The Context Data Cloud offers innovative solutions that can be utilized in various application scenarios.
Semantic Positioning Solutions
The location of a user is the fundamental factor shaping location-based services (LBS) and is usually computed solely in terms of WGS84 coordinates without taking the semantics of the location into consideration. Furthermore, traditional proactive LBS require manual effort in defining geofences beforehand around all relevant location entities, which is very time-consuming and not feasible in the LBS development process. Another challenge is related to Geocoding Services where incomplete or ambiguous address data often leads to wrong results.
In order to overcome these drawbacks, we provide several Semantic Positioning Solutions as integral parts of the Context Data Cloud platform that overcome the limitations of classic geocoding as well as geofencing methods and add semantic features to proactive self-referencing and cross-referencing LBS. The applicability of our Semantic Tracking and Semantic Geocoding approaches is showcased within the Friend Tracker as well as Popular Places services of the CDCApp and our OpenMobileNetwork Geocoding Web Interface.
For more information, see [Uzun et al., 2013] and [Uzun et al., 2014].
In order to facilitate easy discovery of potentially useful context data within the LOD Cloud, we have developed a Context Search service that makes use of the Context Meta Ontology and its corresponding Context Meta Ontology Directory.
For this purpose, we have crawled the datasets of the OpenMobileNetwork, Linked Crowdsourced Data as well as Enipedia and created Context Meta Ontologies by categorizing the major concepts and predicates of these datasets into context facets in a semi-automatic fashion. Examples for context facets are LocationContextFacet, NetworkContextFacet, and EnvironmentalContextFacet. In addition, availability constraints in terms of time and location have been included, thus allowing for a fine grained search.
The categorized meta data is made available within the Context Meta Ontology Directory. Furthermore, there are a number of JSON API endpoints that allow for quicker access without needing knowledge about the construction of SPARQL queries.
A specific use case for context data discovery could look as follows:
A mobile application developer is looking for relevant context data in Berlin. To get an overview of what datasets are available, he uses the JSON API to find out:
He learns that there is environmental, location and weather context data available in general. Since he is only interested in environmental data for Berlin, he sends another JSON API query:
But is the data up-to-date? This question can also easily be answered:
Location Analytics Framework
Through the OpenMobileNetwork, we have conducted thousands of network measurements by smartphone users comprising an exact GPS location, a list of received WiFi access points as well as the respective signal strength, the current Cell-ID of the mobile network the smartphone is connected to and information about running services and the traffic generated by those services.
Based on the datasets of the OpenMobileNetwork and Linked Crowdsourced Data, a Location Analytics Framework is implemented that provides insight on what is going on in different mobile network regions. This insight is based on diverse data: Context information and location preferences of users, their movements as well as stationary phases and external data, such as public events or weather information. The fusion of this data yields scope to identify correlations and trends as they are coming up.