Challenges and opportunities of big data analytics for maritime and shipping industry /

dc.campusChennai
dc.contributor.authorRao, B.V. Ramalingeswara
dc.contributor.authorAmritha, C.S.
dc.date.accessioned2025-03-07T09:16:52Z
dc.date.accessioned2025-03-31T10:11:22Z
dc.date.available2025-03-07T09:16:52Z
dc.date.issued2024-01-01
dc.description.abstractBig Data is more than just large amounts of data. Big Data allows companies to use enormous amounts of data from non-traditional sources. A non-traditional source is time-sensitive data, not just past recorded data, used to optimize the industry and ports. Big Data includes texts, audios, videos, and real-time information. Big data is a big category which includes the data in both structured and unstructured forms stored in the cloud. For e-commerce, the supply chain data source might be order management systems, warehouses, payroll, inventory systems and carrier data. For the Maritime analytics market traditional data may come from dockyards, ships, vessel operations, bill of lading, traditional (fixed) data is used to analyze profits and losses. Nontraditional is time sensitive and not always quantifiable. Examples of nontraditional data are weather data, traffic and location data and movement of freight via transportation.
dc.identifier.urihttps://doi.org/10.46647/ijetms.2024.v08i01.011
dc.identifier.urihttps://dspacenew8-imu.refread.com/handle/123456789/2122
dc.language.isoen
dc.publisherIJETMS
dc.schoolSchool of Nautical Studies
dc.subjectMaritime
dc.subjectShipping
dc.subjectBig data Analytics
dc.subjectDigitalization
dc.titleChallenges and opportunities of big data analytics for maritime and shipping industry /
dc.typeArticle

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