Metadata Generation: Processes, People and Tools

Greenberg, Jane

doi:10.1002/bult.269

Cited by 29 publications

(11 citation statements)

References 1 publication

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“…Metadata is the information that describes, annotates, or adds onto digital data [13]. Though there is a fluid relationship between what is referred to as 'data' and that which is described as 'metadata', in this work we are centrally interested in metadata as a way to examine the relationship between digital systems and the people that use them.…”

Section: System Dimensions and Conceptsmentioning

confidence: 98%

Curatorial Agents

Gulotta

Sciuto

Kelliher

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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As people increasingly turn to digital channels to share, store, and reflect on their lives and experiences, the processes by which they manage the diverse collection of information generated over the course of their lives are changing. These processes, once a matter of hands-on curation and personal meaning making, are now deeply rooted in interactions with digital systems. In this work, we drew from prior research from personalization, memory, and information management to create four interactive, provocative systems. Through sessions with 12 adults from Pittsburgh, PA we used a combination of these systems and interviews to examine how systems might play a role in the near and long term resurfacing of personal and familial digital information. Findings point to an opportunity to create systems that can openly mediate the curation and transmission of digital content, and ways to draw meaning from the differences between how systems and people recall and represent their experiences.

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Section: System Dimensions and Conceptsmentioning

confidence: 98%

Curatorial Agents

Gulotta

Sciuto

Kelliher

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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“…Although this phrase is a bit nebulous, it accurately captures the uber‐level, and dare I say meta‐meaning, for metadata. Metadata is a snippet of information (data with meaning) that describes or represents a larger chunk of content, and that content can be any “entity, form or mode” of information [, p. 16]. More sophisticated definitions for metadata reference intellectual aspects such as data structuring and classification, and they identify metadata‐supported functions such as discovery, provenance tracking, rights management, authentication and other aspects of life‐cycle management [].…”

Section: Metadata and Costmentioning

confidence: 99%

Metadata capital: Raising awareness, exploring a new concept

Greenberg¹

2014

Bull of the Asso Info Sci

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EDITOR'S SUMMARY While the value of information is widely recognized, the next step is recognizing metadata as an economic asset. Generating metadata involves costs in technological and human resources, but failure to generate and use metadata can lead to lost opportunity costs. Metadata activities are ultimately motivated by a drive for return on investment. The view of metadata as capital emphasizes that it has value that can rise with reuse. Metadata capital is defined as an asset that captures contextual knowledge about any information object, is produced by human labor or automated processes and yields a product or facilitates some service and can benefit the public. Expediting reuse of data and metadata is the key to maximizing their value, and early research demonstrates value in the linked open vocabulary environment and in reusing URIs. A collaborative project of the National Consortium for Data Science, the Metadata Capital Initiative, is focusing on the value of metadata through reuse in a big data setting to document its specific contributions to technology methods and intellectual advances.

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“…Greenberg.j [2] identifies four different people which may be involved in metadata tagging. Professional metadata creator, technical metadata creators, content creator and community enthusiasts so that annotation when done manually is a labor intensive, time consuming and costly activity.…”

Section: Descriptionmentioning

confidence: 99%

Extracting IEEE LOM 9. 4 using Memetic Algorithm for Textual Learning Objects

Arjaria¹,

Tomar²,

Roy³

2014

IJCA

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With the growing number of learning objects & their increasing use for learning, it is required to search the desired learning material in the least time. So it is needed to have the quality learning object. The quality of learning objects means they are tagged with correct & complete metadata. IEEE LOM set up a standard for uniformity of Meta data values. The IEEE LOM has 9 different categories for metadata. These categories are used to describe the learning object when filled with metadata values and in turn they are useful searching and understanding the learning object without actually opens it. IEEE LOM 9 category belongs to the classification and under this category, a important subcategory of IEEE LOM 9.4 lies which actually belongs to keywords of learning objects. This paper uses the memetic algorithm based approach to extract the keywords for each learning objects of different classes. The correct keywords strongly reflect the learning object class and in turn it is very useful for user's point of view to decide whether this is the learning object he is searching for.

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Metadata Generation: Processes, People and Tools

Cited by 29 publications

References 1 publication

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Curatorial Agents

Metadata capital: Raising awareness, exploring a new concept

Extracting IEEE LOM 9. 4 using Memetic Algorithm for Textual Learning Objects

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