Widgets vs Portlets

本文探讨了Widgets与Portlets在企业应用和社会协作工具中的不同作用。Widgets因其易用性和RESTful服务的支持而受到欢迎,但对于高交易量的应用,Portlets提供了更稳定的选择。此外,文章还讨论了门户产品如何支持Widgets并利用Portlets实现组件间的交互。

Widgets vs Portlets

by Michael Porter on June 16th, 2010

Whenever I go to a major conference or I deal with social collaboration or web 2.0 tools, I see a lot of widget options.  Here’s my take on the whole widget vs portlet question:

 

1. Widgets are great because they are easy to create and use RESTful services.

 

2. Widgets do have a problem if you are trying to create a more enterprise type portlet. If it will have a lot of transactions and you want to bullet proof it, you will probably want to create a portlet.

 

3. Portlets work best when you have interaction between portlets and pages. That’s not to say that widgets don’t pass data parameters around but portal and JSr 286 have matured enough to make it much easier to user.

 

4. Widgets aren’t going away anytime soon.  IBM has widget based solution in their mashup server.  Oracle has the Ensemble product which should still be in the Fusion stack.

 

5. Portal vendors are supporting mashups in their products.  IBM has support for widgets via a portlet. Liferay has a decent blog post on the topic as well.  The GateIn Portal (formerly JBoss Portal) also provides gadget support.

 

Also, I found a good blog post on a deeper view of widgets vs portlets at the WebSphere Portal Blog.

 

I’m probably going to change my mind at some point but right now I don’t see widgets or portlets going away soon.  Portal vendors are going to support the whole widget approach as it matures.  Portal developers are going to continue to develop portlets and use existing portlets already developed.  Widgets become just another option to use on your portal or a way to even push portal functionality out of your portal.

内容概要:本文围绕EKF SLAM(扩展卡尔曼滤波同步定位与地图构建)的性能展开多项对比实验研究,重点分析在稀疏与稠密landmark环境下、预测与更新步骤同时进行与非同时进行的情况下的系统性能差异,并进一步探讨EKF SLAM在有色噪声干扰下的鲁棒性表现。实验考虑了不确定性因素的影响,旨在评估不同条件下算法的定位精度与地图构建质量,为实际应用中EKF SLAM的优化提供依据。文档还提及多智能体系统在遭受DoS攻击下的弹性控制研究,但核心内容聚焦于SLAM算法的性能测试与分析。; 适合人群:具备一定机器人学、状态估计或自动驾驶基础知识的科研人员及工程技术人员,尤其是从事SLAM算法研究或应用开发的硕士、博士研究生和相关领域研发人员。; 使用场景及目标:①用于比较EKF SLAM在不同landmark密度下的性能表现;②分析预测与更新机制同步与否对滤波器稳定性与精度的影响;③评估系统在有色噪声等非理想观测条件下的适应能力,提升实际部署中的可靠性。; 阅读建议:建议结合MATLAB仿真代码进行实验复现,重点关注状态协方差传播、观测更新频率与噪声模型设置等关键环节,深入理解EKF SLAM在复杂环境下的行为特性。稀疏 landmark 与稠密 landmark 下 EKF SLAM 性能对比实验,预测更新同时进行与非同时进行对比 EKF SLAM 性能对比实验,EKF SLAM 在有色噪声下性能实验
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