Recommendations for in-situ data Near Real Time Quality Control

With the construction of operational oceanography systems, the need for real-time has become more and more important. A lot of work had been done in the past, within National Data Centres (NODC) and International Oceanographic Data and Information Exchange (IODE) to standardise delayed mode quality control procedures. Concerning such quality control procedures applicable in real-time (within hours to a maximum of a week from acquisition), which means automatically, some recommendations were set up for physical parameters but mainly within projects without consolidation with other initiatives. During the past ten years the EuroGOOS community has been working on such procedures within international programs such as Argo, OceanSites or GOSUD, or within EC projects such as Mersea, MFSTEP, FerryBox, ECOOP, and MyOcean. In collaboration with the FP7 SeaDataNet project that is standardizing the delayed mode quality control procedures in NODCs, and MyOcean GMES FP7 project that is standardizing near real time quality control procedures for operational oceanography purposes, the DATA-MEQ working group decided to put together this document to summarize the recommendations for near real-time QC procedures that they judged mature enough to be advertised and recommended to EuroGOOS.

Using SeaDataNet Management System to preserve the XBT Data-Set of the Mediterranean Sea

significant amount of Expendable Bathythermograph (XBT) data has been collected in the Mediterranean Sea since 1999 in the framework of operational oceanography activities. The management and storage of such a volume of data poses significant challenges and opportunities. The SeaDataNet project, a pan-European infrastructure for marine data diffusion, provides a convenient way to avoid dispersion of these temperature vertical profiles and to facilitate access to a wider public. The XBT data flow, along with the recent improvements in the quality check procedures and the consistence of the available historical data set are described. The main features of SeaDataNet services and the advantage of using this system for long-term data archiving are presented. Finally, focus on the Ligurian Sea is included in order to provide an example of the kind of information and final products devoted to different users can be easily derived from the SeaDataNet web portal.

The necessary alignment between technology innovation effectiveness and operational effectiveness

Organisations are increasingly investing in complex technological innovations such as enterprise information systems with the aim of improving the operations of the business, and in this way gaining competitive advantage. However, the implementation of technological innovations tends to have an excessive focus on either technology innovation effectiveness (also known as system effectiveness), or the resulting operational effectiveness; focusing on either one of them is detrimental to the long-term enterprise benefits through failure to achieve the real value of technological innovations. The lack of research on the dimensions and performance objectives that organisations must be focusing on is the main reason for this misalignment. This research uses a combination of qualitative and quantitative, three-stage methodological approach. Initial findings suggest that factors such as quality of information from technology innovation effectiveness, and quality and speed from operational effectiveness are important and significantly well correlated factors that promote the alignment between technology innovation effectiveness and operational effectiveness.

Workflow simulation for operational decision support

Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of workflow management. To do this we exploit not only the workflow’s design, but also use logged data describing the system’s observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.