Design of Adaptive Business Rules Model for High Frequency Data Processing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F14%3APU112024" target="_blank" >RIV/00216305:26230/14:PU112024 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.fit.vutbr.cz/research/pubs/all.php?id=10669" target="_blank" >http://www.fit.vutbr.cz/research/pubs/all.php?id=10669</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Design of Adaptive Business Rules Model for High Frequency Data Processing

Popis výsledku v původním jazyce

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 needs to be analyzed in real time in order to identify the trends in data and to react to these trends automatically. Traditional approach for detecting anomalies on stock market has been statistical analysis, but a CEP-based approach is able to react faster than the traditional approach.

Název v anglickém jazyce

Design of Adaptive Business Rules Model for High Frequency Data Processing

Popis výsledku anglicky

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 needs to be analyzed in real time in order to identify the trends in data and to react to these trends automatically. Traditional approach for detecting anomalies on stock market has been statistical analysis, but a CEP-based approach is able to react faster than the traditional approach.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ISAT Monograph Series

ISBN

978-83-7493-346-9

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

Wroclaw University of Technology

Místo vydání

Szklarska Poręba

Místo konání akce

SZKLARSKA PORĘBA

Datum konání akce

21. 7. 2014

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—