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Big Data (2012.09109v1)

Published 15 Dec 2020 in cs.CY

Abstract: The Internet of Things, crowdsourcing, social media, public authorities, and other sources generate bigger and bigger data sets. Big and open data offers many benefits for emergency management, but also pose new challenges. This chapter will review the sources of big data and their characteristics. We then discuss potential benefits of big data for emergency management along with the technological and societal challenges it poses. We review central technologies for big-data storage and processing in general, before presenting the Spark big-data engine in more detail. Finally, we review ethical and societal threats that big data pose.

Summary

  • The paper presents an extensive review of how diverse Big Data sources, including IoT and social media, bolster emergency management efforts.
  • It utilizes distributed computing methods like HDFS and Apache Spark for processing complex, real-time data in critical scenarios.
  • The study highlights challenges such as data quality issues, ethical concerns, and infrastructure demands that influence future research.

Big Data in Emergency Management: An Analytical Review

The chapter authored by Andreas L. Opdahl and Vimala Nunavath presents an exhaustive examination of the role and potential of Big Data in the context of emergency management. The chapter delineates the multifaceted aspects of Big Data, ranging from its diverse sources to its application in emergency scenarios, while also addressing associated technological, societal, and ethical challenges.

Defining Big Data: Characteristics and Infrastructure

The chapter begins by clarifying the concept of Big Data, characterized by the volume, velocity, and variety — often referred to as the "three Vs." It further extends this definition to include veracity and value, underlying the importance of data's accuracy and utility. Big Data is recognized for its ability to integrate large-scale datasets from various sources such as the Internet of Things (IoT), social media, and public authorities, demonstrating its technical intricacies and the need for robust infrastructures like distributed computing systems, including HDFS and Spark.

Big Data Sources for Emergency Management

The authors identify multiple avenues for sourcing Big Data relevant to emergency management. Organizations like the Humanitarian Data Exchange and PreventionWeb offer curated overviews of datasets pertinent to crisis scenarios. Furthermore, technological tools such as OpenStreetMap, Wikipedia, and government databases, alongside proprietary sources like Google Crisis Map, are highlighted as essential repositories. The IoT and social media further contribute real-time data, critical for timely and effective emergency responses.

Applications and Challenges

The practical application of Big Data for emergency management is underscored across several phases, including preparation, detection, response, and recovery. For preparation, Big Data is invaluable for creating baseline models to understand normal conditions, thus facilitating the early detection of anomalies. During an emergency, data from various sensors can provide situational overviews and actionable insights, seen in real-time traffic data, weather sensors, and social media indicators. Recovery efforts also benefit from Big Data through detailed post-mortem analyses, aiding future risk mitigation strategies.

However, leveraging Big Data entails overcoming significant challenges, such as the need for skilled professionals, established computational infrastructures, and efficient data communication techniques. Moreover, data quality, noise filtration, and interoperability of datasets remain crucial technical obstacles.

Technological Analysis: Big Data Storage and Processing

In reviewing central technological paradigms, the authors discuss cloud computing, distributed file systems, and DDBMS. The evolution of tools from Google's foundational MapReduce frameworks to the sophisticated Apache Spark is detailed, emphasizing Spark's ability to process large datasets through in-memory storage and dynamic data-flow pipelines. Spark's Resilient Distributed Datasets (RDDs) offer robust solutions for distributed processing, which are crucial for managing the data-driven demands of emergency management.

Ethical and Societal Considerations

The ethical implications of Big Data usage, particularly concerning privacy and information security, are judiciously explored. The chapter suggests a balanced approach to data utilization, advocating for organizational transparency, informational stewardship, and technical precautionary measures to safeguard personal and sensitive information. Ethical concerns are particularly salient given the potential for misuse of surveillance and personal data during emergencies.

Conclusion and Implications for Future Research

The chapter offers a comprehensive overview of the transformative role Big Data plays in emergency management, underlining both its promises and pitfalls. By enhancing data collection technologies, processing frameworks, and ethical practices, researchers and practitioners stand to augment emergency management capabilities significantly. Future research directions might explore the integration of emerging AI techniques with Big Data, further optimizing emergency response paradigms and advancing societal resilience against crises.