Road network applications, such as navigation, incident detection, and Point-of-Interest (POI) recommendation, make extensive use of network edge weights (e.g., traveling times). Some of these weights can be missing, especially in a road network where traffic data may not be available for every road. In this article, we study the stochastic weight completion (SWC) problem, which computes the weight distributions of missing road edges. This is difficult, due to the intricate temporal and spatial correlations among neighboring edges. Besides, the road network can be sparse , i.e., there is a lack of traveling information in a large portion of the network. To tackle these challenges, we propose a multi-granularity framework for Region-Wise Graph Completion (RegGC) . To learn coarse spatial correlations among distantly located roads, we construct a region-wise hypergraph neural architecture based on semantic region dependencies. For finer spatial correlations, we incorporate contextual road network properties (e.g., speed limits, lane counts, and road types). Moreover, it incorporates recent and periodic dimensions of road traffic. We evaluate RegGC against 10 existing methods on 3 real road network datasets. They show that RegGC is more effective and efficient than state-of-the-art solutions.

Road network applications, such as navigation, incident detection, and Point-of-Interest (POI) recommendation, make extensive use of network edge weights (e.g., traveling times). Some of these weights can be missing, especially in a road network where traffic data may not be available for every road. In this paper, we study the stochastic weight completion (SWC) problem, which computes the weight distributions of missing road edges. This is difficult, due to the intricate temporal and spatial correlations among neighboring edges. Moreover, the road network can be sparse, i.e., there is a lack of traveling information in a large portion of the network. To tackle these challenges, we propose the Contextual Graph Completion (ConGC). We propose to incorporate the contextual properties about the road network (e.g., speed limits, number of lanes, road types) to provide finer granularity of spatial correlations. Moreover, ConGC incorporates temporal and periodic dimensions of the road traffic. We evaluate ConGC against existing methods on three real road network datasets. They show that ConGC is more effective and efficient than state-of-the-art solutions.

In this paper, we study anomalous trajectory detection, which aims to extract abnormal movements of vehicles on the roads. This important problem, which facilitates understanding of traffic behavior and detection of taxi fraud, is challenging due to the varying traffic conditions at different times and locations. To tackle this problem, we propose the deep -probabilistic-based time-dependent anomaly detection algorithm ( DeepTEA ). This method, which employs deep-learning methods to obtain time-dependent outliners from a huge volume of trajectories, can handle complex traffic conditions and detect outliners accurately. We further develop a fast and approximation version of DeepTEA, in order to capture abnormal behaviors in real-time. Compared with state-of-the-art solutions, our method is 17.52% more accurate than seven competitors on average, and can handle millions of trajectories.

Incident detection (ID), or the automatic discovery of anomalies from road traffic data (e.g., road sensor and GPS data), enables emergency actions (e.g., rescuing injured people) to be carried out in a timely fashion. Existing ID solutions based on data mining or machine learning often rely on dense traffic data; for instance, sensors installed in highways provide frequent updates of road information. In this paper, we ask the question: Can ID be performed on sparse traffic data (e.g., location data obtained from GPS devices equipped on vehicles)? As these data may not be enough to describe the state of the roads involved, they can undermine the effectiveness of existing ID solutions. To tackle this challenge, we borrow an important insight from the transportation area, which uses trajectories (i.e., moving histories of vehicles) to derive incident patterns. We study how to obtain incident patterns from trajectories and devise a new solution (called Filter-Discovery-Match (FDM)) to detect anomalies in sparse traffic data. Experiments on a taxi dataset in Hong Kong and a simulated dataset show that FDM is more effective than state-of-the-art ID solutions on sparse traffic data.

Social advertising exploits the interconnectivity of users in social networks to spread advertisement and generate user engagements. A lot of research has focused on how to select the best subset of users in a social network to maximize the number of engagements or the generated revenue of the advertisement. However, there is a lack of studies that consider the advertiser's value-per-engagement, i.e., how much an advertiser is maximally willing to pay for each engagement. Prior work on social advertising is based on the classical framework of influence maximization. In this paper, we propose a model where advertisers compete in an auction mechanism for the influential users within a social network. The auction mechanism can dynamically determine payments for advertisers based on their reported values. The main problem is to find auctions which incentivize advertisers to truthfully reveal their values, and also respect each advertiser's budget constraint. To tackle this problem, we propose a new truthful auction mechanism called TSA. Compared with existing approaches on real and synthetic datasets, TSA performs significantly better in terms of generated revenue.

Stream Processing (SP) is an important Big Data technology enabling continuous querying of data streams. The stream setting offers the opportunity to exploit synergies and, theoretically, share the access and processing costs between multiple different collaborators. But what should be the monetary contribution of each consumer when they do not trust each other and have varying valuations of the differing outcomes? In this article, we present Collaborative Stream Processing (CSP), a model where the costs, which are set exogenously by providers, are shared between multiple consumers, the collaborators. For this, we identify three important requirements for CSP to establish trust between the collaborators and propose a CSP algorithm, ENCSPA, adhering to these requirements. Based on the collaborators' outcome valuations and the costs of the raw data streams, ENCSPA computes the payment for each collaborator. At the same time, ENCSPA ensures that no collaborator has an incentive to manipulate the system by providing misinformation about her/his value, budget, or time limit. We show that ENCSPA can calculate payments in a reasonable amount of time for up to one thousand collaborators.

In graph applications (e.g., biological and social networks), various analytics tasks (e.g., clustering and community search) are carried out to extract insight from large and complex graphs. Central to these tasks is the counting of the number of motifs , which are graphs with a few nodes. Recently, researchers have developed several fast motif counting algorithms. Most of these solutions assume that graphs are deterministic, i.e., the graph edges are certain to exist. However, due to measurement and statistical prediction errors, this assumption may not hold, and hence the analysis quality can be affected. To address this issue, we examine how to count motifs on uncertain graphs, whose edges only exist probabilistically. Particularly, we propose a solution framework that can be used by existing deterministic motif counting algorithms. We further propose an approximation algorithm. Extensive experiments on real datasets show that our algorithms are more effective and efficient than existing solutions.

The World Wide Web is a massive network of interlinked documents. One of the reasons the World Wide Web is so successful is the fact that most content is available free of any charge. Inspired by the success of the World Wide Web, the Web of Data applies the same strategy of interlinking to data. To this point, most of data in the Web of Data is also free of charge. The fact that the data is freely available raises the question of financing these services, however. As we will discuss in this paper, advertisement and donations cannot easily be applied to this new setting. To create incentives to subsidize data providers, we propose that sponsors should pay the providers to promote sponsored data. In return, sponsored data will be privileged over non-sponsored data. Since it is not possible to enforce a certain ordering on the data the user will receive, we propose to split up the data into different batches and deliver these batches with different delays. In this way, we can privilege sponsored data without withholding any non-sponsored data from the user. In this paper, we introduce a new concept of a delayed-answer auction, where sponsors can pay to prioritize their data. We introduce a new model which captures the particular situation when a user access data in the Web of Data. We show how the weighted Vickrey-Clarke-Groves auction mechanism can be applied to our scenario and we discuss how certain parameters can influence the nature of our auction. With our new concept, we build a first step to a free yet financial sustainable Web of Data.