Linked Data Based Analytics for Drug Data on a Global Scale
The Web is currently comprised of vast amounts of heterogeneous data, most of which are unstructured or semi-structured. Health-care data are available as open data on the Web, and the integration and consolidation of such data can provide unprecedented results regarding price comparison of medicine products between countries, finding similar drugs and other relevant statistics, but the different structure of the data makes this a difficult task. The issue that impedes this task is that health-care data for most countries are merely published as 2-star open data, thus forming silos that don’t intercommunicate. Additionally, there is not a central directory where this data can be found, so finding the right drug dataset for each world country by itself can be a challenging job.
Our aim is to create a single dataset incorporating medicine products data from each country, using the Semantic Web technologies and Linked Data principles. These principles enable us to create a dataset of structured data, interlink them with other public health-care datasets and do this in an open way so that efforts were not duplicated. The dataset would allow users to access world drug data from a single endpoint and provide them with an opportunity to examine unseen statistics in the medical field previously.
For this purpose, we have created an automated system for collecting the drug data for each country. So far we have gathered data from 25 countries, but our system is highly scalable, and we plan to incorporate data from all country where it is publicly available on the web. The drug data we use from each country is data extracted from medical databases from the official drug registry in the associated country. This system extracts, cleans and transforms the data from 2-star Open Data to 5-Star Linked Data.
Also, we present a web application which manipulates the data from our dataset in order to provide end users with extensive information about the world drugs and useful statistics.
About the lecturer
Dimitar Trajanov Ph.D. is a professor at Faculty of Computer Science and Engineering – Cyril and Methodius University –Skopje. From March 2011 until September 2015, he was the Dean of the Faculty of Computer Science and Engineering, and in his tenure, the Faculty has become the largest technical Faculty in Macedonia. Dimitar Trajanov obtained his master and Ph.D. in Computer Science and Engineering from the “Ss Cyril and Methodius University” Skopje in 1998 and 2006 respectively. From 1999 to 2010 he was employed at the Faculty of Electrical Engineering and Information Technologies, first as a research and teaching assistant, and the as an Assistant Professor and form 2010 as an Associate Professor.
He is an author of more than 120 journal and conference papers and seven books. He has been involved in more than 40 international and national scientific and applicative projects as a project lead or a participant.
Dimitar Trajanov is the leader of Regional Social Innovation Hub established as a cooperation between UNDP and the Faculty of Computer Science and Engineering. He is also, a member of the National Committee for Innovation and Entrepreneurship where he is working on creating strategies and legislation to encourage innovation and entrepreneurship in the Republic of Macedonia through inspiring the use of technology for economic development.
His research interests include Semantic Web, Data Science, Big Data, Open Data, Social Innovation, Mobile Development, Ad hoc Networks, Parallel Processing, Reliability, System on Chip Design.