# Cyber-Physical-Social Frameworks for Urban Big Data Systems: A Survey
## 1 Introduction
The vast amounts of data obtained by the sensing of physical phenomena by distributed sensor networks and those contributed by city inhabitants through their sensor-enabled smartphones and on online social networks, can offer near real-time large-scale sensing for intelligence mining to facilitate responsive and intelligent services in urban  
Figure 1: Cyber–Physical—Social Systems (CPSS) (adapted from [17]). WSN: Wireless Sensor Networks. settings [13]. Extracting knowledge out of the data, typically through big data analytics techniques, can help to build a picture of urban dynamics, which can enable intelligent applications and services, and guide decision-making, both for city authorities and city inhabitants [21]. Implementations of CPSS applications are emerging in everyday life in smart urban systems, in domains as varied as command and control, smart environments, smart transportation, smart social production systems and so on [19; 30; 31; 32; 33; 34; 35]. Such applications rely on efficient monitoring of the urban physical infrastructure and ambient environment and combine the collected data through intelligent cyber processes to deliver improved services to citizens; for example, by adjusting traffic signal timings based on vehicle and cyclist arrival information [19; 36], developing sustainable waste management systems [37] and recommending events/venues based on citizen preferences, proximity, road and environment (pollution levels) conditions [38; 39]. The resulting urban big data system offers the potential of creating more sustainable and environment-friendly future cities [40]. While a number of CPSS implementations have focused on architectural views including fog [41] and cloud-based implementations [42] as well as multivariate data distribution platforms [43], a more data-centric view is needed to realize CPSSs for urban areas [40]. Such a data-centric perspective can help to create abstractions of the high-dimensional and high-volume data generated and exchanged by the heterogeneous devices, which can then be analyzed and fused through analytics and reasoning techniques to build city intelligence. As the sensed physical world data is largely dependent on its surrounding environment, external information (e.g., locations, time, events gleaned from mobile crowd sensing that may influence sensed data) needs to be taken into account to derive relevant knowledge. A number of recent studies have reviewed urban CPSS from different aspects, with some focusing on the development stages of CPSS and the applicability of different system-level design methods for CPSS design [1; 44]. [PERSON] et al. [45] review the middleware, computation model, data quality and run-time environments necessary for smart building and smart city implementations, while [PERSON] et al. [46] survey approaches for engaging human interaction with urban computing technologies. A survey of mobile crowd sensing approaches [13] that is closely aligned to this paper, presents a taxonomy based on the data collection steps, processing architecture and resulting applications. In contrast to these surveys, this article collects works on urban CPSS implementations and organizes them from a data perspective. The reviewed works are those that include all the three aspects of cyber-physical-social, i.e., the data collected involves both the physical world and mobile crowd sensed (social) data that is manipulated through various data processing techniques to fuse them together for collective urban intelligence. Both research studies and commercial solutions are reviewed, with a focus on methods for cross-space data fusion and collaborative sensing techniques.