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
德惠市| 大冶市| 安阳县| 临邑县| 汶川县| 平远县| 怀远县| 平谷区| 嵩明县| 榆林市| 九江市| 井研县| 繁昌县| 百色市| 双城市| 德阳市| 吉水县| 巴彦县| 双峰县| 五峰| 上思县| 葫芦岛市| 兴安县| 江孜县| 怀柔区| 全南县| 娱乐| 苏州市| 卓尼县| 马龙县| 连州市| 水富县| 成安县| 连南| 横山县| 息烽县| 静乐县| 富裕县| 鄂伦春自治旗| 喀喇沁旗| 东乡县|