Skip to main content

Research Repository

Advanced Search

Marco Ortolani's Outputs (21)

Compounding barriers to fairness in the digital technology ecosystem (2021)
Conference Proceeding
Woolley, S. I., Collins, T., Andras, P., Gardner, A., Ortolani, M., & Pitt, J. (2021). Compounding barriers to fairness in the digital technology ecosystem. . https://doi.org/10.1109/ISTAS52410.2021.9629166

A growing sense of unfairness permeates our quasi-digital society. Despite drivers supporting and motivating ethical practice in the digital technology ecosystem, there are compounding barriers to fairness that, at every level, impact technology inno... Read More about Compounding barriers to fairness in the digital technology ecosystem.

A fog-based hybrid intelligent system for energy saving in smart buildings (2020)
Journal Article
Ortolani. (2020). A fog-based hybrid intelligent system for energy saving in smart buildings. Journal of Ambient Intelligence and Humanized Computing, 2793-2807. https://doi.org/10.1007/s12652-019-01375-2

In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper propose... Read More about A fog-based hybrid intelligent system for energy saving in smart buildings.

Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces (2019)
Journal Article
Casella, E., Ortolani, M., Silvestri, S., & Das, S. K. (2019). Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces. Personal and Ubiquitous Computing, 24, 451-464. https://doi.org/10.1007/s00779-019-01319-9

Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this pa... Read More about Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces.

Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces (2019)
Journal Article
Casella, E., Ortolani, M., Silvestri, S., & Das, S. K. (2020). Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces. Personal Technologies, 24, 451-464. https://doi.org/10.1007/s00779-019-01319-9

Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this pap... Read More about Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces.

A Network Tomography Approach for Traffic Monitoring in Smart Cities (2018)
Journal Article
Ortolani, Zhang, R., Newman, S., Ortolani, M., & Silvestri, S. (2018). A Network Tomography Approach for Traffic Monitoring in Smart Cities. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2268 - 2278. https://doi.org/10.1109/TITS.2018.2829086

Traffic monitoring is a key enabler for several planning and management activities of a Smart City. However, traditional techniques are often not cost efficient, flexible, and scalable. This paper proposes an approach to traffic monitoring that does... Read More about A Network Tomography Approach for Traffic Monitoring in Smart Cities.

An Ambient Intelligence System for Assisted Living (2017)
Book Chapter
Ortolani. (2017). An Ambient Intelligence System for Assisted Living. In 2017 AEIT International Annual Conference, Cagliari, Italy, 20-22 Sep (1 -6). https://doi.org/10.23919/AEIT.2017.8240559

Nowadays, the population's average age is constantly increasing, and thus the need for specialized home assistance is on the rise. Smart homes especially tailored to meet elderly and disabled people's needs can help them maintaining their autonomy, w... Read More about An Ambient Intelligence System for Assisted Living.

Structural Knowledge Extraction from Mobility Data (2016)
Conference Proceeding
Cottone, P., Gaglio, S., Lo Re, G., Ortolani, M., & Pergola, G. (2016). Structural Knowledge Extraction from Mobility Data. In G. Adorni, S. Cagnoni, M. Gori, & M. Maratea (Eds.), AI*IA 2016 Advances in Artificial Intelligence -. https://doi.org/10.1007/978-3-319-49130-1_22

Knowledge extraction has traditionally represented one of the most interesting challenges in AI; in recent years, however, the availability of large collections of data has increased the awareness that “measuring” does not seamlessly translate into “... Read More about Structural Knowledge Extraction from Mobility Data.

Detecting Similarities in Mobility Patterns (2016)
Conference Proceeding
Cottone, P., Ortolani, M., & Pergola, G. (2016). Detecting Similarities in Mobility Patterns. In Frontiers in Artificial Intelligence and Applications (167 - 178). https://doi.org/10.3233/978-1-61499-682-8-167

The wide spread of low-cost personal devices equipped with GPS sensors has paved the way towards the creation of customized services based on user mobility habits and able to track and assist users in everyday activities, according to their current l... Read More about Detecting Similarities in Mobility Patterns.

Gaining insight by structural knowledge extraction (2016)
Conference Proceeding
Cottone, P., Gaglio, S., Lo Re, G., & Ortolani, M. (2016). Gaining insight by structural knowledge extraction.

The availability of increasingly larger and more complex datasets has boosted the demand for systems able to analyze them automatically. The design and implementation of effective systems requires coding knowledge about the application domain inside... Read More about Gaining insight by structural knowledge extraction.

Gl-learning: an optimized framework for grammatical inference (2016)
Conference Proceeding
Cottone, P., Ortolani, M., & Pergola, G. (2016). Gl-learning: an optimized framework for grammatical inference. In CompSysTech '16: Computer Systems and Technologies 2016. https://doi.org/10.1145/2983468.2983502

In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and t... Read More about Gl-learning: an optimized framework for grammatical inference.

A machine learning approach for user localization exploiting connectivity data (2016)
Journal Article
Ortolani. (2016). A machine learning approach for user localization exploiting connectivity data. Engineering Applications of Artificial Intelligence, 125 - 134. https://doi.org/10.1016/j.engappai.2015.12.015

The growing popularity of Location-Based Services (LBSs) has boosted research on cheaper and more pervasive localization systems, typically relying on such monitoring equipment as Wireless Sensor Networks (WSNs), which allow to re-use the same instru... Read More about A machine learning approach for user localization exploiting connectivity data.

SmartBuildings: an AmI system for energy efficiency (2015)
Conference Proceeding
De Paola, A., Re, G. L., Morana, M., & Ortolani, M. (2015). SmartBuildings: an AmI system for energy efficiency. In 2015 Sustainable Internet and ICT for Sustainability (SustainIT). https://doi.org/10.1109/sustainit.2015.7101372

Nowadays, the increasing global awareness of the importance of energy saving in everyday life acts as a stimulus to provide innovative ICT solutions for sustainability. In this scenario, the growing interest in smart homes has been driven both by soc... Read More about SmartBuildings: an AmI system for energy efficiency.

Extracting Structured Knowledge From Sensor Data for Hybrid Simulation (2014)
Book Chapter
Ortolani, M. (2014). Extracting Structured Knowledge From Sensor Data for Hybrid Simulation. In Advances onto the Internet of Things (153-165). Springer. https://doi.org/10.1007/978-3-319-03992-3_11

Obtaining continuous and detailed monitoring of indoor environments has today become viable, also thanks to the widespread availability of effective and flexible sensing technology; this paves the way for the design of practical Ambient Intelligence... Read More about Extracting Structured Knowledge From Sensor Data for Hybrid Simulation.

A Structural Approach to Infer Recurrent Relations in Data (2014)
Book Chapter
Cottone, P., Gaglio, S., & Ortolani, M. (2014). A Structural Approach to Infer Recurrent Relations in Data. In Advances onto the Internet of Things (105-119). Springer. https://doi.org/10.1007/978-3-319-03992-3_8

Extracting knowledge from a great amount of collected data has been a key problem in Artificial Intelligence during the last decades. In this context, the word “knowledge” refers to the non trivial new relations not easily deducible from the observat... Read More about A Structural Approach to Infer Recurrent Relations in Data.

Improving User Experience via Motion Sensors in an Ambient Intelligence Scenario (2013)
Conference Proceeding
Lo Re, G., Morana, M., & Ortolani, M. (2013). Improving User Experience via Motion Sensors in an Ambient Intelligence Scenario. In Proceedings of the 3rd International Conference on Pervasive Embedded Computing and Communication Systems PECCS (29-34). https://doi.org/10.5220/0004306000290034

Ambient Intelligence (AmI) is a new paradigm in Artificial Intelligence that aims at exploiting the information about the environment state in order to adapt it to the user preferences. AmI systems are usually based on several cheap and unobtrusive s... Read More about Improving User Experience via Motion Sensors in an Ambient Intelligence Scenario.

Gesture Recognition for Improved User Experience in a Smart Environment (2013)
Conference Proceeding
Gaglio, S., Lo Re, G., Morana, M., & Ortolani, M. (2013). Gesture Recognition for Improved User Experience in a Smart Environment. In AI*IA 2013: Advances in Artificial Intelligence (493-504). https://doi.org/10.1007/978-3-319-03524-6_42

Ambient Intelligence (AmI) is a new paradigm that specifically aims at exploiting sensory and context information in order to adapt the environment to the user’s preferences; one of its key features is the attempt to consider common devices as an int... Read More about Gesture Recognition for Improved User Experience in a Smart Environment.

QoS-Aware Fault Detection in Wireless Sensor Networks (2013)
Journal Article
De Paola, A., Lo Re, G., Milazzo, F., & Ortolani, M. (2013). QoS-Aware Fault Detection in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, https://doi.org/10.1155/2013/165732

Wireless sensor networks (WSNs) are a fundamental building block of many pervasive applications. Nevertheless the use of such technology raises new challenges regarding the development of reliable and fault-tolerant systems. One of the most critical... Read More about QoS-Aware Fault Detection in Wireless Sensor Networks.