Abdelmounaam Rezgui

Geoinformatics: Transforming data to knowledge for geosciences

An integrative view of Earth as a system, based on multidisciplinary data, has become one of the most compelling reasons for research and education in the geosciences. It is now necessary to establish a modern infrastructure that can support the transformation of data to knowledge. Such an information infrastructure for geosciences is contained within the emerging science of geoinformatics, which seeks to promote the utilization and integration of complex, multidisciplinary data in seeking solutions to geoscience-based societal challenges.

Dimming Cellular Networks

We propose a novel technique called dimming to improve the energy efficiency of cellular networks by reducing the capacity, services, and energy consumption of cells without turning off the cells. We define three basic methods to dim the network: coverage, frequency, and service dimming. We construct a multi-time period optimization problem to implement frequency dimming and extend it to implement both frequency and service dimming together. We illustrate the ability of dimming techniques to adapt the capacity and network services in proportion to the dynamic spatial and temporal load resulting in significant energy savings through numerical results for a sample network.

Utilize cloud computing to support dust storm forecasting

Abstract The simulations and potential forecasting of dust storms are of significant interest to public health and environment sciences. Dust storms have interannual variabilities and are typical disruptive events. The computing platform for a dust storm forecasting operational system should support a disruptive fashion by scaling up to enable high-resolution forecasting and massive public access when dust storms come and scaling down when no dust storm events occur to save energy and costs. With the capability of providing a large, elastic, and virtualized pool of computational resources, cloud computing becomes a new and advantageous computing paradigm to resolve scientific problems traditionally requiring a large-scale and high-performance cluster. This paper examines the viability for cloud computing to support dust storm forecasting. Through a holistic study by systematically comparing cloud computing using Amazon EC2 to traditional high performance computing (HPC) cluster, we find that cloud computing is emerging as a credible solution for (1) supporting dust storm forecasting in spinning off a large group of computing resources in a few minutes to satisfy the disruptive computing requirements of dust storm forecasting, (2) performing high-resolution dust storm forecasting when required, (3) supporting concurrent computing requirements, (4) supporting real dust storm event forecasting for a large geographic domain by using recent dust storm event in Phoniex, 05 July 2011 as example, and (5) reducing cost by maintaining low computing support when there is no dust storm events while invoking a large amount of computing resource to perform high-resolution forecasting and responding to large amount of concurrent public accesses.

Service-Oriented Sensor-Actuator Networks [Ad Hoc and Sensor Networks]

Most approaches developed to query sensor-actuator networks (SANETs) are either application-specific or generic. Application-specific SANETs provide limited reusability, are not cost effective, and may require extensive reprogramming efforts to make the network able to serve new applications. Generic SANETs usually require that a sizeable code be deployed on the nodes regardless of the specific requirements of the application at hand. More important, they may not be optimized to fully exploit the specific characteristics and query patterns of a given application. In this article we introduce service-oriented SANETs (SOSANETs) as a novel approach to building customizable SANETs. SOSANETs provide the benefits of both application-specific SANETs (e.g., energy efficiency, scalability) and generic SANETs (e.g., reusability) and avoid most of their limitations. We implemented our approach in TinySOA, a SOSANET developed on top of TinyOS. We conducted an evaluation of TinySOA that included a comparison with TinyDB, an established query processing system for sensor networks. The obtained empirical results show that TinySOA outperforms TinyDB in many aspects including energy consumption, scalability, and response time.

Using adaptively coupled models and high-performance computing for enabling the computability of dust storm forecasting

Forecasting dust storms for large geographical areas with high resolution poses great challenges for scientific and computational research. Limitations of computing power and the scalability of parallel systems preclude an immediate solution to such challenges. This article reports our research on using adaptively coupled models to resolve the computational challenges and enable the computability of dust storm forecasting by dividing the large geographical domain into multiple subdomains based on spatiotemporal distributions of the dust storm. A dust storm model (Eta-8bin) performs a quick forecasting with low resolution (22 km) to identify potential hotspots with high dust concentration. A finer model, non-hydrostatic mesoscale model (NMM-dust) performs high-resolution (3 km) forecasting over the much smaller hotspots in parallel to reduce computational requirements and computing time. We also adopted spatiotemporal principles among computing resources and subdomains to optimize parallel systems and improve the performance of high-resolution NMM-dust model. This research enabled the computability of high-resolution, large-area dust storm forecasting using the adaptively coupled execution of the two models Eta-8bin and NMM-dust.

A Reputation-Based Approach to Preserving Privacy in Web Services

The Web is an environment where users, Web services, and software agents exchange sensitive personal information. This calls for enforceable strategies to preserve people’s privacy. In most solutions, users define their respective privacy requirements and must themselves make the decision about information disclosure. Personal judgments are usually made based on the sensitivity of the information and the reputation of the party to which the information is to be disclosed. The emerging semantic Web is expected to make the challenge more acute in the sense that it would provide a whole infrastructure for the automation of semantics in the Web. On the privacy front, this means that privacy invasion would net more quality and sensitive personal information. In this paper, we propose a reputation-based approach to automate privacy enforcement in a semantic Web environment. We propose a reputation management system that monitors Web services and collects, evaluates, updates, and disseminates information related to their reputation for the purpose of privacy protection.

CAPRA: a comprehensive approach to product ranking using customer reviews

Online shopping generates billions of dollars in revenues, including both the physical goods and online services. Product images and associated descriptions are the two main sources of information used by the shoppers to gain knowledge about a product. However, these two pieces of information may not always present the true picture of the product. Images could be deceiving, and descriptions could be overwhelming or cryptic. Moreover, the relative rank of these products among the peers may lead to inconsistencies. Hence, a useful and widely used piece of information is “user reviews”. A number of vendors like Amazon have created whole ecosystems around user reviews, thereby boosting their revenues. However, extracting the relevant and useful information out of the plethora of reviews is not straight forward, and is a very tedious job. In this paper we propose a product ranking system that facilitates the online shopping experience by analyzing the reviews for sentiments, evaluating their usefulness, extracting and weighing different product features and aspects, ranking it among similar comparable products, and finally creating a unified rank for each product. Experiment results show the usefulness of our proposed approach in providing an effective and reliable online shopping experience in comparison with similar approaches.

A Scalable Middleware for Web Databases

The emergence of Web databases has introduced new challenges related to their organization, access, integration, and interoperability. New approaches and techniques are needed to provide across-the-board transparency for accessing and manipulating Web databases irrespective of their data models, platforms, locations, or systems. In meeting these needs, it is necessary to build a middleware infrastructure to support flexible tools for information space organization, communication facilities, information discovery, content description, and assembly of data from heterogeneous sources. In this paper, we describe a scalable middleware for efficient data and application access that we have built using the available technologies. The resulting system is called WebFINDIT. It is a scalable and uniform infrastructure for locating and accessing heterogeneous and autonomous databases and applications.

WebDG – A Platform for E-Government Web Services

Web services are deemed as the natural choice for deploying e-government applications. Their use enables e-government to fully get advantage of the envisioned Semantic Web. In this paper, we propose WebDG, a comprehensive Web Service Management System for e-government applications. It aims to improve government-citizen interactions through an infrastructure built around the ”life experience” of citizens. WebDG provides a framework for automatically composing e-government services, optimized querying services, and preserving privacy.

SEMANTIC INTEGRATION IN GEOSCIENCES

We present an approach for the semantic integration of geoscience data, and a system implementing this approach. Specifically, we demonstrate the use of data ontologies and application of markup languages for semantic integration of data and services. We introduce a domain level object ontology, called Earth and Planetary ONTology (EPONT) to explore, extract, and integrate information from heterogeneous geologic data sets. As proof of concept, we define the DIA engine, an extensible infrastructure for the Discovery, Integration, and Analysis of geoscience data, tools, and services. DIA provides a collaborative environment where scientists can share their resources (e.g., geochemical data, filtering services, etc.) by registering them through well-defined ontologies. We envision the DIA infrastructure to also use other classes of ontologies, namely process and service, for knowledge creation.