商品描述
In the 1980s, traditional Business Intelligence (BI) systems focused on the delivery of reports that describe the state of business activities in the past, such as for questions like "How did our sales perform during the last quarter?" A decade later, there was a shift to more interactive content that presented how the business was performing at the present time, answering questions like "How are we doing right now?" Today the focus of BI users are looking into the future. "Given what I did before and how I am currently doing this quarter, how will I do next quarter?" Furthermore, fuelled by the demands of Big Data, BI systems are going through a time of incredible change. Predictive analytics, high volume data, unstructured data, social data, mobile, consumable analytics, and data visualization are all examples of demands and capabilities that have become critical within just the past few years, and are growing at an unprecedented pace. This book introduces research problems and solutions on various aspects central to next-generation BI systems. It begins with a chapter on an industry perspective on how BI has evolved, and discusses how game-changing trends have drastically reshaped the landscape of BI. One of the game changers is the shift toward the consumerization of BI tools. As a result, for BI tools to be successfully used by business users (rather than IT departments), the tools need a business model, rather than a data model. One chapter of the book surveys four different types of business modeling. However, even with the existence of a business model for users to express queries, the data that can meet the needs are still captured within a data model. The next chapter on vivification addresses the problem of closing the gap, which is often significant, between the business and the data models. Moreover, Big Data forces BI systems to integrate and consolidate multiple, and often wildly different, data sources. One chapter gives an overview of several integration architectures for dealing with the challenges that need to be overcome. While the book so far focuses on the usual structured relational data, the remaining chapters turn to unstructured data, an ever-increasing and important component of Big Data. One chapter on information extraction describes methods for dealing with the extraction of relations from free text and the web. Finally, BI users need tools to visualize and interpret new and complex types of information in a way that is compelling, intuitive, but accurate. The last chapter gives an overview of information visualization for decision support and text. Table of Contents: Introduction and the Changing Landscape of Business Intelligence / BI Game Changers: an Industry Viewpoint / Business Modeling for BI / Vivification in BI / Information Integration in BI / Information Extraction for BI / Information Visualization for BI
商品描述(中文翻譯)
在1980年代，傳統的商業智能（BI）系統專注於提供描述過去業務活動狀態的報告，例如回答問題「上個季度的銷售表現如何？」十年後，轉向更具互動性的內容，展示業務當前的表現，回答問題「我們現在的狀況如何？」如今，BI使用者的焦點轉向未來。「根據我之前的做法和本季的表現，下個季度我會怎麼樣？」此外，受到大數據需求的推動，BI系統正經歷著一個令人難以置信的變革時期。預測分析、大量數據、非結構化數據、社交數據、移動、可消費的分析和數據可視化都是過去幾年內變得至關重要且以前所未有的速度增長的需求和能力的例子。本書介紹了與下一代BI系統相關的各個方面的研究問題和解決方案。它以一章節從行業的角度介紹了BI的演變，並討論了改變遊戲規則的趨勢如何徹底重塑了BI的格局。其中一個改變遊戲規則的因素是轉向BI工具的消費者化。因此，為了成功地被業務用戶（而不是IT部門）使用，這些工具需要一個商業模型，而不是數據模型。本書的一章節調查了四種不同類型的商業建模。然而，即使存在用於表達查詢的商業模型，滿足需求的數據仍然是在數據模型中捕獲的。下一章節關於活化解決了商業模型和數據模型之間的差距問題，這差距通常很大。此外，大數據迫使BI系統整合和合併多個且通常差異很大的數據來源。一章節概述了幾種應對這些挑戰的整合架構。雖然本書到目前為止專注於通常的結構化關聯數據，但其餘章節轉向非結構化數據，這是大數據的一個日益增長且重要的組成部分。一章節關於信息提取描述了處理從自由文本和網絡中提取關係的方法。最後，BI使用者需要工具以引人入勝、直觀但準確地視覺化和解釋新的和複雜的信息。最後一章節概述了用於決策支持和文本的信息可視化。目錄：引言和商業智能的變化風景/BI改變遊戲規則：行業觀點/商業建模用於BI/BI中的活化/BI中的信息整合/BI中的信息提取/BI中的信息可視化
