From 7.5th floor:
Data management in the worldwide sensor web draws the big picture in mentioning that now too much attention has been placed on the networking issues of distributed sensing and too little on tools to manage, analyze and understand the data. The authors ask the question weather we can design sensor networks with data quality in mind? They ask a very crucial question, but as often in location-aware computing, it is very unclear on who can claim what quality in location information is or in other words who can answer “how good is good enough?”. Of course it is important to manage temporal and spatial data and handle their inherent uncertainty (e.g. via probabilistic theory) or mask it (e.g. via interpolation) or play with it (seamful design). It seems clear now that my thesis is about acknowledging that situation (uncertainty in the location information, fluctuant quality in the data), but instead of aiming to produce “perfect data”, I plan to provide an understanding and solutions from a human and urban perspective. It comes, at the first place, with the observation of people experiencing location-aware systems in CatchBob!, and making use of location information, in my taxi driver (co-evolution, context and granularity). This observations help me accumulating evidences on the contextual factors influencing the granularity (≈human expectation of quality) of the location information used.
Balazinska, M., Deshpande, A., Franklin, M. J., Gibbons, P. B., Gray, J., Hansen, M., Liebhold, M., Nath, S., Szalay, A., and Tao, V. (2007). Data management in the worldwide sensor web. IEEE Pervasive Computing, 6(2):30–40.
