Wise Cities: 'Old' big data and 'slow' real time. Carrera, F. ![Wise Cities: 'Old' big data and 'slow' real time [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex ‘Big Data’ typically refers to large datasets, mined in bulk from modern electronic devices, that can be analyzed to extract patterns of behavior at both the macro and micro level. More often than not, big data is derived from major systems, such as social networks or telephone providers, or from other sensor networks that compose the big data firehose. Humans have also been recruited to act as sentient sensors that can contribute richer and finer-grained crowdsourced data to this big pile. Nevertheless, big data is not only found in the form of (near) real-time information extracted through reality mining. While the activities that take place in a city are fast and mobile, the immobile structures that make up the physical city evolve at a much slower pace, creating a sedimentary layer of ‘old big data’ that is possibly more useful – certainly as useful – for city planning as the information derived from real-time sensing.Using our efforts in Venice (Italy) to record the historical evolution and current state of the city through many kinds of data, we show how smart-city approaches can be used to capture the backlog of data that predates the current age of the internet, and also to intercept gradual structural changes to the built environment that happen in ‘slow real time’. Such techniques are applicable to all cities, and especially to world heritage sites like Venice where we can accumulate longitudinal big data that can gradually transform a smart city into a wise city.
@misc{carrera_wise_nodate,
title = {Wise {Cities}: '{Old}' big data and 'slow' real time},
url = {https://docs.google.com/document/d/1CPCGMInLI4TgtELP4s8WFRX_EzYM5pTPa4LixmaSiN4/edit?usp=embed_facebook},
abstract = {‘Big Data’ typically refers to large datasets, mined in bulk from modern electronic devices, that can be analyzed to extract patterns of behavior at both the macro and micro level. More often than not, big data is derived from major systems, such as social networks or telephone providers, or from other sensor networks that compose the big data firehose. Humans have also been recruited to act as sentient sensors that can contribute richer and finer-grained crowdsourced data to this big pile. Nevertheless, big data is not only found in the form of (near) real-time information extracted through reality mining. While the activities that take place in a city are fast and mobile, the immobile structures that make up the physical city evolve at a much slower pace, creating a sedimentary layer of ‘old big data’ that is possibly more useful -- certainly as useful -- for city planning as the information derived from real-time sensing.Using our efforts in Venice (Italy) to record the historical evolution and current state of the city through many kinds of data, we show how smart-city approaches can be used to capture the backlog of data that predates the current age of the internet, and also to intercept gradual structural changes to the built environment that happen in ‘slow real time’. Such techniques are applicable to all cities, and especially to world heritage sites like Venice where we can accumulate longitudinal big data that can gradually transform a smart city into a wise city.},
language = {en},
urldate = {2021-11-19},
author = {Carrera, Fabio},
}
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