Analyzing flow of objects or data at different granularities of space and time can unveil interesting insights or trends.For example, transportation companies, by aggregating passenger travel data (e.g., counting passengers travelling from one region to another), can analyze movement behavior.In this paper, we study the problem of finding important trends in passenger movements between regions at different granularities.We define Origin (𝑂), Destination (𝐷), and Time (𝑇 ) patterns (ODT patterns) and propose an algorithm that enumerates them.We propose optimizations that greatly reduce the search space and the computational cost of pattern enumeration.We also propose pattern variants (constrained patterns and top-𝑘 patterns) that could be useful to different application scenarios.We evaluate our methods on three real datasets and identify significant ODT flow patterns in them.

Hypothesis testing is a statistical method used to draw conclusions about populations from sample data, typically represented in tables. With the prevalence of graph representations in real-life applications, hypothesis testing on graphs is gaining importance. In this work, we formalize node, edge, and path hypotheses on attributed graphs. We develop a sampling-based hypothesis testing framework, which can accommodate existing hypothesis-agnostic graph sampling methods. To achieve accurate and time-efficient sampling, we then propose a Path-Hypothesis-Aware SamplEr, PHASE, an m -dimensional random walk that accounts for the paths specified in the hypothesis. We further optimize its time efficiency and propose PHASE opt . Experiments on three real datasets demonstrate the ability of our framework to leverage common graph sampling methods for hypothesis testing, and the superiority of hypothesis-aware sampling methods in terms of accuracy and time efficiency.

With the growth of the Internet of Things (IoT), Smart(er) Cities have been a research goal of researchers, businesses and local authorities willing to adopt IoT technologies to improve their services. Among them, Smart Transportation [7,8], the integrated application of modern technologies and management strategies in transportation systems, refers to the adoption of new IoT solutions to improve urban mobility. These technologies aim to provide innovative solutions related to different modes of transport and traffic management and enable users to be better informed and make safer and 'smarter' use of transport networks. This talk presents SmartCityBus, a data-driven intelligent transportation system (ITS) whose main objective is to use online and offline data in order to provide accurate statistics and predictions and improve public transportation services in the short and medium/long term.

In temporal interaction networks (TINs), vertices correspond to entities, which exchange data quantities (e.g., money, bytes, messages) over time. We study the problem of con-tinuously tracking the origins of quantities at network vertices, as interactions take place over time. We target applications, such as financial exchange networks, where the selected transferred units at each interaction are not specified. We investigate several quantity selection policies that apply to different application scenarios. For each policy, we propose space- and time-efficient meta-data propagation mechanisms for continuously tracking provenance at vertices. For the hard case of proportional selection policy, we reduce the cost of tracking in practice, by either (i) limiting provenance tracking to a subset of vertices or groups of vertices, or (ii) tracking provenance only for quantities that were generated in the near past or limiting the provenance data in each vertex by a budget constraint. Our experimental evaluation on real datasets demonstrate the efficiency and scalability of our techniques compared to baseline approaches that extend flow computation algorithms to track provenance.

Temporal interaction networks capture the history of activities between entities along a timeline. At each interaction, some quantity of data (money, information, traffic) flows from one vertex of the network to another. Flow-based analysis can reveal important information, such as unusually large money transfers in a part of a financial transaction network. In this paper, we introduce the flow computation problem between two vertrices in an interaction network. We propose and study two models of flow computation, one based on a greedy flow transfer assumption and one that finds the maximum possible flow. We show that the greedy flow computation problem can be easily solved by a single scan of the interactions in time order. For the harder maximum flow problem, we propose precomputation and simplification approaches that can greatly reduce its complexity in practice. We also approach the problem of flow pattern enumeration in interaction networks and propose an effective path indexing technique. We evaluate our algorithms using real datasets. The results demonstrate the efficiency and scalability of our algorithms.

In temporal interaction networks, such as financial transaction networks, vertices model entities which exchange quantities (e.g., money) over time. We study the problem of identifying the origin of the quantities that flow into the vertices of the network over time. We consider various models of flow relay, which are related to different application scenarios and develop corresponding techniques for flow provenance.