找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Next-Generation Business Intelligence Software with Silverlight 3; Bart Czernicki Book 2010 Bart Czernicki 2010 Internet.computer.performa

[復(fù)制鏈接]
樓主: Causalgia
11#
發(fā)表于 2025-3-23 10:06:01 | 只看該作者
12#
發(fā)表于 2025-3-23 14:02:31 | 只看該作者
Enhancing Visual Intelligence in Silverlight,esentation from a charting perspective. In this chapter, you will learn how visual intelligence can be enhanced using unique characteristics of Silverlight technology to visualize almost any type of analytical data assets for different environments. This chapter will incorporate the knowledge in the
13#
發(fā)表于 2025-3-23 21:28:11 | 只看該作者
Integrating with Business Intelligence Systems, compelling argument that Silverlight can successfully present BI 2.0 content. It is time to cover how to design Silverlight applications so they can be successfully integrated and deployed across BI systems. In this chapter, you will learn what enterprise components are required to be able to deplo
14#
發(fā)表于 2025-3-23 23:39:14 | 只看該作者
15#
發(fā)表于 2025-3-24 03:43:22 | 只看該作者
Silverlight As a Business Intelligence Client,This chapter introduces Silverlight as a potential world-class BI client. In the first two chapters, you learned about BI 2.0 concepts and Silverlight RIA technology. It is time to see how the combination of BI 2.0 and Silverlight can form very powerful applications.
16#
發(fā)表于 2025-3-24 07:18:29 | 只看該作者
17#
發(fā)表于 2025-3-24 11:40:03 | 只看該作者
Introduction to Data Visualizations,This chapter is the first chapter in a three-part series about data visualizations.
18#
發(fā)表于 2025-3-24 18:43:10 | 只看該作者
19#
發(fā)表于 2025-3-24 22:07:41 | 只看該作者
20#
發(fā)表于 2025-3-25 02:27:59 | 只看該作者
Predictive Analytics (What-If Modeling),This chapter covers creating and applying BI models that are forward looking. In the past chapters, we focused on BI concepts that applied to past or current data. However, BI 2.0 applications can extend the functionality of that data by injecting analytical models that can leverage historical data in order to predict future outcomes.
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-16 16:09
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
西畴县| 牟定县| 鱼台县| 阳山县| 五河县| 玛沁县| 陇西县| 长武县| 兴文县| 余姚市| 衡阳市| 上虞市| 达州市| 孟村| 鸡东县| 五华县| 利津县| 鄂托克旗| 天柱县| 芦山县| 旬阳县| 扎兰屯市| 阳朔县| 兰考县| 大名县| 洪雅县| 新干县| 疏附县| 淮南市| 米易县| 庐江县| 磴口县| 冷水江市| 锦州市| 宁明县| 景洪市| 紫阳县| 和田县| 辽宁省| 晴隆县| 清苑县|