Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Emergence: From Chaos to Order by John H. Holland .

This book provides an in-depth description of event-based systems, covering topics ranging from local event matching and distributed event forwarding algorithms, through a practical discussion of software engineering issues raised by the event-based style, to state-of-the-art research in event-based systems like composite event detection and security. The authors offer a comprehensive overview, and show the power of event-based architectures in modern system design, encouraging professionals to exploit this technique in next generation large-scale distributed applications like information dissemination, network monitoring, enterprise application integration, or mobile systems.

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Distributed event-based systems

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

It is believed that transactions belong to key services of componentbased middleware. However, surprisingly, there is no general agreement on how the component-based middleware support for transactions should look like. In this paper we exploit our experiences with Jironde [4], a flexible framework that extends the Fractal component model [1, 2] with transactions via a set of transactional controllers that manage transactions on behalf of a component. Then we identify several key architectural and technical issues related to enhancing component-based middleware with transactions. We investigate how current technologies address theses issues, and the suitability of current standards to support transaction processing in component-based middleware. 1 Transactions and Components: Architectural Issues Different component models deal with component’s participation in a transaction differently. In the explicit transaction participation, the scenario of involving a component C to a transaction t essentially consists of the three steps as follows: 1) C is registered to t. 2) A client invokes various operations on C. 3) At the time of t’s validation, the transaction manager invokes specific methods of the registered C’s interfaces. These (callback) methods must be implemented by the transactional components. With the implicit transaction participation, components are not obliged to implement any functionality related to transactions. Any time C is visited by a transaction t, the transaction manager of the container keeps all necessary information to manage atomicity, concurrency control, and recovery. Different component standards deal with component participation in transactions differently. CCM use the explicit transaction participation, COM+ uses the implicit one, while EJB mix both. Component-unaware transactions manipulate data without any knowledge on whether they are organized or related to components. For component-aware transactions, components are the data they manipulate with. We believe that all the CCM, EJB, and COM+ transactions are component-unaware. A component is transaction-unaware if its code does not use any transactional primitives and is not therefore in any way dependent on any transactional standard, while a transactionaware component is the opposite. Component’s awareness of transactions reflects the implicit/explicit transaction participation but includes also some hidden expectations of the component design. Transaction-unaware container does not deal with On Enhancing Component-Based Middleware with Transactions 2 transactions, which are managed at the application level instead. Transaction-aware container provides some transaction management features, such as containerdemarcation, transaction propagation, concurrency control, etc. EJB, CCM and COM+ are examples that provide such containers. 2 Transactions and Components: Technical Issues The technical issues related to transactional component include concurrency control, recovery, and transaction context propagation. As for concurrency control, the current technologies either use a simple read/write model or do not allow any concurrent access to component instances at all. It would be beneficial to exploit the concurrency potential of components by e.g. the use of conflict matrixes defined on all the methods of all implemented interfaces. Both concurrency control and recovery of components reflect the architectural patterns presented in the previous section. Another important issue is the transaction context propagation. There are several options of how to specify a transaction propagation policy, as well as whether to define it either during the component’s development or during its deployment. A last issue is to support the definition of new propagation policies, like JOTDF [5] does.

On The Move to meaningful Internet systems 2003 : OTM 2003 Workshops : OTM Confederated International Workshops : HCI-SWWA, IPW, JTRES, WORM, WMS, and WRSM 2003, Catania, Sicily, Italy, November 3-7, 2003 : proceedings

Modeling and execution of event stream processing in business processes

The Internet of Things and Cyber-physical Systems provide enormous amounts of real-time data in form of streams of events Businesses can benefit from the integration of this real-world data; new services can be provided to customers, or existing business processes can be improved Events are a well-known concept in business processes However, there is no appropriate abstraction mechanism to encapsulate event stream processing in units that represent business functions in a coherent manner across the process modeling, process execution, and IT infrastructure layer In this paper we present Event Stream Processing Units (SPUs) as such an abstraction mechanism SPUs encapsulate application logic for event stream processing and enable a seamless transition between process models, executable process representations, and components at the IT layer We derive requirements for SPUs and introduce a BPMN extension to model SPUs We present a runtime infrastructure that executes SPUs and supports implicit invocation and completion semantics We illustrate our approach using a logistics process as running example

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Event stream processing units in business processes

The concept of Cloud Computing is by now at the peak of public attention and adoption Driven by several economic and technological enablers, Cloud Computing is going to change the way we have to design, maintain and optimise large-scale data-intensive software systems in the future Moving large-scale, data-intensive systems into the Cloud may not always be possible, but would solve many of today's typical problems In this paper we focus on the opportunities and restrictions of current Cloud solutions regarding the data model of such software systems We identify the technological issues coming along with this new paradigm and discuss the requirements to be met by Cloud solutions in order to provide a meaningful alternative to on-premise configurations

Aspects of data-intensive cloud computing

Today's social web platforms, such as Facebook, Twitter, Google+, and LinkedIn, increasingly have to process large volumes of user-generated data on the fly. As the role of such platforms shifts from being portals for largely historic data towards providing platforms for real-time data analytics, we observe that their architectures incrementally move from storage-centric designs, based on distributed data management technologies, towards event-based models exploiting queueing and stream processing systems.We believe that it is time to rethink fundamentally the software architecture for social web platforms and base them on a content-based communication model, that is explicitly designed to disseminate and partition incoming request flows on a cluster of servers. A content-based publish/subscribe system thus acts as a scalable and elastic, highly responsive data distribution backbone. By focusing on fresh data, such an architecture can optimize the routing of data to match the topology of the data center, dynamically adapt data flows to alleviate hot spots, and elastically scale to more servers when required by computationally expensive on-the-fly data analytics applications.

http://www.dvs.tu-darmstadt.de/publications/pdf/a6-eyers.pdf

Living in the present: on-the-fly information processing in scalable web architectures

Today's production processes are characterized by global supply chains, short lifecycles, and an increasing personalization of goods. To satisfy the demands for agility we must integrate the production with the logistics processes and knowledge about the underlying transportation services and infrastructure. This requires continuous monitoring and reacting to events. Service-oriented architectures have provided a platform for structuring services within and across enterprises. However, for an effective monitoring and timely reaction to emerging situations it is crucial to integrate event processing and service orientation. In this position paper we show how event processing and service orientation can be combined into an effective delivery platform for an integrated coordination of the flow of goods. We show how simple events, e.g. RFID tag detections or simple sensor readings, can be integrated into abstract events that are meaningful to invoke logistics services and improve the celerity of responses. We propose filtering, aggregating, and on-the-fly analysis of the continuous flow of events and make events persistent in an event warehouse for auditability and input to future planning processes.

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Sebastian Frischbier

Papers

Modeling and execution of event stream processing in business processes

Event stream processing units in business processes

Aspects of data-intensive cloud computing

Living in the present: on-the-fly information processing in scalable web architectures

Event-driven services: integrating production, logistics and transportation