Big Data

Bhadani, Abhay Kumar; Jothimani, Dhanya

doi:10.4018/978-1-5225-0182-4.ch001

Cited by 80 publications

(23 citation statements)

References 25 publications

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“…The result is petabytes of data that document ocean soundscapes. Efficiently extracting this critical information and comparing it to other datasets in the context of ecosystem-based research management is a Big Data challenge that traditional desktop processing methods cannot address (Marx, 2013;Bhadani and Jothimani, 2016). Machine learning and artificial intelligence are increasingly playing a role in research applications for PAM datasets (Shamir et al, 2014;Shiu et al, 2020;Allen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The Next Wave of Passive Acoustic Data Management: How Centralized Access Can Enhance Science

et al. 2021

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Passive acoustic data collection has grown exponentially over the past decade resulting in petabytes of data that document our ocean soundscapes. This effort has resulted in two big data challenges: (1) the curation, management, and global dissemination of passive acoustic datasets and (2) efficiently extracting critical information and comparing it to other datasets in the context of ecosystem-based research and management. To address the former, the NOAA National Centers for Environmental Information recently established an archive for passive acoustic data. This fast-growing archive currently contains over 100 TB of passive acoustic audio files mainly collected from stationary recorders throughout waters in the United States. These datasets are documented with standards-based metadata and are freely available to the public. To begin to address the latter, through standardized processing and centralized stewardship and access, we provide a previously unattainable comparison of first order sound level-patterns from archived data collected across three distinctly separate long-term passive acoustic monitoring (PAM) efforts conducted at regional and national scales: NOAA/National Park Service Ocean Noise Reference Station Network, the Atlantic Deepwater Ecosystem Observatory Network, and the Sanctuary Soundscape Monitoring Project. Nine sites were selected from these projects covering the Alaskan Arctic, Northeast and Central Pacific, Gulf of Mexico, Caribbean Sea, and Mid and Northwest Atlantic. Sites could generally be categorized into those strongly influenced by anthropogenic noise (e.g., vessel traffic) and those that were not. Higher sound levels, specifically for lower frequencies (<125 Hz), and proximity to densely populated coastal zones were common characteristics of sites influenced by anthropogenic noise. Conversely, sites with lower overall sound levels and away from dense populations resulted in soundscape patterns influenced by biological sources. Seasonal variability in sound levels across selected decidecade bands was apparent for most sites and often represented changes in the presence or behavior of sound-producing species. This first order examination of levels across projects highlights the utility of these initial metrics to identify patterns that can then be examined in more detail. Finally, to help the PAM community collectively and collaboratively move forward, we propose the next frontier for scalable data stewardship, access, and processing flow.

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Section: Introductionmentioning

confidence: 99%

The Next Wave of Passive Acoustic Data Management: How Centralized Access Can Enhance Science

et al. 2021

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“…Hence, the Big Data ecosystem offers the required tools to improve ontology-based systems. Whereas HDFS, HBase, and MongoDB permit to store a large volume of ontology instances and provide a sufficient running space regardless of their size (Bhadani and Jothimani, 2016). Besides, MapReduce, Storm, and Spark can ensure a quick processing framework.…”

Section: Background Of the Studymentioning

confidence: 99%

An intelligent system for energy management in smart cities based on big data and ontology

Sayah

Kazar

Lejdel³

et al. 2020

SASBE

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PurposeThis research paper aims at proposing a framework based on semantic integration in Big Data for saving energy in smart cities. The presented approach highlights the potential opportunities offered by Big Data and ontologies to reduce energy consumption in smart cities.Design/methodology/approachThis study provides an overview of semantics in Big Data and reviews various works that investigate energy saving in smart homes and cities. To reach this end, we propose an efficient architecture based on the cooperation between ontology, Big Data, and Multi-Agent Systems. Furthermore, the proposed approach shows the strength of these technologies to reduce energy consumption in smart cities.FindingsThrough this research, we seek to clarify and explain both the role of Multi-Agent System and ontology paradigms to improve systems interoperability. Indeed, it is useful to develop the proposed architecture based on Big Data. This study highlights the opportunities offered when they are combined together to provide a reliable system for saving energy in smart cities.Practical implicationsThe significant advancement of contemporary applications (smart cities, social networks, health care, IoT, etc.) requires a vast emergence of Big Data and semantics technologies in these fields. The obtained results provide an improved vision of energy-saving and environmental protection while keeping the inhabitants’ comfort.Originality/valueThis work is an efficient contribution that provides more comprehensive solutions to ontology integration in the Big Data environment. We have used all available data to reduce energy consumption, promote the change of inhabitant’s behavior, offer the required comfort, and implement an effective long-term energy policy in a smart and sustainable environment.

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“…Third, data can be differentiated based on the 3V model (i.e. volume, velocity and variety) of Big Data (Bhadani and Jothimani, 2016; Gandomi and Haider, 2015). Volume refers to the sheer amount of data generated, often via electronically supported business processes as described above.…”

Section: The Data Divide: Managers’ Data and Academics’ Insightsmentioning

confidence: 99%

Bridging the data divide between practitioners and academics

Benoit

Klose

Wirtz

et al. 2019

JOSM

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Purpose Organizations (data gatherers in the context) drown in data while at the same time seeking managerially relevant insights. Academics (data hunters) have to deal with decreasing respondent participation and escalating costs of data collection while at the same time seeking to increase the managerial relevance of their research. The purpose of this paper is to provide a framework on how, managers and academics can collaborate better to leverage each other’s resources. Design/methodology/approach This research synthesizes the academic and the managerial literature on the realities and priorities of practitioners and academics with regard to data. Based on the literature, reflections from the world’s leading service research centers, and the authors’ own experiences, the authors develop recommendations on how to collaborate in research. Findings Four dimensions of different data realities and priorities were identified: research problem, research resources, research process and research outcome. In total, 26 recommendations are presented that aim to equip academics to leverage the potential of corporate data for research purposes and to help managers to leverage research results for their business. Research limitations/implications This paper argues that both practitioners and academics have a lot to gain from collaborating by exchanging corporate data for scientific approaches and insights. However, the gap between different realities and priorities needs to be bridged when doing so. The paper first identifies data realities and priorities and then develops recommendations on how to best collaborate given these differences. Practical implications This research has the potential to contribute to managerial practice by informing academics on how to better collaborate with the managerial world and thereby facilitate collaboration and the dissemination of academic research for the benefit of both parties. Originality/value Whereas the previous literature has primarily examined practitioner–academic collaboration in general, this study is the first to focus specifically on the aspects related to sharing corporate data and to elaborate on academic and corporate objectives with regard to data and insights.

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Big Data

Cited by 80 publications

References 25 publications

The Next Wave of Passive Acoustic Data Management: How Centralized Access Can Enhance Science

The Next Wave of Passive Acoustic Data Management: How Centralized Access Can Enhance Science

An intelligent system for energy management in smart cities based on big data and ontology

Bridging the data divide between practitioners and academics

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