Hyperautomation: The Key to HyperautomationOntological Models in 2022
Everything that can be automated should be automated while optimizing business processes. However, niche automation cannot become the basis for integration; hyperautomation is needed, which has received real reinforcement in the form of an ontological approach to describing the subject area and graph bases, as an adequate tool for implementing large-scale projects.
Modern corporate IT is a set of methods and tools for processing the same corporate data. Various trendy technologies and methodologies aim to improve the status quo by automating certain operations. For example, to solve changing business problems, modular approaches to automatic layout and recombination of application systems are actively developed (Kubernetes – automating the deployment, scaling and management of containerized applications), and machine learning specialists seek to introduce automation elements into the execution of routine operations depending on the conditions tasks to be solved. Ezaaz Hyperautomation
However, niche automation cannot become the basis of integration, and it is precisely this fragmentation that has become the main strategic problem for enterprise IT today. Gartner analysts called hyperautomation one of the key modern technology trends: everything that can be automated should be automated while simultaneously optimizing business processes. In other words, analysts have declared the battle to be legacy, suboptimal for systems to support changing business processes, without specifying, however, how to win it.
Data-centric architecture for Hyperautomation
Enterprise data has a number of properties that must be taken into account when creating the IT architecture of a digital enterprise.
Integrated informatization and digital business processes are fundamentally changing the paradigm of corporate data processing for Hyperautomation. While it used to be economical to store data and provide access to it, today the key is to reuse existing data in different contexts.
Companies collect ever larger and more detailed arrays of information about their operations, customers, participants in internal and external business processes, objects of business operations, and their characteristics, including taking into account the entire history. ezaaz Hyperautomation
However, there is no need to talk about the effectiveness of using this data, although there are attempts to work with anonymized data accumulated by third-party organizations and companies, which implies an exponential increase in the number of internal integrations between various application systems. From this follows another key property of modern enterprise data – connectedness. Links between data generate, in particular, analytical inferences and new calculated characteristics that enrich the source data and turn corporate data into corporate knowledge.
Reuse and connectivity are redefining the required IT architecture: it is not the data itself that matters, but it’s model.
The best way to meet the requirements of hyperautomation is the data-centric architecture of supporting the logical connectivity of all data and ensuring that a variety of applications work with them, including predictive analytics and machine learning software.
To create a single description of data that provides support for their connectivity and reuse, semantic models are excellent, which add a new level to the structure of a corporate information system – virtual entities (images of real-world entities: a part, a device, an employee, etc.). ) and links between them.
Ontological models are the most developed version of the semantic description of the subject area today, formalizing knowledge about it. In general, they include a vocabulary of domain terms and a set of logical relationships. In this capacity, ontologies claim to be a universal model for representing knowledge for various subject areas. It is critical that ontological models are suitable for machine processing and therefore provide the ability to automatically work with knowledge (logical inference) to obtain new knowledge in a style similar to the logical reasoning of a human expert.
A prominent representative of the semantic paradigm of computer resources is the Semantic Web, which is based on ontological resources, and the Web Ontology Language (OWL), which has become the standard for many technological areas (information retrieval in large arrays of unstructured data, text processing in natural language, etc.), where special linguistic ontologies are used. Ontological models are also used in corporate information systems, where the problem of integrating data from heterogeneous sources is acute for Hyperautomation.
A means of integrating data in the form of a logically organized structure for easy access and exchange in a distributed environment is a data matrix (data fabric) – business users encounter it when working with a “knowledge graph” (knowledge graph), visualizing objects and relationships, describing the knowledge of a particular ontological model. A new level of abstraction appears in the corporate information system – semantic, which opens up the following possibilities:
- flexibility of descriptions of data and their complexes;
- unified description of data and processes;
- prompt introduction of changes (information systems change in real-time following changes in business processes);
- enrichment of the semantic description as business processes develop and problems solving.
In general, ontologies perform the function of integration, providing a common semantic basis in decision-making and data mining processes, as well as a single platform for combining various information systems. At the same time, it becomes possible to naturally solve several tasks that are relevant for the IT department.
Ensuring a systematic approach to data accumulation. It’s no secret that a huge effort in IT departments today is spent cleaning raw data, validating it, and validating it. Thinking in terms of models fundamentally changes the situation: there is unification in the description of various data, taking into account their semantics.
Creation of industry and typical corporate data models. Such models are modified when new business tasks appear by adding the necessary attributes, entities, and relationships.
Implementation of simple and convenient mechanisms for working with data, available from a single storefront, formed from a distributed decentralized storage to support a variety of analytical processing and data enrichment with new analytical features. This opens up opportunities for creating predictive models of any complexity in an intuitive form accessible to business users.
Providing a natural environment for the consolidation and exchange of experience among employees. This task becomes especially important as business mobility grows
In other words, ontological models become intermediaries between business users and the information system. In particular, the functions of describing and managing business logic can be transferred to business employees, and this is nothing more than one of the options for implementing the Low-code approach to transferring business process automation functions from IT specialists to business users. Ontologies allow eliminating the artificial separation of the functionality of business analysts and IT: the former can now manage the data structure themselves, making changes to the ontology that describes both the data structure and the logic of their processing. ezaaz Hyperautomation
So, based on ontological models, a semantic structure of a single data-centric corporate storage is created, focused on the sharing of a single data space. Based on this storage, digital images of real-world entities (digital clients, digital products and services, digital “things”, digital business processes) can be formed and combined into digital complexes and ecosystems.
The role of business processes for Hyperautomation
Digital business processes in the semantic architecture play the role of a link between individual “things” and other objects (entities) involved in the digital business activities of enterprises. This role becomes even more important as the detail of information objects grows, for example, when creating a digital twin of an enterprise.
The modern digital business process management platform includes, in particular, the management of business process regulations, tasks and cases (Adaptive Case Management, ACM), workflow, tasks and resources, and provides integration with IoT and RPA mechanisms, as well as rapid creation and modification digital solutions for any subject areas, including through Low-code mechanisms. As a result, BPMS becomes the core of hyper-automation.
Semantic approaches become the key to the implementation of the concept of hyperautomation, integrating various technologies into a single whole based on a single semantic field (ontological models and graph bases).
Practical implementation for Hyperautomation model
To implement the ontological model, graph databases are used, such as HyperGraphDB – a multigraph model, ArangoDB and OrientDB – multi-model DBMS, GraphX - a distributed framework for working with graphs in the Hadoop ecosystem using the Spark computing engine. All graph bases provide support for a decentralized data structure and distributed structures.
If the logic of business applications is decentralized, then NP-complete (allowing you to solve any computational problem) smart contracts, such as Ethereum, EOS, or Cardano, can support it in a native language. In addition, such databases implement data connectivity through the semantic layer of the IT architecture and storage of both structured and unstructured information. All these properties are key for ontological models. ezaaz Hyperautomation
Since the main feature of a graph database is the description of connections (relationships) between objects of the subject area, it can contain data of any type, as well as any basic intellectual complexes of ontologies. For this reason, graph bases are quite flexible and allow automatic logical inference — solving such intellectual tasks as retail supply chain management or decision support when servicing digital deposits based on ontological storage data.
The idea of hyperautomation associated with the total digitalization of objects and processes that incorporate information systems has received real reinforcement in the form of an ontological approach to describing the subject area and graph databases as an adequate tool for implementing large-scale projects.
Both the approach and the tools will gain popularity as businesses move towards hyper-automation. Graph databases are used in Facebook to manage a social network, and in Amazon to operate a recommendation service. Sberbank is experimenting with a prototype business solution based on super-large graphs, hoping to use them to interactively solve tasks with billions of connections: from searching for affiliates and organizations to product recommendations.
