The research themes of NDB are briefly described below. Note that there is overlap between some of
Advanced data modelling
Data modeling concerns structuring the chosen universe of discourse into a set of meaningful interrelated
concepts, using constructs such as object classes and relationships (OO modeling), entities
and relationships (E/R modeling), or facts, measures, and dimensions (multidimensional
modeling). The activities of NDB concerns the modeling of complex or advanced types of data such as temporal,
spatio-temporal, or multidimensional data.
Business intelligence is a collection of related technologies for data analysis, including data warehousing
(integrating data from diverse sources into a common, analysis-oriented database), On-Line Analytical
Processing (OLAP: fast aggregate querying of dimensional data) and data mining (machine-assisted
discovery of hidden patterns in the data). The activities of NDB include handling complex multidimensional
data, using pre-aggregated data for query performance, distributed OLAP, and 3D visual data mining.
Location-based services (LBS)
Location-Based Services (LBS) are mobile internet services that use the location of the user, e.g., obtained
via GPS satellites, to customize the service to the particular time and place of the user. LBS are expected
to be a killer application for the mobile internet in the years to come. Example applications include
traffic coordination and management, location-aware advertising, integrated tourist services, safety-related
services, and position-varying information in industrial environments. Data management for LBS is a challenge
because the positions of users change continuously, invalidating most traditional database techniques
such as indexing. The activities of NDB explore how data models, query languages, and indexing and query
processing techniques should be changed to meet the needs of LBS.
The most widely used query language today is SQL, which is supported by all commercial relational database
management systems. However, SQL is often cumbersome and inefficient for special-purpose querying, e.g.,
querying temporal data. The activities of NDB seek to develop SQL extensions, or totally new query languages,
to support advanced queries on temporal, spatio-temporal, and multidimensional data.
Query processing and indexing
Standard indexing techniques such as B-trees and accompanying query processing techniques such as nested-loop
index join work well for traditional data. However, for advanced types of data such as continuously changing
positions, these techniques fail. The activities of NDB explore new query processing and indexing techniques
for advanced data types such as temporal, spatio-temporal, and multidimensional data.
Spatio-temporal data management
Data that contain both spatial and temporal information, i.e., spatio-temporal data, occur in
many systems, e.g., Location-Based Services. Managing both these issues, which are in themselves very
complex, concurrently is a challenge for any database system. The activities of NDB explore data modeling,
query languages, query processing, and indexing for spatio-temporal data.
Temporal data management
Almost all applications need to handle the temporal aspects of data. Indeed, it is estimated that more
than 30% of all SQL code (often the most complex parts of the code) is related to temporal data management.
Also, query performance for temporal queries is often bad. The activities of NDB seek to ease these problems
by working with data modeling, query languages, query processing, and indexing for temporal data.
Web data management
Extensible Markup Language (XML) is fast becoming the new standard for data representation and exchange
on the World Wide Web. The rapid emergence of XML data on the web, e.g., business-to-business (B2B) e-commerce,
is making it necessary for database technologies to handle XML data as well as traditional data formats.
The activities of NDB focus on extending OLAP systems to support XML sources, and on versioning mechanisms
for XML data.