Workshop on Modeling and Management of Big Data

MoBiD’19 is to be an international forum for exchanging ideas on the latest and best proposals for modeling and managing Big Data in this new data-driven paradigm. Papers focusing on novel applications and using conceptual modeling approaches for any aspects of Big Data such as Hadoop and its ecosystems, Big Data Analytics, social networking, security and privacy, hybrid cloud, Big Data warehousing, data science topics, etc. are highly encouraged. The workshop will be a forum for researchers and practitioners who are interested in the different facets related to the use of the conceptual modeling approaches for the development of next generation applications based on Big Data.


  • Agile modeling for Big Data
  • Advanced applications with Hadoop or Spark frameworks
  • Application design and architecture of Big Data environment
  • Big Data Analytics
  • Business Process Modeling
  • Business Intelligence applications and modeling
  • Conceptual modeling approaches for Big Data
  • Conceptualization for data-drive paradigm
  • Data-driven businesses
  • Using data science approaches for novel analysis and applications
  • Enterprise modeling and architectures for Big Data projects
  • Data integration in Big Data environments
  • Data Integration and management for Hadoop ecosystems
  • Data virtualization, ELT, or ETL for data integration
  • Information packaging
  • Knowledge management for Big Data
  • Metamodeling
  • Modeling and management for social network data
  • Novel applications in Big Data
  • Interface design and visualization for Big Data
  • Model-driven development methodologies and approaches
  • Provenance modeling
  • Requirements modeling for Big Data applications
  • Security and privacy in social networks
  • Software as a Service (SaaS) modeling solutions
  • Analytics for complex data
  • Cloud-based analytics
  • Data mining and warehousing over the cloud
  • ETL over the cloud
  • Hybrid cloud
  • Modeling and management in IOT domains
  • Smart Cities
  • Smart health
  • Education for Big Data and data science
  • Blockchains

Submission Guidelines

Since the proceedings will be published by Springer in the LNCS series, authors must submit manuscripts using the LNCS style. See for style files and details. The page limit for submitted papers (as well as for final, camera-ready papers) is 10. Manuscripts not submitted in the LNCS style or exceeding the page limit will not be reviewed and automatically rejected.

Submission is done through EasyChair. Please select the Workshop track when submitting. EasyChair Submission: – please pay attention to select the correct track.

Workshop Organizers

Il-Yeol Song is professor in the College of Computing and Informatics of Drexel University. He served as Deputy Director for NSF Research Center on Visual & Decision Informatics between 2012-2014. He is an ACM Distinguished Scientist and an ER Fellow. He received Peter P. Chen Award in Conceptual Modeling in 2015. His research interests include conceptual modeling, data warehousing & analytics, CRM, Big Data technologies and management, and smart aging. Dr. Song published over 200 peer-reviewed papers and co-edited 24 proceedings. He is a co-Editor-in-Chief of Journal of Computing Science and Engineering (JCSE) and is in an editorial board member of DKE, JDM, IJDS, and JDFSL. He won the Best Paper Award in the IEEE CIBCB 2004. He won 14 research awards from competitions of annual Drexel Research Days. Dr. Song currently serves as the Steering Committee member of the ER conference. He is also a steering committee member of DOLAP and ADFSL conferences. He served as a program/general chair of over 20 international conferences/workshops including DOLAP’98-13, CIKM’99, ER’03, FP-UML’06, DaWaK’07- ‘08, , DESRIST’09, CIKM ‘09, and MoBiD’13-17. He delivered keynote speeches at several conferences, including at the First Asia-Pacific iSchool Conference in 2014, ACM SAC 2015, ER2015, EDB 2016, and A-LIEP 2016.

Juan Trujillo is a Full Professor at the Department of Software and Computing Systems in the University of Alicante, Spain, and the leader of the Lucentia Research Group. His main research topics include Business Intelligence applications, Business Intelligence 2.0, Key Performance Indicators (KPIs), data warehouse development, OLAP, data mining, UML, MDA, and data warehouse security & quality. He has participated in the official registration of different tools related to Data Warehouse modelling. He has advised 11 PhD students and published more than 180 papers in different highly impact conferences such as the ER, UML or CAiSE, and more than 30 papers in highly ranked peer-reviewed journals such as the DKE, DSS, ISOFT, IS, or JDM. He has also been co-editor of five special issues in different JCR journals such as DKE, DSS, and IJDWM. He has also been PC member of conferences and reviewer of JCR journals such as ER, CIKM, ICDE, DOLAP, DSS, JDM, or DKE, and PC co-Chair of DOLAP’05, DAWAK’05-’06, FP-UML’05-’09, and ER2013. Further information on his main research publications can be found on:

Alejandro Maté is an assistant professor at the Department of Software and Computing Systems. He has a degree in Computer Science at the University of Alicante (Spain). Master in Computer Science at the University of Alicante in 2010. He has carried out a PhD stay at the University of Trento (Italy). His research focuses on the requirements analysis and conceptual design of data warehouses, model-driven development, and traceability in model-driven approaches. He has published more than 50 papers in JCR journals and highly ranked conferences related to his research.