# Cyber-Physical-Social Frameworks for Urban Big Data Systems: A Survey
## 8 Discussion  
In this section, we discuss the key research or technical challenges that need to be met for truly multimodal, cross-space CPSS framework deployments as well as the involved societal challenges.  
From a technology perspective, a key feature that has emerged from this review of CPSS in urban areas is concerned with the highly inhomogeneous and distributed nature of the data sources, including that of the sensors themselves and their interaction mechanisms [97]. With the increasing deployment of mobile sensor nodes in typical smart city implementations, data source detection and data collection mechanisms need to evolve to manage the resultant mobility issues. An interesting search mechanism for retrieving observation data from mobile sources has been detailed in [62]. As pointed out in [98], data collection methods need to leverage Big Data techniques and distributed intelligence to be able to facilitate cooperative sensing and transparent access to data processing mechanisms.  
A number of system modeling methodologies [1; 44; 99; 100; 101] have been proposed for the cyber-physical parts of a CPSS, however, current approaches lack feasible methods to uniformly model the physical, cyber and social spaces. Concurrently, the models need to take into account the fact that the cross-space data can either fit continuous time models (i.e., physical data) or be unstructured (e.g., tweets from the Twitter social network). An initial effort in this direction is outlined in [1] where the design decisions of a single-user CPSS are modeled with a flow based model that formally captures the relationships between the physical object, human and cyber actors. The data flow is represented through directed acyclic graphs.  
Current reasoning strategies employed for data analysis and fusion fall under the deductive reasoning category. This could be combined with probabilistic reasoning methods to handle uncertainty situations. The approach consists of truth tables which give the \"probabilities of what is likely to be true\" [102], which can provide estimates on the likelihood of events. Another possible aligned approach concerned with non-stationary environments, which are typical in CPSS scenarios, is that of learning in dynamic or evolving environments. The approach, termed concept drift learning [103; 104], is used to detect changes in the environment with some degree of confidence in order to trigger adaptive strategies.  
The growing prevalence of mobile crowd sensing in urban scenarios is transforming citizens into data sources. However, this engenders societal issues that will need to be solved, such as the trust level of the data, specially where it is involved in decision making processes. Annotating data with provenance metadata, such as the W3C provenance ontology, PROV-O [105], can be a possible solution to provide a degree of trust to data contributed by citizen smartphones. Closely aligned with the issue of trust is that of privacy and control of data. In current implementations, the service providers, who have access to usage statistics and users' personal data, become the de-facto owners after the data collection step. Some studies [5] propose the use of a personal Databox [106] system, which stores personal data along with access policies, which is then contacted by service providers to negotiate on the data access.