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  • In this paper we would like to discuss high frequency data processing and the use of complex event platform in combination with business rules approach. For such a high volume of data, it is suitable to use complex event platform (CEP), because CEP allows for big data processing in real time. We would like to focus on improvement of decision making process under the condition of dynamical adaptation of the process on the fly. We will use pattern recognition for detecting and predicting the trends in data by mining this information from historical data. After the distinguishing patterns we will build the set of business rules according to which the process runs and we will control the process flow by defining the restrictions. We would like to use this model for building trading systems. Algorithmic trading applies complex event processing by calculating complex algorithms that indicate when to sell or buy based on real-time processing. Market data can be viewed as events. This data nee
  • In this paper we would like to discuss high frequency data processing and the use of complex event platform in combination with business rules approach. For such a high volume of data, it is suitable to use complex event platform (CEP), because CEP allows for big data processing in real time. We would like to focus on improvement of decision making process under the condition of dynamical adaptation of the process on the fly. We will use pattern recognition for detecting and predicting the trends in data by mining this information from historical data. After the distinguishing patterns we will build the set of business rules according to which the process runs and we will control the process flow by defining the restrictions. We would like to use this model for building trading systems. Algorithmic trading applies complex event processing by calculating complex algorithms that indicate when to sell or buy based on real-time processing. Market data can be viewed as events. This data nee (en)
Title
  • Design of Adaptive Business Rules Model for High Frequency Data Processing
  • Design of Adaptive Business Rules Model for High Frequency Data Processing (en)
skos:prefLabel
  • Design of Adaptive Business Rules Model for High Frequency Data Processing
  • Design of Adaptive Business Rules Model for High Frequency Data Processing (en)
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  • RIV/00216305:26230/14:PU112024!RIV15-MSM-26230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 10450
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26230/14:PU112024
http://linked.open...riv/jazykVysledku
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  • CEP, business process, business rules, adaptive rules, HFD, market data (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [8055F37F6377]
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  • SZKLARSKA PORĘBA
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  • Szklarska Poręba
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  • ISAT Monograph Series
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http://linked.open...UplatneniVysledku
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  • Kreslíková, Jitka
  • Zámečníková, Eva
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Politechnika Wrocławska
https://schema.org/isbn
  • 978-83-7493-346-9
http://localhost/t...ganizacniJednotka
  • 26230
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