Name
Abstract | ||
|---|---|---|
We present an approach for Business Intelligence (BI), where market share changes are tracked, evaluated, and prioritized dynamically and interactively. Out of all the hundreds or thousands of possible combinations of sub-markets and players, the system brings to the user those combinations where the most significant changes have happened, grouped into related insights. Time-series prediction and user interaction enable the system to learn what “significant” means to the user, and adapt the results accordingly. The proposed approach captures key insights that are missed by current top-down aggregative BI systems, and that are hard to be spotted by humans (e.g., Cisco’s US market disruption in 2010). |
Year | Venue | Field |
|---|---|---|
2016 | ADMA | Multiplicative model,Computer science,Synthetic data,Artificial intelligence,Business intelligence,Market share,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Renato Keshet | 1 | 338 | 27.26 |
| Alina Maor | 2 | 0 | 0.34 |
| George Kour | 3 | 0 | 0.34 |