Application platforms provide runtime environments for application logic. They manage the life cycle of an application or application component, and ensure the availability, reliability, scalability, security and monitoring of application logic. They typically support distributed application deployments across multiple nodes. Some also support cloud-style operations (elasticity, multitenancy and self-service).
Gartner defines business process automation (BPA) tools as software that automates business processes by enabling orchestration and choreography of diverse sets of actors (humans, systems and bots) involved in the execution of the process. BPA tools provide an environment for developing and running applications that incorporate process models (and optionally other business, decision and data models) enabling digitization of business operations
Gartner defines business processes as the coordination of the behavior of people, systems and things to produce specific business outcomes. 'Things' in this context refers to devices that are part of the Internet of Things (IoT). A BPM platform minimally includes: a graphical business process and/or rule modeling capability, a process registry/repository to handle the modeling metadata, a process execution engine and a state management engine or rule engine (or both). The three types of BPM platforms — basic BPM platforms, business process management suites (BPMSs), and intelligent business process management suites (iBPMSs) — can help solution architects and business outcome owners accelerate application development, transform business processes, and digitalize business processes to exploit business moments by providing capabilities that manage different aspects of the business process life cycle.
EBPA is a comprehensive approach toward business and process modeling aimed at transforming and improving business performance with an emphasis on cross-viewpoint (strategy, analysis, architecture, automation), cross-functional analysis to support strategic and operational decisions.
The market for ESP platforms consists of software subsystems that perform real-time computation on streaming event data. They execute calculations on unbounded input data continuously as it arrives, enabling immediate responses to current situations and/or storing results in files, object stores or other databases for later use. Examples of input data include clickstreams; copies of business transactions or database updates; social media posts; market data feeds; images; and sensor data from physical assets, such as mobile devices, machines and vehicles.
IMDGs provide a lightweight, distributed, scale-out in-memory object store — the data grid. Multiple applications can concurrently perform transactional and/or analytical operations in the low-latency data grid, thus minimizing access to high-latency, hard-disk-drive-based or solid-state-drive-based data storage. IMDGs maintain data grid durability across physical or virtual servers via replication, partitioning and on-disk persistence. Objects in the data grid are uniquely identified through a primary key, but can also be retrieved via other attributes. The most typical use of IMDGs is for web-scale transaction processing applications. However, adoption for analytics, often in combination with Apache Spark and Hadoop or stream analytics platforms, is growing fast — for example, for fraud detection, risk management, operation monitoring, dynamic pricing and real-time recommendation management.
Reviews for 'Internet of Things - Others'
Process mining platforms are designed to discover, monitor and improve processes by extracting knowledge from events captured in information systems to continuously deliver visibility and insights. Process mining includes automated process discovery (extracting process models from an event log), conformance checking (monitoring deviations by comparing model and log), social network/organizational mining, automated construction of simulation models, model extension, model repair, case prediction and history-based recommendations. Process mining platforms extend process mining capabilities by advanced process analytics, process improvement detection and process improvement recommendations, partly driven by AI and generative AI (GenAI).
A digital twin of an organization (DTO) is a dynamic software model of any organization that relies on operational and contextual data to understand how an organization operationalizes its business model, connects with its current state, responds to changes, deploys resources and delivers customer value.