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Semantic Web Technologies for Business Intelligence

Copyright © 2012. 30 pages.
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DOI: 10.4018/978-1-61350-038-5.ch014|
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MLA

Berlanga, Rafael, Oscar Romero, Alkis Simitsis, Victoria Nebot, Torben Bach Pedersen, Alberto Abelló and María José Aramburu. "Semantic Web Technologies for Business Intelligence." Business Intelligence Applications and the Web: Models, Systems and Technologies. IGI Global, 2012. 310-339. Web. 23 Nov. 2014. doi:10.4018/978-1-61350-038-5.ch014

APA

Berlanga, R., Romero, O., Simitsis, A., Nebot, V., Pedersen, T. B., Abelló, A., & Aramburu, M. J. (2012). Semantic Web Technologies for Business Intelligence. In M. Zorrilla, J. Mazón, Ó. Ferrández, I. Garrigós, F. Daniel, & J. Trujillo (Eds.) Business Intelligence Applications and the Web: Models, Systems and Technologies (pp. 310-339). Hershey, PA: Business Science Reference. doi:10.4018/978-1-61350-038-5.ch014

Chicago

Berlanga, Rafael, Oscar Romero, Alkis Simitsis, Victoria Nebot, Torben Bach Pedersen, Alberto Abelló and María José Aramburu. "Semantic Web Technologies for Business Intelligence." In Business Intelligence Applications and the Web: Models, Systems and Technologies, ed. Marta E. Zorrilla, Jose-Norberto Mazón, Óscar Ferrández, Irene Garrigós, Florian Daniel and Juan Trujillo, 310-339 (2012), accessed November 23, 2014. doi:10.4018/978-1-61350-038-5.ch014

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Abstract

This chapter describes the convergence of two of the most influential technologies in the last decade, namely business intelligence (BI) and the Semantic Web (SW). Business intelligence is used by almost any enterprise to derive important business-critical knowledge from both internal and (increasingly) external data. When using external data, most often found on the Web, the most important issue is knowing the precise semantics of the data. Without this, the results cannot be trusted. Here, Semantic Web technologies come to the rescue, as they allow semantics ranging from very simple to very complex to be specified for any web-available resource. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The chapter first presents a motivating running example, followed by an introduction to the relevant SW foundation concepts. The chapter then goes on to survey the use of SW technologies for data integration, including semantic data annotation and semantics-aware extract, transform, and load processes (ETL). Next, the chapter describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms, and the use of advanced SW reasoning functionality on MD models. Finally, the chapter describes in detail a number of directions for future research, including SW support for intelligent BI querying, using SW technologies for providing context to data warehouses, and scalability issues. The overall conclusion is that SW technologies are very relevant for the future of BI, but that several new developments are needed to reach the full potential.
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Motivating Scenario And Running Example

BI technology is aimed at gathering, transforming and summarizing available data from existing sources to generate analytical information suitable for decision making tasks. A typical BI scenario can be roughly structured into three layers:

  • the data sources layer, which regards all the potential data of any nature (e.g., relational, object-oriented, semi-structured, and textual) that can help to fulfill the analysis goals,

  • the integration layer, which is in charge of normalizing and cleansing the data gathered from the sources, as well as of storing it in an appropriate format for the subsequent analysis, and

  • the analysis layer, which contains a series of tools for generating the information from the normalized data so that it will be presented to analysts.

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Complete Chapter List

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Table of Contents
Foreword
Panos Vassiliadis
Chapter 1
Adriana Marotta, Laura González, Lorena Etcheverry, Bruno Rienzi, Raúl Ruggia, Flavia Serra, Elena Martirena
Web Warehouses (WW) are data warehouses that consolidate data from the Web. The process of building them presents several challenges, most of them... Sample PDF
Quality Management in Web Warehouses
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Chapter 2
Fadila Bentayeb, Nora Maïz, Hadj Mahboubi, Cécile Favre, Sabine Loudcher, Nouria Harbi, Omar Boussaïd, Jérôme Darmont
Research in data warehousing and OLAP has produced important technologies for the design, management, and use of Information Systems for decision... Sample PDF
Innovative Approaches for Efficiently Warehousing Complex Data from the Web
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Chapter 3
Ramón A. Carrasco, Miguel J. Hornos, Pedro Villar, María A. Aguilar
In this chapter, we address the problem of integrating semantically heterogeneous data (including data expressed in natural language), which are... Sample PDF
An Extraction, Transformation, and Loading Tool Applied to a Fuzzy Data Mining System
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Chapter 4
Byung-Kwon Park, Il-Yeol Song
As the amount of data grows very fast inside and outside of an enterprise, it is getting important to seamlessly analyze both data types for total... Sample PDF
Incorporating Text OLAP in Business Intelligence
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Chapter 5
Flavius Frasincar, Wouter IJntema, Frank Goossen, Frederik Hogenboom
News items play an increasingly important role in the current business decision processes. Due to the large amount of news published every day it is... Sample PDF
A Semantic Approach for News Recommendation
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Chapter 6
Vincenzo Pallotta, Lammert Vrieling, Rodolfo Delmonte
In this chapter we present the major challenges of a new trend in business analytics, namely Interaction Mining. With the proliferation of... Sample PDF
Interaction Mining: Making Business Sense of Customers Conversations through Semantic and Pragmatic Analysis
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Chapter 7
Alexandra Balahur, Ester Boldrini, Andrés Montoyo, Patricio Martínez-Barco
The past years have marked the birth and development of the Social Web, where people freely express and search for opinions on all possible topics.... Sample PDF
OpAL: A System for Mining Opinion from Text for Business Applications
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Chapter 8
Andreas Henschel, Erik Casagrande, Wei Lee Woon, Isam Janajreh, Stuart Madnick
For decision makers and researchers working in a technical domain, understanding the state of their area of interest is of the highest importance.... Sample PDF
A Unified Approach for Taxonomy-Based Technology Forecasting
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Chapter 9
Moez Essaidi, Aomar Osmani
In recent years, the data warehousing infrastructures have undergone many changes in various aspects. This is usually due to many factors: the... Sample PDF
Business Intelligence-as-a-Service: Studying the Functional and the Technical Architectures
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Chapter 10
Marta E. Zorrilla, Diego García
In this chapter we present a BI application delivered as a service on-demand. In particular, it is a data mining service that aims to help... Sample PDF
A Data Mining Service to Assist Instructors Involved in Virtual Education
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Chapter 11
Matteo Golfarelli, Federica Mandreoli, Wilma Penzo, Stefano Rizzi, Elisa Turricchia
Cooperation is seen by companies as one of the major means for increasing flexibility and innovating. Business intelligence (BI) platforms are aimed... Sample PDF
BIN: Business Intelligence Networks
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Chapter 12
Henrike Berthold, Philipp Rösch, Stefan Zöller, Felix Wortmann, Alessio Carenini, Stuart Campbell
The success of organizations and business networks depends on fast and well-founded decisions taken by the relevant people in their specific area of... Sample PDF
Towards Ad-Hoc and Collaborative Business Intelligence
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Chapter 13
Maik Thiele, Wolfgang Lehner
In the past, data-warehouse systems served as information providers for key management members and knowledge workers; today, they are the central... Sample PDF
Real-Time BI and Situational Analysis
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Chapter 14
Rafael Berlanga, Oscar Romero, Alkis Simitsis, Victoria Nebot, Torben Bach Pedersen, Alberto Abelló, María José Aramburu
This chapter describes the convergence of two of the most influential technologies in the last decade, namely business intelligence (BI) and the... Sample PDF
Semantic Web Technologies for Business Intelligence
$30.00
List Price: $37.50