Research

Research Interests

  • Database Technology
    • Data management on modern hardware
      • Co-processor-accelerated query optimization
      • Efficient algorithms for query (co-)processing on heterogeneous hardware (e.g., GPUs, Intel Xeon Phi, NUMA Systems)
      • Genome data analysis using main-memory databases
      • Multi-dimensional index structures for main-memory databases
    • Graph database management systems
    • Large-scale and cloud data management
      • NoSQL databases
      • Transaction management in the cloud
      • Data integrity in the cloud
      • Parallel entity resolution
      • Self-tuning for cloud storage clusters
  • Feature-Oriented Software Development (FOSD)
    • Product-line configuration recommender systems
    • Prioritization for software product line testing
    • Migration of cloned product variants to a software product line
    • Variability-aware refactoring
    • Variability-aware code smells
    • Formal specification and verification of software product lines
    • Analysis of variability models
    • Multi software product lines
  • Variability in Embedded Systems / Heterogenous Hardware
    • Composition and adaptation of software product lines at runtime
    • Syntactical and semanticle interoperability in heterogeneous (embedded) systems
    • Data management in embedded systems and sensor networks

Current Funded Projects

  • SPL Testing
  • Southeast Asia Research Network: Digital Engineering
  • Supporting Advanced Data Management Features for the Cloud Environment (Clustering the Cloud, Consistent data management for cloud gaming)
  • Supporting Advanced Data Management Features for the Cloud Environment

    Description: the aim of this project is to support advanced features of cloud data management. The project has two basic directions. The focus of the first direction is (self-) tuning for cloud data management clusters that are serving one or more applications with divergent workload types. It aims to achieve dynamic clustering to support workload based optimization. This approach is based on logical clustering within a DB cluster based on different criteria such as: data, optimization goal, thresholds, and workload types. The second direction focuses on the design of Cloud-based massively multiplayer online games. It aims to provide a scalable available efficient and reusable game architecture. Our approach is to manage data differently in multiple storage systems (file system, NoSQL system and RDBMS) according to their data management requirements, such as data type, scale, and consistency.

    Members:Siba Mohammad
    Ziqiang Diao
    Keywords:Cloud data management, online games, self tuning

    Clustering the Cloud - A Model for Self-Tuning of Cloud Datamangement Systems

    Over the past decade, cloud data management systems became increasingly popular, because they provide on-demand elastic storage and large-scale data analytics in the cloud. These systems were built with the main intention of supporting scalability and availability in an easily maintainable way. However, the (self-) tuning of cloud data management systems to meet specific requirements beyond these basic properties and for possibly heterogeneous applications becomes increasingly complex. Consequently, the self-management ideal of cloud computing is still to be achieved for cloud data management. The focus of this PhD project is (self-) tuning for cloud data management clusters that are serving one of more applications with divergent workload types. It aims to achieve dynamic clustering to support workload based optimization. Our approach is based on logical clustering within a DB cluster based on different criteria such as: data, optimization goal, thresholds, and workload types.

    Type:Drittmittelprojekt
    Funded by:Syrian Ministry of Higher Education and DAAD
    Funded:October 2011 - March 2015
    Members:Siba Mohammad

    Consistent data management for cloud gaming

    Cloud storage systems are able to meet the future requirements of the Internet by using non-relational database management systems (NoSQL DBMS). NoSQL system simplifies the relational database schema and the data model to improve system performances, such as system scalability and parallel processing. However, such properties of cloud storage systems limit the implementation of some Web applications like massively multi-player online games (MMOG). In the research described here, we want to expand existing cloud storage systems in order to meet requirements of MMOG. We propose to build up a transaction layer on the cloud storage layer to offer flexible ACID levels. As a goal the transaction processing should be offered to game developers as a service. Through the use of such an ACID level model both the availability of the existing system and the data consistency during the interactivity of multi-player can be converted according to specific requirements.

    Type:Drittmittelprojekt
    Funded by:Graduate Funding of Saxony-Anhalt
    Funded:July 2012 - December 2014
    Members:Zigiand Diao
  • Nachhaltiges Variabilit√§tsmanagement von Feature-orientierten Software-Produktlinien (NaVaS)
  • EXtracting Product Lines from vAriaNTs (EXPLANT)
  • Secure Data Outsourcing to Untrusted Clouds
  • See also here

Other Research Projects

Completed Projects

Past Conference

Database Systems for Business, Technology, and Web (BTW)

The 15th BTW conference on "Database Systems for Business, Technology, and Web" (BTW 2013) of the Gesellschaft für Informatik (GI) will take place from March 11th to March 15th, 2013 at the Otto-von-Guericke-University of Magdeburg, Germany.

Website:Conference-Website

Past Workshops