Continuously Learning Systems With In-Memory Computing
The Internet of Things (IoT) requires systems for data processing that provide sufficient computing power. In-memory computing is one solution to overcome the challenges.
A challenge of many IoT applications is that they combine many different systems that can change in the shortest possible time.
For example, unpredictable weather conditions or difficult traffic conditions in parts of a country can affect the vehicle fleet. Systems that plan their routes and calculate delivery times should be able to account for deviations in real time. This requirement is met by continuously learning systems based on machine learning. They can constantly adjust their calculations based on the latest data.
However, only continuously learning systems will prevail in the industry, which is also available at an affordable price.
Here are some of the business use cases conceivable for the IoT that can use the in-memory computing solutions.
Logistics companies and manufacturers can equip their trucks with IoT-enabled sensors to track the location of the vehicles or monitor the condition of the goods. The information gained thereby helps to automatically adapt logistics and procurement planning to changing conditions and thus to optimize production capacities.
IoT-enabled sensors can also be used to monitor machine condition or other operating conditions to maximize production operations. If the location of technicians and spare parts, as well as their availability, are known to the system in real time in the event of a machine failure, it can optimally plan the repair and thereby minimize downtimes.
To help cities curb congested streets, they can use data from multiple sources, including IoT-enabled devices. If information about current events, accidents or roadblocks, as well as data from the evaluation of traffic cameras and weather stations are fed into the systems that optimize the traffic flow, they can be updated in real time. This information can be forwarded to autonomous vehicles, which then adjust their routes accordingly and thus reduce traffic jams.
All of these applications require the right computing power to use the vast amount of information collected in real time. When these applications are built on machine learning, and in-memory computing platform architecture or Hybrid Transactional/Analytical Processing (HTAP) architecture, the continuous learning systems are significantly more powerful than systems based on non-updateable decision models and thus contribute to business success.
The key benefit of this technology is that it combines the power of transactional and analytical databases. It makes extraction, transformation and loading processes superfluous. Such an architecture is thus ideal for real-time use cases because there are no time delays. It enables HTAP real-time data analysis of live data without affecting transaction performance.
In order to realize HTAP and thus continuously learning systems, a whole range of technologies are necessary. In-memory computing has all these features:
An in-memory datagrid is distributed across clusters of local servers or cloud storage. You can use the entire compute and storage capacity of the cluster for computation, and you can scale it by adding more machines to the cluster. Another advantage of in-memory datagrids is that they can be easily inserted between the data and application layers of existing applications without having to replace the existing database.
In-memory databases are fully functional databases that use memory and provide RDBMS functionality along with SQL support. For existing applications, in-memory databases are difficult because the data layer needs to be replaced. But when developing new applications, the advantages should be based on in-memory databases from the beginning.
Streaming Analytics Engines are necessary to handle the complexity of data flows and event processing. An engine can use in-memory computing and the associated speed to query live data without compromising the performance of the application.
Continuous learning lets you process records up to petabyte size in HTAP speed. This is possible through program libraries that have been optimized with both Machine Learning (ML) and Deep Learning (DL) to handle massive amounts of data in parallel. The data can be distributed to the memory of each computer in an in-memory cluster and the system is still able to process it locally using ML and DL algorithms. Such an architecture allows the ML or DL models to be continually updated with new data without compromising database performance.
To really benefit from the digital transformation, companies will have to rely on continuous learning systems for many IoT use cases. In-memory computing offers the solutions for the computing power and scalability required for this.
However, the mere existence of these technologies is not enough – companies also need to be able to use them cost-effectively. It has already been achieved in some industries including Insurance, Financial Services, Transportation, Retail, Telecommunications and more through Insight Edge HTAP solutions by GigaSpaces. There, it is possible to build up extensive HTAP infrastructures through affordable main memory and sophisticated in-memory computing software, thus enabling continuous learning systems.
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