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Shipping and Logistics Analytics using Big Data
Shipping and Logistics Analytics using Big Data
Knygos.lt klubas Knygos.lt nariams
297,21 €
-30%
Įprastai
424,59 €
  • Planuojame turėti už 149 d.
With the advancement of information and communication technology, a data-driven approach applying the tools and methodologies of big data analytics has become quite common across industry. In maritime shipping, automatic identification system (AIS) vessel movement data is reliable and improving and has been used for the detection of suspicious vessels and to support vessel navigation. However, since detailed cargo items and loading/unloading conditions cannot be directly obtained from AIS data,…
  • Leidėjas:
  • Metai: 2026
  • Puslapiai: 488
  • ISBN-10: 1032505346
  • ISBN-13: 9781032505343
  • Kalba: Anglų

Shipping and Logistics Analytics using Big Data (el. knyga) (skaityta knyga) | knygos.lt

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With the advancement of information and communication technology, a data-driven approach applying the tools and methodologies of big data analytics has become quite common across industry. In maritime shipping, automatic identification system (AIS) vessel movement data is reliable and improving and has been used for the detection of suspicious vessels and to support vessel navigation. However, since detailed cargo items and loading/unloading conditions cannot be directly obtained from AIS data, research using this data in the fields of logistics, shipping, and shipbuilding is still limited. This book introduces the state of the art on the worldwide use of AIS and other maritime big data for shipping and logistics analysis, with data-oriented case studies, for the maritime and related industries and for university use in maritime economics, industrial engineering, and transport engineering.

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  • Leidėjas:
  • Metai: 2026
  • Puslapiai: 488
  • ISBN-10: 1032505346
  • ISBN-13: 9781032505343
  • Kalba: Anglų

With the advancement of information and communication technology, a data-driven approach applying the tools and methodologies of big data analytics has become quite common across industry. In maritime shipping, automatic identification system (AIS) vessel movement data is reliable and improving and has been used for the detection of suspicious vessels and to support vessel navigation. However, since detailed cargo items and loading/unloading conditions cannot be directly obtained from AIS data, research using this data in the fields of logistics, shipping, and shipbuilding is still limited. This book introduces the state of the art on the worldwide use of AIS and other maritime big data for shipping and logistics analysis, with data-oriented case studies, for the maritime and related industries and for university use in maritime economics, industrial engineering, and transport engineering.

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