The ALBA architecture encompasses the following main modules for executing scientific experiments: 1. Experiment engine: The experiment engine inter-
faces with the Matlab environment and executes the code related to clustering methods. The core of the computation is a Matlab function, activated by Java code through a suitable JMatLink1 inter-
face. The whole Java code is exposed as a Web service. Single functions can also be isolated as
ALBA Cooperative Environment for Scientific Experiments
independent Web services and allocated on dif- ferent nodes for reuse purposes. The creation and deploy of Web services is based on Apache Axis on the Apache Tomcat2 Web container.
2. Data extraction module: Almost the totality of DNA-microrrays data is currently present in three formats, each coming in different variants. A wide choice of data is available for example on the Cancer Program Data Set of the Broad Institute, MA. The Data Extraction Module is a Java module that maps different kinds of formats into a unique, uniform representation. The module is exposed as a Web service.
3. Visualization module: The visualization module of ALBA deals with the suitable visualization of experiment results. The results can be returned as graphs or as textual descriptions; they are re- versed into an HTML page accessible from any Web browser. Also the Visualization Module is a virtual resource and, as such, can be (re)used in subsequent experiments as an independent mod- ule to be included in the experiment workflow of other organizations.
The three modules are depicted in Figure 1. They implement an integrated environment where experi- ments can be executed as workflows of services on heterogeneous platforms.
FuturE trEndS
In future works, the classes of experiments of inter- est could be extended, to implement the proposed structure in specific application fields where its need looks of primary relevance, and of course to detail the sequences of interaction among actors in the specific use cases. The approach should be deeper investigated by enlarging the scope of the experiments of interest, such as, for instance, brain dynamics investigation, geo-informatics, drug discovery.
concLuSIon
We have illustrated the elements of the ALBA proj- ect, aimed at defining models and tools for scientific cooperative services to be designed and executed in a distributed manner over the Internet. The proposed cooperative framework for distributed experiments is quite general and flexible, being adaptable to different contexts.
Given the challenge of evaluating the effects of applying emerging Web service technology to the scientific community, the evaluation performed up to now takes a flexible and multi-faceted approach: it aims at assessing task-user-system functionality and can be extended incrementally according to the continuous evolution of scientific cooperative environment.
Figure 1. The ALBA modules
data Extraction Module data Matlab instance Visualization Module Jmatlink Experiment Engine
A
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KEY tErMS
Bioinformatics: The application of the ICT tools
to advanced biological problems, like transcriptomics and proteomic, involving huge amounts of data.
Clustering: Automatic aggregation of data in classes
according to a given distance (usually Euclidean). It is supervised if a subset of data is used in order to learn the classification embedded rule to be applied to the rest of the data; otherwise unsupervised.
Cooperative Information Systems: Independent,
federated information systems that can either autono- mously execute locally or cooperate for some tasks toward a common organizational goal.
Drug Discovery: Forecasting of the properties of a
candidate new drug on the basis of a computed combina- tion of the known properties of its main constituents.
E-Experiment: Scientific experiment executed on
an ICT distributed environment centred on cooperative tools and methods
E-Science: Modality of performing experiments in
silico in a cooperative way by resorting to information and communication technology (ICT)
Grid Computing: Distributed computation over
a grid of nodes dynamically allocated to the process in execution.
Interoperability: Possibility of performing com-
putation in a distributed heterogeneous environment without altering the technological and specification structure at each involved node.
Web Services: Software paradigm enabling peer-
to-peer computation in distributed environments based on the concept of “service” as an autonomous piece of code published in the network.
EndnotES
1 http://jmatlink.sourceforge.net/ 2 http://tomcat.apache.org/