FUnctionality Sharing In Open eNvironments
Heinz Nixdorf Chair for Distributed Information Systems

Dr.-Eng. Javad Chamanara

Scientific Staff

Ernst-Abbe-Platz 2 , 07743 Jena , Room: 3236


Join date: 2011-07-01

Curriculum Vitae

I am working on designing QUIS, a declarative computer language, to process various kinds of data in a unified manner. The work is in progress in the FUSION group at the Department of Mathematics and Computer Science, Friedrich Schiller University of Jena, Germany.

In addition to the research and teaching duties, I am also taking part in the BExIS++ project, which is a generic, modular, and web-based research data management platform.

For more information about my current work and publications please visit: http://uni-jena.academia.edu/JavadChamanara

Research Area

  • Computer Language Design
  • Data Management and Analysis
  • Distributed Processing and Workflow Orchestration
  • Earth Digitization and Visualization



BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data
Javad Chamanara, Jitendra Gaikwad, Roman Gerlach, Alsayed Algergawy, Andreas Ostrowski and Birgitta König-Ries
Biodiversity Data Journal


Querying Heterogeneous Data in an In-situ Unified Agile System
Digitale Bibliothek Thüringen (DBT) 8.5.2018


QUIS: in-situ heterogeneous data source querying
Javad Chamanara, Birgitta König-Ries, H. V. Jagadish
Journal of Proceedings of the VLDB Endowment
Munich, Germany 28.8.2017


An Introduction to the Data Retrieval Facilities of the XQt Language for Scientific Data
Javad Chamanara, Birgitta König-Ries
In: Galhardas, H. and Rahm, E., Data Integration in the Life Sciences, Lecture Notes in Computer Science 8574, pp 143-150
Springer International Publishing Switzerland 1.10.2014


A conceptual model for data management in the field of ecology
Javad Chamanara, Birgitta König-Ries
Journal of Ecological Informatics 1.12.2013


Supervised Theses


Build Automatisation in BExIS using Jenkins
Nicole K. Stender - Projektarbeit
FSU Jena


Current Projects

The Heinz-Nixdorf Endowed Chair for Practical Computer Science at the Friedrich-Schiller-University (FSU) of Jena, Germany is working with the Institute of Ecology (FSU), the Institute for Geography (FSU), the University Data Center (FSU) and the Max Planck Institute for Biogeochemistry for the further development of the established Biodiversity-Exploratories Information System (BExIS). Furthermore, the project will focus on strategic work such as concepts and method development to foster long-term data preservation in biodiversity research at the Friedrich-Schiller-University. Ideally, the further development of the existing BExIS software will serve three purposes: Technical Goals The core of the BExIS software will be revised…
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Completed Projects

QUIS: Query Heterogeneous Data In-Situ
Data of interest are often found in a variety of data sources, many of which are not relational databases, but have their own data organization and query capabilities. To answer questions of interest, one has to run queries across data from these heterogeneous sources. The traditional approach is to perform multiple individual data transformation tasks, one per data source, to import the data into a common repository where they can be queried and analyzed. Drawbacks of this approach include the manual effort and the cost of transforming and importing potentially large data sets, and the lost opportunity to exploit any query facilities provided by the data sources. QUIS (QUery In-Situ) proposes an approach for querying the data "in-situ" to the greatest extent possible, by taking the user query and transforming appropriate portions of it into corresponding query expressions on individual data sources. Realizing this approach requires the development of a unified query model. This model can extract sub-queries matching heterogeneous capabilities of individual sources, perform heterogeneous joins on intermediate results as necessary, and deal with barriers such as incompatible type systems en route. Early experiments have shown that QUIS almost eliminates the time to prepare the data while paying only a small cost in query execution time compared to a fully integrated, indexed, and loaded relational database.
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