Enterprise Integration Patterns (EIP) are a collection of widely used stencils for integrating enterprise applications and business processes. These patterns represent a "de-facto" standard reference for design decisions when integrating enterprise applications. For each of these patterns we present the integration semantics (model) and the conceptual translation (syntax) to the Business Process Model and Notation (BPMN), which is a "de-facto" standard for modelling business process semantics and their runtime behavior.
Our approach stresses on the control flow, data flow and modeling capabilities of BPMN as well as its execution semantics. Recent work on Data in Business Processes" @cite_7 shows that besides (COREPRO) @cite_2 @cite_3 @cite_13 , which mainly deals with data-driven process modeling and (business) object status management, and UML activity diagrams, BPMN achieves the highest coverage in the categories relevant for our approach. Compared to BPMN and apart from the topic of object state" representation, neither @cite_4 nor petri nets do support data modeling at all @cite_7 . Based on that work, BPMN was further evaluated with respect to data dependencies within BPMN processes @cite_11 @cite_9 , however, not towards control and data flow as in our approach.
@cite_4 @cite_7 @cite_9 @cite_3 @cite_2 @cite_13 @cite_11
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Enacting business processes in process engines requires the coverage of control flow, resource assignments, and process data. While the first two aspects are well supported in current process engines, data dependencies need to be added and maintained manually by a process engineer. Thus, this task is error-prone and time-consuming. In this paper, we address the problem of modeling processes with complex data dependencies, e.g., m:n relationships, and their automatic enactment from process models. First, we extend BPMN data objects with few annotations to allow data dependency handling as well as data instance differentiation. Second, we introduce a pattern-based approach to derive SQL queries from process models utilizing the above mentioned extensions. Therewith, we allow automatic enactment of data-aware BPMN process models. We implemented our approach for the Activiti process engine to show applicability. Software-as-a-service (SaaS) providers are further expanding their offering by growing into the space of business process outsourcing (BPO). Therefore, the SaaS provider wants to administer and manage the business process steps according to a service level agreement. Outsourcing of business processes results in decentralized business workflows. However, current business process modeling languages, e.g. Business Process Execution Language (BPEL), Business Process Model and Notation (BPMN), are based highly on a centralized execution model and current BPMN engines offer limited constructs for federation and decentralized execution. To guarantee execution of business processes according to a service level agreement, different parties involved in a federated workflow must be able to inspect the state of external workflows. This requires advanced inspection interfaces and monitoring facilities. Current business process modeling languages must thus be extended to support monitoring in the s pecification, support modeling and support deployment of decentralized workflows. In this paper, correlation and monitoring extensions for BPMN are described. These extensions to BPMN are described such that the existing specification can still be used as is in a backwards compatible way. Business process modeling has become an important tool for managing organizational change and for capturing requirements of software. A central problem in this area is the fact that the acquisition of as-is models consumes up to 60 of the time spent on process management projects. This is paradox as there are often extensive documentations available in companies, but not in a ready-to-use format. In this paper, we tackle this problem based on an automatic approach to generate BPMN models from natural language text. We combine existing tools from natural language processing in an innovative way and augmented them with a suitable anaphora resolution mechanism. The evaluation of our technique shows that for a set of 47 text-model pairs from industry and textbooks, we are able to generate on average 77 of the models correctly. We present an approach for dynamic information flow control across the application and database. Our approach reduces the amount of policy code required, yields formal guarantees across the application and database, works with existing relational database implementations, and scales for realistic applications. In this paper, we present a programming model that factors out information flow policies from application code and database queries, a dynamic semantics for the underlying @math core language, and proofs of termination-insensitive non-interference and policy compliance for the semantics. We implement these ideas in Jacqueline, a Python web framework, and demonstrate feasibility through three application case studies: a course manager, a health record system, and a conference management system used to run an academic workshop. We show that in comparison to traditional applications with hand-coded policy checks, Jacqueline applications have 1) a smaller trusted computing base, 2) fewer lines of policy code, and 2) reasonable, often negligible, additional overheads.