Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity

Human mobility:Models and applications

Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between short-range and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.Comment: 126 pages, 45+ figure

Network Alignment: Theory, Algorithms, and Applications

Networks are central in the modeling and analysis of many large-scale human and technical systems, and they have applications in diverse fields such as computer science, biology, social sciences, and economics. Recently, network mining has been an active area of research. In this thesis, we study several related network-mining problems, from three different perspectives: the modeling and theory perspective, the computational perspective, and the application perspective. In the bulk of this thesis, we focus on network alignment, where the data provides two (or more) partial views of the network, and where the node labels are sometimes ambiguous. Network alignment has applications in social-network reconciliation and de-anonymization, protein-network alignment in biology, and computer vision. In the first part of this thesis, we investigate the feasibility of network alignment with a random-graph model. This random-graph model generates two (or several) correlated networks, and lets the two networks to overlap only partially. For a particular alignment, we define a cost function for structural mismatch. We show that the minimization of the proposed cost function (assuming that we have access to infinite computational power), with high probability, results in an alignment that recovers the set of shared nodes between the two networks, and that also recovers the true matching between the shared nodes. The most scalable network-alignment approaches use ideas from percolation theory, where a matched node-couple infects its neighboring couples that are additional potential matches. In the second part of this thesis, we propose a new percolation-based network-alignment algorithm that can match large networks by using only the network structure and a handful of initially pre-matched node-couples called seed set. We characterize a phase transition in matching performance as a function of the seed-set size. In the third part of this thesis, we consider two important application areas of network mining in biology and public health. The first application area is percolation-based network alignment of protein-protein interaction (PPI) networks in biology. The alignment of biological networks has many uses, such as the detection of conserved biological network motifs, the prediction of protein interactions, and the reconstruction of phylogenetic trees. Network alignment can be used to transfer biological knowledge between species. We introduce a new global pairwise-network alignment algorithm for PPI networks, called PROPER. The PROPER algorithm shows higher accuracy and speed compared to other global network-alignment methods. We also extend PROPER to the global multiple-network alignment problem. We introduce a new algorithm, called MPROPER, for matching multiple networks. Finally, we explore IsoRank, one of the first and most referenced global pairwise-network alignment algorithms. Our second application area is the control of epidemic processes. We develop and model strategies for mitigating an epidemic in a large-scale dynamic contact network. More precisely, we study epidemics of infectious diseases by (i) modeling the spread of epidemics on a network by using many pieces of information about the mobility and behavior of a population; and by (ii) designing personalized behavioral recommendations for individuals, in order to mitigate the effect of epidemics on that network

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Esa 12th Conference: Differences, Inequalities and Sociological Imagination: Abstract Book

Esa 12th Conference: Differences, Inequalities and Sociological Imagination: Abstract Boo