Azadeh Ghari Neiat

A service computing manifesto: the next 10 years

Mapping out the challenges and strategies for the widespread adoption of service computing.

Spatio-temporal Composition of Sensor Cloud Services

We propose a new framework for composing Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. To evaluate spatio-temporal Sensor-Cloud services, two new quality attributes are introduced. We present a heuristic algorithm based on A* to compose Sensor-Cloud services in terms of spatio-temporal aspects. In addition, a new spatio-temporal technique based on 3D R-tree to access Sensor-Cloud services is proposed. Analytical and simulation results are presented to show the performance of the proposed approach.

Failure-proof spatio-temporal composition of sensor cloud services

We propose a new failure-proof composition model for Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. To evaluate Sensor-Cloud services, a novel spatio-temporal quality model is introduced. We present a new failure-proof composition algorithm based on D* Lite to handle QoS changes of Sensor-Cloud services at run-time. Analytical and simulation results are presented to show the performance of the proposed approach.

Spatio-Temporal Composition of Crowdsourced Services

We propose a new composition approach for crowdsourced services based on dynamic features such as spatio-temporal aspects. The proposed approach is defined based on a formal crowdsourced service model that abstracts the functionality of crowdsourced data on the cloud in terms of spatio-temporal features. We present a new QoS-aware spatio-temporal union composition algorithm to efficiently select the optimal crowdsourced composition plan. Experimental results validate the performance of the proposed algorithm.

Crowdsourced Coverage as a Service: Two-Level Composition of Sensor Cloud Services

We present a new two-level composition model for crowdsourced Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. The proposed approach is defined based on a formal Sensor-Cloud service model that abstracts the functionality and non-functional aspects of sensor data on the cloud in terms of spatio-temporal features. A spatio-temporal indexing technique based on the 3D R-tree to enable fast identification of appropriate Sensor-Cloud services is proposed. A novel quality model is introduced that considers dynamic features of sensors to select and compose Sensor-Cloud services. The quality model defines Coverage as a Service which is formulated as a composition of crowdsourced Sensor-Cloud services. We present two new QoS-aware spatio-temporal composition algorithms to select the optimal composition plan. Experimental results validate the performance of the proposed algorithms.

Spatio-Temporal Linear Composition of Sensor Cloud Services

The major contribution of this chapter is that it proposes a novel QoS-based sensor cloud service composition framework using the power of the service paradigm. Two major components are involved in this framework. The first component is a sensor cloud service management framework that comprises a service model and an indexing model of sensor cloud services. Therefore, we present a new service model which aims to abstract a sensor cloud service by conceptualizing the spatio-temporal aspect of the service as its functional attributes and the qualitative aspects of the service as its non-functional attributes. The indexing model aims to spatio-temporally index sensor cloud services to enable an effective and efficient search of the services. We also define novel QoS attributes for evaluating sensor cloud services based on dynamic features of the sensor cloud. The composition combines sensor cloud services to provide a new sensor cloud service. Therefore, the second component of the proposed framework is a spatio-temporal linear composition algorithm which enables users to select optimal composition plans based on their own functional and non-functional requirements.

A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services

This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into regions. The availability prediction of a mobile crowdsourced service at a certain location and time is then formulated as a classification problem. To determine the availability duration of predicted mobile crowdsourced services, we formulate a forecasting task of time series using the Gramian Angular Field. We validated the effectiveness of the proposed framework through multiple experiments.

Confidence-Aware Reputation Bootstrapping in Composite Service Environments

We propose a novel reputation bootstrapping approach for both composite and atomic services in service-oriented environments. We consider multiple factors which may implicitly represent reputations of new services. Our approach does not rely on empirical assumptions. In contrast, we propose a data-driven method to determine how much a factor can represent service reputation. The reputation-related factors are modelled in a layer-based framework. This aims to quantitatively describe the importance of factors in reputation bootstrapping. Furthermore, we define confidence to represent how reliable the bootstrapped reputation of a new service is. We evaluate our approach based on a real-world dataset. The experimental results demonstrate the feasibility and outperformance of our approach.