Publications
Publications#
Journal Paper:
Naeima Hamed, Andrea Gaglione, Alex Gluhak, Omer Rana, and Charith Perera. (2023). Query Interface for Smart City Internet of Things Data Marketplaces: A Case Study. ACM Transactions on Internet of Things, 4(3), Article 19, 39 pages. https://doi.org/10.1145/3609336
Abstract:
Cities are increasingly becoming augmented with sensors through public, private, and academic sector initiatives. Most of the time, these sensors are deployed with a primary purpose (objective) in mind (e.g., deploy sensors to understand noise pollution) by a sensor owner (i.e., the organization that invests in sensing hardware, e.g., a city council). Over the past few years, communities undertaking smart city development projects have understood the importance of making the sensor data available to a wider community—beyond their primary usage. Different business models have been proposed to achieve this, including creating data marketplaces. The vision is to encourage new startups and small and medium-scale businesses to create novel products and services using sensor data to generate additional economic value. Currently, data are sold as pre-defined independent datasets (e.g., noise level and parking status data may be sold separately). This approach creates several challenges, such as (i) difficulties in pricing, which leads to higher prices (per dataset); (ii) higher network communication and bandwidth requirements; and (iii) information overload for data consumers (i.e., those who purchase data). We investigate the benefit of semantic representation and its reasoning capabilities toward creating a business model that offers data on demand within smart city Internet of Things data marketplaces. The objective is to help data consumers (i.e., small and medium enterprises) acquire the most relevant data they need. We demonstrate the utility of our approach by integrating it into a real-world IoT data marketplace (developed by the synchronicity-iot.eu project). We discuss design decisions and their consequences (i.e., tradeoffs) on the choice and selection of datasets. Subsequently, we present a series of data modeling principles and recommendations for implementing IoT data marketplaces.
Book Chapter:
Naeima Hamed, Omer Rana, Pablo Orozco-terWengel, Benoît Goossens, and Charith Perera. (2023). FOO: An Upper-Level Ontology for the Forest Observatory. In Pesquita, C., et al. The Semantic Web: ESWC 2023 Satellite Events. ESWC 2023. Lecture Notes in Computer Science, vol 13998. Springer, Cham. https://doi.org/10.1007/978-3-031-43458-7_29
Abstract:
Wildlife and preservation research activities in the tropical forest of Sabah, Malaysia, can generate a wide variety of data. However, each research activity manages its data independently. Since these data are disparate, gaining unified access to them remains a challenge. We propose the Forest Observatory Ontology (FOO) as a basis for integrating different datasets. FOO comprises a novel upper-level ontology that integrates wildlife data generated by sensors. We used existing ontological resources from various domains (i.e., wildlife) to model FOO’s concepts and establish their relationships. FOO was then populated with multiple semantically modelled datasets. FOO structure and utility are subsequently evaluated using specialised software and task-based methods. The evaluation results demonstrate that FOO can be used to answer complex use-case questions promptly and correctly.
Journal Paper:
Naeima Hamed, Omer Rana, Pablo Orozco-terWengel, Benoît Goossens, and Charith Perera. (2024). A Comparison of Open Data Observatories. Journal of Data and Information Quality (Just Accepted, November 2024). https://doi.org/10.1145/3705863
Abstract:
Open Data Observatories refer to online platforms that provide real-time and historical data for a particular application context, e.g., urban/non-urban environments or a specific application domain. They are generally developed to facilitate collaboration within one or more communities through reusable datasets, analysis tools, and interactive visualizations. Open Data Observatories collect and integrate various data from multiple disparate data sources—some providing mechanisms to support real-time data capture and ingest. Data types can include sensor data (soil, weather, traffic, pollution levels) and satellite imagery. Data sources can include Open Data providers, interconnected devices, and services offered through the Internet of Things. The continually increasing volume and variety of such data require timely integration, management, and analysis, yet presented in a way that end-users can easily understand. Data released for open access preserve their value and enable a more in-depth understanding of real-world choices. This survey compares thirteen Open Data Observatories and their data management approaches - investigating their aims, design, and types of data. We conclude with research challenges that influence the implementation of these observatories, outlining some strengths and limitations for each one and recommending areas for improvement. Our goal is to identify best practices learned from the selected observatories to aid the development of new Open Data Observatories
Journal Paper:
Naeima Hamed, Omer Rana, Pablo Orozco-terWengel, Benoît Goossens, and Charith Perera. (2024). FooDS: Ontology-based Knowledge Graphs for Forest Observatories. ACM Journal on Computing and Sustainable Societies (Just Accepted, November 2024). https://doi.org/10.1145/3707637
Abstract:
Wildlife research activities generate data on ecosystems and species interactions from varied independent projects. Forest Observatories are online platforms that curate, integrate, and analyze wildlife research data for forest monitoring. However, integrating data from disparate sources can be challenging due to data heterogeneity. This study, in collaboration with a research facility in the forest of Sabah, Malaysian Borneo, proposes a novel approach to integrate heterogeneous wildlife data for Forest Observatories. We used the Forest Observatory Ontology (FOO) to standardize wildlife data entities generated by sensors. Four semantically modeled wildlife datasets populated FOO, resulting in an ontology-based knowledge graph named FooDS (Forest Observatory Ontology Data Store). We evaluated FOO and FooDS using specialized open-source ontology scanners, domain experts’ feedback, and applied use cases. This study contributes FooDS, the first ontology-based knowledge graph for Forest Observatories, which provides accurate query responses, reasoning about data, and granular data acquisition from diverse datasets.
Journal Paper:
Naeima Hamed, Omer Rana, Pablo Orozco-terWengel, Benoît Goossens, and Charith Perera. (2024). PoachNet: Predicting Poaching Using an Ontology-Based Knowledge Graph. Sensors, 24(24), Article 8142. https://doi.org/10.3390/s24248142
AbstractS:
Poaching poses a significant threat to wildlife and their habitats, necessitating advanced tools for its prediction and prevention. Existing tools for poaching prediction face challenges such as inconsistent poaching data, spatiotemporal complexity, and translating predictions into actionable insights for conservation efforts. This paper presents PoachNet, a novel predictive system that integrates deep learning with Semantic Web reasoning to infer poaching likelihood. Using elephant GPS data extracted from an ontology-based knowledge graph, PoachNet employs a sequential neural network to predict future movements, which are semantically modelled and incorporated into the graph. Semantic Web Rule Language (SWRL) is applied to infer poaching risk based on these geo-location predictions and poaching rule-based logic. By addressing spatiotemporal complexity and integrating predictions into an actionable semantic rule, PoachNet advances the field, with its geo-location prediction model outperforming state-of-the-art approaches.