# Cyber-Physical-Social Frameworks for Urban Big Data Systems: A Survey
## 3 Data-Centric Cyber-Physical-Social Urban Big Data Systems  
There have been a number of recent initiatives on data-oriented CPSS architecture implementations. However, these have largely orientated towards a particular focus, for instance, the underlying technologies being used (e.g., semantic web-based mechanisms [38; 55]), data abstraction requirements [56] or application [33; 57]. To arrive at a conceptual framework for urban CPSS, we start from the well-documented layer diagram [4] of a CPS, as shown in Figure 3a. The elements of a CPS implementation are recognized to include the sensor and actuator networks that sense and interact with the physical world; control components that retrieve the sensor data, perform local processing and manage the actuators in accordance to specified goals; data analytics (software) components that are responsible for the storage, processing and filtering of data obtained from the control elements; computation elements that are used to adapt the CPS to internal or external changes through \"switching between operation modes\" [4]; and communication elements that encompass a variety of technologies to support connectivity in the system. Figure 3: From Cyber–Physical Systems (CPS) to a data-driven CPSS; (**a**) main elements of a CPS framework and (**b**) data-oriented CPSS functional elements. For a data-oriented perspective of a CPSS implementation in urban settings, the emphasis needs to be on the data sourcing, collection and analysis mechanisms in order to obtain city intelligence [29; 40]. Thus, we recognize that the communication elements are not the focus in a CPSS framework conceptualization since they can be assumed to be taken into account by the public/private institutions deploying the sensor networks (e.g., wired or wireless networks for data transfer), part of the enabling technologies (3 G, 4 G or WiFi networks) in the case of mobile crowd-sensing by smartphone-accompanied citizens or implicitly present as is the case in crowd observations posted on online social networks. Since urban CPSS are not designed to be tightly bound control loop architectures, the control and computation elements are also not translated into a CPSS conceptualization. Furthermore, it has been recognized in the current state of the art that CPSS computing requires multitier computation based on the DIKW dimensions, supported by mechanisms that deliver increasing abstractions to make the data human-understandable [29]. Each level of such a multi-tiered framework consists of a series of horizontal and vertical operators, with the horizontal operators responsible for mapping the multimodal data to common concepts to support integration within each level; and the vertical operators in charge of the translations from raw data to higher-level knowledge. We thus identify the following requirements of a data-driven CPSS framework, derived from the layer architecture of [40]:  
* Collaborative sensing sources: since different sources may provide data about the same physical resource (i.e., location, phenomenon, etc.) from different aspects due to their inherent capabilities, the resulting data must be combined to achieve collaborative sensing. Smartphone-carrying citizens are valuable sensing resources due to their inherent mobility around different parts of a city, together with observations made by citizens on online social networks about specific city-relevant situations. Citizen-contributed data can be a cooperative source of relevant data to complement that obtained from physical sensor networks. * Data analysis: in addition to preliminary steps such as data cleaning, redundancy elimination etc., data analysis should consider the inherent correlation between the data from different spaces (i.e., online or physical world) through detection of patterns and thematic-spatiotemporal context relevance [58].