Overview

Enhancing data quality is critical as data is at the heart of all activities within organizations; poor data quality leads to inaccurate analysis and reporting which will impact business decisions. Here we will address four different data quality issues and how to identify them using CAS actions in Viya.

Data Completeness: The degree to which values are present in a data collection.

Data Uniqueness: A measure of unwanted duplication existing within or across systems for a particular field, record, or data set.

Data Consistency: Violation of semantic rules defined over the dataset. .

Data Accuracy: Data are accurate when data values stored in the database correspond to real-world values.

Overview

Learn how to build a model for real-time detection of malfunctioning light groupings. These light groupings are part of a Smart Campus floodlight circuits located in a parking lot.

The data set consists of energy consumption values captured every five minutes from six floodlight circuits, over a span of about three months. 

We will use the subspace tracking (SST) algorithm packaged in SAS Event Stream Processing Studio to detect outliers in real time using streaming data. It is a method to detect anomalies and degradation in systems that generate high-frequency, high-dimensional data. It can be used for data containing a single measure for sensors from various devices operating under similar conditions (e.g., energy output from multiple panels in a Solar farm), or multiple measures for sensors from a single device operating under similar conditions (e.g., turbofan in an aircraft).

Overview

Imagine you are the owner of a retail store and want to better understand your customers. Wouldn't it be great to know what percentage of customers in your store looked happy as they browsed your shelves, or perhaps which aisles attract men versus women customers?

In this example, we used two products that are embedded in the SAS Analytics for IoT solution, SAS Visual Data Mining and Machine Learning (VDMML) and SAS Event Stream Processing (ESP) to show you how to train and deploy various demographic analytic models to create a single solution that connects directly to a USB-connected security camera. With this connection, we can process incoming images using pre-training analytical models and produce demographic statistics.

This application provides:
     •  Face Detection - process incoming images and detect human faces in the video stream
     •  Age Classification - determine approximate age grouped by category
     •  Gender Classification - determine male or female
     •  Emotion Classification - group by these classes: Happy, Neutral, Sad, Fear, Surprise, Angry

Overview

Imagine you are responsible for manufacturing quality at your company. Wouldn't it be great to know what percentage of product contains flaws? Maybe replace human oversite with automation or collect data to perform a root cause analysis. SAS Analytics for IoT is a perfect solution to solve this or any Computer Vision (CV) problem. This comprehensive AI-embedded solution provides so many capabilities because it integrates a number of key SAS products.

In this use case, we are going to simulate our manufacturing process using a Rubik's cube. Our cube represents a part which is being produced, and it is up to us to determine whether it was manufactured correctly.

Overview

Learn how to build an anomaly detection model and deploy it for real-time detection of malfunctioning Air Handling Units (AHUs). These AHUs are part of a smart campus heating, ventilating and air-conditioning (HVAC) system.

We will use the Support Vector Data Description (SVDD) algorithm, packaged in  SAS Visual Data Mining and Machine Learning (VDMML), and deploy it in SAS Event Stream Processing Studio (ESP) to detect outliers in real time using streaming data.

Overview

Due to the recent pandemic, several governments have implemented social distancing measures to limit the spread of the disease.  In this use case, we show you how to track these social distancing rules using Computer Vision on camera image.

The overall goal of this project is to identify objects (i.e., people) who are following social distancing guidelines and those who are not. This is accomplished by way of the following:
  •  Transform images and object coordinates into a two-dimensional map
  •  Calculate real world distances between detected objects 
  •  Detect crowds that exceed a specified parameter
  •  Visualize the results on the camera image

Overview

Bird watching is a great and relaxing hobby, and by getting a bird feeder, we can enjoy as the birds come by during the day for a little snack. It's also nice to take pictures of the birds so we can find out the species and show them to our friends. Taking pictures of the birds requires us to have our phone or a camera handy whenever watching the action at the bird feeder, and being able to take a picture without scaring the birds away. Also, what if some great birds stop by for a nibble and we aren't around?

To address these issues, and bring the IoT to bird watching, we created a fun project with SAS software to make sure we never miss the action at our bird feeder.