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| 1 | Big Data Analytics in Telecommunications: Literature Review and Architecture Recommendations显示文摘This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabytes to petabytes of data on a daily basis. Io T applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts(POC) on a severely limited BDA technology stack(as compared to the available technology stack), i.e.,we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation(called Lambda Tel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines.We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe Lambda Tel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises. | Hira Zahid Tariq Mahmood Ahsan Morshed Timos Sellis | 2020 | IEEE/CAA Journal of Automatica Sinica2020，7，1： | 6 |
| 2 | Big Data Analytics in Healthcare——A Systematic Literature Review and Roadmap for Practical Implementation显示文摘The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale.Analysis of this big data allows for boundless potential outcomes for discovering knowledge.Big data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost savings.However,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners.In this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature review.We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL databases.We also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare research.We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm.We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare.Finally,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work.The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successfully implement BDA initiatives in their organizations. | Sohail Imran Tariq Mahmood Ahsan Morshed Timos Sellis | 2021 | IEEE/CAA Journal of Automatica Sinica2021，8，1： | 2 |
| 3 | ARKTOS: towards the modeling, design, control and execution of ETL processes显示文摘 | Panos Vassiliadis Zografoula Vagena Spiros Skiadopoulos Nikos Karayannidis Timos Sellis | 2001 | Infornation Systems2001，26，8： | 2 |
| 4 | Efficient Cost Models for Spatial Queries Using R-trees 显示文摘 | Yannis Theodoridis Emmanuel Stefanakis Timos Sellis | 2000 | IEEE Transactions on Knowledge and Data Engineering (S1041-4347)2000，12，1： | 1 |
| 5 | Designing Data Warehouses显示文摘 | Theodoratos Dimitri Sellis Timos | 1999 | Data and Knowledge Engineering1999，31，3： | 1 |
| 6 | Designing data warehouses 显示文摘 | Dimitri Theodoratos Timos Sellis | 1999 | Data & Knowledge Engineering1999，31，： | 1 |
| 7 | Designing data warehouses显示文摘 | Dimitri Theodoratos Timos Sellis | 1999 | Data and Knowledge Engineering1999，31，3： | 1 |
| 8 | A Survey on Logical Models for OLAP Databases 显示文摘 | Panos Vassiliadis Timos Sellis | 1999 | ACM SIGMOD Record1999，28，4： | 1 |
| 9 | Storing and Indexing Spatial Data in P2P Systems 显示文摘 | VERENA KANTERE SPIROS SKIADOPOULOS TIMOS SELLIS | 2009 | IEEE Transactions on Knowledge and Data Engineering2009，21，2： | 1 |
| 10 | Storing and Indexing Spatial Data in P2P Systems 显示文摘 | VERENA KANTERE SPIROS SKIADOPOULOS TIMOS SELLIS | 2009 | IEEE Transactions on Knowledge and Data Engineering2009，21，2： | 1 |
| 11 | Qualitative Representation of Spatial Knowledge in Two-Dimensional Space显示文摘 | Dimitris Papadias Timos Sellis | 1994 | Very Large Data Bases Jouranl1994，3，4： | 1 |
| 12 | Designing Data Warehouses显示文摘 | Dimitri Theodoratos Timos Sellis | 1999 | Data and Knowledge Engineering1999，31，3： | 1 |
| 13 | Multiple-query optimization显示文摘 | Timos K Sellie | 1998 | ACM trans-actions on database system1998，13，1： | 1 |
| 14 | Designing Data Warehouses显示文摘 | Dimitri Theodoratos Timos sellis | 1999 | Data & Knowledge Engineering1999，，31： | 1 |
| 15 | Hierarchically compressed wavelet synopses显示文摘 | Dimitris Sacharidis Antonios Deligiannakis Timos Sellis | 2009 | The VLDB Journal2009，，1： | 1 |
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