Enterprise content management

Summary

Enterprise content management (ECM) extends the concept of content management by adding a timeline for each content item and, possibly, enforcing processes for its creation, approval, and distribution. Systems using ECM generally provide a secure repository for managed items, analog or digital. They also include one (or more) methods for importing content to bring manage new items, and several presentation methods to make items available for use. Although ECM content may be protected by digital rights management (DRM), it is not required. ECM is distinguished from general content management by its cognizance of the processes and procedures of the enterprise for which it is created. Definitions

Late 2005: The technology was used to capture, manage, store, preserve, and deliver content and documents related to organizational processes
Early 2006: ECM tools and strategies allowed the management of an organization's unstructured information, wherever that information exists.

Official source

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

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The course integrates concepts from media studies, machine learning, multimedia and network science to characterize social practices and analyze content in sites like Facebook, Twitter and YouTube. Students will learn computational methods to infer individual and networked phenomena in social media.

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Historical newspaper semantic segmentation using visual and textual features

Raphaël Barman

Mass digitization and the opening of digital libraries gave access to a huge amount of historical newspapers. In order to bring structure into these documents, current techniques generally proceed in two distinct steps. First, they segment the digitized images into generic articles and then classify the text of the articles into finer-grained categories. Unfortunately, by losing the link between layout and text, these two steps are not able to account for the fact that newspaper content items have distinctive visual features. This project proposes two main novelties. Firstly, it introduces the idea of merging the segmentation and classification steps, resulting in a fine- grained semantic segmentation of newspapers images. Secondly, it proposes to use textual features under the form of embeddings maps at segmentation step. The semantic segmentation with four categories (feuilleton, weather forecast, obituary, and stock exchange table) is done using a fully convolutional neural network and reaches a mIoU of 79.3%. The introduction of embeddings maps improves the overall performances by 3% and the generalization across time and newspapers by 8% and 12%, respectively. This shows a strong potential to consider the semantic aspect in the segmentation of newspapers and to use textual features to improve generalization.

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Special Section on Social Media as Sensors

Daniel Gatica-Perez, Jie Luo

Social media have transformed the Web into an interactive sharing platform where users upload data and media, comment on, and share this content within their social circles. Each content item is associated with an abundance of metadata and related information such as location, tags, comments, favorites and mood indicators, access logs, and so on. At the same time, all this information is implicitly or explicitly interconnected based on various properties such as social links among users, groups, communities, and sharing patterns. These properties transform social media into data sources of an extremely dynamic nature that reflect topics of interests, events, and the evolution of community opinion and focus. Social media processing offers a unique opportunity to structure and extract information and to benefit multiple areas ranging from computer vision to psychology and marketing. The objective of this special section is to provide an overview of the current research in emerging topics related to applications where social media can act as sensors of real-life phenomena and case studies that reveal valuable insights usually not possible with existing, limited, controlled, and laboratory-based datasets.

Ieee-Inst Electrical Electronics Engineers Inc2013