Detail publikačního výsledku

Novel Big Data Approach for Text Supported Service Operations Management

POVODA, L.; BURGET, R.; RAJNOHA, M.; BREZANY, P.

Originální název

Novel Big Data Approach for Text Supported Service Operations Management

Anglický název

Novel Big Data Approach for Text Supported Service Operations Management

Druh

Kapitola, resp. kapitoly v odborné knize

Originální abstrakt

Operations management is being applied across all industry sectors and offers more accurate decision making, cost saving and a path to lean production. All depend on proper information, which is a valuable input for operations management and can represent the key to gain competitive advantage. Thanks to the information, we can improve and optimize processes and react to problems before they even appear. Unfortunately, getting this knowledge is often a challenging task. It is estimated that only a small portion of publicly available data can be considered as valuable. Considering the size of today's data storage, it is out of possibilities for anyone to read them all and operatively react to the changing environment and competition. In case of the text data, they are often written in many different languages, use different expert domains or customer segments, who have their own specific language style. To gain valuable information from this data, it is necessary to know each particular language (not necessarily human, but also machine generated) and grammar. This chapter presents the latest advances in artificial intelligence for the text data analysis and operations management. First, we provide the state of the art of the text processing approaches, then we discuss selected use-cases from the field of operations management and how the latest methods can help to solve those problems. The last part of the chapter outlines some ideas for further improvement of the current approaches and how to effectively analyse data in a multilingual environment and decrease memory demands.

Anglický abstrakt

Operations management is being applied across all industry sectors and offers more accurate decision making, cost saving and a path to lean production. All depend on proper information, which is a valuable input for operations management and can represent the key to gain competitive advantage. Thanks to the information, we can improve and optimize processes and react to problems before they even appear. Unfortunately, getting this knowledge is often a challenging task. It is estimated that only a small portion of publicly available data can be considered as valuable. Considering the size of today's data storage, it is out of possibilities for anyone to read them all and operatively react to the changing environment and competition. In case of the text data, they are often written in many different languages, use different expert domains or customer segments, who have their own specific language style. To gain valuable information from this data, it is necessary to know each particular language (not necessarily human, but also machine generated) and grammar. This chapter presents the latest advances in artificial intelligence for the text data analysis and operations management. First, we provide the state of the art of the text processing approaches, then we discuss selected use-cases from the field of operations management and how the latest methods can help to solve those problems. The last part of the chapter outlines some ideas for further improvement of the current approaches and how to effectively analyse data in a multilingual environment and decrease memory demands.

Klíčová slova

Big data; Decision making; Digital storage; Text processing; Changing environment; Competitive advantage; Cost saving; Data storage; Decisions makings; Industry sectors; Management IS; Operation management; Service operations management; Text data;, Competition

Klíčová slova v angličtině

Big data; Decision making; Digital storage; Text processing; Changing environment; Competitive advantage; Cost saving; Data storage; Decisions makings; Industry sectors; Management IS; Operation management; Service operations management; Text data;, Competition

Autoři

POVODA, L.; BURGET, R.; RAJNOHA, M.; BREZANY, P.

Rok RIV

2025

Vydáno

01.12.2022

Nakladatel

Springer Science and Business Media Deutschland GmbH

ISBN

978-3-030-87304-2

Kniha

Big Data and Blockchain for Service Operations Management: Studies in Big Data

Edice

98

Strany od

163

Strany do

189

Strany počet

27

URL

BibTex

@inbook{BUT183094,
  author="Lukáš {Povoda} and Radim {Burget} and Martin {Rajnoha} and Peter {Brezany}",
  title="Novel Big Data Approach for Text Supported Service Operations Management",
  booktitle="Big Data and Blockchain for Service Operations Management: Studies in Big Data",
  year="2022",
  publisher="Springer Science and Business Media Deutschland GmbH",
  series="98",
  pages="163--189",
  doi="10.1007/978-3-030-87304-2\{_}6",
  isbn="978-3-030-87304-2",
  url="https://books.google.cz/books?id=I7NeEAAAQBAJ&pg=PA163&lpg=PA163&dq=10.1007/978-3-030-87304-2_6&source=bl&ots=Vhot6SHok2&sig=ACfU3U0rOHVxYsT1yVAIafaxLAv1J6_WVw&hl=cs&sa=X&ved=2ahUKEwiDudej5879AhWGQ_EDHQFiC7gQ6AF6BAgIEAM#v=onepage&q=10.1007%2F978-3-030-87304-2_6&f=false"
}