Trajectory Pattern Mining for Urban Computing in the Cloud

Altomare, Albino; Cesario, Eugenio; Comito, Carmela; Marozzo, Fabrizio; Talia, Domenico

doi:10.1109/tpds.2016.2565480

Cited by 25 publications

(17 citation statements)

References 15 publications

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“…Although the purpose of this paper also includes efficient communication in IoT systems, developing a unified middleware application is beyond the scope of our study. Recently, there has been similar studies in the smart city domain, such as [22], [23]. However, while these studies contributed to the smart city systems in their 3. http://www.onem2m.org/ 4. https://www.w3.org/TR/vocab-ssn/ own right, none of these studies tackle the semantic interoperability, data aggregation, complex event processing and quality of information all at the same time in the semantic web environment for smart city systems.…”

Section: Smart City Frameworkmentioning

confidence: 99%

Observing the Pulse of a City: A Smart City Framework for Real-Time Discovery, Federation, and Aggregation of Data Streams

Kolozali

Bermúdez-Edo

Farajidavar

et al. 2019

IEEE Internet Things J.

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An increasing number of cities are confronted with challenges resulting from the rapid urbanisation and new demands that a rapidly growing digital economy imposes on current applications and information systems. Smart city applications enable city authorities to monitor, manage and provide plans for public resources and infrastructures in city environments, while offering citizens and businesses to develop and use intelligent services in cities. However, providing such smart city applications gives rise to several issues such as semantic heterogeneity and trustworthiness of data sources, and extracting up-to-date information in real time from large-scale dynamic data streams. In order to address these issues, we propose a novel framework with an efficient semantic data processing pipeline, allowing for real-time observation of the pulse of a city. The proposed framework enables efficient semantic integration of data streams and complex event processing on top of real-time data aggregation and quality analysis in a Semantic Web environment. To evaluate our system, we use real-time sensor observations that have been published via an open platform called Open Data Aarhus by the City of Aarhus. We examine the framework utilising Symbolic Aggregate Approximation to reduce the size of data streams, and perform quality analysis taking into account both single and multiple data streams. We also investigate the optimisation of the semantic data discovery and integration based on the proposed stream quality analysis and data aggregation techniques.

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Section: Smart City Frameworkmentioning

confidence: 99%

Observing the Pulse of a City: A Smart City Framework for Real-Time Discovery, Federation, and Aggregation of Data Streams

Kolozali

Bermúdez-Edo

Farajidavar

et al. 2019

IEEE Internet Things J.

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“…Comito, et al [34] Presented a novel methodology to extract and analyze the time-and geo-references associated with social data so as to mine information about human dynamics and behaviors within urban context. In another study [35] presented a cloud-based software environment specifically designed for urban computing supporting smart city applications and described in detail the design and workflow for the implementation of the application and its execution by a workflow engine integrated in the environment. Brimicombe and Li [36] developed city intelligence idea that measures city ability to produce favorable conditions to get metropolitan operators (i.e., inhabitants, systems, and public/private groups) and Cheng, et al [37] investigated the interrelation between the smart city and urban planning.…”

Section: Related Workmentioning

confidence: 99%

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Muhammad

Wan

Cervantes

et al. 2018

IJGI

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Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as "Weibo") in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference.

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“…For executing the load tests and measuring the performance of the system we used Apache JMeter 7 . According to the evaluation approach proposed in [26], the following performance parameters have been considered:…”

Section: Experimental Setup and Performance Parametersmentioning

confidence: 99%

A scalable middleware for context-aware mobile applications

Belcastro

Marozzo

Trunfio

2019

IJAHUC

Self Cite

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A core functionality of context-aware mobile applications is storing, indexing, and retrieving information about users, places, events and other resources. The goal of this work is to design and provide a service-oriented middleware, called Geocon, which can be used by mobile developers to implement such functionality. To represent information about users, places, events and resources of context-aware applications, Geocon defines a metadata model that can be extended to match specific application requirements. The middleware includes a geocon-service for storing, searching and selecting metadata about users, resources, events and places of interest, and a geocon-client library that allows mobile applications to interact with the service through the invocation of local methods. The paper describes the Geocon middleware and presents an experimental evaluation of its scalability on a cloud platform with a real-world mobile application.

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Trajectory Pattern Mining for Urban Computing in the Cloud

Cited by 25 publications

References 15 publications

Observing the Pulse of a City: A Smart City Framework for Real-Time Discovery, Federation, and Aggregation of Data Streams

Observing the Pulse of a City: A Smart City Framework for Real-Time Discovery, Federation, and Aggregation of Data Streams

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

A scalable middleware for context-aware mobile applications

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