Biological networks are a very convenient modelling and visualisation tool to discover knowledge from modern high-throughput genomics and postgenomics data sets. Indeed, biological entities are not isolated, but are components of complex multi-level systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems. We present the causal formalism and bring it out in the context of biological networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression. We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set.
With the coming tsunami of 'Smart Meter' and 'Smart Grid' data SP Energy Networks (SPEN) considered that it needed to explore 'Dynamic Data Modelling' within 'Master Data' structures. This step is considered essential in determining the future approach to Data Management within this predicted 'Big Data' environment. A project called Distribution Intelligence for Network Operators (DINO) was formulated to facilitate phase 1 of this exploration. This initial investigation consists of enhancement of operational insight from a high volume 'Alarm' data output that is generated within our existing Network Management System (NMS). The dynamic data source is a high volume, bespoke relationship, alarm event historian system called PSAlerts. Novel dynamic data modelling techniques were explored to relate this dynamic data source to a specific integrated network, communication and protection master data model. This 'Integrated Network Model' (INM) was crafted from a snapshot of the overall NMS enterprise data relating to the network automation business area; along with a locally held, informally managed, semistructured radio communication dataset. Daily experience with the 'Automation' team business process led to specific use cases that could utilise this dynamic data stream analysis, but are currently masked in the noise due to the volumes of data. Complex Event Processing (CEP) translated the dynamic data streams into the required network intelligence. Final integration of both these into a Proof of Concept (PoC) dynamic 'Smart Network Model' (SNM) was completed to provide the final output visualisation and follow-on action capability.
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