Skip to content. | Skip to navigation

Personal tools
You are here: Home Theses A Rule Based Recommendation System for Data Trend Visualisation


Prof. Dr. S. Decker
RWTH Aachen
Informatik 5
Ahornstr. 55
D-52056 Aachen
Tel +49/241/8021501
Fax +49/241/8022321

How to find us

Annual Reports





A Rule Based Recommendation System for Data Trend Visualisation

Thesis type
  • Master
Status Finished
Submitted in 2019
Proposal on 22. Jan 2019 16:15
Proposal room Seminarraum I5
Add proposal to calendar vCal
Presentation on 09. Oct 2019 15:00
Presentation room Seminarraum I5
Add presentation to calendar vCal

Recommendation systems help users make decisions. With the advent of large, high-dimensional datasets there is a need for tools that can support rapid visual analysis. For a data analysts or a normal business user, it is a difficult task to select the related visualisation for a given dataset which is also the most appropriate one. Thus the main aim of this thesis work is to build a rule engine for time series data which will be able to collect the context of the data and can recommend visualisations accordingly.  The output of this thesis work will be used to build a visualization recommender system for mining the data for interesting values, trends, and patterns to speed up data analysis

Time-series dataset shows the time trend of the data. For the time series data, there are different forecasting methods to get the data trend for a particular dataset. Thus the proposed recommendation engine will recommend the corresponding charts by taking the data trend into account. The recommendation system should be based on the knowledge-based recommendation technique. Since we are not considering the user history part to the visualization, the other recommendation technique like collaborative filtering, content-based filtering or hybrid recommendation is not appropriate to the rule engine. In this way, we eliminate the drawback of having a "cold start" problem where user history is very important to the system.




Document Actions