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Big Data Business Rules

Stenlander, Richard LU (2016) In LU-CS-EX 2016-43 EDA920 20161
Department of Computer Science
Abstract
Business Rules Engines are systems created to deliver relevant business information to applications and business processes.
Given that rule engines are meant to be heavily queried, efficient rule search is essential.
However, since the advent of Big Data, it has become increasingly difficult to maintain the rule engine performance - more data often leads to more rules.
In this thesis we prototype a rule engine in the Big Data framework Apache Spark, opening up the world of business rules for Big Data applications.
Furthermore, a performance study is conducted, benchmarking the performance and scalability of the prototype against the Amadeus Business Rules engine, ABR.
The measurements show that in terms for execution time, Amadeus... (More)
Business Rules Engines are systems created to deliver relevant business information to applications and business processes.
Given that rule engines are meant to be heavily queried, efficient rule search is essential.
However, since the advent of Big Data, it has become increasingly difficult to maintain the rule engine performance - more data often leads to more rules.
In this thesis we prototype a rule engine in the Big Data framework Apache Spark, opening up the world of business rules for Big Data applications.
Furthermore, a performance study is conducted, benchmarking the performance and scalability of the prototype against the Amadeus Business Rules engine, ABR.
The measurements show that in terms for execution time, Amadeus could benefit from doing the rule search in parallel for already existing rule sets. (Less)
Popular Abstract (Swedish)
Business Rules är regler som definierar en del av en affärsverksamhet.
I stora IT-system är det viktigt att snabbt kunna hitta den regel som skall appliceras, en uppgift som blir allt mer tidskrävande när antalet regler ökar.
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author
Stenlander, Richard LU
supervisor
organization
course
EDA920 20161
year
type
H3 - Professional qualifications (4 Years - )
subject
keywords
Amadeus, Big Data, Business Rules, MapReduce, Spark
publication/series
LU-CS-EX 2016-43
report number
LU-CS-EX 2016-43
ISSN
1650-2884
language
English
id
8894532
date added to LUP
2016-11-03 09:32:00
date last changed
2016-11-03 09:32:00
@misc{8894532,
  abstract     = {Business Rules Engines are systems created to deliver relevant business information to applications and business processes.
Given that rule engines are meant to be heavily queried, efficient rule search is essential.
However, since the advent of Big Data, it has become increasingly difficult to maintain the rule engine performance - more data often leads to more rules. 
In this thesis we prototype a rule engine in the Big Data framework Apache Spark, opening up the world of business rules for Big Data applications. 
Furthermore, a performance study is conducted, benchmarking the performance and scalability of the prototype against the Amadeus Business Rules engine, ABR.
The measurements show that in terms for execution time, Amadeus could benefit from doing the rule search in parallel for already existing rule sets.},
  author       = {Stenlander, Richard},
  issn         = {1650-2884},
  keyword      = {Amadeus,Big Data,Business Rules,MapReduce,Spark},
  language     = {eng},
  note         = {Student Paper},
  series       = {LU-CS-EX 2016-43},
  title        = {Big Data Business Rules},
  year         = {2016},
}