The pre-deployment dataset contains expression measures on 22034 transcripts.
We repeated the same numerical experiments described above on post-deployment dataset from Breen et al. We used the same set up parameters in ARACNe and VIPER.
As highlighted earlier, while the In-dataset display the deployment of at least three language-varieties (English, Hindi, Gujarati), including at least two scripts (Latin, Devanagri), the Sw-dataset displays the use of only one language-variety (Swedish) in the Latin script.
Such datasets enable the deployment of robust 3D analysis algorithms.
Testing have been performed using an on-field recorded dataset from a pervasive deployment in Cambridge (UK), encompassing several weeks.
We include a discussion of the relative performance of the used tools on our case study, and an overview of lessons learnt for practical deployment of OBDA on real-world datasets.
Based on the analysis of a dataset from two 21-weeks deployment studies and nine semi-structured interviews, we conducted an in-depth formative study trying to understand the communication asymmetry among older adults and their relatives.
We demonstrate the effectiveness of our approach on a number of inference algorithms and evaluate the implementation on a suite of datasets in a real network deployment.
At the same time, workflow management systems (WFMS) may support data parallelism over big datasets by providing scalable, distributed deployment and execution of the workflow over a cloud infrastructure.
This allows for the secure deployment of both public and pre-publication datasets from the same server and interface.
