Extracting user spatio-temporal profiles from location based social networks

Report de RecercaLocation Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These social network provide a low rate sampling of user's location information during large intervals of time that can be used to discover complex behaviors, including mobility profiles, points of interest or unusual events. This information is important for different domains like mobility route planning, touristic recommendation systems or city planning. Other approaches have used the data from LSBN to categorize areas of a city depending on the categories of the places that people visit or to discover user behavioral patterns from their visits. The aim of this paper is to analyze how the spatio-temporal behavior of a large number of users in a well limited geographical area can be segmented in different profiles. These behavioral profiles are obtained by means of clustering algorithms that show the different behaviors that people have when living and visiting a city. The data analyzed was obtained from the public data feeds of Twitter and Instagram inside the area of the city of Barcelona for a period of several months. The analysis of these data shows that these kind of algorithms can be successfully applied to data from any city (or any general area) to discover useful profiles that can be described on terms of the city singular places and areas and their temporal relationships. These profiles can be used as a basis for making decisions in different application domains, specially those related with mobility inside and outside a city.Preprin

Recognizing activities of daily living from patterns and extraction of web knowledge

The ability to infer and anticipate the activities of elderly individuals with cognitive impairment has made it possible to provide timely assistance and support, which in turn allows them to lead an independent life. Traditional non-intrusive activity recognition approaches are dependent on the use of various machine learning techniques to infer activities given the collected object usage data. Current activity recognition approaches are also based on knowledge driven techniques that require extensive modelling of the activities that needs to be inferred. These models can be seen as too restrictive, prescriptive and static as they are based on a finite set of activities. In this paper, we propose a novel “top down” approach to recognising activities based on object usage data, which detects patterns associated with the activity-object relationship and utilizes web knowledge in order to build dynamic activity models based on the objects used to perform the activity. Experimental results using the Kasteren dataset shows it is comparable to existing approaches

Let Opportunistic Crowdsensors Work Together for Resource-efficient, Quality-aware Observations

International audienceOpportunistic crowdsensing empowers citizens carrying hand-held devices to sense physical phenomena of common interest at a large and fine-grained scale without requiring the citizens' active involvement. However, the resulting uncontrolled collection and upload of the massive amount of contributed raw data incur significant resource consumption, from the end device to the server, as well as challenge the quality of the collected observations. This paper tackles both challenges raised by opportunistic crowdsensing, that is, enabling the resource-efficient gathering of relevant observations. To achieve so, we introduce the BeTogether middleware fostering context-aware, collaborative crowdsensing at the edge so that co-located crowdsensors operating in the same context, group together to share the work load in a cost- and quality-effective way. We evaluate the proposed solution using an implementation-driven evaluation that leverages a dataset embedding nearly 1 million entries contributed by 550 crowdsensors over a year. Results show that BeTogether increases the quality of the collected data while reducing the overall resource cost compared to the cloud-centric approach

Routine pattern discovery and anomaly detection in individual travel behavior

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling

Mining frequent spatio-temporal patterns from location based social networks

Report de recercaLocation Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These social network provide a low rate sampling of user's location information during large intervals of time that can be used to discover complex behaviors, including frequent routes, points of interest or unusual events. This information is important for different domains like route planning, touristic recommendation systems or city planning. Other approaches have used the data from LSBN to categorize areas of a city depending on the categories of the places that people visit or to discover user behavioral patterns from their visits. The aim of this paper is to analyze the frequent spatio-temporal patterns that users share when visiting a city. This behavior is studied in a well limited geographical area by means of frequent itemsets algorithms in order to establish some causal dependence between visits that can be interpreted as interesting routes or spatio-temporal connections. The data analyzed was obtained from the public data feeds of Twitter and Instagram inside the area of the cities of Barcelona and Milan for a period of several months. The analysis of these data shows that these kind of algorithms can be successfully applied to data from any city (or general area) to discover useful patterns that can be interpreted on terms of the city singular places and areas and that these patters can be used as a the elements of a knowledge base for different applications.Preprin