KEY PUBLICATIONS
June 25, 2017
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 quality of service parameters. Proposed framework is a 4-stage filtering algorithm, to select social-sensor cloud services based on user query and quality of service demands. 4-stage filtering is based on spatial correlation, textual correlation, visual features and quality of service parameters. Analytical results are presented to show the performance of the proposed approach.
November 13, 2017
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 and related posted information. The proposed framework is a 4-stage algorithm, to select social-sensor cloud services based on the user queries. The selection algorithm is based on spatio-temporal indexing, spatio-temporal and textual correlations, and quality of services. Analytical results are presented to prove the efficiency of the proposed approach in comparison to a traditional approach of image processing.
July 2, 2018
We propose a new social-sensor cloud services trust model. We propose to represent social media data streams, i.e., images' meta-data and related posted information, as social-sensor cloud services. Images' meta-data and the related posted information are abstracted as the functional and non-functional aspects of the social-sensor cloud services. The trustworthiness of a social-sensor cloud service is measured based on the users' stance based trust model. We use the textual features of the social-sensor cloud services, i.e., comments and meta-data, e.g., spatio-temporal information to gather the trust-rate of the service. Analytical results are presented to show the performance of the proposed model with real datasets.
February 18, 2020
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 existing attempts however ignore the temporal-semantic relevance and spatio-temporal evolution of the images and direction-oriented scene reconstruction. We propose a novel social-sensor cloud (SocSen) service composition approach to form tapestry scenes for scene analysis. The novelty lies in utilising images and image meta-information to bypass expensive traditional image processing techniques to reconstruct scenes. Metadata, such as geolocation, time and angle of view of an image are modelled as non-functional attributes of a SocSen service.
To Date
PUBLICATIONS
Conferences:
Heuristics based Mosaic of Social-Sensor Services for Scene Reconstruction. Tooba Aamir, Hai Dong, and Athman Bouguettaya. WISE 2020, Amsterdam and Leiden, Netherlands.
Social-Sensor Composition for Scene Analysis. Tooba Aamir, Hai Dong, and Athman Bouguettaya. ICSOC 2018, Hangzhou, China.
Stance and Credibility Based Trust in Social-Sensor Cloud Services. Tooba Aamir, Hai Dong, and Athman Bouguettaya. WISE 2018, Dubai, United Arab Emirates.
Trust in Social-Sensor Cloud Service. Tooba Aamir, Hai Dong, and Athman Bouguettaya. ICWS 2018, San Francisco, CA, USA.
Social-Sensor Cloud Service for Scene Reconstruction. Tooba Aamir, Athman Bouguettaya, Hai Dong, Sajib Mistry, and Abdelkarim Erradi. ICSOC 2017, Malaga, Spain.
Social-Sensor Cloud Service Selection. Tooba Aamir, Athman Bouguettaya, Hai Dong, Abdelkarim Erradi, and Rachid Hadjidj. ICWS 2017, Honolulu, HI, USA.
Journals:
Social-Sensor Composition for Tapestry Scenes. Tooba Aamir, Hai Dong, and Athman Bouguettaya. IEEE Transactions on Services Computing (2020).