Holger Ziekow

Event-based monitoring of process execution violations

Process-aware information systems support business operations as they are typically defined in a normative process model. Often these systems do not directly execute the process model, but provide the flexibility to deviate from the normative model. This paper proposes amethod for monitoring control-flow deviations during process execution. Our contribution is a formal technique to derive monitoring queries from a process model, such that they can be directly used in a complex event processing environment. Furthermore, we also introduce an approach to filter and aggregate query results to provide compact feedback on deviations. Our techniques is applied in a case study within the IT service industry.

Optimizing Event Pattern Matching Using Business Process Models

A growing number of enterprises use complex event processing for monitoring and controlling their operations, while business process models are used to document working procedures. In this work, we propose a comprehensive method for complex event processing optimization using business process models. Our proposed method is based on the extraction of behavioral constraints that are used, in turn, to rewrite patterns for event detection, and select and transform execution plans. We offer a set of rewriting rules that is shown to be complete with respect to the all, seq, and any patterns. The effectiveness of our method is demonstrated in an experimental evaluation with a large number of processes from an insurance company. We illustrate that the proposed optimization leads to significant savings in query processing. By integrating the optimization in state-of-the-art systems for event pattern matching, we demonstrate that these savings materialize in different technical infrastructures and can be combined with existing optimization techniques.

Six Case Studies

In this chapter we described six case studies illustrating the use and potential of RFID in the manufacturing industry. In all case studies we first analyzed the current situation on the shop floor. Then we illustrated how the current production could be optimized by applying RFID technology in diverse scenarios. We concluded the case studies with a cost and benefits discussion for each portrayed RFID application.

RFID and the Internet of Things: Technology, Applications, and Security Challenges

Radio Frequency Identification (RFID) has started to exert a major influence on modern supply chain management. In manufacturing, RFID changes the way objects are tracked on the shop floor and how manufactured goods interact with the production environment. In logistics, RFID is used to track and trace pallets or individual objects on a global scale. In retail, RFID is used to identify objects, retrieve related information, and prevent theft. Sometimes the tags remain attached to the objects post-sale, thus facilitating additional services. Overall, enterprises have much more detailed information about the objects: the use and produce, their location, their trajectories, and their physical state.

Optimising Complex Event Queries over Business Processes Using Behavioural Profiles

Complex event processing emerged as a technology that promises tight integration of business process management with the flow of products in a supply chain. As part of that, complex event querying is used to monitor and analyse streams of events. The amount of data that needs to be processed along with the distribution of the event-emitting sources impose serious challenges for efficient event querying mechanisms. In this paper, we assume that the business process to which the events relate is defined in terms of a normative process model. Based thereon, we show how this knowledge can be leveraged to optimise complex event queries and their processing. To this end, we use the formal concept of behavioural profiles as a behavioural abstraction of the process model.

A probabilistic approach for cleaning RFID data

The inherent uncertainty in RFID signals requires an RFID middleware system to clean the input data after capturing. Typically these systems employ a low pass filter for reducing errors. In this paper we propose an approach for data cleaning that exploits basic characteristics of RF signals as well as maximum likelihood operations. With our filter we improve proximity detection of RFID tags. Our solution enables reasoning about the position of RFID tags in the readers range without measuring the signal strength of tag responses. It is therefore applicable on top of standard reader interfaces. Our solution improves data cleaning wherever the tag to reader distance is relevant. For instance this enables correct ordering of items that pass by a reader on a conveyor or enhances tracking scenarios with RFID equipped fork lifts. We demonstrate the benefits of our approach compared to low pass filtering.

Sharing RFID and complex event data among organizations

Integrating RFID data from various sources in a value chain is an important requirement for leveraging the full potential of the technology. To facilitate this crucial integration, the global standardization organization GS1 is currently developing a range of service specifications. Yet, the services specified so far do not enable event-based interaction schemes and do not leverage the detection of distributed complex events. In this paper we propose to fill this gap by means of a peer-to-peer based broker network that can realize event-based interaction on top of existing specifications. For this infrastructure we present a new method to improve in-network processing. As shown by our experiments, our method significantly reduces overall system load by exploiting a priori knowledge about the observed processes.

RFID in Manufacturing: From Shop Floor to Top Floor

The purpose of this chapter was to lay the foundations for the upcoming discussion of RFID in manufacturing. A key observation concerns the fact that RFID develops its full potential only if it is tightly integrated with any existing IT infrastructures. A tight integration with existing ERP and MES systems makes it particularly more likely that RFID will lead to concrete and local productivity improvements in the short and medium terms. Such concrete improvements—especially if they are purely intra-enterprise, i.e., independent of any coordination with supply chain partners—facilitate the adoption decision considerably. Cost–benefit calculations are less complex, and there is less uncertainty about the medium-term profitability of an investment in RFID technology.

Modeling trade-offs in the design of sensor-based event processing infrastructures

Systems for distributed event processing have recently gained increasing attention in a broad range of application domains. This raises the demand for methods to adapt the system design to application-specific needs. Our approach considers (1) trade-offs regarding the hardware infrastructure and (2) trade-offs in the software design. For the underlying model we categorize events along the dimensions of temporal complexity and physical distribution. This yields a categorization of events that drives trade-offs in the infrastructure design. The presented model supports design decisions in dependence on application-specific event properties and design goals.

Designing Sensor-Based Event Processing Infrastructures

Systems for distributed event processing have recently gained increasing attention in a broad range of application domains. This raises the demand for methods to adapt the system design to application-specific needs. Our approach considers (i) tradeoffs regarding the hardware infrastructure and (ii) tradeoffs in the software design. The presented model supports design decisions in dependence of application-specific event properties and design goals.