Architecture for Large Scale Reasoning in Business Intelligence

DOI

10.2991/iccasm.2012.395How to use a DOI?

Keywords

Business intelligence, Production System, MapReduce, Timeliness

Abstract

Business intelligence plays a crucial role in modern business. Nevertheless, present business intelligence is not in a position to provide comprehensive business advices owing to limitations on the scope of data and satisfy the indispensable timeliness for business activities. To address these problems, we propose an architecture for business intelligence which could reason on data from numerous domains and provide different users with disparate business advices and results. Furthermore, in our architecture, the production system used to reason depends on MapReduce programming model to implement production rule matching concurrently in different computers with the Rete algorithm. Adopting MapReduce programming model enables production system to obtain more impressive efficiency in rule matching, especially when it comes to a large-scale rules and facts. What’s more, we also adopt two conflict-resolving polices to decide in which sequence matched production rules are executed. In this paper, we firstly describe the architecture and then illustrate the particular implementation of this architecture.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xinlong Zhang
AU  - Bin Cao
AU  - Yanming Ye
PY  - 2012/08
DA  - 2012/08
TI  - Architecture for Large Scale Reasoning in Business Intelligence
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 1544
EP  - 1547
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.395
DO  - 10.2991/iccasm.2012.395
ID  - Zhang2012/08
ER  -