Towards the cloudification of the social networks analytics

In the last years, with the increase of the available data from social networks and the rise of big data technologies, social data has emerged as one of the most profitable market for companies to increase their benefits. Besides, social computation scientists see such data as a vast ocean of information to study modern human societies. Nowadays, enterprises and researchers are developing their own mining tools in house, or they are outsourcing their social media mining needs to specialised companies with its consequent economical cost. In this paper, we present the first cloud computing service to facilitate the deployment of social media analytics applications to allow data practitioners to use social mining tools as a service. The main advantage of this service is the possibility to run different queries at the same time and combine their results in real time. Additionally, we also introduce twearch, a prototype to develop twitter mining algorithms as services in the cloud.Peer ReviewedPostprint (author’s final draft

Evaluation of Simulation Engines for Crowdsensing Activities

The goal of this paper is to analyze existing simulation engines and assess how well-suited they are for simulating the formation, existence and dissolution of dynamic social networks, with a special emphasis on networks formed around crowdsensing efforts. The crowd in this context is a loosely-coupled social network of people, who use their mobile devices to collect and share data and receive some sort of service or satisfaction in return. Often it is hard to predict whether users would like a certain future crowdsensing application, therefore it is necessary to simulate the expected behavior of the crowd in a pre-specified simulation environment. This paper proposes an urban parking scenario, in which the drivers collect and share parking related events. The main part of this research is the analysis of three simulation engines, which will show which is the best suited for simulating dynamic social networks formed around crowdsensing efforts. The results will show that there are generic simulation environments capable of simulating large crowds, which also possess suitable visualization tools and integration with geospatial data

Development of a Middleware between SUMO simulation tool and JaCaMo framework

Smart City has an infrastructure that works with technology to reduce daily problems. The lack of parking spots is one of these problems that can be controlled by creating Smart Parking systems. The MAPS (Multi-Agent Parking System) project studies and develops multi-agent systems for smart parking using JaCaMo framework to implement agents and artifacts. This paper presents the so-called MAPS-SUMO, a middleware between the MAPS project and SUMO, a simulation tool used for urban traffic. Our middleware allows a graphical representation for the simulation executed by MAPS agents

Exploring the Applicability of Location Based Services to Determine the State Routes Transport Networks Integratedness in the City of Johannesburg

In cities of the developing countries, particularly in African, Asian and Latin American continents; there have been growing concerns in terms of the state of public transportation systems. One of the main among the concerns have been lack of well-integrated, reliable and efficient public transport systems. This is particularly so in urban centres, due to rapid growth of the urban population coincided with the end of colonialism, giving rise to large scale economic, spatial and structural transformation of urban landscapes. The consciousness of the need for well-functioning innovative public transport systems by all spheres of governments and the private sector institutions has prompted precipitate action in the past decades to invest in innovative transport systems. Conversely, just like any other rapid growing metropolitan municipalities in developing and emerging economies, the city of Johannesburg has not been released with regards continuous public transport challenges. In the past decade, the City of Johannesburg has actively participated in the development of the first fast train system; the Gautrain in conjunction with two other metropolitan cities within the province. To support the innovative train system, the city also invested in and developed the Rea Vaya; a rapid bus system. However, the state of connectedness of the rail and road route networks within the city have not been well documented. Therefore, this study aims to delineate the extent routes network integration among Gautrain and Rea Vaya within the Johannesburg urban public transport system and how working relationships could be improved. The study adopted a phenomelogical case study survey design that applied mixed-method approaches to gather spatial, qualitative and quantitative data. The exploratory approach was used to formulate the research problem for precise investigation whilst the descriptive approach was used to gather complete and accurate information. Research techniques such as crowdsourcing, interviews, social media was used to collected data. Whilst data analysis and interpretations were conducted with techniques such as main content analysis, Geographic Information Technologies and Echo-Echo. Research findings; indicate that there are limited areas where the route networks between the public transport systems are connected. The large sections of the networks are disintegrated. The work recommends conscious efforts in planning and developing both rail and road route networks that are integrated to promote efficiency of public transport systems

A statistical approach for studying urban human dynamics

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThis doctoral dissertation proposed several statistical approaches to analyse urban dynamics with aiming to provide tools for decision making processes and urban studies. It assumed that human activity and human mobility compose urban dynamics. Initially, it studied geolocated social media data and considered them as a proxy for where and when people carry out what it is defined as the human activity. It employed techniques associated with generalised linear models, functional data analysis, hierarchical clustering, and epidemic data, to explain the spatio-temporal distribution of the places where people interact with their social networks. Afterwards, to understand the mobility in urban environments, data coming from an underground railway system were used. The information was considered repeated daily measurements to capture the regularity of human behaviour. By implementing methods from functional principal components data analysis and hierarchical clustering, it was possible to describe the system and identify human mobility patterns

Identifying the use of a park based on clusters of visitors\u27 movements from mobile phone data

Planning urban parks is a burdensome task, requiring knowledge of countless variables that are impossible to consider all at the same time. One of these variables is the set of people who use the parks. Despite information and communication technologies being a valuable source of data, a standardized method which enables landscape planners to use such information to design urban parks is still broadly missing. The objective of this study is to design an approach that can identify how an urban green park is used by its visitors in order to provide planners and the managing authorities with a standardized method. The investigation was conducted by exploiting tracking data from an existing mobile application developed for Cardeto Park, an urban green area in the heart of the old town of Ancona, Italy. A trajectory clustering algorithm is used to infer the most common trajectories of visitors, exploiting global positioning system and sensor-based tracks. The data used are made publicly available in an open dataset, which is the first one based on real data in this field. On the basis of these user-generated data, the proposed data-driven approach can determine the mission of the park by processing visitors\u27 trajectories whilst using a mobile application specifically designed for this purpose. The reliability of the clustering method has also been confirmed by an additional statistical analysis. This investigation reveals other important user behavioral patterns or trends