Enterprise application integration. Enterprise application integration (EAI) is the use of software and computer systems' architectural principles to integrate a set of enterprise computer applications. Overview[edit] Many types of business software such as supply chain management applications, ERP systems, CRM applications for managing customers, business intelligence applications, payroll and human resources systems typically cannot communicate with one another in order to share data or business rules. For this reason, such applications are sometimes referred to as islands of automation or information silos.
This lack of communication leads to inefficiencies, wherein identical data are stored in multiple locations, or straightforward processes are unable to be automated. In the words of the Gartner Group, EAI is the "unrestricted sharing of data and business processes among any connected application or data sources in the enterprise Improving connectivity[edit] points, is given by . Purposes[edit] Patterns[edit] Bus/hub. Spring Integration. Introduction Using the Spring Framework encourages developers to code using interfaces and use dependency injection (DI) to provide a Plain Old Java Object (POJO) with the dependencies it needs to perform its tasks. Spring Integration takes this concept one step further, where POJOs are wired together using a messaging paradigm and individual components may not be aware of other components in the application.
Such an application is built by assembling fine-grained reusable components to form a higher level of functionality. WIth careful design, these flows can be modularized and also reused at an even higher level. In addition to wiring together fine-grained components, Spring Integration provides a wide selection of channel adapters and gateways to communicate with external systems. Channel Adapters are used for one-way integration (send or receive); gateways are used for request/reply scenarios (inbound or outbound). Features Quick Start. Spring Integration Reference Manual.
Spring Integration provides Channel Adapters for receiving and sending messages using the Advanced Message Queuing Protocol (AMQP). The following adapters are available: Inbound Channel AdapterOutbound Channel AdapterInbound GatewayOutbound Gateway Spring Integration also provides a point-to-point Message Channel as well as a publish/subscribe Message Channel backed by AMQP Exchanges and Queues. In order to provide AMQP support, Spring Integration relies on Spring AMQP ( which "applies core Spring concepts to the development of AMQP-based messaging solutions".
Spring AMQP provides similar semantics as Spring JMS ( Whereas the provided AMQP Channel Adapters are intended for unidirectional Messaging (send or receive) only, Spring Integration also provides inbound and outbound AMQP Gateways for request/reply operations. 10.2 Inbound Channel Adapter advice-chain="" Spring Batch - Spring Batch. Many applications within the enterprise domain require bulk processing to perform business operations in mission critical environments.
These business operations include automated, complex processing of large volumes of information that is most efficiently processed without user interaction. These operations typically include time based events (e.g. month-end calculations, notices or correspondence), periodic application of complex business rules processed repetitively across very large data sets (e.g. insurance benefit determination or rate adjustments), or the integration of information that is received from internal and external systems that typically requires formatting, validation and processing in a transactional manner into the system of record.
Batch processing is used to process billions of transactions every day for enterprises. Spring Batch is part of Spring. For runtime concerns and a container for running a Job as a service see the Spring Batch Admin project. Roadmap. Mining Unstructured Data: Practical Applications: Strata 2012 - O'Reilly Conferences, February 28 - March 01, 2012. The challenge of unstructured data is a top priority for organizations that are looking for ways to search, sort, analyze and extract knowledge from masses of documents they store and create daily. Text mining uses knowledge-driven algorithms to make sense of documents in a similar way a person would do by reading them.
Lately, text mining and analytics tools became available via APIs, meaning that organizations can take immediate advantage these tools. We discuss three examples of how such APIs were utilized to solve key business challenges. Most organizations dream of paperless office, but still generate and receive millions of print documents. Digitizing these documents and intelligently sharing them is a universal enterprise challenge. In the area of forensics, intelligence and security, manual monitoring of masses of unstructured data is not feasible.