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Presentation Information

Presentation Title:   Using Informix to create Cloud DB Service and Edge Computing Service
Primary Speaker:   Shawn Moe
Bio:   Shawn Moe is a long time Informix’er, having worked in a variety of roles over 16+ years with the product family. He is currently based at the IBM lab in Lenexa, Kansas and serves as an architect at large in the Informix Competitive Technologies team, looking at SQL compatibility, application development, and tooling support in and around IDS. Recent Informix assignments have included postings in quality assurance, migration tooling, and most recently, with the Optim Data Studio product family.
Presentation
Abstract:
  
In this proposal, we will introduce two service based on
Informix--Cloud DB Service (HCloudDB) and Edge Computing
Service (SmartEdge).
(1) HCloudDB Service, which is hosted on SuperVessel cloud,
allows you to store and query time series data, JSON data
and relational data in the same database. It provides 4
types of service APIs to store and query data: JDBC APIs,
Mongo APIs, REST APIs and IoT REST APIs. Applications can
use the Mongo community drivers or IBM Informix JDBC driver
to store, update, and query the data in HCloudDB Database.
Powerful REST APIs and simple IoT REST APIs are also
provided for application to access HCloudDB Database by HTTP
protocol. HCloudDB performs special optimizations for time
series data. Time series data is usually generated by IoT
(Internet of Things) devices, such as metering sensors or
temperature sensors. The simple IoT REST APIs, which is
based on Unified JSON Time Series Data Model, are provided
for IoT applications to store and query huge amount time
series sensor data. Furthermore, HCloudDB offers advance
analysis APIs for time series data analysis, such as
aggregating the values in a time series using a new time
interval or aggregating time series values by time from
multiple sensors.
(2) Edge Computing Service, which is a solution service
based on Informix to complement IoT Foundation and IoT Real
time Insight to enable distributed IoT edge Computing. By
deploying this solution on edge side of gateway devices, the
customer can do local analytics, collect data from sensors
and forward to IoT foundation and IoT Real Time Insight. The
solution includes the following features:
- Local window aggregation/preprocessing to reduce data
transformation to cloud
- Local rolling window historian for local query and
persistence with less (50%+ saving) storage size consumption
than other popular used approach¡ªlike SQLite.
- Low latency rule based local real time analytics.
- Drag and drop edge analytics definition through IoT real
time insight extension on the cloud.
- Enable Remote analytics definition/update on Cloud and
push to Edge side for execution dynamically
- Enable edge side device connectivity abstraction (physical
connection to message topic) for better integration with
data model on IoT Foundation.
Co-Speakers
or Panelists:
  
None