Uppsala DataBase Laboratory
The research direction of the group concentrates on developing
methods for scalable and extensible processing of queries
analyzing different kinds of distributed data in terms of
semantic 'NoSQL' data representations. Of particular interest is
scalable processing of high level queries analyzing high volume
data streams. A challenge is to provide scalable processing as
the data volume increases and the analyzes become increasingly
costly. Our approach is to develop smart query transformation
techniques and distributed execution strategies in an
extensible platform where external systems, algorithms, and data
managers can be plugged-in.
Our research system Amos II
provides a platform for scalable processing of queries to many
different kinds of heterogeneous data sources. The queries are
expressed in terms of a high level semantic data model. The
system enables integration of external storage managers,
databases, and computational systems though APIs in several
Road Benchmark simulation of a toll expressway system on
your notebook: SCSQ-LR.
Our massively parallel implementation of the Linear Road
has the highest published performance.
The start-up company Stream Analyze develops technology for scalable on-line analyses of streamed data based on our research.
the query processor is extended to provide flexible and scalable
preservation and querying of relational databases using the
standard semantic web query language SPARQL.
SciSPARQL uses the Amos II platform to provide processing of queries combining numeric data and meta-data by extending SPARQL with array capabilities.
We are responsible for the Doctoral
study subject area on Computer
Science with specialization in Database Technology, TNDAVE04.
There is a doctoral study plan in Swedish
We are teaching doctoral student level courses on modern
database technology within the programme.
Looking for MSc project on modern
database technology? Contact Tore
participate in SMART
VORTEX, a Large Integrated Project co-financed by the
European Union within the Seventh Framework Programme. The goal
of the project is to provide a technological infrastructure
consisting of a comprehensive suite of interoperable tools,
services, and methods for intelligent management and analysis of
massive data streams to achieve better collaboration and
decision making in industrial product life cycles. The project
is a collaboration between a number of European universities and
industrial partners. In the project we have developed the
general platform SVALI for
scalable parallel processing of high volume data streams
involving expensive computations.
We participate in eSSENCE,
a Swedish e-science research collaboration to create a research
environment where the interplay between different e-science
competences will open up the field for novel applications, more
realistic simulations, and new scientific solutions, models and
We organized EDBT: 14th International Conference on Extending Database Technology, and ICDT: 14th International Conference on Database Theory, March 21-25, 2011.In the iStreams project (supported by Vinnova and ASTRON) we developed SCSQ (SuperComputer Stream Query processor), a Data Stream Management System (DSMS) to process very high volume data streams. SCSQ runs in a massively parallel and heterogeneous computing environment containing Linux clusters and an IBM BlueGene computer. SCSQ executes data stream queries that filter, transform, and join data from receivers of low frequence space radio signals.
In the GSDM project (supported by Vinnova) we developed a stream database query manager for high volume queries. This project was in cooperation with IRFU and ASTRON.
Within the EU project Advanced eGovernment Information Service Bus we developed scalable methods to search wrapped eGovernment virtual repositories in terms of semantic web representations and queries. The RDFViewer system enables encapsulating (wrapping) different kinds of data sources to make them available for semantic web tools to query using the query languages SparQL, RDQL, or SQL.The SWARD (Semantic Web Abridged Relational Databases) subsystem wraps relational databases according to some ontology and SWATM (Semantic Web Abridged Topic Maps) wraps Topic Map data.
We participated in the project Personalized Access to Distributed Learning Repositories (PADLR) which was part of the Wallenberg global learning network research program on distributed collaborative learning innovations. Within PADLR we developed PSELO, a semantic web based query processing system for searching learning material from a peer-to-peer based educational infrastructure.Uppsala map Uppsala right now!
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