Relevant bibliographies by topics / Big Data in Pension Management

Contents

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

Academic literature on the topic 'Big Data in Pension Management'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Big Data in Pension Management"

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Astriawati, Rika. "Optimizing Public Policy in Developing Digital-Based Pension Funds in Indonesia." Jurnal Abdimas Peradaban 5, no. 1 (2024): 27–36. https://doi.org/10.54783/d118yn47.

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The increasing elderly population in Indonesia shows the urgent need for effective pension fund development. The gap between pension needs and fund availability adds to the urgency to improve the conventional pension system. The development of digital technology in Indonesia provides an opportunity for transformation in pension fund management, despite challenges in cybersecurity and digital literacy. This study investigates strategies for optimizing public policy in developing digital-based pension funds in Indonesia. The research method used is a qualitative approach, with data obtained from
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Zhu, Yingxin. "Intelligent Nursing System for the Elderly Based on Big Data." International Journal of Healthcare Information Systems and Informatics 19, no. 1 (2024): 1–16. http://dx.doi.org/10.4018/ijhisi.337285.

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The current problem of providing for the aged in China is extremely serious. As a weak member of society, the basic right of the elderly is the harmonious and stable development of society, especially in China, where the problem of aging is extremely severe, and the general pension institutions cannot fully and meticulously meet the precise needs of different elderly people. Therefore, this paper mainly studies the intelligent nursing system for the elderly based on big data, aiming to meet the requirements of the elderly of different ages for the elderly through the combination of big data an
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Kabade, Satish, and Akshay Sharma. "Cloud-Native AI Solutions for Sustainable Pension Investment Strategies." Cloud-Native AI Solutions for Sustainable Pension Investment Strategies 13, no. 3 (2025): 3930–39. https://doi.org/10.5281/zenodo.15597132.

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The use of cloud-native AI in pension fund investment management is rapidly transforming future financial decision-making, risk assessment, and sustainability. Taking advantage of Microsoft Azure's powerful AI and ML, pension funds can use the cloud to enhance their decision-making process by providing the best approach to investing their resources. In other words, by integrating AI into pension funds, it would be possible to personalize and diversify the portfolio and achieve ESG objectives. This paper aims to describe how the cloud- native AI solutions in Azure are o
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Xu, Guoquan, Fang-Chun Liu, Hsiao-Tang Hsu, and Jerry W. Lin. "The impact of pension governance practices on the public defined benefit pension performance." Benchmarking: An International Journal 27, no. 1 (2019): 192–214. http://dx.doi.org/10.1108/bij-08-2018-0265.

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Purpose The purpose of this paper is to investigate the impact of the public pension governance practices on the public defined benefit pension (DBP) fund performance. Design/methodology/approach To provide a holistic evaluation of public DBP performance, this study first employs the Data Envelopment Analysis (DEA) approach to construct a relative performance measure that simultaneously takes into account the association between investment inputs and performance outputs across DBPs in our sample. A DEA regression model is then constructed to empirically examine the impact of pension governance
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Kabade, Satish, and Akshay Sharma. "Intelligent Automation in Pension Service Purchases with AI and Cloud Integration for Operational Excellence." Intelligent Automation in Pension Service Purchases with AI and Cloud Integration for Operational Excellence 3, no. 1 (2023): 725–35. https://doi.org/10.48175/IJARSCT-14100J.

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Abstract: Digital transformation is the main driver for the modernization of pension services from integrated intelligent automation technologies such as Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA), cloud computing. Inspired by their previous solutions across other industries, they chose to build the plugin to enhance performance of traditional pension service workflows, which are primarily manual and semi-automated and often suffer from major inefficiencies, high operational costs, compliance risk as well as poor scalability.
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Pavía, Jose M., and Josep Lledó. "qlifetable: An R package for constructing quarterly life tables." PLOS ONE 20, no. 2 (2025): e0315937. https://doi.org/10.1371/journal.pone.0315937.

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The big data revolution has greatly expanded the availability of microdata on vital statistics, providing researchers with unprecedented access to large and complex datasets on birth, death, migration, and population, sometimes even including exact dates of demographic events. This has led to the development of a novel methodology for estimating sub-annual life tables that offers new opportunities for the insurance industry, also potentially impacting on the management of pension funds and social security systems. This paper introduces the qlifetable package, an R implementation of this method
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Cheptoo, Sang Hilda, and James Kilika. "DISCRETIONARY BENEFITS AND EMPLOYEE RETENTION IN STATE AGENCIES: A CASE OF REGULATORY STATE AGENCIES UNDER THE NATIONAL TREASURY, NAIROBI CITY COUNTY, KENYA." Edith Cowan Journal of Strategic Management 1, no. 1 (2021): 10–18. http://dx.doi.org/10.55077/edithcowanjournalofstrategicmanagement.v1i1.23.

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Purpose: Retention of employees in Kenya’s state agencies has been cited as a big challenge that has continued to undermine effective and efficient service delivery to the public. Employee retention is key to these agencies and failure to retain employees significantly affects their operations thus having a great impact on their service delivery, sustainable development and contribution towards Vision 2030. This study explored the influence of discretionary benefits on employee retention in Kenya’s state regulatory Agencies operating under the National Treasury.
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Dubinin, S. "The Modern Financial Conglomerates Competition." World Economy and International Relations 65, no. 8 (2021): 14–21. http://dx.doi.org/10.20542/0131-2227-2021-65-8-14-21.

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The national and worldwide financial markets were dramatically reorganized in the after financial crisis 2007–2009 years period. The financial systems development faced new macroeconomic environment. The financial conglomerates are actively competing on the financial markets in now day situation. The financial institutes’ competitiveness is determined more and more by the factor of financial innovations on the information technology base. And the competition is escalating. This is the result of the IT companies, eCommerce firm’s invasion in the financial area. They are using the marketplace, i
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Journal, of Global Research in Electronics and Communications (JGREC). "The Impact of Digital Transformation on Annuities - Personalization, Investment Strategies, and Regulatory Challenges." Journal of Global Research in Electronics and Communications (JGREC) 01, no. 04 (2025): 01–07. https://doi.org/10.5281/zenodo.15235600.

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Annuities, as a part of retirement planning, have become an important player with tax deferral on growth and income for life to help address the longevity risk. This paper provides an exhaustive depiction of annuities, distinguishing the features of annuity customization through guaranteed periods and death benefits, and withdrawal rules, and comparing different types of annuities, e.g., single life, joint life, level, and escalating annuities. It relates the historical development of the annuity market from its modest beginnings just before the Great Depression to its expansion with economic
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Journal, of Global Research in Mathematical Archives(JGRMA). "The Impact of Digital Transformation on Annuities: Personalization, Investment Strategies, and Regulatory Challenges." Journal of Global Research in Mathematical Archives (JGRMA) 11, no. 12 (2024): 01–07. https://doi.org/10.5281/zenodo.15279540.

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Annuities, as a part of retirement planning, have become an important player with tax deferral on growth and income for life to help address the longevity risk. This paper provides an exhaustive depiction of annuities, distinguishing the features of annuity customization through guaranteed periods and death benefits, and withdrawal rules, and comparing different types of annuities, e.g., single life, joint life, level, and escalating annuities. It relates the historical development of the annuity market from its modest beginnings just before the Great Depression to its expansion with economic
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Dissertations / Theses on the topic "Big Data in Pension Management"

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Tran, Viet-Trung. "Scalable data-management systems for Big Data." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00920432.

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Big Data can be characterized by 3 V's. * Big Volume refers to the unprecedented growth in the amount of data. * Big Velocity refers to the growth in the speed of moving data in and out management systems. * Big Variety refers to the growth in the number of different data formats. Managing Big Data requires fundamental changes in the architecture of data management systems. Data storage should continue being innovated in order to adapt to the growth of data. They need to be scalable while maintaining high performance regarding data accesses. This thesis focuses on building scalable data manage
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Tudoran, Radu-Marius. "High-Performance Big Data Management Across Cloud Data Centers." Electronic Thesis or Diss., Rennes, École normale supérieure, 2014. http://www.theses.fr/2014ENSR0004.

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La puissance de calcul facilement accessible offerte par les infrastructures clouds, couplés à la révolution du "Big Data", augmentent l'échelle et la vitesse auxquelles l'analyse des données est effectuée. Les ressources de cloud computing pour le calcul et le stockage sont répartis entre plusieurs centres de données de par le monde. Permettre des transferts de données rapides devient particulièrement important dans le cadre d'applications scientifiques pour lesquels déplacer le traitement proche de données est coûteux voire impossible. Les principaux objectifs de cette thèse consistent à ana
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Kalibjian, Jeff. ""Big Data" Management and Security Application to Telemetry Data Products." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579664.

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ITC/USA 2013 Conference Proceedings / The Forty-Ninth Annual International Telemetering Conference and Technical Exhibition / October 21-24, 2013 / Bally's Hotel & Convention Center, Las Vegas, NV<br>"Big Data" [1] and the security challenge of managing "Big Data" is a hot topic in the IT world. The term "Big Data" is used to describe very large data sets that cannot be processed by traditional database applications in "tractable" periods of time. Securing data in a conventional database is challenge enough; securing data whose size may exceed hundreds of terabytes or even petabytes is even mo
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Luchentsova. "BIG DATA FOR REGIONAL AND STATE MANAGEMENT." Thesis, Київ 2018, 2018. http://er.nau.edu.ua/handle/NAU/33808.

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TREVISAN, MARTINO. "Big Data for Traffic Monitoring and Management." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2726624.

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The last two decades witnessed tremendous advances in the Information and Com- munications Technologies. Beside improvements in computational power and storage capacity, communication networks carry nowadays an amount of data which was not envisaged only few years ago. Together with their pervasiveness, network complexity increased at the same pace, leaving operators and researchers with few instruments to understand what happens in the networks, and, on the global scale, on the Internet. Fortunately, recent advances in data science and machine learning come to the res- cue of network a
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Laurila, M. (Mikko). "Big data in Finnish financial services." Bachelor's thesis, University of Oulu, 2017. http://urn.fi/URN:NBN:fi:oulu-201711243156.

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This thesis aims to explore the concept of big data, and create understanding of big data maturity in the Finnish financial services industry. The research questions of this thesis are “What kind of big data solutions are being implemented in the Finnish financial services sector?” and “Which factors impede faster implementation of big data solutions in the Finnish financial services sector?”. Big data, being a concept usually linked with huge data sets and economies of scale, is an interesting topic for research in Finland, a market in which the size of data sets is somewhat limited by the si
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Flike, Felix, and Markus Gervard. "BIG DATA-ANALYS INOM FOTBOLLSORGANISATIONER En studie om big data-analys och värdeskapande." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20117.

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Big data är ett relativt nytt begrepp men fenomenet har funnits länge. Det går att beskriva utifrån fem V:n; volume, veracity, variety, velocity och value. Analysen av Big Data har kommit att visa sig värdefull för organisationer i arbetet med beslutsfattande, generering av mätbara ekonomiska fördelar och förbättra verksamheten. Inom idrottsbranschen började detta på allvar användas i början av 2000-talet i baseballorganisationen Oakland Athletics. Man började värva spelare baserat på deras statistik istället för hur bra scouterna bedömde deras förmåga vilket gav stora framgångar. Detta ledde
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Rystadius, Gustaf, David Monell, and Linus Mautner. "The dynamic management revolution of Big Data : A case study of Åhlen’s Big Data Analytics operation." Thesis, Jönköping University, Internationella Handelshögskolan, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48959.

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Background: The implementation of Big Data Analytics (BDA) has drastically increased within several sectors such as retailing. Due to its rapidly altering environment, companies have to adapt and modify their business strategies and models accordingly. The concepts of ambidexterity and agility are said to act as mediators to these changes in relation to a company’s capabilities within BDA. Problem: Research within the respective fields of dynamic mediators and BDAC have been conducted, but the investigation of specific traits of these mediators, their interconnection and its impact on BDAC is
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Medlej, Maguy. "Big data management for periodic wireless sensor networks." Thesis, Besançon, 2014. http://www.theses.fr/2014BESA2029/document.

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Les recherches présentées dans ce mémoire s’inscrivent dans le cadre des réseaux decapteurs périodiques. Elles portent sur l’étude et la mise en oeuvre d’algorithmes et de protocolesdistribués dédiés à la gestion de données volumineuses, en particulier : la collecte, l’agrégation etla fouille de données. L’approche de la collecte de données permet à chaque noeud d’adapter sontaux d’échantillonnage à l’évolution dynamique de l’environnement. Par ce modèle le suréchantillonnageest réduit et par conséquent la quantité d’énergie consommée. Elle est basée surl’étude de la dépendance de la variance
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Weibl, Johannes [Verfasser]. "Managing Data Resources in the Era of Big Data / Johannes Weibl." Berlin : epubli, 2020. http://d-nb.info/1204318557/34.

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Books on the topic "Big Data in Pension Management"

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García Márquez, Fausto Pedro, and Benjamin Lev, eds. Big Data Management. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-45498-6.

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Li, Jianhui, Xiaofeng Meng, Ying Zhang, Wenjuan Cui, and Zhihui Du, eds. Big Scientific Data Management. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28061-1.

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Edd, Dumbill, Croll Alistair, Steele Julie (Graphic designer), and Loukides Michael Kosta, eds. Planning for big data. O'Reilly Media, 2012.

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Lossio-Ventura, Juan Antonio, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, and Hugo Alatrista-Salas, eds. Information Management and Big Data. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76228-5.

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Fernandez, Renny, and Terrance Frederick Fernandez. Big data management in Sensing. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337355.

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Lossio-Ventura, Juan Antonio, and Hugo Alatrista-Salas, eds. Information Management and Big Data. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90596-9.

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Kutsche, Ralf-Detlef, and Esteban Zimányi, eds. Big Data Management and Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61627-4.

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Shen, Bairong, ed. Healthcare and Big Data Management. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6041-0.

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Lossio-Ventura, Juan Antonio, Denisse Muñante, and Hugo Alatrista-Salas, eds. Information Management and Big Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11680-4.

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Lossio-Ventura, Juan Antonio, and Hugo Alatrista-Salas, eds. Information Management and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55209-5.

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Book chapters on the topic "Big Data in Pension Management"

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Schröder, Kay, Poornima Belavadi, Martina Ziefle, and André Calero Valdez. "The Pension Story - Data-Driven Storytelling with Pension Data." In Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Health, Operations Management, and Design. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06018-2_28.

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Taori, Peeyush, and Hemanth Kumar Dasararaju. "Big Data Management." In International Series in Operations Research & Management Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-68837-4_4.

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Helmold, Marc. "Big Data Management." In Erfolgreiche Transformation zum digitalen Champion. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-44020-6_10.

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Christakos, Constantine K. "Big Data." In Encyclopedia of Security and Emergency Management. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-69891-5_5-1.

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Christakos, Constantine K. "Big Data." In Encyclopedia of Security and Emergency Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-70488-3_5.

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Ritter, Daniel. "The Business Network Data Management Platform." In Big Data. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39467-6_20.

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Niemann, Jörg, and Adrian Pisla. "Big Data." In Life-Cycle Management of Machines and Mechanisms. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56449-0_8.

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Mbaiossoum, Bery, Ladjel Bellatreche, and Stéphane Jean. "Towards Performance Evaluation of Semantic Databases Management Systems." In Big Data. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39467-6_12.

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Arbeleche, Santiago, Michael A. H. Dempster, Elena A. Medova, Giles W. P. Thompson, and Michael Villaverde. "Portfolio Management for Pension Funds." In Intelligent Data Engineering and Automated Learning. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45080-1_63.

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Chiera, Belinda A., and Małgorzata W. Korolkiewicz. "Visualizing Big Data: Everything Old Is New Again." In Big Data Management. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45498-6_1.

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Conference papers on the topic "Big Data in Pension Management"

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Wang, Jin. "Data Analysis on Consumer Pension Plans for Chinese Residents." In Proceedings of the 3rd International Conference on Public Management and Big Data Analysis, PMBDA 2023, December 15–17, 2023, Nanjing, China. EAI, 2024. http://dx.doi.org/10.4108/eai.15-12-2023.2345408.

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Wu, Guoqiang, and Qingping Li. "Research on the Innovation of China's Pension Insurance Transfer and Succession Model——Based on Big Data Technology." In 2020 International Conference on Big Data Economy and Information Management (BDEIM). IEEE, 2020. http://dx.doi.org/10.1109/bdeim52318.2020.00011.

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Qian, Xinyu, Zhenyu Wang, and Guo'an Xu. "Optimization ideas of Hubei Province's two-pillar pension insurance system in response to the challenges of aging." In ICBDDM 2024: 2024 International Conference on Big Data and Digital Management. ACM, 2024. http://dx.doi.org/10.1145/3696500.3696571.

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Du, Tingting. "Design Framework of Smart Pension Platform Based on Big Data Technology." In 2022 IEEE International Conference on Computation, Big-Data and Engineering (ICCBE). IEEE, 2022. http://dx.doi.org/10.1109/iccbe56101.2022.9888211.

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Prasertsom, Patipan, Intouch Kunakorntum, Atthavit Tulyathan, Nontawit Cheewaruangroj, Kanyawee Pornsawangdee, and Napassawan Pasuthip. "Benefit Recommendation System: A Use Case for Pension Fund Data Analytics." In 2020 1st International Conference on Big Data Analytics and Practices (IBDAP). IEEE, 2020. http://dx.doi.org/10.1109/ibdap50342.2020.9245453.

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Grillenberger, Andreas. "Big data and data management." In the tenth annual conference. ACM Press, 2014. http://dx.doi.org/10.1145/2632320.2632325.

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Rossi, Rogério, and Kechi Hirama. "Characterizing Big Data Management." In InSITE 2015: Informing Science + IT Education Conferences: USA. Informing Science Institute, 2015. http://dx.doi.org/10.28945/2192.

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[The final form of this paper was published in the journal Issues in Informing Science and Information Technology.] Considering that big data is a reality for an increasing number of organizations in many areas, its management represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial dimensions to facilitate the management of big data in any organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management must be sup
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Borkar, Vinayak, Michael J. Carey, and Chen Li. "Inside "Big Data management"." In the 15th International Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2247596.2247598.

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Cardoso, Joao, Tomasz Miksa, and Jose Borbinha. "Debunking Active Data Management Plans." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8621860.

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Frisiello, Antonella, Quynh Nhu Nguyen, and Claudio Rossi. "Gamified crowdsourcing for disaster risk management." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258370.

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Reports on the topic "Big Data in Pension Management"

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

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Cao, Larry. T-Shaped Teams: Organizing to Adopt AI and Big Data at Investment Firms. CFA Institute, 2021. http://dx.doi.org/10.56227/22.1.13.

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Focusing on questions raised by the AI Pioneers in Investment Management report, this publication explains how cross-functional teams can help investment professionals and organizations develop investment processes that incorporate AI and big data.
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Vizmanos, Jana Flor, Jose Ramon Albert, Mika Muñoz, et al. Addressing Data Gaps with Innovative Data Sources. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.55.

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With the advent of digital transformation, information and communications technology innovations have also led to a "data revolution" wherein more data is being captured, produced, stored, accessed, analyzed, archived, and reanalyzed at an exponential pace. An examination of new data sources, including big data and crowd-sourced data, can complement traditional sources of statistics and unlock insights that can ultimately lead to interventions for better outcomes by informing policies and actions toward attaining robust, sustainable, and inclusive development. This study will examine PIDS webs
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García-Mantilla, Daniel. PLAC Network Best Practices Series: Target-Income Design of Incentives, Benchmark Portfolios and Performance Metrics for Pension Funds. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003599.

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In defined contribution systems, at the end of the accumulation phase the assets in the retirement account are exchanged for a pension. The conversion rate from assets to retirement income (which depends on the level of interest rates) is very volatile, and its variations constitute the main investment risk facing pension fund affiliates. In this sense, performance metrics, management fees and benchmark portfolios that focus on assets (and asset returns) and ignore the variations in the conversion rate, embed several problems: i. they send wrong signals to regulators, fund managers and workers
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Choquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011568.

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DATE: Wednesday, May 1, 2019 TIME: 2:00 - 3:30 p.m. ET PRESENTER: Gary Choquette, PRCI CLICK DOWNLOAD/BUY TO ACCESS THE REGISTRATION LINK FOR THIS WEBINAR Systems that manage large sets of data are becoming more common in the energy transportation industry. Having access to the data offers the opportunity to learn from previous experiences to help efficiently manage the future. But how does one manage to digest copious quantities of data to find nuggets within the ore? This webinar will outline some of the data management best practices learned from the research projects associated with CEPM.
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Toivonen, Tuuli, Aina Brias Guinart, Johanna Eklund, Hästbacka Matti, Leppämäki Tatu, and Torkko Jussi. Potential of mobile big data for visitor monitoring : Report of the MOBICON workshop held in Helsinki 28.9.2023. Digital Geography Lab, University of Helsinki, 2023. http://dx.doi.org/10.31885/2024.030501.

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The Changes in Nature Visitation and the Potential of Mobile Big Data for Visitor Monitoring workshop was held in Helsinki on 28.9.2023. We organized the workshop as part of the five-year MOBICON research project (Mobile Big Data for Understanding People in Nature - Detecting short- and long-term changes and their implications for biodiversity conservation) funded by Kone Foundation. The aim of the workshop was to collect expert opinions related to the changes in the recreational use of nature, the monitoring needs related to the changing visitations, and to discuss the possibilities of variou
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Ley, Matt, Tom Baldvins, Hannah Pilkington, David Jones, and Kelly Anderson. Vegetation classification and mapping project: Big Thicket National Preserve. National Park Service, 2024. http://dx.doi.org/10.36967/2299254.

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The Big Thicket National Preserve (BITH) vegetation inventory project classified and mapped vegetation within the administrative boundary and estimated thematic map accuracy quantitatively. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, vegetation classification field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetings took place during May, 2
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Coscia, Jordan, Wendy Cass, and Leighton J. Evaluation of 20 years of vegetation change in the Big Meadows landscape of Shenandoah National Park in response to mowing and burning: Application of historic data to identify best practices for future meadow management. National Park Service, 2025. https://doi.org/10.36967/2312599.

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The Big Meadows landscape is an open habitat that supports unique biodiversity not found elsewhere in Shenandoah National Park. To support this biodiversity and to maintain the Meadow’s historic landscape characteristics, Park staff have managed the meadows using prescribed burns, mowing, and the targeted cutting and removal of woody plants since 1998. To assess the impacts of these management actions on the plant community in Big Meadows and to guide future land management recommendations, we visualized changes in the plant community composition between 1998 and 2022 using non-metric multidim
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Cimene, Dr Francis Thaise A. Emerging Technological Trends and Business Process Management: Preparing the Philippines for the Future. Asian Productivity Organization, 2024. https://doi.org/10.61145/dktv2301.

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The Philippine IT-BPM sector plays a vital role in driving economic growth and global competitiveness. This mini-report highlights how emerging technologies such as cloud computing, IoT, and big data analytics are transforming traditional business processes. Grounded in endogenous growth theory, the report emphasizes the impact of innovation and human capital on productivity. Policy recommendations are provided to bolster the nation’s position as a leading outsourcing hub and prepare for future technological advancements.
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