Data
User-OTT-Consumption-Profile---2019

User-OTT-Consumption-Profile---2019

active ARFF Attribution 4.0 International (CC BY 4.0) Visibility: public Uploaded 23-03-2022 by Dustin Carrion
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  • Computer Systems Machine Learning
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Context With the growing dependency that our society has on the Internet, the amount of data that goes through networks keeps increasing. Network monitoring and analysis of consumption behavior represents an important aspect for network operators allowing to obtain vital information about consumption trends in order to offer new data plans aimed at specific users and obtain an adequate perspective of the network. Over-the-top (OTT) media and communications services and applications are shifting the Internet consumption by increasing the traffic generation over the different available networks. OTT refers to applications that deliver audio, video, and other media over the Internet by leveraging the infrastructure deployed by network operators but without their involvement in the control or distribution of the content and are known by their large consumption of network resources. This dataset presents the summarization of the consumption behavior of users inside Universidad del Cauca Network between different days of April, May and June 2019. The users are classified between Low, Medium and High Consumption users. Content This dataset contains 1249 instances and 114 attributes on a single CSV file. Each instance represents a users consumption profile which holds summarized information about the consumption behavior of the user related to 56 OTT applications identified in the different IP flows captured in order to create the dataset. The user behavior is summarized with two types of attributes: time occupation (seconds) and data occupation (Bytes) per application. The application labels include: Amazon, AmazonVideo, Apple, AppleIcloud, AppleItunes, AppleStore, Datasaver, Deezer, Dropbox, eBay, Facebook, GMail, Google, GoogleDocs, GoogleDrive, GoogleHangoutDuo, GoogleMaps, GooglePlus, GoogleServices, HTTP and HTTPProxy (Browsing), IMO, Instagram, Linkedin, LotusNotes, Messenger, MSOneDrive, MSN, NetFlix, Playstation, PlayStore, PlayStation_VUE, Sina (weibo), Skype, SkypeCall, Snapchat, Soundcloud, Spotify, Steam, TeamViewer, Telegram, TikTok, Tuenti, Twitch, Twitter, Viber, Waze, WeChat, WhatsApp, WhatsAppCall, WhatsAppFiles, Wikipedia, Xbox, Yahoo, YouTube, Zoom. For further information you can read and please cite the following papers: IEEE Xplore: https://ieeexplore.ieee.org/document/9258898 Research Gate: https://www.researchgate.net/publication/345990587_Smart_User_Consumption_Profiling_Incremental_Learning-based_OTT_Service_Degradation Acknowledgements I would like to thank Universidad Del Cauca and MEDIANETS Group from Budapest University of Technology and Economics for supporting this research and MinCiencias for my PhD scholarship.

113 features

src_ip_numeric (ignore)numeric973 unique values
0 missing
Amazon_time_occupationnumeric1144 unique values
0 missing
AmazonVideo_time_occupationnumeric869 unique values
0 missing
Apple_time_occupationnumeric543 unique values
0 missing
AppleiCloud_time_occupationnumeric433 unique values
0 missing
AppleiTunes_time_occupationnumeric477 unique values
0 missing
AppleStore_time_occupationnumeric313 unique values
0 missing
DataSaver_time_occupationnumeric331 unique values
0 missing
Deezer_time_occupationnumeric2 unique values
0 missing
Dropbox_time_occupationnumeric581 unique values
0 missing
eBay_time_occupationnumeric38 unique values
0 missing
Facebook_time_occupationnumeric1121 unique values
0 missing
GMail_time_occupationnumeric1088 unique values
0 missing
Google_time_occupationnumeric1206 unique values
0 missing
GoogleDocs_time_occupationnumeric922 unique values
0 missing
GoogleDrive_time_occupationnumeric984 unique values
0 missing
GoogleHangoutDuo_time_occupationnumeric192 unique values
0 missing
GoogleMaps_time_occupationnumeric367 unique values
0 missing
GooglePlus_time_occupationnumeric436 unique values
0 missing
GoogleServices_time_occupationnumeric1193 unique values
0 missing
HTTP_time_occupationnumeric1180 unique values
0 missing
HTTP_Proxy_time_occupationnumeric757 unique values
0 missing
IMO_time_occupationnumeric20 unique values
0 missing
Instagram_time_occupationnumeric831 unique values
0 missing
LinkedIn_time_occupationnumeric788 unique values
0 missing
LotusNotes_time_occupationnumeric18 unique values
0 missing
Messenger_time_occupationnumeric651 unique values
0 missing
MS_OneDrive_time_occupationnumeric814 unique values
0 missing
MSN_time_occupationnumeric997 unique values
0 missing
NetFlix_time_occupationnumeric275 unique values
0 missing
Playstation_time_occupationnumeric157 unique values
0 missing
PlayStore_time_occupationnumeric780 unique values
0 missing
PS_VUE_time_occupationnumeric5 unique values
0 missing
Sina_Weibo_time_occupationnumeric34 unique values
0 missing
Skype_time_occupationnumeric1024 unique values
0 missing
SkypeCall_time_occupationnumeric84 unique values
0 missing
Snapchat_time_occupationnumeric82 unique values
0 missing
SoundCloud_time_occupationnumeric73 unique values
0 missing
Spotify_time_occupationnumeric362 unique values
0 missing
Steam_time_occupationnumeric30 unique values
0 missing
TeamViewer_time_occupationnumeric209 unique values
0 missing
Telegram_time_occupationnumeric117 unique values
0 missing
TikTok_time_occupationnumeric27 unique values
0 missing
Tuenti_time_occupationnumeric1 unique values
0 missing
Twitch_time_occupationnumeric5 unique values
0 missing
Twitter_time_occupationnumeric969 unique values
0 missing
Viber_time_occupationnumeric2 unique values
0 missing
Waze_time_occupationnumeric30 unique values
0 missing
WeChat_time_occupationnumeric4 unique values
0 missing
WhatsApp_time_occupationnumeric839 unique values
0 missing
WhatsAppCall_time_occupationnumeric191 unique values
0 missing
WhatsAppFiles_time_occupationnumeric1 unique values
0 missing
Wikipedia_time_occupationnumeric571 unique values
0 missing
Xbox_time_occupationnumeric369 unique values
0 missing
Yahoo_time_occupationnumeric777 unique values
0 missing
YouTube_time_occupationnumeric1116 unique values
0 missing
Zoom_time_occupationnumeric22 unique values
0 missing
Amazon_data_occupationnumeric1144 unique values
0 missing
AmazonVideo_data_occupationnumeric868 unique values
0 missing
Apple_data_occupationnumeric541 unique values
0 missing
AppleiCloud_data_occupationnumeric430 unique values
0 missing
AppleiTunes_data_occupationnumeric472 unique values
0 missing
AppleStore_data_occupationnumeric313 unique values
0 missing
DataSaver_data_occupationnumeric331 unique values
0 missing
Deezer_data_occupationnumeric2 unique values
0 missing
Dropbox_data_occupationnumeric574 unique values
0 missing
eBay_data_occupationnumeric38 unique values
0 missing
Facebook_data_occupationnumeric1116 unique values
0 missing
GMail_data_occupationnumeric1082 unique values
0 missing
Google_data_occupationnumeric1206 unique values
0 missing
GoogleDocs_data_occupationnumeric890 unique values
0 missing
GoogleDrive_data_occupationnumeric963 unique values
0 missing
GoogleHangoutDuo_data_occupationnumeric192 unique values
0 missing
GoogleMaps_data_occupationnumeric364 unique values
0 missing
GooglePlus_data_occupationnumeric397 unique values
0 missing
GoogleServices_data_occupationnumeric1193 unique values
0 missing
HTTP_data_occupationnumeric1180 unique values
0 missing
HTTP_Proxy_data_occupationnumeric753 unique values
0 missing
IMO_data_occupationnumeric19 unique values
0 missing
Instagram_data_occupationnumeric767 unique values
0 missing
LinkedIn_data_occupationnumeric735 unique values
0 missing
LotusNotes_data_occupationnumeric18 unique values
0 missing
Messenger_data_occupationnumeric644 unique values
0 missing
MS_OneDrive_data_occupationnumeric789 unique values
0 missing
MSN_data_occupationnumeric995 unique values
0 missing
NetFlix_data_occupationnumeric275 unique values
0 missing
Playstation_data_occupationnumeric151 unique values
0 missing
PlayStore_data_occupationnumeric753 unique values
0 missing
PS_VUE_data_occupationnumeric6 unique values
0 missing
Sina.Weibo._data_occupationnumeric33 unique values
0 missing
Skype_data_occupationnumeric1010 unique values
0 missing
SkypeCall_data_occupationnumeric87 unique values
0 missing
Snapchat_data_occupationnumeric82 unique values
0 missing
SoundCloud_data_occupationnumeric73 unique values
0 missing
Spotify_data_occupationnumeric362 unique values
0 missing
Steam_data_occupationnumeric30 unique values
0 missing
TeamViewer_data_occupationnumeric209 unique values
0 missing
Telegram_data_occupationnumeric117 unique values
0 missing
TikTok_data_occupationnumeric27 unique values
0 missing
Tuenti_data_occupationnumeric1 unique values
0 missing
Twitch_data_occupationnumeric5 unique values
0 missing
Twitter_data_occupationnumeric946 unique values
0 missing
Viber_data_occupationnumeric3 unique values
0 missing
Waze_data_occupationnumeric30 unique values
0 missing
WeChat_data_occupationnumeric4 unique values
0 missing
WhatsApp_data_occupationnumeric838 unique values
0 missing
WhatsAppCall_data_occupationnumeric189 unique values
0 missing
WhatsAppFiles_data_occupationnumeric1 unique values
0 missing
Wikipedia_data_occupationnumeric566 unique values
0 missing
Xbox_data_occupationnumeric366 unique values
0 missing
Yahoo_data_occupationnumeric776 unique values
0 missing
YouTube_data_occupationnumeric1115 unique values
0 missing
Zoom_data_occupationnumeric22 unique values
0 missing
clusternumeric3 unique values
0 missing

19 properties

1249
Number of instances (rows) of the dataset.
113
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
113
Number of numeric attributes.
0
Number of nominal attributes.
0.09
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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