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Powerful In-memory Analytics At the core of the Sterna Business Positioning System™ (BPS) is the Sterna BPS Server, an in-memory analytics engine, which performs real-time computations on data originating from within the organization, as well as from external sources.
The Sterna BPS Server is highly scalable. Using patented technology, Sterna BPS leverages today’s ever-increasing computation power of 64-bit processors to perform complex time-based mathematical calculations on very large data sets, on demand, in-memory, and in real time. Using mathematical constraints, Sterna BPS defines and manages a Business Dependency Network that reflects the business interdependencies within the organization. Finally, Sterna BPS in-memory analytics platform integrates operational BI, strategic BI and predictive analytics capabilities in a single solution, and in a single user interface, which is extraordinarily simple to use.
The Business Dependency Network (BDN) is a mathematical model of the organization’s business dependencies. The BDN functions as a constraint propagation network, whereby any change in value of one of the data inputs to the system is instantaneously propagated throughout the network system and surfaced to the user as may be relevant. Thus, Sterna BPS is able to process data updates at any refresh rate, effectively making real-time analytics a reality. As importantly, an action taken in one area of the business is instantaneously reflected in the interdependent areas of the business, ensuring that organizational alignment is maintained at all times.
The building blocks of the constraint propagation network are the “Business Matrix”, and the “Business Formula”, as described below.
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The Sterna BPS in-memory analytics engine extracts data from virtually any data source, using flexible data Collectors. The Collectors are capable of handling all common types of data sources e.g. RDBMS, OLAP, ODBC, XML, Web pages, formatted text files, and many more.
Collected data is transformed and uploaded into memory, where it is stored in highly compact data structures called Sterna Business Matrices. A Business Matrix separates time from the raw data, enabling massive volumes of time-based data to be efficiently stored and processed.
Moreover, data in the Business Matrices is maintained completely separately from the mathematical computations that are applied to the data, providing tremendous flexibility in defining and maintaining the BDN and providing the basis for real-time in-memory analytics such as predictive analytics and simulation capabilities.
Mathematical computations are performed in real time on the data stored in the Business Matrices. These computations are defined using formulae, reflecting the dependencies between the Business Matrices. The formulae are defined using an intuitive business syntax, which hides the complexity of index-based element-wise algebra.
Sterna BPS in-memory analytics platform loads the most up-to-date data from relevant data sources, instantaneously performs bottom-up propagation throughout the constraint propagation network, and executes the mathematical computations in real time, and upon user request. |
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