The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
An Approach towards Real-Time Data Exchange Platform System Architecture (concise contribution)
PERCOM '08 Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications
The Unreasonable Effectiveness of Data
IEEE Intelligent Systems
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Proceedings of the 13th international conference on Ubiquitous computing
Discovering regions of different functions in a city using human mobility and POIs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
City-scale traffic estimation from a roving sensor network
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
T-share: A large-scale dynamic taxi ridesharing service
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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Classic paradigm of scientific modeling is mainly based on a set of previously, accepted or assumed theories about the target phenomena and a validation procedure by limited observations. Therefore, normally data has a supporting role in the modeling process. On the other hand, recent advances in computing technology have brought us a data deluge that may change the classic paradigm of scientific modeling. Information flows and data streams have reached a level of maturity that they can play the main role in modeling of the real systems, without relying on lots of assumptions and rules in the first step. This turn may cause an inversion in the concept of modeling as a rational process. The proposed theoretical idea in this work is that traditional theory-driven models have a theoretical limit in modeling complex systems, known as curse of dimensionality and further, to highlight the fact that massive urban data streams can open up a new data-driven modeling approach, which goes beyond simple data driven analytics or eye catching info-graphics toward operational models of complex phenomena. In this work we describe a conceptual framework for modeling city wide traffic dynamics that proposes a way to encapsulate the complexity based on abstraction power of Markov chains in a coexistence with continuous data streams. Therefore, finally as an experimental set up, we applied the proposed model to a real data set, consisting of GPS traces of taxi cabs in Beijing and the results have been explained.