PapersFlow Research Brief
Big Data and Digital Economy
Research Guide
What is Big Data and Digital Economy?
Big Data and Digital Economy refers to the intersection of big data technologies with cloud computing in digital economic systems, emphasizing security, energy efficiency, machine learning, IoT, privacy protection, resource allocation, cybersecurity, and mobile sensing.
This field encompasses 49,888 papers that address data optimization, secure communication, and privacy-preserving strategies in cloud-based environments. Khan et al. (2014) in "Big Data: Survey, Technologies, Opportunities, and Challenges" note that over 2 billion people worldwide are connected to the Internet, generating information at rates exceeding traditional processing boundaries. Growth over the last 5 years is not specified in available data.
Topic Hierarchy
Research Sub-Topics
Privacy-Preserving Techniques in Big Data
This sub-topic investigates methods like differential privacy, homomorphic encryption, and federated learning to protect data in cloud environments. Researchers evaluate trade-offs between utility, security, and computational overhead in large-scale analytics.
Energy-Efficient Resource Allocation in Cloud Computing
Researchers develop algorithms for dynamic resource provisioning, VM consolidation, and workload scheduling to minimize energy consumption in data centers. Studies benchmark performance against carbon footprints and cost savings in big data workloads.
Big Data Analytics for IoT Applications
This sub-topic explores scalable processing of IoT streams using edge-cloud architectures, anomaly detection, and real-time analytics. Research focuses on handling heterogeneity, latency, and volume in smart city and industrial IoT deployments.
Machine Learning Optimization in Big Data Systems
Studies optimize distributed ML frameworks like Spark MLlib and TensorFlow for big data, addressing scalability, hyperparameter tuning, and model deployment. Researchers tackle challenges in fault tolerance and convergence on massive datasets.
Cybersecurity Frameworks for Cloud Big Data
This sub-topic develops intrusion detection, blockchain-based access control, and threat modeling for big data platforms in clouds. Research assesses resilience against attacks like data poisoning and insider threats in multi-tenant environments.
Why It Matters
Big Data and Digital Economy impacts sectors like finance, genomics, and network security through applications in cloud computing and data processing. Khan et al. (2014) highlight how big data technologies handle information from over 5 billion individuals' devices, enabling opportunities in IT industries for data optimization and machine learning. In genome informatics, Stein (2010) in "The case for cloud computing in genome informatics" demonstrates cloud use for scalable analysis, processing large datasets efficiently. Gai et al. (2017) in "A survey on FinTech" cover financial technology applications, integrating big data for secure transactions. Guo and Yu (2022) in "A survey on blockchain technology and its security" address blockchain's role in decentralized digital economies, supporting immutability and fault-tolerance in economic transactions.
Reading Guide
Where to Start
"Big Data: Survey, Technologies, Opportunities, and Challenges" by Khan et al. (2014), as it provides a foundational survey of big data scale, with specifics like over 2 billion Internet users, accessible before specialized topics like blockchain or FinTech.
Key Papers Explained
Khan et al. (2014) in "Big Data: Survey, Technologies, Opportunities, and Challenges" establishes big data foundations, which Stein (2010) in "The case for cloud computing in genome informatics" applies to cloud scalability; Gai et al. (2017) in "A survey on FinTech" extends to financial digital economy uses, while Guo and Yu (2022) in "A survey on blockchain technology and its security" builds on security needs across these areas. Zhang et al. (2018) and Chen et al. (2020) connect via machine learning advancements for graph data in economic modeling.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Recent emphasis remains on blockchain security and FinTech surveys, with Guo and Yu (2022) detailing consensus algorithms; no preprints or news from the last 6-12 months indicate ongoing focus on established cloud and IoT security without new public developments.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | Computer science as empirical inquiry | 1976 | Communications of the ACM | 2.3K | ✕ |
| 2 | Communication Complexity | 2005 | — | 1.7K | ✕ |
| 3 | An End-to-End Deep Learning Architecture for Graph Classification | 2018 | Proceedings of the AAA... | 1.5K | ✓ |
| 4 | Big Data: Survey, Technologies, Opportunities, and Challenges | 2014 | The Scientific World J... | 535 | ✓ |
| 5 | The case for cloud computing in genome informatics | 2010 | Genome Biology | 524 | ✓ |
| 6 | Electromagnetic radiation from video display units: An eavesdr... | 1985 | Computers & Security | 517 | ✕ |
| 7 | A survey on FinTech | 2017 | Journal of Network and... | 513 | ✕ |
| 8 | A survey on blockchain technology and its security | 2022 | Blockchain Research an... | 494 | ✓ |
| 9 | Simple and Deep Graph Convolutional Networks | 2020 | arXiv (Cornell Univers... | 399 | ✓ |
| 10 | Why general artificial intelligence will not be realized | 2020 | Humanities and Social ... | 365 | ✓ |
Frequently Asked Questions
What technologies are central to Big Data and Digital Economy?
Core technologies include big data, cloud computing, machine learning, IoT, and blockchain. Khan et al. (2014) describe big data's role in managing vast information from over 2 billion Internet-connected people. Guo and Yu (2022) outline blockchain features like decentralization and immutability for secure economic applications.
How does big data connect to the digital economy?
Big data drives the digital economy via cloud-based processing for security and resource allocation. Gai et al. (2017) survey FinTech integrations of big data for financial services. Stein (2010) shows cloud computing enabling genome data handling in economic contexts.
What security challenges exist in this field?
Challenges include privacy protection, cybersecurity, and secure communication in IoT and cloud systems. Guo and Yu (2022) survey blockchain security features like fault-tolerance and anonymity. Van Eck (1985) in "Electromagnetic radiation from video display units: An eavesdropping risk?" identifies risks in data display security.
What is the scale of research in Big Data and Digital Economy?
The field includes 49,888 papers focused on information systems applications. Keywords cover energy efficiency, mobile sensing, and resource allocation. Khan et al. (2014) report rapid information growth exceeding traditional boundaries.
How does machine learning apply here?
Machine learning processes graph-structured data and supports deep learning architectures. Zhang et al. (2018) in "An End-to-End Deep Learning Architecture for Graph Classification" propose neural networks for arbitrary graphs. Chen et al. (2020) in "Simple and Deep Graph Convolutional Networks" address shallow GCN limitations for real-world datasets.
What role does cloud computing play?
Cloud computing facilitates big data handling in digital economies, including genome informatics. Stein (2010) argues for its use in scalable genomic analysis. It supports resource allocation and energy efficiency across IoT applications.
Open Research Questions
- ? How can privacy protection be enhanced in IoT-driven big data systems for digital economies?
- ? What resource allocation strategies optimize energy efficiency in cloud computing for mobile sensing?
- ? Which consensus algorithms best secure blockchain applications in FinTech digital economies?
- ? How do deep graph convolutional networks scale to larger datasets in big data economic models?
- ? What eavesdropping risks persist in modern video display units despite advances in cybersecurity?
Recent Trends
The field holds 49,888 papers with unspecified 5-year growth; Guo and Yu in "A survey on blockchain technology and its security" represents recent work (2022) surveying blockchain's decentralization for digital economies, building on earlier FinTech by Gai et al. (2017).
2022Chen et al. advances graph networks, but no preprints or news from the last 6-12 months show shifts.
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