Name
Abstract | ||
|---|---|---|
A system and a method is proposed for collecting and aggregating crowd-sourced data from data files based on parameters and measures of relevance of underlying content provided by the intelligent crowd. A user's data may be combined with already existing collective data to generate relevant mark-ups for a document or other consumable data file, such as audio or video. The marked-up version of the document or data fie is then displayed to other users to, inter alia, enhance efficiency and comprehension for reading, listening or viewing. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-08979-9_32 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Visualization and data mining,Mining Social Media,Crowd-sourcing,knowledge extraction | Information retrieval,Computer science,Active listening,Information extraction,Artificial intelligence,Knowledge extraction,Data file,Database,Machine learning,Comprehension | Conference |
Volume | ISSN | Citations |
8556 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Indu Mati Anand | 1 | 0 | 0.34 |
| Anurag Wakhlu | 2 | 0 | 0.34 |
| Pranav Anand | 3 | 260 | 19.70 |