Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben

2012 | Buch

Data Warehouses: Next Challenges Kapitel lesen Erstes Kapitel lesen

Business Intelligence

First European Summer School, eBISS 2011, Paris, France, July 3-8, 2011, Tutorial Lectures

herausgegeben von: Marie-Aude Aufaure, Esteban Zimányi

Verlag: Springer Berlin Heidelberg

Buchreihe : Lecture Notes in Business Information Processing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Einloggen, um Zugang zu erhalten
insite
SUCHEN

Über dieses Buch

Business Intelligence (BI) promises an organization the capability of collecting and analyzing internal and external data to generate knowledge and value, providing decision support at the strategic, tactical, and operational levels. Business Intelligence is now impacted by the Big Data phenomena and the evolution of society and users, and needs to take into account high-level semantics, reasoning about unstructured and structured data, and to provide a simplified access and better understanding of diverse BI tools accessible trough mobile devices. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications.

The lectures held at the First European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI technologies like data warehouses, OLAP query processing, or performance issues, but extend into new aspects that are important in this new environment and for novel applications, e.g., semantic technologies, social network analysis and graphs, services, large-scale management, or collaborative decision making.

Combining papers by leading researchers in the field, this volume will equip the reader with the state-of-the-art background necessary for inventing the future of BI. It will also provide the reader with an excellent basis and many pointers for further research in this growing field.

Inhaltsverzeichnis

Frontmatter
Data Warehouses: Next Challenges
Summary
Data Warehouses are a fundamental component of today’s Business Intelligence infrastructure. They allow to consolidate heterogeneous data from distributed data stores and transform it into strategic indicators for decision making. In this tutorial we give an overview of current state of the art and point out to next challenges in the area. In particular, this includes to cope with more complex data, both in structure and semantics, and keeping up with the demands of new application domains such as Web, financial, manufacturing, genomic, biological, life science, multimedia, spatial, and spatiotemporal applications. We review consolidated resaerch in spatio-temporal databases, and open research fields, like real-time Business Intelligence and Semantic Web Data Warehousing and OLAP.
Alejandro Vaisman, Esteban Zimányi
Data Warehouse Performance: Selected Techniques and Data Structures
Summary
Data stored in a data warehouse (DW) are retrieved and analyzed by complex analytical applications, often expressed by means of star queries. Such queries often scan huge volumes of data and are computationally complex. For this reason, an acceptable (or good) DW performance is one of the important features that must be guaranteed for DW users. Good DW performance can be achieved in multiple components of a DW architecture, starting from hardware (e.g., parallel processing on multiple nodes, fast disks, huge main memory, fast multi-core processor), through physical storage schemes (e.g., row storage, column storage, multidimensional store, data and index compression algorithms), state of the art techniques of query optimization (e.g., cost models and size estimation techniques, parallel query optimization and execution, join algorithms), and additional data structures improving data searching efficiency (e.g., indexes, materialized views, clusters, partitions). In this chapter we aim at presenting only a narrow aspect of the aforementioned technologies. We discuss three types of data structures, namely indexes (bitmap, join, and bitmap join), materialized views, and partitioned tables. We show how they are being applied in the process of executing star queries in three commercial database/data warehouse management systems, i.e., Oracle, DB2, and SQL Server.
Robert Wrembel
OLAP Query Personalisation and Recommendation: An Introduction
Summary
The aim of this lecture is to present how popular user-centric techniques, namely personalisation and recommendation, can be adapted to an OLAP context. The presentation begins with an overview of query personalisation and query recommendation in relational databases. Then it introduces the approaches proposed for personalising OLAP queries with user preferences, and the approaches proposed for recommending OLAP queries. All the approaches are characterized in terms of formulation effort, prescriptiveness, proactiveness, expressiveness, and in terms of the data leveraged: the current state of the database, its history, or external information.
Patrick Marcel
The GoOLAP Fact Retrieval Framework
Summary
We discuss the novel problem of supporting analytical business intelligence queries over web-based textual content, e.g., BI-style reports based on 100.000’s of documents from an ad-hoc web search result. Neither conventional search engines nor conventional Business Intelligence and ETL tools address this problem, which lies at the intersection of their capabilities. Three recent developments have the potential to become key components of such an ad-hoc analysis platform: significant improvements in cloud computing query languages, advances in self-supervised keyword generation techniques and powerful fact extraction frameworks. We will give an informative and practical look at the underlying research challenges in supporting ”Web-Scale Business Analytics” applications that we met when building GoOLAP, a system that already enjoys a broad user base and over 6 million objects and facts.
Alexander Löser, Sebastian Arnold, Tillmann Fiehn
Business Intelligence 2.0: A General Overview
Summary
Business Intelligence (BI) solutions allow decision makers to query, understand, and analyze business data in order to make better decisions. However, as the technology and society evolve, faster and better informed decisions are required. Nowadays, it is not enough to use only the information from the own organization and making isolated decisions, but rather requiring also to include information present in the web like opinions or information about competitors, while using collective intelligence, collaborating through social networks, and supporting the BI system with cloud computing. In response to this situation, a vision of a new generation of BI, BI 2.0, based on the evolution of the web and the emerging technologies, arises. However, researchers differ in their vision of this BI evolution. In this paper, we provide an overview of the aspects proposed to be included in BI 2.0. We describe which success factors and technologies have motivated each aspect. Finally, we review how tool developers are including these new features in the next generation of BI solutions.
Juan Trujillo, Alejandro Maté
Graph Mining and Communities Detection
Summary
The incredible rising of on-line social networks gives a new and very strong interest to the set of techniques developed since several decades to mining graphs and social networks. In particular, community detection methods can bring very valuable informations about the structure of an existing social network in the Business Intelligence framework. In this chapter we give a large view, firstly of what could be a community in a social network, and then we list the most popular techniques to detect such communities. Some of these techniques were particularly developed in the SNA context, while other are adaptations of classical clustering techniques. We have sorted them in following an increasing complexity order, because with very big graphs the complexity can be decisive for the choice of an algorithm.
Etienne Cuvelier, Marie-Aude Aufaure
Semantic Technologies and Triplestores for Business Intelligence
Summary
The Semantic Web is the next generation Web of data, which extends the current Web with means to provide well-defined meaning of information and easily find, integrate and analyze relevant information from different sources. Semantic Databases, or triplestores, play a very important role in the realization of the Semantic Web vision, since they provide the means to integrate, store and query the vast amounts of metadata generated on the Semantic Web every day. This paper presents a brief overview of the Semantic Web goals and related standards, with a special focus on the advantages, design and performance factors for triplestores. Finally, the paper provides an overview of Business Intelligence related scenarios where Semantic Technologies and triplestores in particular provide valuable advantage and differentiation.
Marin Dimitrov
Service-Oriented Business Intelligence
Summary
The traditional way to manage Information Technologies (IT) in the companies is having a data center, and licensing monolithic applications based on the number of CPUs, allowed connections, etc. This also holds for Business Intelligence environments. Nevertheless, technologies have evolved and today other approaches are possible. Specifically, the service paradigm allows to outsource hardware as well as software in a pay-as-you-go model. In this work, we will introduce the concepts related to this paradigm and analyze how they affect Business Intelligence (BI). We will analyze the specificity of services and present specific techniques to engineering service systems (e.g., Cloud Computing, Service-Oriented Architectures -SOA- and Business Process Modeling -BPM-). Then, we will also analyze to which extent it is possible to consider Business Intelligence just a service and use these same techniques on it. Finally, we store the other way round. Since service companies represent around 70% of the Gross Domestic Product (GDP) in the world, special attention must be paid to their characteristics and how to adapt BI techniques to enhance services.
Alberto Abelló, Oscar Romero
Collaborative Business Intelligence
Summary
The idea of collaborative BI is to extend the decision-making process beyond the company boundaries thanks to cooperation and data sharing with other companies and organizations. Unfortunately, traditional BI applications are aimed at serving individual companies, and they cannot operate over networks of companies characterized by an organizational, lexical, and semantic heterogeneity. In such distributed business scenarios, to maximize the effectiveness of monitoring and decision making processes there is a need for innovative approaches and architectures. Data warehouse integration is an enabling technique for collaborative BI, and has been investigated along three main directions: warehousing approaches, where the integrated data are physically materialized, federative approaches, where the integration is virtual and based on a global schema, and peer-to-peer approaches, that do not rely on a global schema to integrate the component data warehouses. In this paper we explore and compare these three directions by surveying the available work in the literature. Then we outline a new peer-to-peer framework, called Business Intelligence Network, where peers expose querying functionalities aimed at sharing business information for the decision-making process. The main features of this framework are decentralization, scalability, and full autonomy of peers.
Stefano Rizzi
Backmatter
Metadaten
Titel
Business Intelligence
herausgegeben von
Marie-Aude Aufaure
Esteban Zimányi
Copyright-Jahr
2012
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-642-27358-2
Print ISBN
978-3-642-27357-5
DOI
https://doi.org/10.1007/978-3-642-27358-2

Premium Partner

    Bildnachweise