数据仓库与挖掘应用：趋势与策略解析

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"数据仓库与挖掘应用领域趋势与对策探讨了数据仓库技术及数据挖掘的最新发展，并提供了相关案例分析。这本书由Pedro Nuno San-Bento Furtado撰写，旨在解析这两个领域的未来趋势和应对策略。" 在信息技术日益发展的今天，数据仓库和数据挖掘作为关键的数据管理工具，其应用领域正在不断拓展。数据仓库是一种专门设计用于支持决策制定的系统，它整合了来自多个源的数据，提供了一个统一的历史视角，以便于分析和理解业务状况。而数据挖掘则是从大量数据中发现有用信息和知识的过程，它包括分类、关联规则学习、聚类、预测等多种方法。该书可能涵盖了以下主要知识点： 1. 数据仓库设计与构建：介绍了如何设计高效、可扩展的数据仓库架构，包括星型、雪花型和网状模型等，以及ETL（提取、转换、加载）过程的重要性。 2. 数据预处理：数据清洗、缺失值处理、异常值检测和数据规范化等步骤，这些是确保数据质量的关键。 3. 数据挖掘技术：包括分类算法（如决策树、随机森林）、回归分析、聚类算法（如K-means、DBSCAN）、关联规则挖掘（Apriori、FP-Growth）等，并讨论它们在实际问题中的应用。 4. 大数据分析：随着大数据的崛起，数据仓库和挖掘技术如何适应大数据环境，例如Hadoop和Spark等分布式计算框架的应用。 5. 实时与流式数据处理：数据仓库如何处理实时数据流，以满足业务实时决策的需求，如Apache Kafka和Spark Streaming的使用。 6. 安全与隐私：在数据存储和分析过程中，如何保障数据的安全性，遵循法规如GDPR，并实施数据脱敏等隐私保护措施。 7. 应用案例分析：书中可能会展示不同行业的实际案例，如零售业的销售预测、金融行业的风险评估、医疗保健的疾病诊断等，来说明数据仓库和数据挖掘在各领域的价值。 8. 未来趋势：作者可能探讨了人工智能和机器学习对数据仓库和数据挖掘的影响，以及云存储和边缘计算等新技术如何改变这两个领域。通过深入理解和掌握这些知识点，读者能够更好地适应数据驱动的商业环境，利用数据仓库和数据挖掘技术解决复杂问题，从而推动业务增长和创新。本书对于IT专业人士、数据分析师、数据科学家以及对企业信息系统感兴趣的学者和学生都具有很高的参考价值。

tions, and discusses which architectural patterns should be used to solve those requirements. It further

describes how the concept of service-orientation may inuence future data warehouse architectures and

solutions as well.

Chapter 2, Improving Expressive Power in Modeling Data Warehouse and OLAP Applications, by

Elzbieta Malinowski, discusses how the conceptual multidimensional model can be used to facilitate

the representation of complex hierarchies and different kinds of dimensions in comparison to their

representation in a relational model and commercial OLAP tools. This chapter is in itself an excellent

reference on multidimensional modeling, representation and implementation issues.

Chapter 3, From Conventional to Multiversion Data Warehouse: Practical Issues, by Khurram

Shahzad, concerns data warehouse versioning. Versioning is quite important in real-world projects, since

operational sources or the data warehouse structure itself may evolve. Conventional data warehouses

are not prepared to handle these modications. The chapter, while not a comprehensive survey on the

subject, takes a very practical perspective, collecting and integrating concepts, issues and solutions of

multiversion data warehouses in a tutorial-like approach, to provide a unied source for users that need

to understand version functionality and mechanisms.

Chapter 4, Compression Schemes of High Dimensional Data for MOLAP, by K. M. Azharul Hasan,

surveys data compression techniques relevant to multidimensional OLAP and discusses important

quality issues of MOLAP compression and of existing techniques. Compression is indeed an important

issue faced in implementations of data warehouses and in particular for multidimensional OLAP, due

to possibly huge size and sparsity of MOLAP representations.

Section 2. Application Issues and Trends in Data Warehousing and OLAP

Chapter 5, View Management Techniques and Their Application to Data Stream Management, by Chris-

toph Quix et al., is a very interesting and insightful chapter on the subjects of view management and

data stream management, starting with a suggestion that data stream processing shares many similarities

with view management in data warehousing. The chapter provides an overview of view maintenance and

view selection methods, explains the fundamental issues of data stream management, and discusses how

view management techniques from data warehousing are related to data stream management. Finally, it

provides directions for future research in view management, data streams, and data warehousing.

Chapter 6, A Framework for Data Warehousing and Mining in Sensor Stream Application Domains,

by Nan Jiang provides insight into how data collected from sensor devices can be fed into data ware-

houses and mined. This is a relevant subject, since sensors are increasingly used in many different ap-

plications, from weather and environmental monitoring to hospital and factory operation sites, trafc

monitoring and so on. The chapter presents a general framework for domain-driven mining of sensor

stream applications, and evaluates the proposed framework with experiments on trafc management

and environmental monitoring.

Chapter 7, A Data Warehousing Approach for Genomics Data Meta-Analysis, by Martine Collard et

al., takes a very different application domain, genomics, and shows how data warehousing and mining

are relevant in that context. Since experimental micro-array data and sources of biological knowledge

are now available on public repositories, comparative analyses involving several experiments become

conceivable and hold potentially relevant knowledge. However, manually navigating and searching for

similar tendencies in such huge spaces is impracticable. In this context, the authors propose a semantic

data warehousing solution based on semantic web technologies that allows to monitoring both the di-

versity and the volume of all related data.

xvi

Chapter 8, A Multidimensional Model for Correct Aggregation of Geographic Measures, by Sandro

Bimonte et al., discusses aggregation issues in multidimensional, geostatistic, GIS and Spatial OLAP

models. The chapter provides a good review of those models and discusses why the multidimensional

aggregation of geographic objects (geographic measures) exhibits theoretical and implementation

problems. The authors then proceed to propose a solution to that problem within a GeoCube multidi-

mensional model.

Section 3. Foundations and Applications of Data Mining and Data Analysis

Chapter 9, Novel Trends in Clustering, by Claudia Plant and Christian Böhm, is a very interesting work

featuring state-of-the-art and current trends analysis on data clustering. It is useful for both researchers

and practitioners, providing an overview on emerging trends in clustering, including subspace and pro-

jected clustering, correlation clustering, semi-supervised clustering, spectral clustering and parameter-

free clustering. Requirements from concrete example applications in life sciences and the web provide

motivation for the discussion of novel approaches to clustering in this chapter.

Chapter 10, Recent Advances of Exception Mining in Stock Market, by Chao Luo et al., offers a sur-

vey of current advanced technologies for exception mining in stock markets. Additionally, it proposes

and analyses improved approaches for exception mining and discusses future research directions and

related issues.

Chapter 11, Analysis of Content Popularity in Social Bookmarking Systems, by Symeon Papadopou-

los et al., embraces a very current issue of social networks and social bookmarking systems. Modern

SBS-based applications permit their users to submit their preferred content, comment on and rate the

content of other users and establish social relations with each other. The chapter provides a unied treat-

ment of the phenomenon by studying four aspects of popularity of socially bookmarked content: (a) the

distributional properties of content consumption, (b) its evolution in time, (c) the correlation between

the semantics of online content and its popularity, and (d) the impact of online social networks on the

content consumption behavior of individuals.

Chapter 12, Using Data Mining Techniques to Probe the Role of Hydrophobic Residues in Protein

Folding and Unfolding Simulations, by Catarina Silva et. al. shows how data mining approaches, in this

case hierarchical clustering and association rules mining, is useful in molecular dynamics simulation

experiments related to the study of protein folding problem. The protein folding problem is the iden-

tication of rules that determine the acquisition of the native, functional, three-dimensional structure

of a protein from its linear sequence of amino-acids. Its importance stems from the fact that functional

properties of proteins can frequently be related to protein conformation issues, and data mining meth-

ods – hierarchical clustering and association rules – are applied on the simulation results to characterize

important aspects for analysis.

Chapter 13, A Geostatistically Based Probabilistic Risk Assessment Approach, by Claudia Cherubini,

poses the question of how to determine the levels of risk of contamination in environmental pollution

risk assessment of zones, taking into consideration a high degree of variability and uncertainty that is

inherent to the problem. The author uses an uncertainty modeling approach based on geostatistics for

determining the parameters which enter as input to the probabilistic procedure of risk assessment. Al-

though the focus of this work does not classify strictly as classical data mining but rather as statistical

analysis, the discussion and approaches used provide insight and are relevant in any kind of analysis

of spatial data.

Chapter 1

Data Warehouse Architecture:

Practices and Trends

Xuegang Huang

Danske Bank Group, Denmark

IntroductIon

Over the past decades, the concept of data ware-

housing has been spread out to everywhere in the

business world. Organizations have been practic-

ing hard on achieving successful data warehouse

architectures. Lessons have been learnt from those

who have succeeded, as well as those who have not.

Several key developments in the data warehousing

industry denote the past and present of this discipline.

Specifically, starting with very few industry vendors

in the 80s such as Teradata, many IT companies like

Microsoft, IBM and Oracles are extending their

database management systems (DBMSs) to have

sufficient support on data warehouses. The early

years’ data warehousing theory and engineering

practices have been well recorded in the publications

of Inmon (Inmon, 2005) and Kimball (Kimball &

Ross, 2002).

Academic research of data warehousing tech-

nologies started in the early 90s. The database

research community began with a focus on incor-

ABStrAct

The wide adoption of business intelligence applications has let more and more organizations to build

and maintain data warehouse systems. Concepts like “unied view of data” and “one version of the

truth” have been the main drive of creating data warehouses. The dynamics of the business world poses

the challenges of managing large volume, complex data in data warehouses while the real-time inte-

gration and master data needs are presented. This chapter summarizes the past and present patterns

of typical data warehouse architectures and describes how the concept of service-oriented architecture

inuences the future evolvement of data warehouse architecture. The discussion takes many real world

requirements in data warehouse solutions and lists considerations on how architecture patterns can

solve these requirements.

DOI: 10.4018/978-1-60566-816-1.ch001

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