Sensor Data Visualisation: A Composition-Based Approach to Support Domain Variability

Logre, Ivan; Mosser, Sébastien; Collet, Philippe; Riveill, Michel

doi:10.1007/978-3-319-09195-2_7

Cited by 25 publications

(38 citation statements)

References 19 publications

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“…The complete list of methods can be consulted in Table 7. Configuration files 3 [20] [26] [28] Model-driven 3 [12] [21] [9] Agents 2 [16] [14] SPL 2 [27] [15] Pre-defined templates 2 [25] [9] Semantic reasoner 1 [18] Inclusive user modeling 1 [30] Context-aware generator 1 [24] Indicator ontology 1 [23] Knowledge graphs 1 [23]…”

Section: Mq8 Which Methods Have Been Used For Enabling Variability?mentioning

confidence: 99%

“…The results cover from 2011 to 2018, with a work placed in 2007 [9]. A few records were published in 2011 [10; 11], 2012 [12], 2013 [13], 2014 [14][15][16] and 2016 [17; 18]. However, the majority of records are distributed between 2017 [19][20][21][22][23][24] and 2018 [25][26][27][28][29][30][31], six and seven papers respectively.…”

Section: Mq1 How Many Studies Were Published Over the Years?mentioning

confidence: 99%

“…Regarding customizable and/or personalized dashboards, it can be seen that Business Intelligence (BI) is the most common application domain (Figure 3), followed by the Internet of Things (IoT), Learning Analytics (LA), services monitoring and social science domains. IoT 2 [16] [18] Learning Analytics 2 [22] [24] Services monitoring 2 [20] [26] Disaster situations 1 [30] Economics 1 [31] Emergency management 1 [17] Energy monitoring 1 [13] Generic 1 [29] Interface evaluation 1 [28] Microservices monitoring 1 [19] Physics 1 [11] Sensor monitoring 1 [15] Social sciences 1 [27] 4.5 MQ5. Which are the factors that condition the dashboards' variability process?…”

Section: Mq4 To Which Contexts Have Been the Variability Processes Amentioning

confidence: 99%

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Tailored information dashboards

Vázquez-Ingelmo

García-Peñalvo

Therón

2019

Proceedings of the XX International Conference on Human Computer Interaction

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Information dashboards are extremely useful tools to exploit knowledge. Dashboards enable users to reach insights and to identify patterns within data at-a-glance. However, dashboards present a series of characteristics and configurations that could not be optimal for every user, thus requiring the modification or variation of its features to fulfill specific user requirements. This variation process is usually referred to as customization, personalization or adaptation, depending on how this variation process is achieved. Given the great number of users and the exponential growth of data sources, tailoring an information dashboard is not a trivial task, as several solutions and configurations could arise. To analyze and understand the current state-of-the-art regarding tailored information dashboards, a systematic mapping has been performed. This mapping focus on answering questions regarding how existing dashboard solutions in the literature manage the customization, personalization and/or adaptation of its elements to produce tailored displays. P O S TGiven the large amount of content customization and/or personalization possibilities, and the potential misconception of those terms, this paper aims to investigate the existing literature regarding the customization, adaptation and/or personalization of information dashboards, focusing on mapping [4] the collected studies to understand the existing solutions and research lines of this area.The remainder of this paper is organized as follows. Section 2 outlines the research method followed to perform the systematic mapping. Section 3 describes the data extraction process for analyzing the collected works. Section 4 presents the results of the systematic mapping, finishing with Section 5 where the results are discussed and Section 6, where the work's conclusions are shared. RESEARCH METHODThis study is based on the guidelines suggested by Kitchenham and Charters [5] for systematic literature studies and the guidelines suggested by Petersen [6] for mapping studies. The mapping process is organized in a series of phases; first, the planning phase, where the main goals and research questions to be answered are defined. Second, the conducting phase, where the search strategy is generated and the selection, assessment and data extraction of the studies are performed. The final stage is the reporting phase, where the results are disseminated. P O S T

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Section: Mq8 Which Methods Have Been Used For Enabling Variability?mentioning

confidence: 99%

Section: Mq1 How Many Studies Were Published Over the Years?mentioning

confidence: 99%

Section: Mq4 To Which Contexts Have Been the Variability Processes Amentioning

confidence: 99%

See 1 more Smart Citation

Tailored information dashboards

Vázquez-Ingelmo

García-Peñalvo

Therón

2019

Proceedings of the XX International Conference on Human Computer Interaction

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show abstract

“…In addition, the huge amount of available widgets (e.g., D3.js offers 235 widgets on January 2015) increase the difficulty to select a suitable visualization. There is a lack of effort in the categorization of those new visualization capabilities [4]. These last two points strengthen the difficulty to cooperate with other domains, considering the gap between the conceptual role of the RE and the implementation role of the DD, and because of the incompatible constraints imposed by the chosen libraries on data format then reduce reusability.…”

Section: Visualization Dashboardsmentioning

confidence: 98%

Composition challenges for sensor data visualization

Logre

Mosser

Riveill

2015

Companion Proceedings of the 14th International Conference on Modularity

Self Cite

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Connected objects and monitoring systems continuously produce data about their environment. Dashboards are then designed to aggregate and present these data to end-users. Technologies used to design and implement visualization dashboards are babbling from a software engineering point of view. This paper highlights how this domain could benefit from leveraging separation of concerns and software composition paradigms to support dashboard design.

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“…From the first application on several public product matrices [38], this technique was applied and adapted in different contexts, such as web configurators [39] or competing visualization APIs for dashboards [40]. It is also used in the digital signage MSPL evoked in the previous section, so to populate it from different input SPLs.…”

Section: Reverse Engineeringmentioning

confidence: 99%

Domain Specific Languages for Managing Feature Models: Advances and Challenges

Collet

2014

Leveraging Applications of Formal Methods, Verification and Validation. Technologies for Mastering Change

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Abstract. Managing multiple and complex feature models is a tedious and error-prone activity in software product line engineering. Despite many advances in formal methods and analysis techniques, the supporting tools and APIs are not easily usable together, nor unified. In this paper, we report on the development and evolution of the Familiar Domain-Specific Language (DSL). Its toolset is dedicated to the large scale management of feature models through a good support for separating concerns, composing feature models and scripting manipulations. We overview various applications of Familiar and discuss both advantages and identified drawbacks. We then devise salient challenges to improve such DSL support in the near future.

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Sensor Data Visualisation: A Composition-Based Approach to Support Domain Variability

Cited by 25 publications

References 19 publications

Tailored information dashboards

Tailored information dashboards

Composition challenges for sensor data visualization

Domain Specific Languages for Managing Feature Models: Advances and Challenges

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