| <!DOCTYPE html> |
| <html> |
| |
| <head> |
| <%- partial("partials/_meta.ejs", { title: "Home" }) %> |
| </head> |
| |
| <body> |
| |
| <%- partial("partials/_nav.ejs", { title: "Use Cases" }) %> |
| |
| <%- partial("partials/_breadcumb.ejs", { title: "Use Cases"}) %> |
| |
| <section class="elements-area section-padding-50"> |
| <div class="container"> |
| <div class="row"> |
| <!-- ========== Buttons ========== --> |
| <div class="col-12"> |
| <h2 class="page-section-title">Industrial IoT</h2> |
| <div class="row"> |
| <div class="col-md-6 col-12"> |
| <h5 class="feature-item-margin"> |
| <i class="fas fa-check sp-color-green"></i> Integrated adapters such as OPC UA and MQTT and data processors tailored at analyzing high-frequency sensor data make StreamPipes a great choice to <span |
| class="feature-highlights-bg">quickly analyze machine data.</span> |
| </h5> |
| <h5 class="feature-item-margin"> |
| <i class="fas fa-check sp-color-green"></i> The ability to create geographically distributed pipelines make StreamPipes suitable for <span |
| class="feature-highlights-bg">edge computing</span> use cases. |
| </h5> |
| <h5 class="feature-item-margin"> |
| <i class="fas fa-check sp-color-green"></i> Data sinks for popular databases such as Elasticsearch and the ability to integrate Machine Learning models into data processors enable use cases such as<span |
| class="feature-highlights-bg">predictive analytics</span> and <span class="feature-highlights-bg">anomaly detection.</span> |
| </h5> |
| </div> |
| <div class="col-md-6 col-12"> |
| <img src="/img/usecases/production-line.png"> |
| </div> |
| |
| </div> |
| </div> |
| </div> |
| </div> |
| </section> |
| |
| <section class="elements-area section-padding-50 feature-section-gray"> |
| <div class="container"> |
| <div class="row"> |
| |
| <!-- ========== Buttons ========== --> |
| <div class="col-12"> |
| <h2 class="page-section-title">Applications</h2> |
| <div class="row"> |
| <div class="col-md-4 col-12"> |
| <div style="margin-left:auto;"><h5><span |
| class="feature-highlights-bg">Incident Detection</span></h5> |
| <div class="usecases-applications-item"> |
| <span>StreamPipes allows to immediately detect incidents you'd like to avoid. We support algorithms ranging from simple threshold-based tracking of sensor measurements over trend analysis over time periods up to the integration of custom-tailored predictive maintenance algorithms.</span> |
| </div> |
| </div> |
| </div> |
| <div class="col-md-4 col-12"> |
| <div style="margin-left:auto;"><h5><span |
| class="feature-highlights-bg">Data Harmonization</span></h5> |
| <div class="usecases-applications-item"> |
| <span>StreamPipes helps to create a clean data lake based on sensor measurements from machines and other assets. Various data harmonization algorithms (e.g., filters, aggregations and unit converters) allow to easily clean and enrich data in a continuous fashion.</span> |
| </div> |
| </div> |
| </div> |
| <div class="col-md-4 col-12"> |
| <div style="margin-left:auto;"><h5><span |
| class="feature-highlights-bg">Monitoring</span></h5> |
| <div class="usecases-applications-item"> |
| <span>See what's happening right now: Use StreamPipes as your real-time window into your current production performance. A live dashboard and a wide range of available notification channels allow you to monitor KPI's in a flexible and customizable manner.</span> |
| </div> |
| </div> |
| </div> |
| </div> |
| </div> |
| </div> |
| </div> |
| </section> |
| |
| <%- partial("partials/_footer.ejs") %> |
| </body> |
| |
| </html> |