The sensors can measure different meteorological phenomena: Wind Direction, Wind Speed, Temperature, Relative Humidity, Precipitation, Global Radiation, Atmospheric Pressure and Net Radiation. The dataset supplies information regarding the current flowing through the distribution lines and details about how the distribution lines are spread over the Trentino territory. The first number is proportional to the number of calls issued from the area B to the province A, the second one is proportional to the number of calls from the province A to the area B.the spatial aggregation is the Trentino GRID squares and the Italian provinces.the temporeal aggregation values are in timeslots of ten minutes. designed the dataset and wrote the paper. Telecom Italia dataset elds. Defined as type 2; Heavy: precipitation quantity equal to in [10,100] mm/h. Users can get more data about the municipality (e.g., boundaries, population) using the acheneID as a primary key in the Administrative Regions; created: Tweet time in ISO format YYYY-MM-DDTHH: mm: SS, Europe/Rome timezone; geometry: approximate position of the tweet, in geoJSON format. You are free: The ODbL also requires you to share any improvements you make to this databases under the ODbL as well. The dataset contains measurements about temperature, precipitation and wind speed/direction taken in 36 Weather Stations placed around the Province of Trentino. The precipitation datasets provide information about precipitation intensity and type over the geographical area. In addition to the data described in this paper, the second edition also provides private mobility data (trips performed by customers of some car security and insurance companies), demographic data from Telecom Italia (e.g., gender, age-range and living area) and detailed Italian companies' information (e.g., number of employees, size and locations). Telecom Italia's Big Data Challenge - Data Collaboratives 10 20 0.0101 0.0693 0. . Since the text of the news articles is not provided, a service like diffbot (http://www.diffbot.com) or any other similar service (e.g., Apache Tika) could be used to extract the text from a given url. The goal of this challenge was to come up with technological ideas related to big data that in return. EPJ Data Science 4, 4 (2015). plot_maps.py Shows the thematic maps of Fig. Nonetheless, there is also a soft weekly seasonality observable especially comparing Sunday and Monday. 10. Node Centrality Metrics for Hotspots Analysis in Telecom Big Data Quantifying the impact of human mobility on malaria. R.L. Unfortunately, since it was not possible to share the input (raw) files, this code can not be executed to perfectly reproduce the datasets. The data are accessible from the Harvard Dataverse repository but also from a public API provided by Dandelion (http://dandelion.eu) which is the original platform where the data were published for the Big Data Challenge. FBK takes is the scientific partner on big data and open data policy. In order to get a first grasp of the geographical location of the grids, we suggest importing them into the free software QGIS, adding an OpenStreetMap layer as well. Timestamp: timestamp value with the following format: YYYYMMDDHHmm; Square id: id of a given square of Milan/Trentino GRID; Intensity: intensity value of the precipitation. arXiv preprint arXiv: 1407.4885 (2014). PLoS ONE 10, e0128692 (2015). Gianni Barlacchi and Marco De Nadai: These authors contributed equally to this work. The Precipitation dataset [Data citations 14,15] contains values about the type and the intensity of the precipitation. The news datasets contain all the articles published on the websites http://www.milanotoday.it and http://www.trentotoday.it. This dataset contains measurements about temperature, precipitation and wind speed/direction taken in 36 Weather Stations.15 minutes time interval. The Grid dataset [Data citations 2,3] provides the geographical reference of each square which composes the grid in the reference system: WGS 84EPSG:4326. square id: identification string of a given square of the Milan or Trentino GRID; Time Interval: The cell geometry expressed as geoJSON and projected in WGS84 (EPSG:4326). SpazioDati is the technological partner hosting the data distribution platform. 31. Its main role was to provide an affordable way to access to all the data related to the challenge and Dandelion is the original platform where all of this data was published.Its not the first time that some large datasets are made available to the public, through a controlled access: we can cite the public data sets published on Amazon S3, for example.But its the first time that there is an official Open Data release starting from some Big Data sets: we know that its an hot topic.Using your account on dandelion.eu to access the data, let us to collect some useful insights on the real demand side of the Open Data value chain.Well publish these statistics of usage as Open Data, to make all the community involved more aware about the data value chain.Its also useful to give some real perceptions on the Smart Cities and Smart Communities visions. The obfuscation of the username has been done using the hash function SHA-1, and two random generated strings (SALT1 and SALT2): The dataTXT is a tool to identify meaningful sequences of one or more terms, and then to link them to the most appropriate Wikipedia page. (t) follows the rule: where k is a constant defined by Telecom Italia, which hides the true number of calls, SMS and connections. This datatset is available both for Trentino province and city of Milan. Provided by the Springer Nature SharedIt content-sharing initiative, Scientific Data (Sci Data) The third contains census variables, divided into eight different groups: residential population, foreign population, families, education level, work status, commuting, accommodations info and building composition. Defined as type 1; Moderate: precipitation quantity equal in [2,10] mm/h. This dataset provides, for specific instances, the total current flowing through the lines. Figure 4 shows the process we have done to transform the original dataset to the shared one. A prototypical example is offered by Orange's Data for Development (D4D) initiative in 2013 (ref. Bajardi, P., Delfino, M., Panisson, A., Petri, G. & Tizzoni, M. Unveiling patterns of international communities in a global city using mobile phone data. Different sensors can share the same location. The presented datasets can be enriched by using census data provided by the Italian National Institute of Statistics (ISTAT) (http://www.istat.it/en/), a public research organization and the main provider of official statistics in Italy. Discover more. 156 Recommendations 0 Learn more about stats on ResearchGate Abstract In this work, we are interested in the applications of big data in the telecommunication domain, analysing two weeks of. Cartography and Geographic Information Science 41, 260271 (2014). In the Telecommunications and Social pulse datasets, we provided record level data which are not algorithmically aggregated on purpose. It is a value between 0 and 3; Coverage: percentage value of the quadrant covered by the precipitation; Type: type of the precipitation. The Milano Grid is provided in GeoJSON format. Thank you for visiting nature.com. master 1 branch 0 tags Go to file Code dwhitena Update README.md 398c34c on Apr 15, 2015 3 commits README.md Update README.md 8 years ago call_in_mgrid.png Initial Commit F.A. Algorithms | Free Full-Text | Citywide Cellular Traffic Prediction Telecom Italia and OPNET Datasets for Network Traffic Prediction Cell ID T imestamp Recevied SMS Activity Sent SMS Activity Incoming Calls Activity Outgoing Calls Activity. 5) have a strong daily seasonal component which starts in the early morning and increases during the day, having a peak around 22:00. plot.py Shows the time-series of the SMS, Calls, Tweets and Internet CDRs of Milan (see Fig. PLoS Computational Biology 10, 1003716 (2014). The SMSs are received in the nation identified by the Country code; Call-in activity: activity proportional to the amount of received calls inside the Square id during a given Time interval. Milan is the main industrial, commercial, and financial centre of Italy. Data for development: the d4d challenge on mobile phone data. This information is directly provided by ARPA (Agenzia Regionale per la Protezione dellAmbiente).Temporal aggregation 1 hour. In this context, research challenges that provide access to a large number of research teams to the same dataset are becoming a truly valuable framework to advance the state of the art in the field. Dynamic population mapping using mobile phone data. Quercia, D., Ellis, J., Capra, L. & Crowcroft, J. Tracking gross community happiness from tweets. The Grid dataset for the city of Milan (i.e., data citation 2 in the paper), which describes the tessellation of space into the areas over which such information is aggregated The challenge was organized by Telecom Italia, in association with EIT ICT Labs, SpazioDati, MIT Media Lab, Polytechnic University of Milan, Fondazione Bruno Kessler,University of Trento and TrentoRISE.The data provided in the dataset of the Big Data Challenge is geo-referenced (areas: Milan and the Autonomous Province of Trento Italy) and anonymized. These data was used during the Big Data Challenge 2014, an online call for developers, researchers and designers from all over the world to come up with brand-new big data services and applications. Scientific reports 4 (2014). This dataset provides information regarding the level of interaction between the Province of Trento and the Italian provinces. We refer to this grid as the Trentino Grid. Original data sources include ISTAT and Eurostat data. ), which require different amount of electricity. The dataset describes precipitation intensity over the province of Trento.the spatial aggregation is the Trentino GRID squares.The temporal values are provided every ten minutes. Exploratory Data Analysis On Telecom Italia Big Data Challenge To get some useful insight about the data we further describe and visualize activity and connectivity maps from Telecom Italia data sets and mobility from Telekom Srbija data set. Updated 2 years ago. 3). This dataset provides information regarding the level of interaction between the Province of Trento and the Italian provinces.The level of interaction between an area A of the Province of Trento and a province B is given as a pair of decimal numbers. The dataset used for analysis is for Telecom Italia for the city of Milano and is made public in 2014 after the contest of Big data challenge . You can do anything you want, as you remain under the terms and conditions of the ODbL license conditions. Telecom Italia: As part of the "Big Data Challenge", consists of data about telecommunication activity in the city of Milan and in the province of Trentino. From Figs 5 and 6 it is possible to observe a strong daily seasonality which usually starts at 7:00, when people turn on their phones and probably commute to work and then slowly decreases in the evening when people return home and sleep. 7), the selected areas show very different behavioural patterns. EPJ Data Science 4, 1 (2015). It is a rich, open multi-source aggregation of telecommunications, weather, news, social networks and electricity data. It can also be useful to visualize the data and the distribution of the events inside the geographical areas. CAS The Call Detail Records (CDRs) of the 6.8 billion mobile phone subscribers worldwide (http://www.itu.int/en/ITU-D/Statistics/Pages/facts/default.aspx, date of access 06/08/2014) potentially represent the most invaluable proxy for people's communication and mobility habits at a global scale. The Orange Telecom's Churn Dataset, which consists of cleaned customer activity data (features), along with a churn label specifying whether a customer canceled the subscription, will be used to develop predictive models. Call Details Record Analysis: A Spatiotemporal Exploration toward CDRs log the user activity for billing purposes and network management.The spatial aggregation values are provided for the squares of the Tretino GRID.The temporal values are aggregated in timeslots of ten minutes. Telecom Italia made a dataset of its own mobile phone data (millions of anonymized and geo-referenced records of calls from Milan and . Telecommunications Premium Premium statistics Industry-specific and extensively researched technical data (partially from exclusive partnerships). This dataset [Data citation 19] provides information about the current administrative regions of Milan and in the Province of Trentino. The software is written in Python 2.7 and can be found at [Data citation 1]. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Hence, it is possible to capture the evolution observing permanent hotspots (places that are important all day), intermittent (with a lifespan of only few hours per day) and intermediate (with a lifespan ~ 12h). This dataset contains data derived from an analysis of geolocalized tweets originated from Milan during the months of November and December.Each row corresponds to a tweet. Proceedings of ICMI, 427434 (2014). The second set is composed of the administrative boundaries used in the last three censuses. processed the data and wrote the paper. The data are released on 7 Italian cities: Bari, Milan, Naples, Rome, Turin, Venice and Palermo. Nature comm. A.V. At the beginning of 2014, Telecom Italia, in collaboration with several international partners, launched the Telecom Italia Big Data Challenge. The network learns the temporal and spatial dependence of cellular traffic data. A multi-source dataset of urban life in the city of Milan and the Province of Trentino. 5) and the Boxplots shown in Fig. Telecom Italia received late last year a preliminary bid of 50.5 euro cents a share from KKR. CAS The Telecommunications and Social pulse data make it possible to identify the hotspots of the city, defined as areas with high activity density with respect to the rest of the city. In the 2014 edition they provided data of two Italian areas: the city of Milan and the Province of Trentino. Bogomolov, A. et al. For this reason we provide some useful examples in [Data citation 1] which display this information. Exploring the mobility of mobile phone users. Telecom Churn Dataset | Kaggle Physica A: statistical mechanics and its applications 392, 14591473 (2013). Moreover, the emergence of new geo-located Information and Communications Technology (ICT) services like Twitter and Foursquare introduces further opportunities for researchers to inspect quantitatively different aspects of human behaviour such as the social well-being of individuals and communities19, socio-economic status of geographical regions20, and people's mobility21. designed the dataset and wrote the paper. The contest made available to developers, designers and scientists a large dataset of 30+ kinds of data (mobile, weather, energy, etc.) Journal of Machine Learning Research 12, 28252830 (2011). A pair of decimal numbers is given as the level of interaction. Recently, the dataset used for the contest was made open to the public via their website. The Telecom Italia Big Data Challenge now is Open Data Home Highlights At the beginning of 2014, Telecom Italia, in collaboration with several international partners, launched the Telecom Italia Big Data Challenge. For the latter, each task is performed for predicting service-specific traffic data based on a fully connected network. A plain language summary of the ODbL is available on the Open Data Commons website. Dataset with 6 projects 1 file 1 table. Article The output is written in the same directory where the script resides. Isaacman, S. et al. Thus, the area of Milan is composed of a grid overlay of 1,000 (squares with size of about 235235meters and Trentino is composed of a grid overlay of 6,575 squares (see Fig. On the real-world Telecom Italia dataset, simulation results demonstrate the effectiveness of our proposal through prediction performance measure, spatial pattern comparison and statistical distribution verification. PLoS ONE 9, e105184 (2014). Telecom Italia Big Data Challenge MIT Media Lab For privacy issues the user id has been obfuscated. ADS The dataset has been released to the whole research community and here we provide a detailed description of the data records' structure, and present the methodology used in the data collection/aggregation process. publicly available is the dataset published by Telecom Italia in 2014 as "the Big Data Challenge" [5]. Science 328, 1029 (2010). Some of the datasets are spatially aggregated using a regular grid overlayed on the territory. The data provides information of Telecom Italia's customers interacting with the network and of other people using it while roaming. Italy: Telecom Italia mobile data traffic 2014-2018 | Statista Since it is not possible to have a well-established ground truth for the data, some important events with expected high importance for Milan were selected to validate it. In Milan, the type and the intensity of the phenomena are continuously measured by different sensors located within the city limit. It is composed by two subsets of data. However, this information is summarized in the Customer site dataset where for each square grid the number of customer sites is recorded along with the information about the power line they are connected to. Song, C., Qu, Z., Blumm, N. & Barabasi, A. There is no spatial aggregation and the data is aggregated in 60min time-slots. Introduction Cellular network is an important communication network, which provides call, message, and data services to the end users in the range covered by the base stations. First Online: 20 June 2020 Part of the Contributions to Statistics book series (CONTRIB.STAT.) This is spatiotemporal data because it contains both spatial and temporal aspects of subscribers and networks. Get the most important science stories of the day, free in your inbox. Science 338, 267270 (2012). Two types of CDR datasets were also produced to measure the interaction intensity between different locations: one from a particular area (Trentino/Milan) to any of the Italian provinces and one quantifying the interactions within the city/province (e.g., Milan to Milan). Telecom Data Data Card Code (4) Discussion (1) About Dataset No description available Usability info License Unknown adult.data ( 3.97 MB) get_app fullscreen chevron_right Unable to show preview Unexpected end of JSON input Data Explorer Version 1 (5.98 MB) insert_drive_file adult.data insert_drive_file adult.test Summary arrow_right folder 2 files Some of the datasets referring to the Milan urban area are spatially aggregated using a grid. Date in the format YYYY-MM-DD HH24 : MI; Value: the ampere value of the current passing through a given powerline (Line id) at a given Timestamp. A multi-source dataset of urban life in the city of Milan and the Province of Trentino. Telecom Italia's board of directors has agreed to the spin-off of its 23 data centers into a separate business. In each traffic component, the spatial-temporal attention module is designed to capture the dynamic spatial-temporal correlation of cellular traffic; the spatial-temporal convolution module. Google Scholar. The Open Database License (ODbL) explicitly covers data and not just creative works like photographs or text. The results of the proposed networks are then validated using the Telecom Italia Dataset. 4 SETlayers. de Montjoye, Y., Smoreda, Z., Trinquart, R., Ziemlicki, C. & Blondel, V. D4d-senegal: The second mobile phone data for development challenge. On the decomposition of cell phone activity patterns and their connection with urban ecology. PDF Big Data Analysis of Spatio-temporal Data

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