Name
Abstract | ||
|---|---|---|
Using graph data models for business intelligence applications is a novel and promising approach. In contrast to traditional data warehouse models, graph models enable the mining of relationship patterns. In our prior work, we introduced an approach to graph-based data integration and analytics called BIIIG (Business Intelligence with Integrated Instance Graphs). In this work, we compare state-of-the-art systems for graph data management and analytics with regard to the support for our approach in Big Data scenarios. To exemplify the analytical value of graph models for business intelligence, we propose an analytical workflow to extract knowledge from graph-integrated business data. Finally, we show how we use Gradoop, a novel framework for distributed graph analytics, to implement our approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1515/itit-2016-0006 | IT-INFORMATION TECHNOLOGY |
Keywords | Field | DocType |
Graph-based database models, data mining, parallel and distributed DBMSs, business intelligence, property graph model, graph pattern mining | Data science,Graph,Computer science,Business intelligence,Scalability | Journal |
Volume | Issue | ISSN |
58 | 4 | 1611-2776 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| André Petermann | 1 | 51 | 6.17 |
| Martin Junghanns | 2 | 50 | 5.48 |