Owen Molloy

A QFD based performance measurement tool

Abstract The re-engineering of its business processes should allow an organisation to obtain dramatic performance improvements while also emphasising the focus on the customer. It is widely accepted that in order to improve the performance of a process, it must be measured and this forms the basis of the approach undertaken and described in this paper. This performance measurement approach identifies a range of high level, customer oriented performance measures that can be used to measure any business process. A Quality Function Deployment (QFD) based tool incorporating this approach is described. This QFD based tool allows a user to develop a set of performance measures (or use the measures provided) that accurately measure the performance of a particular process.

Using semantic rules to determine access control for web services

Semantic Web technologies are bring increasingly employed to solve knowledge management issues in traditional Web technologies. This paper follows that trend and proposes using Semantic rule languages to construct rules for defining access control rules for Web Services. Using these rules, a system will be able to manage access to Web Services and also the information accessed via these services.

Real-time business activity monitoring and analysis of process performance on big-data domains

Timely access to performance data is essential to optimise business processes.We introduce a cloud-based infrastructure that monitors process performance in real-time on big data environments.We propose an event correlation algorithm that links a massive number of consecutive events.We demonstrate that our IT solution is able to generate metrics at very low latency rates using low hardware costs. Real-time access to business performance information is critical for corporations to run a competitive business and respond to a continuously changing business environment with ever-higher levels of competition. The timely analysis and monitoring of business processes are essential to identify non-compliant situations and react immediately to those inconsistencies in order to respond quickly to competitors. In this regard, the integration of business intelligence (BI) systems with Process Aware Information Systems (PAIS) can become a key tool for business users in decision making. However, current BI systems are not suitable for optimising and improving end-to-end processes since these are normally business domain specific and are not sufficiently process-aware to support the needs of process improvement type activities. In addition, highly transactional business environments may produce vast amounts of event data that cannot be efficiently managed by the use of traditional storage systems which are not designed to manage vast amounts of event data. We introduce a cloud-based architecture that leverages big-data technology to support performance analysis on any business domain, in a timely manner and regardless of the underlying concerns of the operational systems. Likewise, we demonstrate the ability of the solution to provide real-time business activity monitoring on big-data environments with low hardware costs.

Building a Process Performance Model for Business Activity Monitoring

A formal business process model serves as a common understanding of how business tasks are carried out to achieve end goals. The business process life cycle is managed using Business Process Management tools and methodologies. Business Activity Monitoring provides (near) real-time visibility into process execution notifying relevant personnel of process exceptions. Business process modelling captures business and execution semantics, but lacks any foundation for process analysis. This chapter will outline a model for process performance management for use in the monitoring phase of the process life cycle and how this model is leveraged within the iWISE architecture. iWISE provides a single view of business processes spanning disparate systems and departments.

StoRHm: a protocol adapter for mapping SOAP based Web Services to RESTful HTTP format

A protocol adapter ideally suited to enable enterprises to gradually transition from SOAP Web Services to RESTful HTTP Web Services without impacting existing clients is presented in this paper. The inherent advantage of such a transition is the visibility of RESTful HTTP messages to Web intermediaries such as caches. In contrast, SOAP messages are opaque, which disables Web intermediaries. While both approaches can use HyperText Transfer Protocol (HTTP) for message transfer, the paradigms contrast sharply. SOAP uses an interface specific approach whereas RESTful HTTP uses a Uniform Interface approach. SOAP marks up its payload with eXtensible Markup Language (XML) whereas in certain situations RESTful HTTP requires no XML. We present the disadvantages of the SOAP approach and outline how the RESTful HTTP approach solves these issues. We present results showing opaque SOAP messages transformed into transparent RESTful HTTP messages. We present StoRHm (SOAP toRESTful HTTP mapping), a protocol adapter which maps SOAP messages to RESTful HTTP format.

Using Description Logic and Rules to Determine XML Access Control

The quantity of generated information we store and need to access is colossal. Security of this information is becoming an issue of greater importance as the techniques and granularity with which it can be accessed become more advanced. Availability of information is a key component of any security system, although the information must be protected, it must also be available to the people who need it as and when they request it. However, increasing the methods by which it is accessable automatically increases the chance it may be compromised. Security systems are now using advanced levels of encryption, digital signatures containing biometric data and highly complex access control policies. We are proposing an access control system which reduces the complexity involved in defining authorisation permissions, particularly in structured documents such as XML where the user may be granted restricted access. Our solution employs techniques usually reserved for intelligent systems and the semantic web.

Using event-based process modelling to support six sigma quality

Collaborative and operational process visibility requires a process performance monitoring framework that provides traceability in near real-time. Process variability produces non-conforming or defective products and services. The focus of a six sigma quality programme is to reduce variability using statistical methods to highlight variance. Business interactions may be modelled using an event-based process model. In addition, business object information may be packaged with enterprise events. Process models lack the performance and quality information necessary for complete process lifecycle management and portability across disparate process management systems. This paper will describe the combination of process model, event and defect information to support a six sigma reporting mechanism for achieving process transparency. A proposition to supplement current process definition languages with such performance information is also included.

Integration of Event Data from Heterogeneous Systems to Support Business Process Analysis

Business Intelligence (BI) systems have traditionally been warehouse based, and have not been sufficiently process-aware to support the needs of process improvement type activities. It has been a challenge to leverage BI (and increasingly Analytics) functionality within the context of an overall process model. The ability to drill down into process data, track specific chains of process events, perform what-if type analysis, as well as monitoring overall aggregate performance is where process-aware Business Activity Monitoring (BAM) systems can play a significant role in improving performance. This paper presents a system prototype with the capabilities of integrating event data flowing through different heterogeneous systems such as business process execution language (BPEL) engines, enterprises resource planning (ERP) systems, workflows, legacy systems, etc., as well as storing this data into a global process execution repository. A new language for querying the stored event information is presented.

Dynamic Configuration of Collaboration in Networked Organisations

Levels of collaboration between networked organisations are at an all time high. There is a need for some control in the connections made between organisations and the information they pass to one another. Controlling the collaboration between two or more organisations involves providing an information structure to which messages between all communicating organisations must adhere to when passing information and specifying a protocol or protocols that must be used by the organisations to pass these messages. Any system employed to undertake the communication management between organisations must be dynamically configurable; therefore no organisation is “offline” while being configured.

Coupling Simulation with Machine Learning: A Hybrid Approach for Elderly Discharge Planning

Healthcare systems are increasingly challenged by the phenomenal growth of population ageing. Healthcare executives are, and will be, in an inevitable need of evidence-based artifacts for decision making. The paper addresses issues in the context of discharge planning for elderly patients with application to hip fracture care in Ireland. A hybrid approach is embraced that integrates simulation modeling with machine learning in an attempt to improve the validity of the simulation model outputs. In terms of simulation modeling, a discrete event simulation model is used to model the elderly patients journey through the care scheme of hip fracture. In tandem with the simulation model, predictive models are used to guide the simulation model. Specifically, the predictive models are used to make predictions on the inpatient length of stay and discharge destination of simulation-generated patients. On a population basis, the simulation model provides demand predictions for healthcare resources related to discharge destinations, with a focus on long-stay care such as nursing homes. Our results suggest that there may be a need to reconsider the geographic distribution of nursing homes within particular areas in Ireland in order to keep abreast of the foreseen shift in demographics. Furthermore, the incorporation of machine learning within simulation modeling is claimed to improve the predictive power of the simulation model.