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MIOedge is a data-centric application platform for delivering applications that adapt to your business needs.Use MIOedge to deliver business value in the cloud through business continuity, customer self-service, marketing campaigns, and object-oriented analytics applications—all data-driven.
Rapid Application Development Platform
Cost Savings and Improved Performance
Real-Time and Historical Pairing
ingest huge amounts of data
Standard EdgeUnit (EU)*-- $3.92 per EU-hour-- $320 per EU-month
MIOdb® Database-- .02 per GB-hour-- 1.20 per GB-month
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
Aggregated User Rating
Ease of use
Features & Functionality
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MIOedge technologies to automatically distribute processing and database queries. Any code you write, whether application logic or database query, is simple with no reference to parallelism or concurrency. MIOedge does the hard work of taking that code and figuring out how to distribute and concurrently execute. MIOedge will automatically use as many slices as allocated to your application container.
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MIOedge is a data-centric application platform for delivering applications that adapt to business needs, it deliver business value in the cloud through business continuity, customer self-service, marketing campaigns, and object-oriented analytics applications all data-driven. MIOedge Provides online, non-downloadable software, namely, enterprise application software for accounting, financial systems, business intelligence, business process management, content management, customer relationship management, enterprise resource planning, human resources management, supply chain management, and work flow. MIOedge’s context database stores entities (e.g., customers) in contexts where all like data is clustered in these contexts. This allows MIOedge to efficiently ingest, cluster and analyze vast amounts of data on the fly. This even includes pairing streaming data (e.g., sensor data) with historical data in real-time. This is a paradigm shift from the current generation of streaming analytic tools. Being able to have both streaming and historical data together creates a much richer view of the data. For example, even in the world of big data, this allows to move beyond working with generalizations about business. Instead, user can finally tailor decisions to individual entities. In MIOedge’s database, a distributed unsupervised machine learning clustering algorithm drives data integration. This allows developers to build real-time contextual applications that leverage complex event processing.
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