# Cyber-Physical-Social Frameworks for Urban Big Data Systems: A Survey
## 3 Data-Centric Cyber-Physical-Social Urban Big Data Systems
Thematic-spatiotemporal context awareness consists of associating the physical world numerical sensing data with external influencing information (e.g., locations, time, events that may influence sensed data), since urban data is often highly localized. * Cross-space data fusion: with the multimodal data collected from heterogeneous data sources, advanced mining techniques are needed to fuse the data which may be in different scales of measurement [59], for instance, physical sensor data which is usually in interval or ratio scale (involving quantitative variables) and open datasets which correspond to nominal or ordinal scale (involving qualitative classifications). Following the identification of the requirements, the layers for the proposed conceptual framework can be identified to consist of four layers: data sources, data processing and analytics, data fusion, and applications, as shown in Figure 3b. **Data sources**: this layer involves heterogeneous data sources for gathering urban data, including data from both fixed and mobile sites as well as user-contributed data. The corresponding data collection mechanisms cover a wide range, including data upload and retrieval from sensor gateways through Representational State Transfer (RESTful) Application Programming Interface (API) [60] or Constrained Application Protocol (CoAP) [61], opportunistic data transmission from mobile sensors [15; 62], such as those mounted on public transport vehicles. User-contributed data involves citizens explicitly uploading data from their sensor-rich mobile phones as well as urban data information extracted from online social networks. **Data processing**: this layer details the various machine learning-based or logic-based data processing methods that are employed in the state of the art to derive information from the physical sensor networks data or from user data, usually in isolation, before being merged. **Data fusion**: this layer consists of various statistical or logic-based methods to integrate the outputs from the data processing layer in order to achieve a cohesive view of urban awareness. Crucial to achieving this urban intelligence is the fusion of cross-space, heterogeneous and multimodal data. **Applications**: this layer consists of the different kinds of applications that are enabled by CPSS. _A Taxonomy and Conceptual Framework for CPSS Solutions_  
Following the identification of the data-specific elements for an urban CPSS implementation and its constituent layers, Table 1 provides a summary of the surveyed works and commercial solutions, delineating the methods applied for each of the layer implementations. In addition to the data-specific elements identified above, Table 1 also lists the **system components** encountered in the reviewed works (if specified in the related articles). To analyze the various disparate existing methods for data manipulation and the resulting applications in the CPSS domain as reviewed in this article, we outline the various functional blocks of a conceptual CPSS framework, as presented in Figure 4.  
Figure 4 maps the identified CPSS elements from Figure 3b to the four layers of data sources, data processing, data fusion, and applications. The following sections of this article elaborate and categorize the various techniques and mechanisms employed for the different data-specific tasks. Figure 4: A conceptual framework for CPSS.