Big Data Business Rules
(2016) In LU-CS-EX 2016-43 EDA920 20161Department 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.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8894532
- author
- Stenlander, Richard LU
- supervisor
-
- Peter Exner LU
- Pierre Nugues LU
- organization
- course
- EDA920 20161
- year
- 2016
- 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}}, language = {{eng}}, note = {{Student Paper}}, series = {{LU-CS-EX 2016-43}}, title = {{Big Data Business Rules}}, year = {{2016}}, }