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ALKET CECAJ

DOCENTE A CONTRATTO presso: Dipartimento di Scienze e Metodi dell'Ingegneria


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Pubblicazioni

2020 - Comparing deep learning and statistical methods in forecasting crowd distribution from aggregated mobile phone data [Articolo su rivista]
Cecaj, A.; Lippi, M.; Mamei, M.; Zambonelli, F.
abstract

Accurately forecasting how crowds of people are distributed in urban areas during daily activities is of key importance for the smart city vision and related applications. In this work we forecast the crowd density and distribution in an urban area by analyzing an aggregated mobile phone dataset. By comparing the forecasting performance of statistical and deep learning methods on the aggregated mobile data we show that each class of methods has its advantages and disadvantages depending on the forecasting scenario. However, for our time-series forecasting problem, deep learning methods are preferable when it comes to simplicity and immediacy of use, since they do not require a time-consuming model selection for each different cell. Deep learning approaches are also appropriate when aiming to reduce the maximum forecasting error. Statistical methods instead show their superiority in providing more precise forecasting results, but they require data domain knowledge and computationally expensive techniques in order to select the best parameters.


2020 - Forecasting Crowd Distribution in Smart Cities [Relazione in Atti di Convegno]
Cecaj, A.; Lippi, M.; Mamei, M.; Zambonelli, F.
abstract

In this work we present a forecasting method that can be used to predict crowd distribution across the city. Specifically, we analyze and forecast cellular network traffic and estimate crowd on such basis. Our forecasting model is based on a neural network combined with time series decomposition techniques. Our analysis shows that this approach can give interesting results in two directions. First, it creates a forecasting solution that fits all the variability in our dataset without having to create specific features and without complex search procedures for optimal parameters. Second, the method performs well, showing to be robust even in the presence of spikes in the data thus enabling better applications such as event management and detection of crowd gathering.


2019 - Investigating economic activity concentration patterns of co-agglomerations through association rule mining [Articolo su rivista]
Cecaj, A.; Mamei, M.
abstract

Economic activity tends to concentrate in particular geographic areas forming agglomerations and co-locations of firms. These agglomerations bring benefits for the firms themselves by increasing productivity, access to human resources, labor pooling, innovation, knowledge spillovers and regional growth. In this paper, we present a method for the discovery and analysis of such agglomerations. The method allows to spot patterns of co-locations in the composition of the agglomerations. Those patterns identify important relationships between the firms compounding the agglomerations thus describing the dynamics that exists inside the agglomeration itself.


2017 - Data fusion for city life event detection [Articolo su rivista]
Cecaj, A.; Mamei, M.
abstract

The automatic detection of events happening in urban areas from mobile phones’ and social networks’ datasets is an important problem that would enable novel services ranging from city management and emergency response, to social and entertainment applications. In this work we present a simple yet effective method for discovering events from spatio-temporal datasets, based on statistical anomaly detection. Our approach can combine multiple sources of information to improve results. We also present a method to automatically generate a keyword-based description of the events being detected. We run experiments in two cities with data coming from a mobile phone operator (call detail records–CDRs) and from Twitter. We show that this method gives interesting results in terms of precision and recall. We analyze the parameters of our approach and discuss its strengths and weaknesses.


2016 - Re-identification and information fusion between anonymized CDR and social network data [Articolo su rivista]
Cecaj, Alket; Mamei, Marco; Zambonelli, Franco
abstract

The analysis of multiple datasets on users’ behaviors opens interesting information fusion possibilities and, at the same time, creates a potential for re-identification and de-anonymization of users’ data. On the one hand, this kind of approaches can breach users’ privacy despite anonymization. On the other hand, combining different datasets is a key enabler for advanced context-awareness in that information from multiple sources can complement and enrich each other. In this work we analyze different anonymized mobility datasets in the direction of highlighting re-identification and information fusion possibilities. In particular we focus on call detail record (CDR) datasets released by mobile telecom operators and datasets comprising geo-localized messages released by social network sites. Results shows that: (1) in line with previous findings, few (about 4) data points are enough to uniquely pin point the majority (90 %) of the users, (2) more than 20 % of CDR users have a single social network user exhibiting a number of matching data points. We speculate that these two users might be the same person. (3) We derive an estimate of the probability of two users begin the same person given the number of data points they have in common, and estimate that for 3 % of the social network users we can find a CDR user very likely (>90 % probability) to be the same person.


2014 - Re-identification of Anonymized CDR datasets Using Social Network Data [Relazione in Atti di Convegno]
Cecaj, Alket; Mamei, Marco; Bicocchi, Nicola
abstract

In this work we examine a large dataset of 335 million anonymized call records made by 3 million users during 47 days in a region of northern Italy. Combining this dataset with publicly available user data, from different social networking ser-vices, we present a probabilistic approach to evaluate the potential of re-identification of the anonymized call records dataset. In this sense, our work explores different ways of analyzing data and data fusion techniques to integrate different mobility datasets together. On the one hand, this kind of approaches can breach users' privacy despite anonymization, so it is worth studying carefully. On the other hand, combining different datasets is a key enabler for advanced context-awareness in that information form multiple sources can complement and enrich each other.


2014 - Social Collective Awareness in Socio-Technical Urban Superorganisms [Capitolo/Saggio]
Bicocchi, Nicola; Cecaj, Alket; Fontana, Damiano; Mamei, Marco; Sassi, Andrea; Zambonelli, Franco
abstract

Smart cities are characterized by the close integration of ICT devices and humans. However, the vast majority of current deployments of smart technologies relies on sensing devices collecting data and data mining techniques squeezing little meanings out of them. Nevertheless, we believe that citizens integrated with ICT technologies could collaboratively constitute large-scale socio-technical superorganisms supporting collective awareness and behaviours. This paper clarifies our vision on urban superorganisms, identifies the key challenges towards their actual deployment and proposes a prototype architecture supporting their development.


2013 - Collective Awareness for Human-ICT Collaboration in Smart Cities [Relazione in Atti di Convegno]
Bicocchi, Nicola; Cecaj, Alket; Fontana, Damiano; Mamei, Marco; Sassi, Andrea; Zambonelli, Franco
abstract

Future urban scenarios will be characterized by the close integration of ICT devices and humans. Citizens using their own capabilities integrated with ICT technologies could collaboratively constitute a large-scale socio-technical superorganism to support collective urban awareness and activities. This position paper, with the help of a representative case study in the area of intelligent transportation systems, identifies the key challenges for future urban superorganisms and proposes a two-tier architecture to support their development.