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  • (Dés)Activation de fonctionnalités (plugins)

    18 février 2011, par

    Pour gérer l’ajout et la suppression de fonctionnalités supplémentaires (ou plugins), MediaSPIP utilise à partir de la version 0.2 SVP.
    SVP permet l’activation facile de plugins depuis l’espace de configuration de MediaSPIP.
    Pour y accéder, il suffit de se rendre dans l’espace de configuration puis de se rendre sur la page "Gestion des plugins".
    MediaSPIP est fourni par défaut avec l’ensemble des plugins dits "compatibles", ils ont été testés et intégrés afin de fonctionner parfaitement avec chaque (...)

  • Activation de l’inscription des visiteurs

    12 avril 2011, par

    Il est également possible d’activer l’inscription des visiteurs ce qui permettra à tout un chacun d’ouvrir soit même un compte sur le canal en question dans le cadre de projets ouverts par exemple.
    Pour ce faire, il suffit d’aller dans l’espace de configuration du site en choisissant le sous menus "Gestion des utilisateurs". Le premier formulaire visible correspond à cette fonctionnalité.
    Par défaut, MediaSPIP a créé lors de son initialisation un élément de menu dans le menu du haut de la page menant (...)

  • Publier sur MédiaSpip

    13 juin 2013

    Puis-je poster des contenus à partir d’une tablette Ipad ?
    Oui, si votre Médiaspip installé est à la version 0.2 ou supérieure. Contacter au besoin l’administrateur de votre MédiaSpip pour le savoir

Sur d’autres sites (5484)

  • What is Behavioural Segmentation and Why is it Important ?

    28 septembre 2023, par Erin — Analytics Tips

    Amidst the dynamic landscape of web analytics, understanding customers has grown increasingly vital for businesses to thrive. While traditional demographic-focused strategies possess merit, they need to uncover the nuanced intricacies of individual online behaviours and preferences. As customer expectations evolve in the digital realm, enterprises must recalibrate their approaches to remain relevant and cultivate enduring digital relationships.

    In this context, the surge of technology and advanced data analysis ushers in a marketing revolution : behavioural segmentation. Businesses can unearth invaluable insights by meticulously scrutinising user actions, preferences and online interactions. These insights lay the foundation for precisely honed, high-performing, personalised campaigns. The era dominated by blanket, catch-all marketing strategies is yielding to an era of surgical precision and tailored engagement. 

    While the insights from user behaviours empower businesses to optimise customer experiences, it’s essential to strike a delicate balance between personalisation and respecting user privacy. Ethical use of behavioural data ensures that the power of segmentation is wielded responsibly and in compliance, safeguarding user trust while enabling businesses to thrive in the digital age.

    What is behavioural segmentation ?

    Behavioural segmentation is a crucial concept in web analytics and marketing. It involves categorising individuals or groups of users based on their online behaviour, actions and interactions with a website. This segmentation method focuses on understanding how users engage with a website, their preferences and their responses to various stimuli. Behavioural segmentation classifies users into distinct segments based on their online activities, such as the pages they visit, the products they view, the actions they take and the time they spend on a site.

    Behavioural segmentation plays a pivotal role in web analytics for several reasons :

    1. Enhanced personalisation :

    Understanding user behaviour enables businesses to personalise online experiences. This aids with delivering tailored content and recommendations to boost conversion, customer loyalty and customer satisfaction.

    2. Improved user experience :

    Behavioural segmentation optimises user interfaces (UI) and navigation by identifying user paths and pain points, enhancing the level of engagement and retention.

    3. Targeted marketing :

    Behavioural segmentation enhances marketing efficiency by tailoring campaigns to user behaviour. This increases the likelihood of interest in specific products or services.

    4. Conversion rate optimisation :

    Analysing behavioural data reveals factors influencing user decisions, enabling website optimisation for a streamlined purchasing process and higher conversion rates.

    5. Data-driven decision-making :

    Behavioural segmentation empowers data-driven decisions. It identifies trends, behavioural patterns and emerging opportunities, facilitating adaptation to changing user preferences and market dynamics.

    6. Ethical considerations :

    Behavioural segmentation provides valuable insights but raises ethical concerns. User data collection and use must prioritise transparency, privacy and responsible handling to protect individuals’ rights.

    The significance of ethical behavioural segmentation will be explored more deeply in a later section, where we will delve into the ethical considerations and best practices for collecting, storing and utilising behavioural data in web analytics. It’s essential to strike a balance between harnessing the power of behavioural segmentation for business benefits and safeguarding user privacy and data rights in the digital age.

    A woman surrounded by doors shaped like heads of different

    Different types of behavioural segments with examples

    1. Visit-based segments : These segments hinge on users’ visit patterns. Analyse visit patterns, compare first-time visitors to returning ones, or compare users landing on specific pages to those landing on others.
      • Example : The real estate website Zillow can analyse how first-time visitors and returning users behave differently. By understanding these patterns, Zillow can customise its website for each group. For example, they can highlight featured listings and provide navigation tips for first-time visitors while offering personalised recommendations and saved search options for returning users. This could enhance user satisfaction and boost the chances of conversion.
    2. Interaction-based segments : Segments can be created based on user interactions like special events or goals completed on the site.
      • Example : Airbnb might use this to understand if users who successfully book accommodations exhibit different behaviours than those who don’t. This insight could guide refinements in the booking process for improved conversion rates.
    3. Campaign-based segments : Beyond tracking visit numbers, delve into usage differences of visitors from specific sources or ad campaigns for deeper insights.
      • Example : Nike might analyse user purchase behaviour from various traffic sources (referral websites, organic, direct, social media and ads). This informs marketing segmentation adjustments, focusing on high-performance channels. It also customises the website experience for different traffic sources, optimising content, promotions and navigation. This data-driven approach could boost user experiences and maximise marketing impact for improved brand engagement and sales conversions.
    4. Ecommerce segments : Separate users based on purchases, even examining the frequency of visits linked to specific products. Segment heavy users versus light users. This helps uncover diverse customer types and browsing behaviours.
      • Example : Amazon could create segments to differentiate between visitors who made purchases and those who didn’t. This segmentation could reveal distinct usage patterns and preferences, aiding Amazon in tailoring its recommendations and product offerings.
    5. Demographic segments : Build segments based on browser language or geographic location, for instance, to comprehend how user attributes influence site interactions.
      • Example : Netflix can create user segments based on demographic factors like geographic location to gain insight into how a visitor’s location can influence content preferences and viewing behaviour. This approach could allow for a more personalised experience.
    6. Technographic segments : Segment users by devices or browsers, revealing variations in site experience and potential platform-specific issues or user attitudes.
      • Example : Google could create segments based on users’ devices (e.g., mobile, desktop) to identify potential issues in rendering its search results. This information could be used to guide Google in providing consistent experiences regardless of device.
    A group of consumers split into different segments based on their behaviour

    The importance of ethical behavioural segmentation

    Respecting user privacy and data protection is crucial. Matomo offers features that align with ethical segmentation practices. These include :

    • Anonymization : Matomo allows for data anonymization, safeguarding individual identities while providing valuable insights.
    • GDPR compliance : Matomo is GDPR compliant, ensuring that user data is handled following European data protection regulations.
    • Data retention and deletion : Matomo enables businesses to set data retention policies and delete user data when it’s no longer needed, reducing the risk of data misuse.
    • Secured data handling : Matomo employs robust security measures to protect user data, reducing the risk of data breaches.

    Real-world examples of ethical behavioural segmentation :

    1. Content publishing : A leading news website could utilise data anonymization tools to ethically monitor user engagement. This approach allows them to optimise content delivery based on reader preferences while ensuring the anonymity and privacy of their target audience.
    2. Non-profit organisations : A charity organisation could embrace granular user control features. This could be used to empower its donors to manage their data preferences, building trust and loyalty among supporters by giving them control over their personal information.
    Person in a suit holding a red funnel that has data flowing through it into a file

    Examples of effective behavioural segmentation

    Companies are constantly using behavioural insights to engage their audiences effectively. In this section, we’ll delve into real-world examples showcasing how top companies use behavioural segmentation to enhance their marketing efforts.

    A woman standing in front of a pie chart pointing to the top right-hand section of customers in that segment
    1. Coca-Cola’s behavioural insights for marketing strategy : Coca-Cola employs behavioural segmentation to evaluate its advertising campaigns. Through analysing user engagement across TV commercials, social media promotions and influencer partnerships, Coca-Cola’s marketing team can discover that video ads shared by influencers generate the highest ROI and web traffic.

      This insight guides the reallocation of resources, leading to increased sales and a more effective advertising strategy.

    2. eBay’s custom conversion approach : eBay excels in conversion optimisation through behavioural segmentation. When users abandon carts, eBay’s dynamic system sends personalised email reminders featuring abandoned items and related recommendations tailored to user interests and past purchase decisions.

      This strategy revives sales, elevates conversion rates and sparks engagement. eBay’s adeptness in leveraging behavioural insights transforms user experience, steering a customer journey toward conversion.

    3. Sephora’s data-driven conversion enhancement : Data analysts can use Sephora’s behavioural segmentation strategy to fuel revenue growth through meticulous data analysis. By identifying a dedicated subset of loyal customers who exhibit a consistent preference for premium skincare products, data analysts enable Sephora to customise loyalty programs.

      These personalised rewards programs provide exclusive discounts and early access to luxury skincare releases, resulting in heightened customer engagement and loyalty. The data-driven precision of this approach directly contributes to amplified revenue from this specific customer segment.

    Examples of the do’s and don’ts of behavioural segmentation 

    Happy woman surrounded by icons of things and activities she enjoys

    Behavioural segmentation is a powerful marketing and data analysis tool, but its success hinges on ethical and responsible practices. In this section, we will explore real-world examples of the do’s and don’ts of behavioural segmentation, highlighting companies that have excelled in their approach and those that have faced challenges due to lapses in ethical considerations.

    Do’s of behavioural segmentation :

    • Personalised messaging :
      • Example : Spotify
        • Spotify’s success lies in its ability to use behavioural data to curate personalised playlists and user recommendations, enhancing its music streaming experience.
    • Transparency :
      • Example : Basecamp
        • Basecamp’s transparency in sharing how user data is used fosters trust. They openly communicate data practices, ensuring users are informed and comfortable.
    • Anonymization
      • Example : Matomo’s anonymization features
        • Matomo employs anonymization features to protect user identities while providing valuable insights, setting a standard for responsible data handling.
    • Purpose limitation :
      • Example : Proton Mail
        • Proton Mail strictly limits the use of user data to email-related purposes, showcasing the importance of purpose-driven data practices.
    • Dynamic content delivery : 
      • Example : LinkedIn
        • LinkedIn uses behavioural segmentation to dynamically deliver job recommendations, showcasing the potential for relevant content delivery.
    • Data security :
      • Example : Apple
        • Apple’s stringent data security measures protect user information, setting a high bar for safeguarding sensitive data.
    • Adherence to regulatory compliance : 
      • Example : Matomo’s regulatory compliance features
        • Matomo’s regulatory compliance features ensure that businesses using the platform adhere to data protection regulations, further promoting responsible data usage.

    Don’ts of behavioural segmentation :

    • Ignoring changing regulations
      • Example : Equifax
        • Equifax faced major repercussions for neglecting evolving regulations, resulting in a data breach that exposed the sensitive information of millions.
    • Sensitive attributes
      • Example : Twitter
        • Twitter faced criticism for allowing advertisers to target users based on sensitive attributes, sparking concerns about user privacy and data ethics.
    • Data sharing without consent
      • Example : Meta & Cambridge Analytica
        • The Cambridge Analytica scandal involving Meta (formerly Facebook) revealed the consequences of sharing user data without clear consent, leading to a breach of trust.
    • Lack of control
      • Example : Uber
        • Uber faced backlash for its poor data security practices and a lack of control over user data, resulting in a data breach and compromised user information.
    • Don’t be creepy with invasive personalisation
      • Example : Offer Moment
        • Offer Moment’s overly invasive personalisation tactics crossed ethical boundaries, unsettling users and eroding trust.

    These examples are valuable lessons, emphasising the importance of ethical and responsible behavioural segmentation practices to maintain user trust and regulatory compliance in an increasingly data-driven world.

    Continue the conversation

    Diving into customer behaviours, preferences and interactions empowers businesses to forge meaningful connections with their target audience through targeted marketing segmentation strategies. This approach drives growth and fosters exceptional customer experiences, as evident from the various common examples spanning diverse industries.

    In the realm of ethical behavioural segmentation and regulatory compliance, Matomo is a trusted partner. Committed to safeguarding user privacy and data integrity, our advanced web analytics solution empowers your business to harness the power of behavioral segmentation, all while upholding the highest standards of compliance with stringent privacy regulations.

    To gain deeper insight into your visitors and execute impactful marketing campaigns, explore how Matomo can elevate your efforts. Try Matomo free for 21-days, no credit card required. 

  • Stream ffmpeg transcoding result to S3

    7 juin 2019, par mabead

    I want to transcode a large file using FFMPEG and store the result directly on AWS S3. This will be done inside of an AWS Lambda that has limited tmp space so I can’t store the transcoding result locally and then upload it to S3 in a second step. I won’t have enough tmp space. I therefore want to store the FFMPEG output directly on S3.

    I therefore created a S3 pre-signed url that allows ’PUT’ :

    var outputPath = s3Client.GetPreSignedURL(new Amazon.S3.Model.GetPreSignedUrlRequest
    
{
    
    BucketName = "my-bucket",
    
    Expires = DateTime.UtcNow.AddMinutes(5),
    
    Key = "output.mp3",
    
    Verb = HttpVerb.PUT,
    
});

    I then called ffmpeg with the resulting pre-signed url :

    ffmpeg -i C:\input.wav -y -vn -ar 44100 -ac 2 -ab 192k -f mp3 https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550427237&Signature=%2BE8Wc%2F%2FQYrvGxzc%2FgXnsvauKnac%3D

    FFMPEG returns an exit code of 1 with the following output :

    ffmpeg version N-93120-ga84af760b8 Copyright (c) 2000-2019 the FFmpeg developers
    
  built with gcc 8.2.1 (GCC) 20190212
    
  configuration: --enable-gpl --enable-version3 --enable-sdl2 --enable-fontconfig --enable-gnutls --enable-iconv --enable-libass --enable-libdav1d --enable-libbluray --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libopus --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libtheora --enable-libtwolame --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libzimg --enable-lzma --enable-zlib --enable-gmp --enable-libvidstab --enable-libvorbis --enable-libvo-amrwbenc --enable-libmysofa --enable-libspeex --enable-libxvid --enable-libaom --enable-libmfx --enable-amf --enable-ffnvcodec --enable-cuvid --enable-d3d11va --enable-nvenc --enable-nvdec --enable-dxva2 --enable-avisynth --enable-libopenmpt
    
  libavutil      56. 26.100 / 56. 26.100
    
  libavcodec     58. 47.100 / 58. 47.100
    
  libavformat    58. 26.101 / 58. 26.101
    
  libavdevice    58.  6.101 / 58.  6.101
    
  libavfilter     7. 48.100 /  7. 48.100
    
  libswscale      5.  4.100 /  5.  4.100
    
  libswresample   3.  4.100 /  3.  4.100
    
  libpostproc    55.  4.100 / 55.  4.100
    
Guessed Channel Layout for Input Stream #0.0 : stereo
    
Input #0, wav, from 'C:\input.wav':
    
  Duration: 00:04:16.72, bitrate: 3072 kb/s
    
    Stream #0:0: Audio: pcm_s32le ([1][0][0][0] / 0x0001), 48000 Hz, stereo, s32, 3072 kb/s
    
Stream mapping:
    
  Stream #0:0 -> #0:0 (pcm_s32le (native) -> mp3 (libmp3lame))
    
Press [q] to stop, [?] for help
    
Output #0, mp3, to 'https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550427237&Signature=%2BE8Wc%2F%2FQYrvGxzc%2FgXnsvauKnac%3D':
    
  Metadata:
    
    TSSE            : Lavf58.26.101
    
    Stream #0:0: Audio: mp3 (libmp3lame), 44100 Hz, stereo, s32p, 192 kb/s
    
    Metadata:
    
      encoder         : Lavc58.47.100 libmp3lame
    
size=     577kB time=00:00:24.58 bitrate= 192.2kbits/s speed=49.1x    
    
size=    1109kB time=00:00:47.28 bitrate= 192.1kbits/s speed=47.2x    
    
[tls @ 000001d73d786b00] Error in the push function.
    
av_interleaved_write_frame(): I/O error
    
Error writing trailer of https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550427237&Signature=%2BE8Wc%2F%2FQYrvGxzc%2FgXnsvauKnac%3D: I/O error
    
size=    1143kB time=00:00:48.77 bitrate= 192.0kbits/s speed=  47x    
    
video:0kB audio:1144kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
    
[tls @ 000001d73d786b00] The specified session has been invalidated for some reason.
    
[tls @ 000001d73d786b00] Error in the pull function.
    
[https @ 000001d73d784fc0] URL read error:  -5
    
Conversion failed!

    As you can see, I have a URL read error. This is a little surprising to me since I want to output to this url and not read it.

    Anybody know how I can store directly my FFMPEG output directly to S3 without having to store it locally first ?

    Edit 1
    I then tried to use the -method PUT parameter and use http instead of https to remove TLS from the equation. Here’s the output that I got when running ffmpeg with the -v trace option.

    ffmpeg version N-93120-ga84af760b8 Copyright (c) 2000-2019 the FFmpeg developers
    
  built with gcc 8.2.1 (GCC) 20190212
    
  configuration: --enable-gpl --enable-version3 --enable-sdl2 --enable-fontconfig --enable-gnutls --enable-iconv --enable-libass --enable-libdav1d --enable-libbluray --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libopus --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libtheora --enable-libtwolame --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libzimg --enable-lzma --enable-zlib --enable-gmp --enable-libvidstab --enable-libvorbis --enable-libvo-amrwbenc --enable-libmysofa --enable-libspeex --enable-libxvid --enable-libaom --enable-libmfx --enable-amf --enable-ffnvcodec --enable-cuvid --enable-d3d11va --enable-nvenc --enable-nvdec --enable-dxva2 --enable-avisynth --enable-libopenmpt
    
  libavutil      56. 26.100 / 56. 26.100
    
  libavcodec     58. 47.100 / 58. 47.100
    
  libavformat    58. 26.101 / 58. 26.101
    
  libavdevice    58.  6.101 / 58.  6.101
    
  libavfilter     7. 48.100 /  7. 48.100
    
  libswscale      5.  4.100 /  5.  4.100
    
  libswresample   3.  4.100 /  3.  4.100
    
  libpostproc    55.  4.100 / 55.  4.100
    
Splitting the commandline.
    
Reading option '-i' ... matched as input url with argument 'C:\input.wav'.
    
Reading option '-y' ... matched as option 'y' (overwrite output files) with argument '1'.
    
Reading option '-vn' ... matched as option 'vn' (disable video) with argument '1'.
    
Reading option '-ar' ... matched as option 'ar' (set audio sampling rate (in Hz)) with argument '44100'.
    
Reading option '-ac' ... matched as option 'ac' (set number of audio channels) with argument '2'.
    
Reading option '-ab' ... matched as option 'ab' (audio bitrate (please use -b:a)) with argument '192k'.
    
Reading option '-f' ... matched as option 'f' (force format) with argument 'mp3'.
    
Reading option '-method' ... matched as AVOption 'method' with argument 'PUT'.
    
Reading option '-v' ... matched as option 'v' (set logging level) with argument 'trace'.
    
Reading option 'https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D' ... matched as output url.
    
Finished splitting the commandline.
    
Parsing a group of options: global .
    
Applying option y (overwrite output files) with argument 1.
    
Applying option v (set logging level) with argument trace.
    
Successfully parsed a group of options.
    
Parsing a group of options: input url C:\input.wav.
    
Successfully parsed a group of options.
    
Opening an input file: C:\input.wav.
    
[NULL @ 000001fb37abb180] Opening 'C:\input.wav' for reading
    
[file @ 000001fb37abc180] Setting default whitelist 'file,crypto'
    
Probing wav score:99 size:2048
    
[wav @ 000001fb37abb180] Format wav probed with size=2048 and score=99
    
[wav @ 000001fb37abb180] Before avformat_find_stream_info() pos: 54 bytes read:65590 seeks:1 nb_streams:1
    
[wav @ 000001fb37abb180] parser not found for codec pcm_s32le, packets or times may be invalid.
    
    Last message repeated 1 times
    
[wav @ 000001fb37abb180] All info found
    
[wav @ 000001fb37abb180] stream 0: start_time: -192153584101141.156 duration: 256.716
    
[wav @ 000001fb37abb180] format: start_time: -9223372036854.775 duration: 256.716 bitrate=3072 kb/s
    
[wav @ 000001fb37abb180] After avformat_find_stream_info() pos: 204854 bytes read:294966 seeks:1 frames:50
    
Guessed Channel Layout for Input Stream #0.0 : stereo
    
Input #0, wav, from 'C:\input.wav':
    
  Duration: 00:04:16.72, bitrate: 3072 kb/s
    
    Stream #0:0, 50, 1/48000: Audio: pcm_s32le ([1][0][0][0] / 0x0001), 48000 Hz, stereo, s32, 3072 kb/s
    
Successfully opened the file.
    
Parsing a group of options: output url https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D.
    
Applying option vn (disable video) with argument 1.
    
Applying option ar (set audio sampling rate (in Hz)) with argument 44100.
    
Applying option ac (set number of audio channels) with argument 2.
    
Applying option ab (audio bitrate (please use -b:a)) with argument 192k.
    
Applying option f (force format) with argument mp3.
    
Successfully parsed a group of options.
    
Opening an output file: https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D.
    
[http @ 000001fb37b15140] Setting default whitelist 'http,https,tls,rtp,tcp,udp,crypto,httpproxy'
    
[tcp @ 000001fb37b16c80] Original list of addresses:
    
[tcp @ 000001fb37b16c80] Address 52.216.8.203 port 80
    
[tcp @ 000001fb37b16c80] Interleaved list of addresses:
    
[tcp @ 000001fb37b16c80] Address 52.216.8.203 port 80
    
[tcp @ 000001fb37b16c80] Starting connection attempt to 52.216.8.203 port 80
    
[tcp @ 000001fb37b16c80] Successfully connected to 52.216.8.203 port 80
    
[http @ 000001fb37b15140] request: PUT /output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D HTTP/1.1
    
Transfer-Encoding: chunked
    
User-Agent: Lavf/58.26.101
    
Accept: */*
    
Connection: close
    
Host: landr-distribution-reportsdev-mb.s3.amazonaws.com
    
Icy-MetaData: 1
    
Successfully opened the file.
    
Stream mapping:
    
  Stream #0:0 -> #0:0 (pcm_s32le (native) -> mp3 (libmp3lame))
    
Press [q] to stop, [?] for help
    
cur_dts is invalid (this is harmless if it occurs once at the start per stream)
    
detected 8 logical cores
    
[graph_0_in_0_0 @ 000001fb37b21080] Setting 'time_base' to value '1/48000'
    
[graph_0_in_0_0 @ 000001fb37b21080] Setting 'sample_rate' to value '48000'
    
[graph_0_in_0_0 @ 000001fb37b21080] Setting 'sample_fmt' to value 's32'
    
[graph_0_in_0_0 @ 000001fb37b21080] Setting 'channel_layout' to value '0x3'
    
[graph_0_in_0_0 @ 000001fb37b21080] tb:1/48000 samplefmt:s32 samplerate:48000 chlayout:0x3
    
[format_out_0_0 @ 000001fb37b22cc0] Setting 'sample_fmts' to value 's32p|fltp|s16p'
    
[format_out_0_0 @ 000001fb37b22cc0] Setting 'sample_rates' to value '44100'
    
[format_out_0_0 @ 000001fb37b22cc0] Setting 'channel_layouts' to value '0x3'
    
[format_out_0_0 @ 000001fb37b22cc0] auto-inserting filter 'auto_resampler_0' between the filter 'Parsed_anull_0' and the filter 'format_out_0_0'
    
[AVFilterGraph @ 000001fb37b0d940] query_formats: 4 queried, 6 merged, 3 already done, 0 delayed
    
[auto_resampler_0 @ 000001fb37b251c0] picking s32p out of 3 ref:s32
    
[auto_resampler_0 @ 000001fb37b251c0] [SWR @ 000001fb37b252c0] Using fltp internally between filters
    
[auto_resampler_0 @ 000001fb37b251c0] ch:2 chl:stereo fmt:s32 r:48000Hz -> ch:2 chl:stereo fmt:s32p r:44100Hz
    
Output #0, mp3, to 'https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D':
    
  Metadata:
    
    TSSE            : Lavf58.26.101
    
    Stream #0:0, 0, 1/44100: Audio: mp3 (libmp3lame), 44100 Hz, stereo, s32p, delay 1105, 192 kb/s
    
    Metadata:
    
      encoder         : Lavc58.47.100 libmp3lame
    
cur_dts is invalid (this is harmless if it occurs once at the start per stream)
    
    Last message repeated 6 times
    
size=     649kB time=00:00:27.66 bitrate= 192.2kbits/s speed=55.3x    
    
size=    1207kB time=00:00:51.48 bitrate= 192.1kbits/s speed=51.5x    
    
av_interleaved_write_frame(): Unknown error
    
No more output streams to write to, finishing.
    
[libmp3lame @ 000001fb37b147c0] Trying to remove 47 more samples than there are in the queue
    
Error writing trailer of https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D: Error number -10054 occurred
    
size=    1251kB time=00:00:53.39 bitrate= 192.0kbits/s speed=51.5x    
    
video:0kB audio:1252kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: unknown
    
Input file #0 (C:\input.wav):
    
  Input stream #0:0 (audio): 5014 packets read (20537344 bytes); 5014 frames decoded (2567168 samples); 
    
  Total: 5014 packets (20537344 bytes) demuxed
    
Output file #0 (https://my-bucket.s3.amazonaws.com/output.mp3?AWSAccessKeyId=AKIAJDSGJWM63VQEXHIQ&Expires=1550695990&Signature=dy3RVqDlX%2BlJ0INlDkl0Lm1Rqb4%3D):
    
  Output stream #0:0 (audio): 2047 frames encoded (2358144 samples); 2045 packets muxed (1282089 bytes); 
    
  Total: 2045 packets (1282089 bytes) muxed
    
5014 frames successfully decoded, 0 decoding errors
    
[AVIOContext @ 000001fb37b1f440] Statistics: 0 seeks, 2046 writeouts
    
[http @ 000001fb37b15140] URL read error:  -10054
    
[AVIOContext @ 000001fb37ac4400] Statistics: 20611126 bytes read, 1 seeks
    
Conversion failed!

    So it looks like it is able to connect to my S3 pre-signed url but I still have the Error writing trailer error coupled with a URL read error.

  • A Guide to GDPR Sensitive Personal Data

    13 mai 2024, par Erin

    The General Data Protection Regulation (GDPR) is one of the world’s most stringent data protection laws. It provides a legal framework for collection and processing of the personal data of EU individuals.

    The GDPR distinguishes between “special categories of personal data” (also referred to as “sensitive”) and other personal data and imposes stricter requirements on collection and processing of sensitive data. Understanding these differences will help your company comply with the requirements and avoid heavy penalties.

    In this article, we’ll explain what personal data is considered “sensitive” according to the GDPR. We’ll also examine how a web analytics solution like Matomo can help you maintain compliance.

    What is sensitive personal data ?

    The following categories of data are treated as sensitive :

      1. Personal data revealing :
        • Racial or ethnic origin ;
        • Political opinions ;
        • Religious or philosophical beliefs ;
        • Trade union membership ;
      2. Genetic and biometric data ;
      3. Data concerning a person’s :
        • Health ; or
        • Sex life or sexual orientation.
    Examples of GDPR Sensitive Personal Data

    Sensitive vs. non-sensitive personal data : What’s the difference ?

    While both categories include information about an individual, sensitive data is seen as more private, or requiring a greater protection. 

    Sensitive data often carries a higher degree of risk and harm to the data subject, if the data is exposed. For example, a data breach exposing health records could lead to discrimination for the individuals involved. An insurance company could use the information to increase premiums or deny coverage. 

    In contrast, personal data like name or gender is considered less sensitive because it doesn’t carry the same degree of harm as sensitive data. 

    Unauthorised access to someone’s name alone is less likely to harm them or infringe on their fundamental rights and freedoms than an unauthorised access to their health records or biometric data. Note that financial information (e.g. credit card details) does not fall into the special categories of data.

    Table displaying different sensitive data vs non-sensitive data

    Legality of processing

    Under the GDPR, both sensitive and nonsensitive personal data are protected. However, the rules and conditions for processing sensitive data are more stringent.

    Article 6 deals with processing of non-sensitive data and it states that processing is lawful if one of the six lawful bases for processing applies. 

    In contrast, Art. 9 of the GDPR states that processing of sensitive data is prohibited as a rule, but provides ten exceptions. 

    It is important to note that the lawful bases in Art. 6 are not the same as exceptions in Art. 9. For example, while performance of a contract or legitimate interest of the controller are a lawful basis for processing non-sensitive personal data, they are not included as an exception in Art. 9. What follows is that controllers are not permitted to process sensitive data on the basis of contract or legitimate interest. 

    The exceptions where processing of sensitive personal data is permitted (subject to additional requirements) are : 

    • Explicit consent : The individual has given explicit consent to processing their sensitive personal data for specified purpose(s), except where an EU member state prohibits such consent. See below for more information about explicit consent. 
    • Employment, social security or social protection : Processing sensitive data is necessary to perform tasks under employment, social security or social protection law.
    • Vital interests : Processing sensitive data is necessary to protect the interests of a data subject or if the individual is physically or legally incapable of consenting. 
    • Non-for-profit bodies : Foundations, associations or nonprofits with a political, philosophical, religious or trade union aim may process the sensitive data of their members or those they are in regular contact with, in connection with their purposes (and no disclosure of the data is permitted outside the organisation, without the data subject’s consent).
    • Made public : In some cases, it may be permissible to process the sensitive data of a data subject if the individual has already made it public and accessible. 
    • Legal claims : Processing sensitive data is necessary to establish, exercise or defend legal claims, including legal or in court proceedings.
    • Public interest : Processing is necessary for reasons of substantial public interest, like preventing unlawful acts or protecting the public.
    • Health or social care : Processing special category data is necessary for : preventative or occupational medicine, providing health and social care, medical diagnosis or managing healthcare systems.
    • Public health : It is permissible to process sensitive data for public health reasons, like protecting against cross-border threats to health or ensuring the safety of medicinal products or medical devices. 
    • Archiving, research and statistics : You may process sensitive data if it’s done for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes.

    In addition, you must adhere to all data handling requirements set by the GDPR.

    Important : Note that for any data sent that you are processing, you always need to identify a lawful basis under Art. 6. In addition, if the data sent contains sensitive data, you must comply with Art. 9.

    Explicit consent

    While consent is a valid lawful basis for processing non-sensitive personal data, controllers are permitted to process sensitive data only with an “explicit consent” of the data subject.

    The GDPR does not define “explicit” consent, but it is accepted that it must meet all Art. 7 conditions for consent, at a higher threshold. To be “explicit” a consent requires a clear statement (oral or written) of the data subject. Consent inferred from the data subject’s actions does not meet the threshold. 

    The controller must retain records of the explicit consent and provide appropriate consent withdrawal method to allow the data subject to exercise their rights.

    Examples of compliant and non-compliant sensitive data processing

    Here are examples of when you can and can’t process sensitive data :

    • When you can process sensitive data : A doctor logs sensitive data about a patient, including their name, symptoms and medicine prescribed. The hospital can process this data to provide appropriate medical care to their patients. An IoT device and software manufacturer processes their customers’ health data based on explicit consent of each customer. 
    • When you can’t process sensitive data : One example is when you don’t have explicit consent from a data subject. Another is when there’s no lawful basis for processing it or you are collecting personal data you simply do not need. For example, you don’t need your customer’s ethnic origin to fulfil an online order.

    Other implications of processing sensitive data

    If you process sensitive data, especially on a large scale, GDPR imposes additional requirements, such as having Data Privacy Impact Assessments, appointing Data Protection Officers and EU Representatives, if you are a controller based outside the EU.

    Penalties for GDPR non-compliance

    Mishandling sensitive data (or processing it when you’re not allowed to) can result in huge penalties. There are two tiers of GDPR fines :

    • €10 million or 2% of a company’s annual revenue for less severe infringements
    • €20 million or 4% of a company’s annual revenue for more severe infringements

    In the first half of 2023 alone, fines imposed in the EU due to GDPR violations exceeded €1.6 billion, up from €73 million in 2019.

    Examples of high-profile violations in the last few years include :

    • Amazon : The Luxembourg National Commission fined the retail giant with a massive $887 million fine in 2021 for not processing personal data per the GDPR. 
    • Google : The National Data Protection Commission (CNIL) fined Google €50 million for not getting proper consent to display personalised ads.
    • H&M : The Hamburg Commissioner for Data Protection and Freedom of Information hit the multinational clothing company with a €35.3 million fine in 2020 for unlawfully gathering and storing employees’ data in its service centre.

    One of the criteria that affects the severity of a fine is “data category” — the type of personal data being processed. Companies need to take extra precautions with sensitive data, or they risk receiving more severe penalties.

    What’s more, GDPR violations can negatively affect your brand’s reputation and cause you to lose business opportunities from consumers concerned about your data practices. 76% of consumers indicated they wouldn’t buy from companies they don’t trust with their personal data.

    Organisations should lay out their data practices in simple terms and make this information easily accessible so customers know how their data is being handled.

    Get started with GDPR-compliant web analytics

    The GDPR offers a framework for securing and protecting personal data. But it also distinguishes between sensitive and non-sensitive data. Understanding these differences and applying the lawful basis for processing this data type will help ensure compliance.

    Looking for a GDPR-compliant web analytics solution ?

    At Matomo, we take data privacy seriously. 

    Our platform ensures 100% data ownership, putting you in complete control of your data. Unlike other web analytics solutions, your data remains solely yours and isn’t sold or auctioned off to advertisers. 

    Additionally, with Matomo, you can be confident in the accuracy of the insights you receive, as we provide reliable, unsampled data.

    Matomo also fully complies with GDPR and other data privacy laws like CCPA, LGPD and more.

    Start your 21-day free trial today ; no credit card required. 

    Disclaimer

    We are not lawyers and don’t claim to be. The information provided here is to help give an introduction to GDPR. We encourage every business and website to take data privacy seriously and discuss these issues with your lawyer if you have any concerns.

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