A master student performed a problem exploration for the PayDIBI project. This is the report he wrote.
Integration of Biological Sources – Exploring the Case of Protein Homology
Tjeerd W. Boerman, Maurice van Keulen, Paul van der Vet, Edouard I. Severing (Wageningen University)
Data integration is a key issue in the domain of bioin- formatics, which deals with huge amounts of heterogeneous biological data that grows and changes rapidly. This paper serves as an introduction in the field of bioinformatics and the biological concepts it deals with, and an exploration of the integration problems a bioinformatics scientist faces. We examine ProGMap, an integrated protein homology system used by bioinformatics scientists at Wageningen University, and several use cases related to protein homology. A key issue we identify is the huge manual effort required to unify source databases into a single resource. Uncertain databases are able to contain several possible worlds, and it has been proposed that they can be used to significantly reduce initial integration efforts. We propose several directions for future work where uncertain databases can be applied to bioinformatics, with the goal of furthering the cause of bioinformatics integration.
[details]

For his “Research Topic” course, MSc student Emiel Hollander experimented with a mapping from Probabilistic XML to the probabilistic relational database Trio to investigate whether or not it is feasible to use Trio as a back-end for processing XPath queries on Probabilistic XML.
Storing and Querying Probabilistic XML Using a Probabilistic Relational DBMS
Emiel Hollander, Maurice van Keulen
This work explores the feasibility of storing and querying probabilistic XML in a probabilistic relational database. Our approach is to adapt known techniques for mapping XML to relational data such that the possible worlds are preserved. We show that this approach can work for any XML-to-relational technique by adapting a representative schema-based (inlining) as well as a representative schemaless technique (XPath Accelerator). We investigate the maturity of probabilistic relational databases for this task with experiments with one of the state-of- the-art systems, called Trio.

The paper will be presented at the 4th International Workshop on Management of Uncertain Data (MUD 2010) co-located with VLDB, 13 September 2010, Singapore [details]

It happens too infrequently with students for my taste, but Irma Veldman, a student of mine, got a paper accepted for the SUM conference about her research project.
Compression of Probabilistic XML documents
Irma Veldman, Ander de Keijzer, Maurice van Keulen
Database techniques to store, query and manipulate data that contains uncertainty receives increasing research interest. Such UDBMSs can be classified according to their underlying data model: relational, XML, or RDF. We focus on uncertain XML DBMS with as representative example the Probabilistic XML model (PXML) of [9]. The size of a PXML document is obviously a factor in performance. There are PXML-specific techniques to reduce the size, such as a push down mechanism, that produces equivalent but more compact PXML documents. It can only be applied, however, where possibilities are dependent. For normal XML documents there also exist several techniques for compressing a document. Since Probabilistic XML is (a special form of) normal XML, it might benefit from these methods even more. In this paper, we show that existing compression mechanisms can be combined with PXML-specific compression techniques. We also show that best compression rates are obtained with a combination of PXML-specific technique with a rather simple generic DAG-compression technique.

The paper will be presented at the third International Conference on Scalable Uncertainty Management (SUM2009), 28-30 Sep 2009, Washington, DC, USA [details]