Article

Sense & response service architecture (SARESA): an approach towards a real-time business intelligence solution and its use for a fraud detection application

Authors:

Tho Manh Nguyen,

Josef Schiefer,

A. Min TjoaAuthors Info & Claims

DOLAP '05: Proceedings of the 8th ACM international workshop on Data warehousing and OLAP

Pages 77 - 86

https://doi.org/10.1145/1097002.1097015

Published: 04 November 2005 Publication History

Abstract

The dynamic business environment of many organizations require massive monitoring of their processes in real-time in order to proactively respond to exceptional situations and to take advantage of time-sensitive business opportunities. The ability to sense and interpret events about a changing business environment requires an event-driven IT infrastructure for pwerforming fast and well-informed decisions and putting them into action. However, traditional Business Intelligence (BI) and Data Warehousing technologies do not directly address time sensitive monitoring and analytical requirements. We introduce an enhanced BI architecture that covers the complete process to sense, interpret, predict, automate and respond to business environments and thereby aims to decrease the reaction time needed for business decisions. We propose an event-driven IT infrastructure to operate BI applications which enable real-time analytics across corporate business processes, notifies the business of actionable recommendations or automatically triggers business operations, and effectively closing the gap between Business Intelligence systems and business processes. A scenario from the area of mobile phone fraud detection was chosen for building a prototype that illustrates the proposed approach by using current available IT technologies.

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https://doi.org/10.1080/08839514.2020.1843833
Nguyen ATuunanen TGardner LSheridan D(2020)Design principles for learning analytics information systems in higher educationEuropean Journal of Information Systems10.1080/0960085X.2020.1816144(1-28)Online publication date: 18-Oct-2020
https://doi.org/10.1080/0960085X.2020.1816144
Alirezaei EParsa SVahedi Z(2019)Predictive Analytics of Hyper-Connected Collaborative NetworkInternational Journal of Business Data Communications and Networking10.4018/IJBDCN.201901010215:1(17-33)Online publication date: Jan-2019
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Index Terms

Sense & response service architecture (SARESA): an approach towards a real-time business intelligence solution and its use for a fraud detection application
1. Information systems
  1. Data management systems
    1. Information integration
      1. Data warehouses
  2. Information systems applications
    1. Decision support systems
      1. Data warehouses

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cover image ACM Conferences

DOLAP '05: Proceedings of the 8th ACM international workshop on Data warehousing and OLAP

November 2005

122 pages

ISBN:1595931627

DOI:10.1145/1097002

General Chair:
Il-Yeol Song
Drexel University
,
Program Chair:
Juan Trujillo
University of Alicante

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 November 2005

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CIKM05

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CIKM05: Conference on Information and Knowledge Management

November 4 - 5, 2005

Bremen, Germany

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Cited By

Mari FMassini AMelatti ITronci E(2021)A Constraint Optimization–Based Sense and Response System for Interactive Business Performance ManagementApplied Artificial Intelligence10.1080/08839514.2020.1843833(1-20)Online publication date: 25-Feb-2021
https://doi.org/10.1080/08839514.2020.1843833
Nguyen ATuunanen TGardner LSheridan D(2020)Design principles for learning analytics information systems in higher educationEuropean Journal of Information Systems10.1080/0960085X.2020.1816144(1-28)Online publication date: 18-Oct-2020
https://doi.org/10.1080/0960085X.2020.1816144
Alirezaei EParsa SVahedi Z(2019)Predictive Analytics of Hyper-Connected Collaborative NetworkInternational Journal of Business Data Communications and Networking10.4018/IJBDCN.201901010215:1(17-33)Online publication date: Jan-2019
https://doi.org/10.4018/IJBDCN.2019010102
Ailamaki ALiarou ETözün PPorobic DPsaroudakis I(2017)Databases on Modern Hardware: How to Stop Underutilization and Love MulticoresSynthesis Lectures on Data Management10.2200/S00774ED1V01Y201704DTM0459:1(1-113)Online publication date: 14-Aug-2017
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Yang HAntunes PTate M(2016)Towards a unified conceptualisation of IS agility2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD.2016.7566000(269-275)Online publication date: May-2016
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Morshed SRana JMilrad M(2016)Real-Time Data Analytics: An Algorithmic PerspectiveData Mining and Big Data10.1007/978-3-319-40973-3_31(311-320)Online publication date: 14-Jun-2016
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Teixeira RAfonso FOliveira BMachado JAbelha ASantos MPortela F(2015)Decision Support in E-Government – A Pervasive Business Intelligence ApproachNew Contributions in Information Systems and Technologies10.1007/978-3-319-16528-8_15(155-166)Online publication date: 2015
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Psaroudakis IWolf FMay NNeumann TBöhm AAilamaki ASattler K(2015)Scaling Up Mixed Workloads: A Battle of Data Freshness, Flexibility, and SchedulingPerformance Characterization and Benchmarking. Traditional to Big Data10.1007/978-3-319-15350-6_7(97-112)Online publication date: 5-Feb-2015
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Nalchigar SYu E(2013)From Business Intelligence Insights to Actions: A Methodology for Closing the Sense-and-Respond Loop in the Adaptive EnterpriseThe Practice of Enterprise Modeling10.1007/978-3-642-41641-5_9(114-128)Online publication date: 2013
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