935 resultados para Smithsonian Institution--Maps.


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Source materials like fine art, over-sized, fragile maps, and delicate artifacts have traditionally been digitally converted through the use of controlled lighting and high resolution scanners and camera backs. In addition the capture of items such as general and special collections bound monographs has recently grown both through consortial efforts like the Internet Archive's Open Content Alliance and locally at the individual institution level. These projects, in turn, have introduced increasingly higher resolution consumer-grade digital single lens reflex cameras or "DSLRs" as a significant part of the general cultural heritage digital conversion workflow. Central to the authors' discussion is the fact that both camera backs and DSLRs commonly share the ability to capture native raw file formats. Because these formats include such advantages as access to an image's raw mosaic sensor data within their architecture, many institutions choose raw for initial capture due to its high bit-level and unprocessed nature. However to date these same raw formats, so important to many at the point of capture, have yet to be considered "archival" within most published still imaging standards, if they are considered at all. Throughout many workflows raw files are deleted and thrown away after more traditionally "archival" uncompressed TIFF or JPEG 2000 files have been derived downstream from their raw source formats [1][2]. As a result, the authors examine the nature of raw anew and consider the basic questions, Should raw files be retained? What might their role be? Might they in fact form a new archival format space? Included in the discussion is a survey of assorted raw file types and their attributes. Also addressed are various sustainability issues as they pertain to archival formats with a special emphasis on both raw's positive and negative characteristics as they apply to archival practices. Current common archival workflows versus possible raw-based ones are investigated as well. These comparisons are noted in the context of each approach's differing levels of usable captured image data, various preservation virtues, and the divergent ideas of strictly fixed renditions versus the potential for improved renditions over time. Special attention is given to the DNG raw format through a detailed inspection of a number of its various structural components and the roles that they play in the format's latest specification. Finally an evaluation is drawn of both proprietary raw formats in general and DNG in particular as possible alternative archival formats for still imaging.

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This chapter introduces a conceptual model to combine creativity techniques with fuzzy cognitive maps (FCMs) and aims to support knowledge management methods by improving expert knowledge acquisition and aggregation. The aim of the conceptual model is to represent acquired knowledge in a manner that is as computer-understandable as possible with the intention of developing automated reasoning in the future as part of intelligent information systems. The formal represented knowledge thus may provide businesses with intelligent information integration. To this end, we introduce and evaluate various creativity techniques with a list of attributes to define the most suitable to combine with FCMs. This proposed combination enables enhanced knowledge management through the acquisition and representation of expert knowledge with FCMs. Our evaluation indicates that the creativity technique known as mind mapping is the most suitable technique in our set. Finally, a scenario from stakeholder management demonstrates the combination of mind mapping with FCMs as an integrated system.

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Our research project develops an intranet search engine with concept- browsing functionality, where the user is able to navigate the conceptual level in an interactive, automatically generated knowledge map. This knowledge map visualizes tacit, implicit knowledge, extracted from the intranet, as a network of semantic concepts. Inductive and deductive methods are combined; a text ana- lytics engine extracts knowledge structures from data inductively, and the en- terprise ontology provides a backbone structure to the process deductively. In addition to performing conventional keyword search, the user can browse the semantic network of concepts and associations to find documents and data rec- ords. Also, the user can expand and edit the knowledge network directly. As a vision, we propose a knowledge-management system that provides concept- browsing, based on a knowledge warehouse layer on top of a heterogeneous knowledge base with various systems interfaces. Such a concept browser will empower knowledge workers to interact with knowledge structures.