An Annotation Management System for Multidimensional Databases

Cabanac, Guillaume; Chevalier, Max; Ravat, Franck; Teste, Olivier

doi:10.1007/978-3-540-74553-2_9

Cited by 11 publications

(17 citation statements)

References 8 publications

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“…To increase the level of automation, situational data sources can also take advantage of the previous annotations and mappings related to multidimensional schemata, like done by Cabanac et al (2007). By using these annotations, several mappings between the situational data sources and fusion cubes can be derived.…”

Section: Schema and Instance Mappingmentioning

confidence: 99%

Fusion Cubes

Abelló

Darmont

Etcheverry

et al. 2013

International Journal of Data Warehousing and Mining

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Section: Schema and Instance Mappingmentioning

confidence: 99%

Fusion Cubes

Abelló

Darmont

Etcheverry

et al. 2013

International Journal of Data Warehousing and Mining

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“…According to Paul et al (2004), the use of collective memory information provides the study participants focused attention on the cognitive-conflict task domain and leads to faster decision-making. In the work of Cabanac et al (2007), the authors proposed expertise memory. That is why they introduced the 'decisional annotation' concept to allow analysts to annotate tables of multidimensional databases, to share comments between coworkers, and to foster opinion exchange within discussion threads.…”

Section: Collective Memorymentioning

confidence: 99%

“…According to Boudebza et al (2013), annotation represents the externalised knowledge which reflects personal knowledge and experiences of the annotator. According to Philippe (2009), the annotation represents the vector of the reusability of the informational elements of the watcher, Barber et al (2005) intend to reduce document reading time by the collective annotation, in the work of Cabanac et al (2007) collective annotation is used to allow discussing and debating on the other hand the annotation proposed in the work of Azouaou and Desmoulins (2006) aims to memorise, to re-use and share knowledge.…”

Section: Ontology-based Model For Annotation Semanticsmentioning

confidence: 99%

Collective memory based on semantic annotation among economic intelligence actors

Aissa¹,

Nader²,

Chalal³

2014

IJCI

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Many enterprises are reflected on strategies and tools that facilitate the knowledge sharing and exploiting the collective intelligence among their actors. Economic intelligence actors collaborate to solve a decisional problem, they use significant mental effort, and so they share a common knowledge that can indicate to other actors directions to follow or avoid. Indeed, whenever an actor explores knowledge or a relevant document, it enriches the collective knowledge of the memory via annotations. To ensure this collaboration in solving a decisional problem among actors in a context of economic intelligence, in this article we propose a conceptual model using ontologies to represent collaborative semantic annotations between economic intelligence actors in order to capitalise on and reuse the knowledge shared in a collective memory.

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“…The social validation process associated with a specific visualization, as proposed in Cabanac, Chevalier, Chrisment, and Julien (2007), enables readers to distinguish between consensual, controversial, and neutral annotations. This display adaptation intends to overcome the scalability issue stated beforehand.…”

Section: Measuring the Social Validity Of Collective Annotationsmentioning

confidence: 99%

“…Foshay, Mukherjee, and Taylor (2007) underline the great need for the transposition of this paper‐based practice to digital counterparts. That is why we introduced the “decisional annotation” concept to allow analysts to annotate tables, to share comments between coworkers, and to foster opinion exchange within discussion threads (Cabanac, Chevalier, Ravat, & Teste, 2007). From this perspective, the social validation process enables people to identify (dis)agreed‐upon analyses.…”

Section: Measuring the Social Validity Of Collective Annotationsmentioning

confidence: 99%

Social validation of collective annotations: Definition and experiment

Cabanac

Chevalier

Chrisment

et al. 2009

J. Am. Soc. Inf. Sci.

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People taking part in argumentative debates through collective annotations face a highly cognitive task when trying to estimate the group's global opinion. In order to reduce this effort, we propose in this paper to model such debates prior to evaluating their "social validation." Computing the degree of global confirmation (or refutation) enables the identification of consensual (or controversial) debates. Readers as well as prominent information systems may thus benefit from this information. The accuracy of the social validation measure was tested through an online study conducted with 121 participants. We compared their human perception of consensus in argumentative debates with the results of the three proposed social validation algorithms. Their efficiency in synthesizing opinions was demonstrated by the fact that they achieved an accuracy of up to 84%. Introduction and MotivationsAnnotating paper documents is a common activity practiced since the early middle ages (Fraenkel & Klein, 1999). Field studies show that readers still make extensive use of annotations nowadays (Marshall, 1998;Wolfe & Neuwirth, 2001). Although seemingly insignificant, they actually facilitate key purposes such as "active reading" by supporting critical thinking while reading (Adler & van Doren, 1972), learning by facilitating document appropriation, and proofreading, among many others. With the widespread adoption of digital documents both in the workplace and at home, people have felt frustrated at not being able to annotate them (Sellen & Harper, 2003, p. 96). Such a need led both research labs and companies to design a plethora of annotation systems-mostly targeting Web documents-since the 1990s (Wolfe, 2002). At first, they implemented the usual paper-based annotation functions, mainly for personal use. Then, taking advantage of modern computer storage and networking capabilities, annotation systems provided novel features for collective use. In particular, they enabled annotation sharing through dedicated servers, so that users could view and access them in context, i.e., within the annotated document. Moreover, subsequent readers may later reply to any annotation or any reply in turn, thus forming a debate anchored to the discussed passage. Such a debate is also called a "discussion thread" (e.g., Figure 1).As an asynchronous way of communicating, collective annotations (i.e., annotations along with their discussion threads) are useful at two levels:• From the readers' point of view, collective annotations enable them to discuss document passages in context. This is an advantage in comparison with Internet bulletin boards and forums where one needs to explain the context of his/her post to be understood. Moreover, feedback about a given annotated document is directly accessible to readers, whereas it would be scattered over multiple sources otherwise. Besides being useful for readers, authors also benefit from collective annotations as they can improve their documents by taking into account the associated remarks.• For syste...

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An Annotation Management System for Multidimensional Databases

Cited by 11 publications

References 8 publications

Fusion Cubes

Fusion Cubes

Collective memory based on semantic annotation among economic intelligence actors

Social validation of collective annotations: Definition and experiment

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