Microsoft Office

Summary

Microsoft Office, or simply Office, is a discontinued family of client software, server software, and services developed by Microsoft. It was first announced by Bill Gates on August 1, 1988, at COMDEX in Las Vegas. Initially a marketing term for an office suite (bundled set of productivity applications), the first version of Office contained Microsoft Word, Microsoft Excel, and Microsoft PowerPoint. Over the years, Office applications have grown substantially closer with shared features such as a common spell checker, Object Linking and Embedding data integration and Visual Basic for Applications scripting language. Microsoft also positions Office as a development platform for line-of-business software under the Office Business Applications brand. It contains a word processor (Word), a spreadsheet program (Excel) and a presentation program (PowerPoint), an email client (Outlook), a database management system (Access), and a desktop publishing app (Publisher). Office is produced in s

Official source

https://en.wikipedia.org/wiki/Microsoft_Office

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (7)

Related publications

Related people (1)

Related people

Related units (2)

Related units

Related concepts (99)

Related concepts

Related courses (29)

Related courses

Related lectures (39)

Related lectures

Related publications (7)

Iterative Adaptation for Mobile Clients Using Existing APIs

Willy Zwaenepoel

Iterative Adaptation is a novel approach to adaptation for resource-limited mobile and wireless environments that supports powerful application-specific adaptations without requiring modifications to the application's source code. Common productivity applications, such as browsers, word processors, and presentation tools, export APIs that allow external applications to control their operation. The novel premise in iterative adaptation is that these APIs are sufficient to support a wide range of adaptation policies for applications running on resource-limited devices. In addition to allowing adaptation without having to change the application's source code, this approach has a unique combination of advantages. First, it supports centralized management of resources across multiple applications. Second, it makes it possible to modify application behavior after the application has been deployed. This paper evaluates the extent to which existing APIs can be used for the purposes of adapting document-based applications to run on bandwidth-limited devices. In particular, we implement a large number of bandwidth adaptations for applications from the Microsoft Office and the OpenOffice productivity suites and for Internet Explorer. Although we find limitations in their APIs, we are able to implement many adaptation policies without much complexity and with good performance. Moreover, iterative adaptation achieves performance similar to an approach that implements adaptation by modifying the application, while requiring only a fraction of the coding effort.

2005

Robust and Hierarchical Stop Discovery in Sparse and Diverse Trajectories

Ngoc Hoan Do, Quoc Viet Hung Nguyen, Le Hung Tran, Zhixian Yan

The advance of GPS tracking technique brings a large amount of trajectory data. To better understand such mobility data, semantic models like “stop/move” (or inferring “activity”, “transportation mode”) recently become a hot topic for trajectory data analysis. Stops are important parts of tra- jectories, such as “working at office”, “shopping in a mall”, “waiting for the bus”. There are several methods such as velocity, clustering, density algorithms being designed to discover stops. However, existing works focus on well-defined trajectories like movement of vehicle and taxi, not working well for heterogeneous cases like diverse and sparse trajectories. On the contrary, our paper addresses three main challenges: (1) provide a robust clustering-based method to discover stops; (2) discover both shared stops and personalized stops, where shared stops are the common places where many trajectories pass and stay for a while (e.g. shopping mall), whilst personalized stops are individual places where user stays for his/her own purpose (e.g. home, office); (3) further build stop hierarchy (e.g. a big stop like EPFL campus and a small stop like an office building). We evaluate our approach with several diverse and spare real-life GPS data, compare it with other methods, and show its better data abstraction on trajectory.

2011

, ,

Multiuser interference (MUI) statistical models for impulse radio ultra-wideband (IR-UWB) systems can be an important in providing an accurate estimate of the channel state. As such, it can have a major impact on the overall system performance. In the literature, MUI in the time domain is often approximated with Middleton class A and Gaussian mixture models. We use measurements from an IR-UWB testbed under office Line of Sight (LOS) environments to assess the validity of these models. We also analysis the statistical properties of MUI in the frequency domain. We expect these findings to form the basis for useful interference models.

IEEE2008