Data management has become more critical and challenging than ever; modern data come in different formats, collected in enormous volumes and arrive at an extremely high ratio from multiple sources. The explosion of Big Data has revealed the need to research and develop systems that will support ultra-low latency service and real-time data analytics. Besides, modern data are becoming increasingly more complex as they can be routinely enriched with different and multiple types of non-traditional information such as space, text, time and graph information. A number of research challenges arise for the in-memory management of such composite data. On one hand, we have the well-documented challenges of big data analysis, i.e., Volume, Velocity, Variety, Veracity and Complexity. In addition, in-memory data systems require special techniques for indexing and laying out the data, parallel query processing, concurrency control and data flow. On the other hand, the very nature of composite data poses extra challenges. Due to their heterogeneity and different semantics, every complex type calls for special treatment, which includes effective storage, indexing and processing.
Data Management group's research interests are in efficient storage, indexing, processing and evaluation techniques, specially tailored for complex and composite data that reside in main memory.
Welcome to the Data Management research group
Panagiotis Bouros, Jun.-Prof. Dr.
Head of the group
