MAPI - Messaging Application Programming Interface

MAPI:微软消息架构与API详解
MAPI(Messaging Application Programming Interface)是微软为Windows系统设计的消息传递架构及基于Component Object Model的API。它允许客户端程序通过调用MAPI子系统函数与特定消息服务器进行交互,实现基本邮件功能。MAPI支持标准邮件协议,如MAPI/RPC,并与Microsoft Outlook等应用集成。

(From Wikipedia)

In simple: MAPI is a COM-based API for MS windows for message transferring. It is implemented by using RPC.

Messaging Application Programming Interface (MAPI) is a messaging architecture and a Component Object Model based API for Microsoft Windows. MAPI allows client programmes to become (e-mail) messaging-enabled, -aware, or -based by calling MAPI subsystem routines that interface with certain messaging servers. While MAPI is designed to be independent of the protocol, it is usually used with MAPI/RPC, the proprietary protocol that Microsoft Outlook uses to communicate with Microsoft Exchange.

Simple MAPI is a subset of 12 functions which enable developers to add basic messaging functionality. Extended MAPI allows complete control over the messaging system on the client computer, creation and management of messages, management of the client mailbox, service providers, and so forth. Simple MAPI ships with Microsoft Windows as part of Outlook Express/Windows Mail while the full Extended MAPI ships with Office Outlook and Exchange.

In addition to the Extended MAPI client interface, programming calls can be made indirectly through the Simple MAPI API client interface, through the Common Messaging Calls (CMC) API client interface, or by the object-based CDO Library interface. These three methods are easier to use and designed for less complex messaging-enabled and -aware applications. (Simple MAPI and CMC were removed from Exchange 2003.)

MAPI was originally designed by Microsoft. The company founded its MS Mail team in 1987, but it was not until it acquired Consumers Software in 1991 to obtain Network Courier that it had a messaging product. Reworked, it was sold as MS PC Mail (or Microsoft Mail for PC Networking). The basic API to MS PC Mail was known as MAPI version 0 (or MAPI0). MAPI uses functions loosely based on the X.400 XAPIA standard.

MAPI includes facilities to access message transports, message stores, and directories.

Service provider interface

The full Extended MAPI interface is required for interfacing messaging-based services to client applications such as Outlook. For example, several non-Microsoft e-mail server product vendors created "MAPI service providers" to allow their products to be accessed via Outlook. Notable examples include Axigen Mail Server, Kerio Connect, Scalix, Zimbra, HP OpenMail, IBM Lotus Notes, Zarafa, and Bynari.

MAPI also had a service provider interface of sorts. Microsoft used this to interface MS Mail to an email system based on Xenix, for internal use.

Extended MAPI is the main e-mail data access method used by Outlook, to interface to Microsoft Exchange, via MAPI service providers shipped with Outlook

MAPI/RPC protocol details

Microsoft has released full details of the MAPI/RPC protocol.[1]

"MAPI protocol" is a colloquial name for the MAPI/RPC. At times, Microsoft has also called it "Exchange RPC" and "Outlook-Exchange Transport Protocol".

Open Source MAPI implementations

Up until recently Open Source implementations of MAPI have been scarce. But there are at least three open source projects working on implementing the MAPI protocol in free open source software (FOSS) libraries for use in other open source applications. This list includes the OpenMapi project,[2] the Zarafa's MAPI4Linux (also part of OpenMapi) and the libmapi[3] subproject of the OpenChange[4] project which is utilized in another OpenChange subproject called Evolution-MAPI.[5] Evolution-MAPI is a connector provider that can be installed within the popular open source Evolution groupware client.

External links

【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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