Design And Implementation Of Data Analysis Components

"This thesis describes the design and implementation of the data analysis components. Many features of modern database systems facilitate the decision-making process. Recently, Online Analytical Processing (OLAP) and data mining are increasingly being used in a wide range of applications. OLAP allows users to analyze data from a wide variety of viewpoints. Data mining is the process of selecting, exploring, and modeling large amounts of data to discover previously unknown patterns for business advantage. Microsoft SQL server 2000 Analysis Services provides a rich set of tools to create and to maintain OLAP and data mining objects. In order to use these tools, users need to fully understand the underlying architectures and the specialized technological terms, which are not related to the data analysis. The complexities in the development challenges prevent the data analysts to use these tools effectively. In this work, we developed several components, which can be used as the foundation in the analytical applications. Using these components in the software applications can hide the technical complexities and can provide tools to build the OLAP and mining model and to access data information from these model systems. Developers can also reuse these components without coding from scratch. The reusability of these components enhances the application's reliability and reduces the development costs and time."--abstract.

Presents strategies for application development, interface design, and enabling Web-based access. Includes numerous case studies and examples from the private and public sectors. Provides information on integrating legacy MIS systems and planning for future developments in database design.

In any software design project, the analysis of stage documenting and designing of technical requirements for the needs of users is vital to the success of the project. This book provides a thorough introduction and survey on all aspects of analysis, including design of E-commerce systems, and how it fits into the software engineering process. The material is based on successful professional courses offered at Columbia University to a diverse audience of advanced students and professionals. An emphasis is placed on the stages of analysis and the presentation of many alternative modeling tools that an analyst can utilise. Particular attention is paid to interviews, modeling tools, and approaches used in building effective web-based E-commerce systems.

With this textbook, Vaisman and Zimányi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes “Fundamental Concepts” including multi-dimensional models; conceptual and logical data warehouse design and MDX and SQL/OLAP. Subsequently, Part II details “Implementation and Deployment,” which includes physical data warehouse design; data extraction, transformation, and loading (ETL) and data analytics. Lastly, Part III covers “Advanced Topics” such as spatial data warehouses; trajectory data warehouses; semantic technologies in data warehouses and novel technologies like Map Reduce, column-store databases and in-memory databases. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Pentaho Business Analytics. All chapters are summarized using review questions and exercises to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available at http://cs.ulb.ac.be/DWSDIbook/, including electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style.

An interactive guide to the statistical tools used to solve problems during product and process innovation End to End Data Analytics for Product Development is an accessible guide designed for practitioners in the industrial field. It offers an introduction to data analytics and the design of experiments (DoE) whilst covering the basic statistical concepts useful to an understanding of DoE. The text supports product innovation and development across a range of consumer goods and pharmaceutical organizations in order to improve the quality and speed of implementation through data analytics, statistical design and data prediction. The book reviews information on feasibility screening, formulation and packaging development, sensory tests, and more. The authors – noted experts in the field – explore relevant techniques for data analytics and present the guidelines for data interpretation. In addition, the book contains information on process development and product validation that can be optimized through data understanding, analysis and validation. The authors present an accessible, hands-on approach that uses MINITAB and JMP software. The book: • Presents a guide to innovation feasibility and formulation and process development • Contains the statistical tools used to solve challenges faced during product innovation and feasibility • Offers information on stability studies which are common especially in chemical or pharmaceutical fields • Includes a companion website which contains videos summarizing main concepts Written for undergraduate students and practitioners in industry, End to End Data Analytics for Product Development offers resources for the planning, conducting, analyzing and interpreting of controlled tests in order to develop effective products and processes.

Most of modern enterprises, institutions, and organizations rely on knowledge-based management systems. In these systems, knowledge is gained from data analysis. Today, knowledge-based management systems include data warehouses as their core components. Data integrated in a data warehouse are analyzed by the so-called On-Line Analytical Processing (OLAP) applications designed to discover trends, patterns of behavior, and anomalies as well as finding dependencies between data. Massive amounts of integrated data and the complexity of integrated data coming from many different sources make data integration and processing challenging. New Trends in Data Warehousing and Data Analysis brings together the most recent research and practical achievements in the DW and OLAP technologies. It provides an up-to-date bibliography of published works and the resource of research achievements. Finally, the book assists in the dissemination of knowledge in the field of advanced DW and OLAP.

Stream processing is a novel distributed computing paradigm that supports the gathering, processing and analysis of high-volume, heterogeneous, continuous data streams, to extract insights and actionable results in real time. This comprehensive, hands-on guide combining the fundamental building blocks and emerging research in stream processing is ideal for application designers, system builders, analytic developers, as well as students and researchers in the field. This book introduces the key components of the stream computing paradigm, including the distributed system infrastructure, the programming model, design patterns and streaming analytics. The explanation of the underlying theoretical principles, illustrative examples and implementations using the IBM InfoSphere Streams SPL language and real-world case studies provide students and practitioners with a comprehensive understanding of such applications and the middleware that supports them.