Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Examining the Limits of Predictability of Human Mobility

We challenge the upper bound of human-mobility predictability that is widely used to corroborate the accuracy of mobility prediction models. We observe that extensions of recurrent-neural network architectures achieve significantly higher prediction accuracy, surpassing this upper bound. Given this discrepancy, the central objective of our work is to show that the methodology behind the estimation of the predictability upper bound is erroneous and identify the reasons behind this discrepancy. In order to explain this anomaly, we shed light on several underlying assumptions that have contributed to this bias. In particular, we highlight the consequences of the assumed Markovian nature of human-mobility on deriving this upper bound on maximum mobility predictability. By using several statistical tests on three real-world mobility datasets, we show that human mobility exhibits scale-invariant long-distance dependencies, contrasting with the initial Markovian assumption. We show that this assumption of exponential decay of information in mobility trajectories, coupled with the inadequate usage of encoding techniques results in entropy inflation, consequently lowering the upper bound on predictability. We highlight that the current upper bound computation methodology based on Fano’s inequality tends to overlook the presence of long-range structural correlations inherent to mobility behaviors and we demonstrate its significance using an alternate encoding scheme. We further show the manifestation of not accounting for these dependencies by probing the mutual information decay in mobility trajectories. We expose the systematic bias that culminates into an inaccurate upper bound and further explain as to why the recurrent-neural architectures, designed to handle long-range structural correlations, surpass this upper limit on human mobility predictability

MAPLE: Mobile App Prediction Leveraging Large Language model Embeddings

Despite the rapid advancement of mobile applications, predicting app usage remains a formidable challenge due to intricate user behaviours and ever-evolving contexts. To address these issues, this paper introduces the Mobile App Prediction Leveraging Large Language Model Embeddings (MAPLE) model. This innovative approach utilizes Large Language Models (LLMs) to predict app usage accurately. Rigorous testing on two public datasets highlights MAPLE's capability to decipher intricate patterns and comprehend user contexts. These robust results confirm MAPLE's versatility and resilience across various scenarios. While its primary design caters to app prediction, the outcomes also emphasize the broader applicability of LLMs in different domains. Through this research, we emphasize the potential of LLMs in app usage prediction and suggest their transformative capacity in modelling human behaviours across diverse fields

Human-centred artificial intelligence for mobile health sensing:challenges and opportunities

Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Big Data Research in Italy: A Perspective

The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains