Demo Data from EDBT 2026 / reviewers in the wild / expert
Mohamoud Ali
dblp:218/7006
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational social science and digital humanities › marketing
customer relationship management |
0.3 | 1 | 2018 | CRM Sales Prediction Using Continuous Time-Evolving Classification · AAAI 2018 |
Data mining
predictive analytics |
0.1 | 1 | 2018 | CRM Sales Prediction Using Continuous Time-Evolving Classification · AAAI 2018 |
Methods — techniques the papers use, named apart from their topics
machine learning · 0.7continuous time-evolving classification · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | CRM Sales Prediction Using Continuous Time-Evolving ClassificationabstractCustomer Relationship Management (CRM) systems play an important role in helping companies identify and keep sales and service prospects. CRM service providers offer a range of tools and techniques that will help find, sell to and keep customers. To be effective, CRM users usually require extensive training. Predictive CRM using machine learning expands the capabilities of traditional CRM through the provision of predictive insights for CRM users by combining internal and external data. In this paper, we will explore a novel idea of computationally learning salesmanship, its patterns and success factors to drive industry intuitions for a more predictable road to a vehicle sale. The newly discovered patterns and insights are used to act as a virtual guide or trainer for the general CRM user population. Mohamoud Ali, Yugyung Lee |
AAAI | 1 |