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• Unable to stream video file from MediaMTX media server to browser via WebRTC

  8 juin 2024, par thegreatjedi

  I took over a repository at work. It's a working demo comprising a web server which receives video and camera feeds from a media server (built from the rtsp-simple-server Docker image) via a RTSP relay server and streams the feeds to the client, all deployed via Docker Compose.

  


  I'm trying to switch over to use WebRTC instead. rtsp-simple-server has upgraded into MediaMTX since the time the demo was created 2 years ago. This is the relevant section of the updated Docker Compose configuration :

  


    media-server:
    image: bluenviron/mediamtx:latest-ffmpeg
    expose:
      - 8889
    init: true
    ports:
      - 8889:8889
    restart: unless-stopped
    volumes:
      - type: bind
        source: ./demo/vids
        target: /vids
      - type: bind
        source: ./demo/mediamtx.yml
        target: /mediamtx.yml


  


  Relevant part of the MediaMTX custom configuration in mediamtx.yml :

  


  ###############################################
# Path settings

# Settings in "paths" are applied to specific paths, and the map key
# is the name of the path.
# Any setting in "pathDefaults" can be overridden here.
# It's possible to use regular expressions by using a tilde as prefix,
# for example "~^(test1|test2)$" will match both "test1" and "test2",
# for example "~^prefix" will match all paths that start with "prefix".
paths:
  # example:
  # my_camera:
  #   source: rtsp://my_camera
  ~^demo\d+$:
    runOnDemand: ffmpeg -re -stream_loop -1 -i /vids/$MTX_PATH.mp4 -c:v libvpx -b:v 0 -crf 18 -qmin 18 -qmax 18 -f webm http://localhost:8889/$MTX_PATH/whip

  # Settings under path "all_others" are applied to all paths that
  # do not match another entry.
  all_others:


  


  I've absolutely no experience with WebRTC. This is my first time hearing of this protocol, let alone working with it. From what I understand, I need to convert my demo mp4 videos (which were successfully streaming via RTSP in the previous implementation) to a compatible video codec, so I've opted for VP8.

  


  Before trying to stream the videos into my web server, I tested the stream directly in the browser (tried with both the latest versions of Chrome and Edge). I went to http://localhost:8889/demo0 (which should convert demo0.mp4 to VP8 and then stream it over WebRTC). The video player loaded in the browser but no video data was received and nothing played. After several seconds, the screen displayed "Error : bad status code 400, retrying in some seconds". In the browser console, it showed :

  


  Failed to load resource : the server responded with a status of 400 (Bad Request)

  


  Inside the MediaMTX container's runtime logs, this is what's displayed :

  


  2024-04-02 14:53:08 ffmpeg version 6.1.1 Copyright (c) 2000-2023 the FFmpeg developers
2024-04-02 14:53:08   built with gcc 13.2.1 (Alpine 13.2.1_git20231014) 20231014
2024-04-02 14:53:08   configuration: --prefix=/usr --disable-librtmp --disable-lzma --disable-static --disable-stripping --enable-avfilter --enable-gpl --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libdav1d --enable-libdrm --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-libmp3lame --enable-libopenmpt --enable-libopus --enable-libplacebo --enable-libpulse --enable-librav1e --enable-librist --enable-libsoxr --enable-libsrt --enable-libssh --enable-libtheora --enable-libv4l2 --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxcb --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-lto=auto --enable-lv2 --enable-openssl --enable-pic --enable-postproc --enable-pthreads --enable-shared --enable-vaapi --enable-vdpau --enable-version3 --enable-vulkan --optflags=-O3 --enable-libjxl --enable-libsvtav1 --enable-libvpl
2024-04-02 14:53:08   libavutil      58. 29.100 / 58. 29.100
2024-04-02 14:53:08   libavcodec     60. 31.102 / 60. 31.102
2024-04-02 14:53:08   libavformat    60. 16.100 / 60. 16.100
2024-04-02 14:53:08   libavdevice    60.  3.100 / 60.  3.100
2024-04-02 14:53:08   libavfilter     9. 12.100 /  9. 12.100
2024-04-02 14:53:08   libswscale      7.  5.100 /  7.  5.100
2024-04-02 14:53:08   libswresample   5.  0.100 /  5.  0.100
2024-04-02 14:53:08   libpostproc    57.  3.100 / 57.  3.100
2024-04-02 14:53:08 Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/vids/demo0.mp4':
2024-04-02 14:53:08   Metadata:
2024-04-02 14:53:08     major_brand     : isom
2024-04-02 14:53:08     minor_version   : 512
2024-04-02 14:53:08     compatible_brands: isomiso2mp41
2024-04-02 14:53:08     encoder         : Lavf58.76.100
2024-04-02 14:53:08   Duration: 00:00:03.47, start: 0.000000, bitrate: 1675 kb/s
2024-04-02 14:53:08   Stream #0:0[0x1](und): Video: mpeg1video (mp4v / 0x7634706D), yuv420p(tv, progressive), 640x360 [SAR 1:1 DAR 16:9], 104857 kb/s, 30 fps, 30 tbr, 90k tbn (default)
2024-04-02 14:53:08     Metadata:
2024-04-02 14:53:08       handler_name    : VideoHandler
2024-04-02 14:53:08       vendor_id       : [0][0][0][0]
2024-04-02 14:53:08     Side data:
2024-04-02 14:53:08       cpb: bitrate max/min/avg: 0/0/0 buffer size: 49152 vbv_delay: N/A
2024-04-02 14:53:08 Stream mapping:
2024-04-02 14:53:08   Stream #0:0 -> #0:0 (mpeg1video (native) -> vp8 (libvpx))
2024-04-02 14:53:08 Press [q] to stop, [?] for help
2024-04-02 14:53:08 [libvpx @ 0x7faa8591b8c0] v1.13.1
2024-04-02 14:53:08 [libvpx @ 0x7faa8591b8c0] Bitrate not specified for constrained quality mode, using default of 256kbit/sec
2024-04-02 14:53:08 Output #0, webm, to 'http://localhost:8889/demo0/whip':
2024-04-02 14:53:08   Metadata:
2024-04-02 14:53:08     major_brand     : isom
2024-04-02 14:53:08     minor_version   : 512
2024-04-02 14:53:08     compatible_brands: isomiso2mp41
2024-04-02 14:53:08     encoder         : Lavf60.16.100
2024-04-02 14:53:08   Stream #0:0(und): Video: vp8, yuv420p(tv, progressive), 640x360 [SAR 1:1 DAR 16:9], q=2-31, 256 kb/s, 30 fps, 1k tbn (default)
2024-04-02 14:53:08     Metadata:
2024-04-02 14:53:08       handler_name    : VideoHandler
2024-04-02 14:53:08       vendor_id       : [0][0][0][0]
2024-04-02 14:53:08       encoder         : Lavc60.31.102 libvpx
2024-04-02 14:53:08     Side data:
2024-04-02 14:53:08       cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A
2024-04-02 14:53:18 2024/04/02 06:53:18 INF [path demo0] runOnDemand command stopped: timed out
2024-04-02 14:53:18 2024/04/02 06:53:18 INF [WebRTC] [session 0f460c76] closed: source of path 'demo0' has timed out
[out#0/webm @ 0x7faa859487c0] video:272kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.042856%
2024-04-02 14:53:18 frame=  315 fps= 32 q=18.0 Lsize=     275kB time=00:00:10.46 bitrate= 215.1kbits/s speed=1.05x    
2024-04-02 14:53:18 Exiting normally, received signal 2.


  


  I'm not sure what this is supposed to mean ? Why isn't the server able to stream this 3-second, 709kb video even once ? The browser connected to the server and the URL successfully, but no data was being transferred.

  


  Just in case, I decided to manually convert all of my mp4 files to webm using ffmpeg, and verified with Window's media player that the webm videos work. Then, I modified MediaMTX's configuration to stream the webm videos directly :

  


  paths:
  # example:
  # my_camera:
  #   source: rtsp://my_camera
  ~^demo\d+$:
    runOnDemand: ffmpeg -re -stream_loop -1 -i /vids/$MTX_PATH.webm -c copy -f webm http://localhost:8889/$MTX_PATH/whip


  


  However, the error persists :

  


  2024-04-02 15:03:58 ffmpeg version 6.1.1 Copyright (c) 2000-2023 the FFmpeg developers
2024-04-02 15:03:58   built with gcc 13.2.1 (Alpine 13.2.1_git20231014) 20231014
2024-04-02 15:03:58   configuration: --prefix=/usr --disable-librtmp --disable-lzma --disable-static --disable-stripping --enable-avfilter --enable-gpl --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libdav1d --enable-libdrm --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-libmp3lame --enable-libopenmpt --enable-libopus --enable-libplacebo --enable-libpulse --enable-librav1e --enable-librist --enable-libsoxr --enable-libsrt --enable-libssh --enable-libtheora --enable-libv4l2 --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxcb --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-lto=auto --enable-lv2 --enable-openssl --enable-pic --enable-postproc --enable-pthreads --enable-shared --enable-vaapi --enable-vdpau --enable-version3 --enable-vulkan --optflags=-O3 --enable-libjxl --enable-libsvtav1 --enable-libvpl
2024-04-02 15:03:58   libavutil      58. 29.100 / 58. 29.100
2024-04-02 15:03:58   libavcodec     60. 31.102 / 60. 31.102
2024-04-02 15:03:58   libavformat    60. 16.100 / 60. 16.100
2024-04-02 15:03:58   libavdevice    60.  3.100 / 60.  3.100
2024-04-02 15:03:58   libavfilter     9. 12.100 /  9. 12.100
2024-04-02 15:03:58   libswscale      7.  5.100 /  7.  5.100
2024-04-02 15:03:58   libswresample   5.  0.100 /  5.  0.100
2024-04-02 15:03:58   libpostproc    57.  3.100 / 57.  3.100
2024-04-02 15:03:58 Input #0, matroska,webm, from '/vids/demo0.webm':
2024-04-02 15:03:58   Metadata:
2024-04-02 15:03:58     COMPATIBLE_BRANDS: isomiso2mp41
2024-04-02 15:03:58     MAJOR_BRAND     : isom
2024-04-02 15:03:58     MINOR_VERSION   : 512
2024-04-02 15:03:58     ENCODER         : Lavf60.16.100
2024-04-02 15:03:58   Duration: 00:00:03.47, start: 0.000000, bitrate: 217 kb/s
2024-04-02 15:03:58   Stream #0:0: Video: vp8, yuv420p(tv, progressive), 640x360, SAR 1:1 DAR 16:9, 30 fps, 30 tbr, 1k tbn (default)
2024-04-02 15:03:58     Metadata:
2024-04-02 15:03:58       HANDLER_NAME    : VideoHandler
2024-04-02 15:03:58       VENDOR_ID       : [0][0][0][0]
2024-04-02 15:03:58       ENCODER         : Lavc60.31.102 libvpx
2024-04-02 15:03:58       DURATION        : 00:00:03.466000000
2024-04-02 15:03:58 Output #0, webm, to 'http://localhost:8889/demo0/whip':
2024-04-02 15:03:58   Metadata:
2024-04-02 15:03:58     COMPATIBLE_BRANDS: isomiso2mp41
2024-04-02 15:03:58     MAJOR_BRAND     : isom
2024-04-02 15:03:58     MINOR_VERSION   : 512
2024-04-02 15:03:58     encoder         : Lavf60.16.100
2024-04-02 15:03:58   Stream #0:0: Video: vp8, yuv420p(tv, progressive), 640x360 [SAR 1:1 DAR 16:9], q=2-31, 30 fps, 30 tbr, 1k tbn (default)
2024-04-02 15:03:58     Metadata:
2024-04-02 15:03:58       HANDLER_NAME    : VideoHandler
2024-04-02 15:03:58       VENDOR_ID       : [0][0][0][0]
2024-04-02 15:03:58       ENCODER         : Lavc60.31.102 libvpx
2024-04-02 15:03:58       DURATION        : 00:00:03.466000000
2024-04-02 15:03:58 Stream mapping:
2024-04-02 15:03:58   Stream #0:0 -> #0:0 (copy)
2024-04-02 15:03:58 Press [q] to stop, [?] for help
2024-04-02 15:04:08 2024/04/02 07:04:08 INF [path demo0] runOnDemand command stopped: timed out
2024-04-02 15:04:08 2024/04/02 07:04:08 INF [WebRTC] [session 829664cb] closed: source of path 'demo0' has timed out
[out#0/webm @ 0x7f04b00515c0] video:281kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.023511%
2024-04-02 15:04:08 size=     284kB time=00:00:10.49 bitrate= 221.3kbits/s speed=1.05x    
2024-04-02 15:04:08 Exiting normally, received signal 2.


  


  This is the same when I try to stream my other videos (demo1.mp4, demo2.mp4 etc.). What am I doing wrong ?

  


• 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.

  

  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.

  

  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.

  

  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.

  

  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 

  

  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. 

• Revision 32594 : plugins en minuscules, et alias pour les noms de sites

  1er novembre 2009, par fil@… — Log

  plugins en minuscules, et alias pour les noms de sites