/jksci.2014.19.3.073.

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15

Desai, Vinod, and Dinesha Hagare Annappaiah. "Reputation-based Security model for detecting biased attacks in BigData." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (March 1, 2023): 1567. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1567-1576.

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As internet of things (IoT) devices are increasing since the emergence of these devices in 2010, the data stored by these devices should have a proper security measure so that it can be stored without getting in hands of an attacker. The data stored has to be analyzed whether the data is safe or malicious, as the malicious data can corrupt the whole information. The security model in BigData has many challenges such as vulnerability to fake data generation, troubles with cryptographic protection, and absent security audits. As cyberattacks are increasing the main objective of each organization is to secure the data efficiently. This paper presents a model of reputation security for the detection of biased attacks on BigData. The proposed model provides various evaluation models to identify biased attack in malicious IoT devices and provide a secure communication metric for BigData. The results show better rates in terms of attack detection rate, attack detection failure rata, system throughput and number of dead nodes when the attack rate is increased when compared with the existing reputation-based security (ERS) model. Moreover, this model reputation-based biased attack detection (RBAD) increases the security of the IoT devices in the BigData and reduces the biased attack coming from various malicious nodes.

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Et. al., Saumya Gupta,. "Privacy Protection in Bigdata: A Survey." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 562–68. http://dx.doi.org/10.17762/turcomat.v12i2.888.

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Bigdata becomes a significant sector and academics research topic. Bigdata is a two-edged sword. The rising volume of information together will increase the likelihood of blundering non-public data privacy. Due to many new technologies and innovations that pervade our everyday lives, like smartphones and social networking apps, and the Internet of Things-based intelligent-world systems, the large amount of data generated in our world has exploded. During this data processing, storage, and the use of the information it can quickly cause personal information exposure and the difficulty of interpreting the information. The aim is to incorporate this range of information into one framework for big data management and to recognize problems regarding privacy. This paper begins with the introduction of bigdata, its process, protection issues, and tools which are used to solve its problems

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17

Zhang, Cizhen, and Jiaming Zhong. "Construction of Mobile Learning Model Based on Network Collaborative Inquiry from Bigdata." MATEC Web of Conferences 232 (2018): 01032. http://dx.doi.org/10.1051/matecconf/201823201032.

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The rapid development of information technology represented by media and network is changing our work, life, and learning style. In recent years, with the development of information age and bigdata, mobile learning is being widely used in numerous industries and areas. It is also gradually penetrated into information education. Through the creation of network collaborative inquiry learning model and its application in teaching, this paper tries to study a mobile learning model of network collaborative inquiry based on bigdata.

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Hou, Yuxin, and Shiduo Qian. "Bigdata Analysis Implementation in Financial Field: Evidence from China Merchants Bank & Ant Group." Highlights in Business, Economics and Management 10 (May 9, 2023): 443–48. http://dx.doi.org/10.54097/hbem.v10i.8137.

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Contemporarily, with the growing demands for data analysis, bigdata and machine learning scenarios are proposed in various aspects in order to handle the issues. Especially in finance field, the analysis approach could rapidly and directly increase the efficiency of daily issues as well as reduce the traditional cost. In addition, it can help to distinguish the risk, dig out the logic chain, as well as evaluate the value in not only bank industry but also the insurance, stocks as well as the Fintech corporations. This study chooses two cases to analysis the measures as well as the routine to add the bigdata approaches into the corporation business mode and daily issues. To be specific, the Ant and China Merchants Bank are selected as the two target companies. Moreover, the limitations as well as the defects as well as the future prospects have been given. Overall, these results shed light on guiding further exploration of implementation the state-of-art bigdata analysis techniques and concepts into finance field.

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Han, Bonian, Ziming Xiong, Xiaohe Xu, and Yuchi Zhang. "Implementation of Bigdata Analysis in Consumer Behavior." Advances in Economics, Management and Political Sciences 69, no. 1 (January 8, 2024): 98–104. http://dx.doi.org/10.54254/2754-1169/69/20231289.

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Nowadays, as the way of bigdata analysis become more and more diverse and specific, some people have already turned their eyes to the implementation of these analysis in consumer behavior. Since the markets are more competitive, it would save time and money that the companies produce what the consumers like. In addition, many markets exist for a long time and companies collect a plenty of consumers data, when they use bigdata analysis on the data collected, they can clear make accurate prediction about future products. In this study, we split the implementation of bigdata analysis in consumer behavior into several parts, including the history of the research, the specific analyzing methods, the realistic applications and limitations. In each part, we combine the facts and the understanding to write the analysis by the reference of some authoritative documentations. As a matter of fact, there are two significances of research, first is to have a comprehensive understanding of the current situation about topic from by-parts investigation; second is to have a future imagination based on both the advantages and disadvantages have now.

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Malik, Vandana. "An Outline of Hadoop in Bigdata." International Journal for Research in Applied Science and Engineering Technology 8, no. 8 (August 31, 2020): 1506–9. http://dx.doi.org/10.22214/ijraset.2020.31243.

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Yamagata, Yoshiki, Daisuke Murakami, and Takahiro Yoshida. "Urban carbon mapping with spatial BigData." Energy Procedia 142 (December 2017): 2461–66. http://dx.doi.org/10.1016/j.egypro.2017.12.183.

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Cancela, Héctor, Andrew Higgins, Adela Pagès-Bernaus, and Lluis Miquel Plà-Aragonès. "Prologue – BigData and DSS in agriculture." Computers and Electronics in Agriculture 161 (June 2019): 1–3. http://dx.doi.org/10.1016/j.compag.2019.05.004.

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Raja, K., and Sabibullah Mohamed Hanifa. "Bigdata Driven Cloud Security: A Survey." IOP Conference Series: Materials Science and Engineering 225 (August 2017): 012184. http://dx.doi.org/10.1088/1757-899x/225/1/012184.

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Dewi, Athanasia Octaviani Puspita. "Big Data di Perpustakaan dengan Memanfaatkan Data Mining." Anuva 4, no. 2 (June 9, 2020): 223–30. http://dx.doi.org/10.14710/anuva.4.2.223-230.

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Artikel ini membahas tentang bigdata dalam perpustakaan dan manfaatnya jika diolah. Hasil dari penelitian ini adalah data yang ada di dalam perpustakaan dapat disebut sebagai bigdata jika memenuhi 3 karakteristik dari big data yaitu volume, velocity, dan variety. Big data kemudian bisa diolah menggunakan data mining, salah satunya dengan menggunakan asosiasi dimana akan menghasilkan keterkaitan antar barang. Jika dalam perpustakaan big data-nya adalah data peminjaman berarti dapat menghasilkan keterkaitan antar koleksi yang dipinjam. Para pustakawan khususnya dapat menentukan tindakan selanjutnya dari hasil informasi yang didapatkan melalui hasil pengolahan big data ini. Sehingga diharapkan akan berperan dalam kemajuan sebuah perpustakaan.

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YUCRA, JAIR EMERSON FERREYROS, RICHARD CONDORI CRUZ, JUAN CARLOS HERRERA MIRANDA, RUBEN PLÁCIDO LERMA TIPO, and JUAN BENITES NORIEGA. "Enhanced Strategic Surveillance And Competitive Intelligence Systems Through Advanced Big Data Capabilities." Migration Letters 21, S4 (February 2, 2024): 482–94. http://dx.doi.org/10.59670/ml.v20i3.7270.

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A systematic review was carried out on the production and publication of research papers related to the study of BigData and Surveillance Systems with the effectiveness of Open Government in Ibero-American countries, under the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) approach. The purpose of the analysis proposed in this document was to know the main characteristics of the publications registered in the Scopus and Wos databases during the study and their scope in the study of the proposed variables, achieving the identification of 33 publications in total. Thanks to this first identification, it was possible to refine the results through the keywords entered in the search button of both platforms, which were BIGDATA and SURVEILLANCE SYSTEMS, reaching a total of 14 documents, excluding duplicates and those that did not meet the analysis criteria. The identified scientific publications were analyzed in the hope of knowing the impact of BigData techniques for the processing of large amounts of information on surveillance systems. The analysis allowed us to identify the position of different authors regarding the proposed topic, as well as the relationship between both variables in the execution of different research projects.

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Pogorilyy, S., and M. Chechula. "GPGPU TECHNOLOGY APPLICATION IN WORKING WITH BIGDATA." Naukovi praci Donec'kogo nacional'nogo tehnicnogo universitetu. Seria, Informatika, kibernetika i obcisluval'na tehnika 2, no. 25 (December 2017): 98–102. http://dx.doi.org/10.31474/1996-1588-2017-2-25-98-102.

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Bharti, Bhavna, and Prof Avinash Sharma. "Memory Management in BigData: A Perpective View." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1993–98. http://dx.doi.org/10.31142/ijtsrd14436.

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V R, Nikinprasad, Selva Vignesh M, Mohamed Arshath R, M. Sujithra, and P. Velvadivu. "Bigdata Analysis on Airline Delay and Cancellation." Journal of Data Mining and Management 5, no. 3 (November 7, 2020): 10–20. http://dx.doi.org/10.46610/jodmm.2020.v05i03.002.

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S., Ashlesha, and R. M. "A Review of Hadoop Ecosystem for BigData." International Journal of Computer Applications 180, no. 14 (January 17, 2018): 35–40. http://dx.doi.org/10.5120/ijca2018916273.

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Fang, Benjamin. "The application of bigdata for Intelligent building." Highlights in Science, Engineering and Technology 1 (June 14, 2022): 78–81. http://dx.doi.org/10.54097/hset.v1i.430.

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Intelligent buildings have become an important development trend in the current construction industry. Compared with traditional buildings, intelligent buildings have shown obvious advantages in many aspects, especially in the embodiment of humanized value, which has attracted great attention. In the information age, any industry is inseparable from the technology of the Internet. With the continuous development and maturity of Internet technology, the construction industry will also combine Internet-related technologies to realize intelligent buildings. This paper summarizes the application of big data in smart buildings, mainly including the application of big data in smart home, smart energy, etc., and summarizes the problems existing in the application of big data in smart buildings, and proposes the future The development suggestions for the application of big data in intelligent buildings.

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Malik, Vandana. "Issues and Security Challenges in Bigdata: Hadoop." International Journal for Research in Applied Science and Engineering Technology 8, no. 8 (August 31, 2020): 1502–5. http://dx.doi.org/10.22214/ijraset.2020.31242.

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Park, Sangsung. "Technology Analysis Using Patent-bigdata Statistical Analysis." Journal of Korean Institute of Intelligent Systems 30, no. 2 (April 30, 2020): 148–53. http://dx.doi.org/10.5391/jkiis.2020.30.2.148.

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Shafakhatullah Khan (Member IAENG), Mohammed, Mohammad Abu Kausar, and Shaik Shah Nawaz. "BigData Analytics Techniques to Obtain Valuable Knowledge." Indian Journal of Science and Technology 11, no. 14 (April 1, 2018): 1–14. http://dx.doi.org/10.17485/ijst/2018/v11i14/120977.

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Hashem, Hadi, and Daniel Ranc. "Pre-Processing and Modeling Tools for Bigdata." Foundations of Computing and Decision Sciences 41, no. 3 (September 1, 2016): 151–62. http://dx.doi.org/10.1515/fcds-2016-0009.

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AbstractModeling tools and operators help the user / developer to identify the processing field on the top of the sequence and to send into the computing module only the data related to the requested result. The remaining data is not relevant and it will slow down the processing. The biggest challenge nowadays is to get high quality processing results with a reduced computing time and costs. To do so, we must review the processing sequence, by adding several modeling tools. The existing processing models do not take in consideration this aspect and focus on getting high calculation performances which will increase the computing time and costs. In this paper we provide a study of the main modeling tools for BigData and a new model based on pre-processing.

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Batrouni, Marwan, Aurélie Bertaux, and Christophe Nicolle. "Scenario analysis, from BigData to black swan." Computer Science Review 28 (May 2018): 131–39. http://dx.doi.org/10.1016/j.cosrev.2018.02.001.

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Deore, Anjali, and . "Introduction to Bigdata and Relation with IoT." International Journal of Engineering & Technology 7, no. 3.8 (July 7, 2018): 151. http://dx.doi.org/10.14419/ijet.v7i3.8.16851.

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Big Data consist of large scale data which is complicated and diverse, so that new and different types of integration of techniques and technologies are required to uncover various hidden values from such big datasets. Big Data surrounding is used to set up and examine the diverse sorts of information. Big Data be data that is so massive in volume, so various in range or moving with excessive speed is referred to as Big Data. Acquiring and analysing Big Data be a challenging job because it consists of large dispersed file systems which must be bendy, fault tolerant and scalable. Diverse technologies used by big data application toward hold the huge quantity of data are Hadoop, Map Reduce, and so on. In this paper, firstly the description of big dataset is provided. In next section the different technologies are described which are used for managing Big Data. After that, Big Data method application and in last section we discuss the relation of Big Data and IoT as well as IoT for Big Data analytics.

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Choi, Mi-Seon, Yong-Hwack Cho, and Jin-Hwa Kim. "Forecasting Market trends of technologies using Bigdata." Journal of Industrial Convergence 21, no. 10 (October 31, 2023): 21–28. http://dx.doi.org/10.22678/jic.2023.21.10.021.

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Pal, Amrit, and Manish Kumar. "Frequent Itemset Mining in Large Datasets a Survey." International Journal of Information Retrieval Research 7, no. 4 (October 2017): 37–49. http://dx.doi.org/10.4018/ijirr.2017100103.

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Frequent Itemset Mining is a well-known area in data mining. Most of the techniques available for frequent itemset mining requires complete information about the data which can result in generation of the association rules. The amount of data is increasing day by day taking form of BigData, which require changes in the algorithms for working on such large-scale data. Parallel implementation of the mining techniques can provide solutions to this problem. In this paper a survey of frequent itemset mining techniques is done which can be used in a parallel environment. Programming models like Map Reduce provides efficient architecture for working with BigData, paper also provides information about issues and feasibility about technique to be implemented in such environment.

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Darapaneni, Chandra Sekhar, Bobba Basaveswara Rao, Boggavarapu Bhanu Venkata Satya Vara Prasad, and Suneetha Bulla. "An Analytical Performance Evaluation of MapReduce Model Using Transient Queuing Model." Advances in Modelling and Analysis B 64, no. 1-4 (December 31, 2021): 46–53. http://dx.doi.org/10.18280/ama_b.641-407.

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Today the MapReduce frameworks become the standard distributed computing mechanisms to store, process, analyze, query and transform the Bigdata. While processing the Bigdata, evaluating the performance of the MapReduce framework is essential, to understand the process dependencies and to tune the hyper-parameters. Unfortunately, the scope of the MapReduce framework in-built functions is limited to evaluate the performance till some extent. A reliable analytical performance model is required in this area to evaluate the performance of the MapReduce frameworks. The main objective of this paper is to investigate the performance effect of the MapReduce computing models under various configurations. To accomplish this job, we proposed an analytical transient queuing model, which evaluates the MapReduce model performance for different job arrival rates at mappers and various job completion times of mappers as well as the reducers too. In our transient queuing model, we appointed an efficient multi-server queuing model M/M/C for optimal waiting queue management. To conduct the experiments on proposed analytics model, we selected the Bigdata applications with three mappers and two reducers, under various configurations. As part of the experiments, the transient differential equations, average queue lengths, mappers blocking probability, shuffle waiting probabilities and transient states are evaluated. MATLAB based numerical simulations presented the analytical results for various combinations of the input parameters like λ, µ1 and µ2 and their effect on queue length.

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Kim, Chae-Hyeong, Seog Kim, and Dong-Won Jang. "DataWarehouse Countermeasures in the Bigdata Era: Focused on DataWarehouse." Journal of Korean Institute of Communications and Information Sciences 44, no. 5 (May 31, 2019): 957–67. http://dx.doi.org/10.7840/kics.2019.44.5.957.

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Keller, Max-Emanuel, Peter Mandl, Alexander Döschl, Daniel Kailer, and Markus Grimm. "Verarbeitung komplexer XML-basierter Massendaten in BigData-Anwendungen." Anwendungen und Konzepte der Wirtschaftsinformatik, no. 6 (December 12, 2017): 20–27. http://dx.doi.org/10.26034/lu.akwi.2017.3181.

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XML ist ein semi-strukturiertes Datenbeschreibungsformat, das aufgrund weiter Verbreitung und steigender Datenmengen auch als Eingabeformat für eine BigData-Verarbeitung relevant ist. Der vorliegende Beitrag befasst sich daher mit der Nutzung komplexer XML-basierter Datenstrukturen als Eingabeformat für BigData-Anwendungen. Werden umfangreiche komplexe XML-Datenstrukturen mit verschiedenen XML-Typen in einer zu verarbeitenden XML-Datei beispielsweise mit Apache Hadoop verarbeitet, kann das Einlesen der Daten die Laufzeit einer Anwendung dominieren. Unser Ansatz befasst sich mit der Optimierung der Eingabephasen, indem Zwischenergebnisse der Verarbeitung im Arbeitsspeicher abgelegt werden. Der Aufwand für die Verarbeitung reduziert sich damit zum Teil erheblich. Anhand einer Fallstudie aus der Musikbranche, in der standardisierte XML-basierte Formate wie das DDEX-Format genutzt werden, wird experimentell gezeigt, dass die Verarbeitung mit unserem Ansatz im Vergleich zur klassischen Abarbeitung von Dateiinhalten deutlich effizienter ist.

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Aiswarya, S., K. Yamuna, D. Sowndariya, and G. Mahalakshmi. "An Effective Analysis of Traffic Optimality Using Bigdata." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 3 (March 30, 2017): 116–20. http://dx.doi.org/10.23956/ijarcsse/v7i3/0125.

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Rohit, Ravikumar Muljibhai. "SWOT of Bigdata Security Using Machine Learning Techniques." International Journal of Information Sciences and Techniques 6, no. 1/2 (March 31, 2016): 123–34. http://dx.doi.org/10.5121/ijist.2016.6213.

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CAO, Li, Xin LI, Lianhua DU, Shuai GUI, Bin WANG, Cai GAO, and Xi'nan TANG. "Applying bigdata technology for campus wireless network optimization." Journal of Shenzhen University Science and Engineering 37, Z1 (October 1, 2020): 200–206. http://dx.doi.org/10.3724/sp.j.1249.2020.99200.

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Lovis, Christian, Christophe Gaudet-Blavignac, Raphaël Chevrier, Arnaud Robert, David Issom, and Vasiliki Foufi. "BigData, intelligence artificielle, [i]blockchain[/i] : guide pratique." Revue Médicale Suisse 14, no. 617 (2018): 1559–63. http://dx.doi.org/10.53738/revmed.2018.14.617.1559.

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Mani, S. Jyothi. "BIGDATA AND PREDICTIVE ANALYTICS IN THE ELECTION CAMPAIGN." International Journal of Advanced Research in Computer Science 9, no. 1 (February 20, 2018): 307–9. http://dx.doi.org/10.26483/ijarcs.v9i1.5365.

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Lai, Bo-Cheng, Chun-Yen Chen, Yi-Da Hsin, and Bo-Yen Lin. "A Two-Directional BigData Sorting Architecture on FPGAs." IEEE Computer Architecture Letters 19, no. 1 (January 1, 2020): 72–75. http://dx.doi.org/10.1109/lca.2020.2993040.

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Durai, S., M. Viswanathan, C. Shyamala Kumari, and S. Florence. "Human Activity Pattern in Bigdata for Healthcare Application." Journal of Computational and Theoretical Nanoscience 15, no. 11 (November 1, 2018): 3427–31. http://dx.doi.org/10.1166/jctn.2018.7637.

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CONDIE, TYSON, ARIYAM DAS, MATTEO INTERLANDI, ALEXANDER SHKAPSKY, MOHAN YANG, and CARLO ZANIOLO. "Scaling-up reasoning and advanced analytics on BigData." Theory and Practice of Logic Programming 18, no. 5-6 (September 2018): 806–45. http://dx.doi.org/10.1017/s1471068418000418.

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AbstractBigDatalog is an extension of Datalog that achieves performance and scalability on both Apache Spark and multicore systems to the point that its graph analytics outperform those written in GraphX. Looking back, we see how this realizes the ambitious goal pursued by deductive database researchers beginning 40 years ago: this is the goal of combining the rigor and power of logic in expressing queries and reasoning with the performance and scalability by which relational databases managed BigData. This goal led to Datalog which is based on Horn Clauses like Prolog but employs implementation techniques, such as semi-naïve fixpoint and magic sets, that extend the bottom-up computation model of relational systems, and thus obtain the performance and scalability that relational systems had achieved, as far back as the 80s, using data-parallelization on shared-nothing architectures. But this goal proved difficult to achieve because of major issues at (i) the language level and (ii) at the system level. The paper describes how (i) was addressed by simple rules under which the fixpoint semantics extends to programs using count, sum and extrema in recursion, and (ii) was tamed by parallel compilation techniques that achieve scalability on multicore systems and Apache Spark. This paper is under consideration for acceptance in Theory and Practice of Logic Programming.

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Srinivas, Jangirala, Ashok Kumar Das, and Joel J. P. C. Rodrigues. "2PBDC: privacy-preserving bigdata collection in cloud environment." Journal of Supercomputing 76, no. 7 (September 11, 2018): 4772–801. http://dx.doi.org/10.1007/s11227-018-2605-1.

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