Thiago H. Silva

A Picture of Instagram is Worth More Than a Thousand Words: Workload Characterization and Application

Participatory sensing systems (PSSs) have the potential to become fundamental tools to support the study, in large scale, of urban social behavior and city dynamics. To that end, this work characterizes the photo sharing system Instagram, considered one of the currently most popular PSS on the Internet. Based on a dataset of approximately 2.3 million shared photos, we characterize users behavior in the system showing that there are several advantages and opportunities for large scale sensing, such as a global coverage at low cost, but also challenges, such as a very unequal photo sharing frequency, both spatially and temporally. We also observe that the temporal photo sharing pattern is a good indicator about cultural behaviors, and also says a lot about certain classes of places. Moreover, we present an application to identify regions of interest in a city based on data obtained from Instagram, which illustrates the promising potential of PSSs for the study of city dynamics.

Large-scale study of city dynamics and urban social behavior using participatory sensing

The ubiquitous availability of computing technology such as smartphones, tablets, and other easily portable devices, and the worldwide adoption of social networking sites make it increasingly possible for one to be connected and continuously contribute to this massively distributed information publishing process. In this scenario, people act as social sensors, voluntarily providing data that capture their daily life experiences, and offering diverse observations on both the physical world (e.g., location) and the online world (e.g., events). This large amount of social data can provide new forms of valuable information that are currently not available on this scale via any traditional data collection methods, and can be used to enhance decision making processes. In this article, we argue that location-based social media systems, such as Instagram and Foursquare, can act as valuable sources of large-scale sensing, providing access to important characteristics of urban social behavior much more quickly than traditional methods. We also discuss different applications and techniques that can exploit the data shared in these systems to enable large-scale and near-real-time analyses and visualization of different aspects of city dynamics.

Visualizing the Invisible Image of Cities

With the recent advances in technology, we have the opportunity to study the city dynamics at a large scale. A fundamental step is to be able to sense extensive areas. Participatory sensor networks have the potential to become a very fundamental tool to study social behavior at a large scale. Currently, there are some location sharing services, such as Foursquare, that record the users location along the time, which is clearly one of dimensions in the city dynamics. In this work, we propose a technique called city image and show its applicability taking as examples eight different cities. The resulting image is a way of summarizing the city dynamics based on transition graphs, which map the movements of individuals in a PSN. This technique is a promising way to better understand the city dynamics, helping us to visualize the invisible images of cities.

Traffic Condition Is More Than Colored Lines on a Map: Characterization of Waze Alerts

Participatory sensor network (PSN) enables the understanding of city dynamics and the urban behavioral patterns of their inhabitants. In this work, we focus our analysis on a specific PSN, derived from Waze, for sensing traffic conditions. Our objective is to characterize the properties of this PSN, its broad and global spatial coverage as well as its limitations. We also bring discussions on different opportunities for application design using this network. We claim that the PSN derived from Waze has the potential to help us in the better understanding of traffic problem reasons. Besides that, it could be useful for improving algorithms used in navigation services: (1) by exploiting the provided real-time traffic information or (2) by helping in the identification of valuable pieces of information that are hard to detect with traditional sensors, such as car accidents and potholes.

Challenges and opportunities on the large scale study of city dynamics using participatory sensing

Cities are not identical and evolve over time, and sensing in large scale can be used to capture these differences. Research in Wireless Sensor Networks has provided several tools, techniques and algorithms to solve the problem of sensing in restricted scenarios (e.g., factory). However, sensing large scale areas, such as big cities, brings many challenges and incurs high costs related to system building and management. Thus, sensing those areas becomes more feasible when people collaborate among themselves using their portable devices, and building what has been named participatory sensing systems. This work analyzes an emerging type of network derived from this type of system, the Participatory Sensor Network (PSN), where nodes are autonomous mobile entities and the sensing depends on whether they want to participate in the sensing process. Based on four datasets of participatory sensing systems (27 million of records), we show that this type of network brings many challenges related to structural problems, e.g. instant coverage very limited, and also because of big data issues, which may restrict the use of this emerging type of network. However, it presents also, as shown here, many advantages and open opportunities, mainly related to large scale study of cities dynamics.

Uncovering properties in participatory sensor networks

A fundamental step to achieve the Ubiquitous Computing vision is to sense the environment. The research in Wireless Sensor Networks has provided several tools, techniques and algorithms to solve the problem of sensing in limited size areas, such as forests or volcanoes. However, sensing large scale areas, such as large metropolises, countries, or even the entire planet, brings many challenges. For instance, consider the high cost associated with building and managing such large scale systems. Thus, sensing those areas becomes more feasible when people collaborate among themselves using their portable devices (e.g., sensor-enabled cell phones). Systems that enable the user participation with sensed data are named participatory sensing systems. This work analyzes a new type of network derived from this type of system. In this network, nodes are autonomous mobile entities and the sensing depends on whether they want to participate in the sensing process. Based on two datasets of participatory sensing systems, we show that this type of network has many advantages and fascinating opportunities, such as planetary scale sensing at small cost, but also has many challenges, such as the highly skewed spatial-temporal sensing frequency.

Studying traffic conditions by analyzing foursquare and instagram data

Traffic jam is a contemporary society problem in urban areas. There are specific sources of information about traffic conditions in the Web, such as Bing Maps. This system presents real-time information about the traffic conditions (e.g., free or congested). Recently, participatory sensing systems, such as Foursquare and Instagram, are becoming very popular. Data shared in these systems have the active participation of users using their portable devices ubiquitously. In this case, these systems can be seen as a kind of sensor network, where users can be considered a social sensor, because the data shared by them are associated with their habits and routines. Thus, can we use data from social sensors, specifically from Foursquare and Instagram, to better understand traffic conditions? This paper shows that data from social sensors and traffic conditions, provided by Bing Maps, are surprisingly very correlated. The social data distribution is equal to the traffic condition distribution, shifted by an offset that can be easily calculated. This information can be extremely valuable, for example, to build more efficient traffic condition predictors.

Aggregating productivity indices for ranking researchers across multiple areas

The impact of scientific research has traditionally been quantified using productivity indices such as the well-known h-index. On the other hand, different research fields---in fact, even different research areas within a single field---may have very different publishing patterns, which may not be well described by a single, global index. In this paper, we argue that productivity indices should account for the singularities of the publication patterns of different research areas, in order to produce an unbiased assessment of the impact of scientific research. Inspired by ranking aggregation approaches in distributed information retrieval, we propose a novel approach for ranking researchers across multiple research areas. Our approach is generic and produces cross-area versions of any global productivity index, such as the volume of publications, citation count and even the h-index. Our thorough evaluation considering multiple areas within the broad field of Computer Science shows that our cross-area indices outperform their global counterparts when assessed against the official ranking produced by CNPq, the Brazilian National Research Council for Scientific and Technological Development. As a result, this paper contributes a valuable mechanism to support the decisions of funding bodies and research agencies, for example, in any research assessment effort.

Sensing in the Collaborative Internet of Things

We are entering a new era of computing technology, the era of Internet of Things (IoT). An important element for this popularization is the large use of off-the-shelf sensors. Most of those sensors will be deployed by different owners, generally common users, creating what we call the Collaborative IoT. This collaborative IoT helps to increase considerably the amount and availability of collected data for different purposes, creating new interesting opportunities, but also several challenges. For example, it is very challenging to search for and select a desired sensor or a group of sensors when there is no description about the provided sensed data or when it is imprecise. Given that, in this work we characterize the properties of the sensed data in the Internet of Things, mainly the sensed data contributed by several sources, including sensors from common users. We conclude that, in order to safely use data available in the IoT, we need a filtering process to increase the data reliability. In this direction, we propose a new simple and powerful approach that helps to select reliable sensors. We tested our method for different types of sensed data, and the results reveal the effectiveness in the correct selection of sensor data.

Community-based endogamy as an influence indicator

Evaluating researchers (individually or in groups) usually depends on qualifying their publications and influence. Here, we aid such crucial task by introducing two new metrics (C-Endo and Comb) that rely on the concept of endogamy for communities of authors who publish in conferences and journals, and produce patents. Endogamy here measures how tightly structured the groups of authors are within a community. We validate and evaluate the metrics by using real datasets, two ground-truth rankings and citation count. We also perform random sampling analysis to account for any unbalance from the ground-truth rankings. Overall, such a thorough evaluation shows that our metrics are successful in defining community-based endogamy as an influence indicator.