Tooba Aamir

The Commonwealth Scientific and Industrial Research Organisation | CSIRO · Data61

We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes. The proposed approach leverages crowdsourced social media images to create an image mosaic to reconstruct a scene at a designated location and an interval of time. The novel approach relies on the set of features defined on the ba...

We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes. The proposed approach leverages crowdsourced social media images to create an image mosaic to reconstruct a scene at a designated location and an interval of time. The novel approach relies on the set of features defined on the ba...

The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events. One of its potential applications is to leverage crowdsourced social media images to create a tapestry scene for scene analysis of designated locations and time intervals. The...

The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events. One of its potential applications is to leverage crowdsourced social media images to create a tapestry scene for scene analysis of designated locations and time intervals. The...

We propose a users’ stance and credibility based social-sensor cloud service trust model. We represent social media data streams, i.e., image meta-data and related posted information, as social-sensor cloud services. We use the textual features of the social-sensor cloud services, i.e., comments, and meta-data, e.g., spatio-temporal information, to...

We consider the scene analysis as a service composition problem. A social-sensor cloud services composition model is proposed for the scene analysis. Our proposed model selects and composes social-sensor cloud services based on the user queries. Textual features of the social-sensor cloud services, i.e., description, comments, and meta-data of the...

We propose a new social-sensor cloud services selection framework for scene reconstruction. The proposed research represents social media data streams, i.e., images’ metadata and related posted information, as social sensor cloud services. The functional and non-functional aspects of social sensor cloud services are abstracted from images’ metadata...

We propose a new framework for social-sensor cloud services selection based on spatio-textual correlation between user's query and service. The proposed research defines a formal social-sensor cloud service model that abstracts the functional and non-functional aspects of social-sensor data on the cloud in terms of spatio-temporal, textual and qual...