Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

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Urban Computing: Concepts, Methodologies, and Applications

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.

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Trajectory Data Mining: An Overview

To summarize, our approach yields important insights into urban phenomena generated by human movements. It represents a quantitative approach to urban analysis, which explicitly identifies ongoing urban transformations.

Detecting the dynamics of urban structure through spatial network analysis

Smartphone-based crowdsourcing fosters the rise of radically novel systems and applications in the context of network monitoring. This article discusses the most significant opportunities offered by this approach, and the major challenges that have to be faced. Our experience in building a smartphone-based crowdsourcing system, Portolan, is also included to provide a practical background to the discussion and to demonstrate the possible benefits.

http://vecchio.iet.unipi.it/vecchio/files/2010/02/article.pdf

Smartphone-based crowdsourcing for network monitoring: Opportunities, challenges, and a case study

Large-scale networks of human interaction, in particular country-wide telephone call networks, can be used to redraw geographical maps by applying algorithms of topological community detection. The geographic projections of the emerging areas in a few recent studies on single regions have been suggested to share two distinct properties: first, they are cohesive, and second, they tend to closely follow socio-economic boundaries and are similar to existing political regions in size and number. Here we use an extended set of countries and clustering indices to quantify overlaps, providing ample additional evidence for these observations using phone data from countries of various scales across Europe, Asia, and Africa: France, the UK, Italy, Belgium, Portugal, Saudi Arabia, and Ivory Coast. In our analysis we use the known approach of partitioning country-wide networks, and an additional iterative partitioning of each of the first level communities into sub-communities, revealing that cohesiveness and matching of official regions can also be observed on a second level if spatial resolution of the data is high enough. The method has possible policy implications on the definition of the borderlines and sizes of administrative regions.

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Delineating geographical regions with networks of human interactions in an extensive set of countries.

The availability of massive network and mobility data from diverse domains has fostered the analysis of human behavior and interactions. Broad, extensive, and multidisciplinary research has been devoted to the extraction of non-trivial knowledge from this novel form of data. We propose a general method to determine the influence of social and mobility behavior over a specific geographical area in order to evaluate to what extent the current administrative borders represent the real basin of human movement. We build a network representation of human movement starting with vehicle GPS tracks and extract relevant clusters, which are then mapped back onto the territory, finding a good match with the existing administrative borders. The novelty of our approach is the focus on a detailed spatial resolution, we map emerging borders in terms of individual municipalities, rather than macro regional or national areas. We present a series of experiments to illustrate and evaluate the effectiveness of our approach.

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Discovering the Geographical Borders of Human Mobility

The analysis of real-world complex networks has been the focus of recent research. Detecting communities helps in uncovering their structural and functional organization. Valuable insight can be obtained by analyzing the dense, overlapping, and highly interwoven k-clique communities. However, their detection is challenging due to extensive memory requirements and execution time. In this paper, we present a novel, parallel k-clique community detection method, based on an innovative technique which enables connected components of a network to be obtained from those of its subnetworks. The novel method has an unbounded, user-configurable, and input-independent maximum degree of parallelism, and hence is able to make full use of computational resources. Theoretical tight upper bounds on its worst case time and space complexities are given as well. Experiments on real-world networks such as the Internet and the World Wide Web confirmed the almost optimal use of parallelism (i.e., a linear speedup). Comparisons with other state-of-the-art k-clique community detection methods show dramatic reductions in execution time and memory footprint. An open-source implementation of the method is also made publicly available.

Parallel $(k)$-Clique Community Detection on Large-Scale Networks

Large-scale network monitoring systems require efficient storage and consolidation of measurement data. Relational databases and popular tools such as the Round-Robin Database show their limitations when handling a large number of time series. This is because data access time greatly increases with the cardinality of data and number of measurements. The result is that monitoring systems are forced to store very few metrics at low frequency in order to grant data access within acceptable time boundaries.

This paper describes a novel compressed time series database named tsdb whose goal is to allow large time series to be stored and consolidated in realtime with limited disk space usage. The validation has demonstrated the advantage of tsdb over traditional approaches, and has shown that tsdb is suitable for handling a large number of time series.

/pdf/tsdb-a-compressed-database-for-time-series-10rheuuxtb.pdf

tsdb: a compressed database for time series

The large base of Internet-enabled smartphones provides an excellent opportunity for a fine-grained observation of the structure of the Internet and a quantitative evaluation of its characteristics. Smartphones can operate as active mobile monitors and, coordinated by a central entity, they can probe the network on a local scale. Then the results produced by a large number of participants can be merged to obtain a detailed graph of the Internet. Besides the design of the measurement framework, this paper describes the implementation and validation of a traceroute-like tool that is compatible with the Android platform. This confirms that smartphone-based crowdsourcing of network properties can be a viable strategy.

http://vecchio.iet.unipi.it/vecchio/files/2010/02/WINMEE-vecchio-final.pdf

On the feasibility of measuring the internet through smartphone-based crowdsourcing

Network security is traditionally based on the analysis and dissection of network packets. The widespread use of data encryption and the increase of network traffic created many challenges in terms of visibility and performance, making security tools less effective and both hard to deploy and maintain as network size and speed increase. The advent of eBPF in modern Linux systems enables introspection and adds the ability to inject code in the kernel at specific tracepoints. This work leverages eBPF to combine system introspection with a novel system-level security policer that enables the creation of fine-grained security policies tailored for specific users, processes and containers. This is a major advance for network security applications that can benefit from system introspection to enrich information extracted from network packets, paving the way for the implementation of systemand network-aware security polices that combine visibility and security at a fraction of the computational cost of exist-

/pdf/combining-system-visibility-and-security-using-ebpf-55s6ae8ahu.pdf

Simone Mainardi

Papers

Discovering the Geographical Borders of Human Mobility

Parallel $(k)$-Clique Community Detection on Large-Scale Networks

tsdb: a compressed database for time series

On the feasibility of measuring the internet through smartphone-based crowdsourcing

Combining System Visibility and Security Using eBPF.