I had the meeting with Pine, discussing the motivation of my proposal, which brings me to rethink about the true motivation of what I am doing, and why is it a contribution.
For the general framework of using data-driven approach to infer meta-data from sensor measurements, I need to show 1> why is this necessary, why will the automated approach better than manually recording? 2> why using data-driven approach instead of other approaches(such as text mining)?
The first question should be addressed in two folds. It’s better in terms of efficiency. Statistics should support that manually recording is time-consuming and error-prone, while the data-driven approach is fast even including the time to collect data. It’s also better in terms of usability. The data-driven approach is trying to map the data into a common name space (e.g., Haystack), while the tag is often named in different ways and hard to interpret and understand. Although the tag naming at the initial step could be based on certain standard scheme, the knowledge required for installers complicate the process of the installation (also need proof?). For the existed buildings with tags being labelled already, it’s also cost expensive to remap the tags to a common namespace. (some examples from me to let people know the time and effort to do the conversion.)
The lack of usability in current tags impede many applications built on BAS to facilitate energy efficiency goals in buildings. examples with statistics? I intuitively think the different naming strategies across buildings or even inside a building largely hinders the generalizability of applications, for example, certain application needs to monitor the temperature in different zone of the building, yet the tag is labelled as “T”, “tp”, “temp”, “Temperature”, and it’s time consuming for people to consider all acronyms for certain measurement. Some examples could be shown from BAS tags used in CMU campus buildings. (Lack of standard labelling code)
As we can see, the problem lies in that there is no standard code to regularize the naming scheme. Despite the efforts from Haystack, IFC, Semantic Sensor web, no one-fit-all solution exists and people are still not using it(why?). It naturally leads to the second question, how can we get usable information without such standard naming code for tags? Two common approaches include IFT and IFD. Why should we use data-driven approach IFD or why should we use the combination of both? Even for IFT/IFD? What are we inferring, do we have a good name space for the information we are inferring? Why cannot we use this name space during the initial installation / manual recording? Besides, is the information inferred from data more complete and structured than that from tags? By looking at the history of data, we may be able to detect change behavior. (how to sustain this?)
Let’s go through some papers discussing the necessity of automated approach.
- W. Jones et. al.: Critical Information for First Responders, Whenever and Wherever it is Needed , 2001
The type of information in buildings is essential to improve the effectiveness of fire fighting operations and safety of the crews. One of the challenge is to interpret sensor signals to know what the environment being detected is.
- L. Luskay et. al.: Methods for Automated and Continuous Commissioning of Building Systems , 2003
The lack of commissioning of buildings lead to lower levels of equipment availability and greater occupant dissatisfaction. Methods for automated commissioning in building systems should be investigated.
- M. Brambley et. al. : Advanced sensors and controls for building applications: Market assessment and potential R & D pathways , 2005
A major barrier impeding the BAS energy savings is the hardware problem of input devices, sensors, transducers, wiring.
- N. Dawes et. al. : Sensor Metadata Management and Its Application in Collaborative Environmental Research 2008
Metadata management in e-science domain.
- X. Liu, B. Akinci : Requirements and Evaluation of Standards for Integration of Sensor Data with Building Information Models , 2009
The need to understand the context of the sensors to analyze the condition of facilities. Sensor readings alone don’t provide support for rich analysis, facility managers often need topological structure of sensors, location information, function and etc. to perform maintenance tasks.
- J. Butler : Point naming standards , 2010
Point name standards: 1> building 2> category 3> equipment type 4> space type 5> point type. Well-chosen point names can provide useful information about installed systems to the people responsible for maintaining, modifying, and interconnecting various building systems. As well, software that performs automated analysis of HVAC system performance may benefit from the consistent
application of a point naming standard.
- J. Lu, K. Whiteman : Smart blueprints: automatically generated maps of homes and the devices within them , 2012
generate a map of home using light and motion sensors, avoid the complicated configuration process. The occupant can just buy off-the-shelf sensors and let the system auto-configure itself.
- Jean-Paul Calbimonte et. al.: Deriving Semantic Sensor Metadata from Raw Measurements , 2012
The meta-data for sensor types is not always complete and coherent, as an example, to indicate a sensor measurement for temperature, different sensors use various tags like ‘T’, ‘tp’, ‘temp’, ‘temperature’, ‘mstemperature’ and etc.