Name
Abstract | ||
|---|---|---|
Big data and analytics are increasingly used in different domains to gain insights and to improve decision-making. Developing big data analytics solutions is a complex task involving multidisciplinary teams and users - with no data science and programming background - to professional data scientists and software engineers. Different stakeholders work with a variety of data types, tasks and concepts in different languages from high- level domain concepts to low level programming languages and technical concepts. In order to advance the level of abstraction beyond low-level data analysis technical details, we demonstrate our BiDaML tool. BiDaML brings all stakeholders around one tool to specify, model and document their big data applications using a novel set of domain-specific visual languages (DSVLs). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/VL/HCC50065.2020.9127196 | 2020 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) |
Keywords | DocType | ISSN |
big data analytics,domain-specific visual languages,BiDaML | Conference | 1943-6092 |
ISBN | Citations | PageRank |
978-1-7281-6901-9 | 0 | 0.34 |
References | Authors | |
11 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hourieh Khalajzadeh | 1 | 13 | 6.05 |
| Andrew J. Simmons | 2 | 4 | 1.78 |
| Mohamed Abdelrazek | 3 | 87 | 14.62 |
| John Grundy | 4 | 0 | 0.34 |
| John G. Hosking | 5 | 1000 | 91.44 |
| Qiang He | 6 | 201 | 21.72 |