On compliance of business processes with business contracts

This paper addresses the problem of ensuring compliance of business processes, implemented within and across organisational boundaries, with the constraints stated in related business contracts. In order to deal with the complexity of this problem we propose two solutions that allow for a systematic and increasingly automated support for addressing two specific compliance issues. One solution provides a set of guidelines for progressively transforming contract conditions into business processes that are consistent with contract conditions thus avoiding violation of the rules in contract. Another solution compares rules in business contracts and rules in business processes to check for possible inconsistencies. Both approaches rely on a computer interpretable representation of contract conditions that embodies contract semantics. This semantics is described in terms of a logic based formalism allowing for the description of obligations, prohibitions, permissions and violations conditions in contracts. This semantics was based on an analysis of typical building blocks of many commercial, financial and government contracts. The study proved that our contract formalism provides a good foundation for describing key types of conditions in contracts, and has also given several insights into valuable transformation techniques and formalisms needed to establish better alignment between these two, traditionally separate areas of research and endeavour. The study also revealed a number of new areas of research, some of which we intend to address in near future.

Modeling control objectives for business process compliance

Business process design is primarily driven by process improvement objectives. However, the role of control objectives stemming from regulations and standards is becoming increasingly important for businesses in light of recent events that led to some of the largest scandals in corporate history. As organizations strive to meet compliance agendas, there is an evident need to provide systematic approaches that assist in the understanding of the interplay between (often conflicting) business and control objectives during business process design. In this paper, our objective is twofold. We will firstly present a research agenda in the space of business process compliance, identifying major technical and organizational challenges. We then tackle a part of the overall problem space, which deals with the effective modeling of control objectives and subsequently their propagation onto business process models. Control objective modeling is proposed through a specialized modal logic based on normative systems theory, and the visualization of control objectives on business process models is achieved procedurally. The proposed approach is demonstrated in the context of a purchase-to-pay scenario.

Compliance aware business process design

Historically, business process design has been driven by business objectives, specifically process improvement. However this cannot come at the price of control objectives which stem from various legislative, standard and business partnership sources. Ensuring the compliance to regulations and industrial standards is an increasingly important issue in the design of business processes. In this paper, we advocate that control objectives should be addressed at an early stage, i.e., design time, so as to minimize the problems of runtime compliance checking and consequent violations and penalties. To this aim, we propose supporting mechanisms for business process designers. This paper specifically presents a support method which allows the process designer to quantitatively measure the compliance degree of a given process model against a set of control objectives. This will allow process designers to comparatively assess the compliance degree of their design as well as be better informed on the cost of non-compliance.

Naltrexone in the treatment of male alcoholics - an effectiveness study in Singapore

Naltrexone has been demonstrated in western studies to be a useful pharmacological adjunct within treatment programmes for alcoholic patients. We report the first study of its efficacy and usefulness in an Asian region. This project was designed to allow naltrexone's performance to be assessed under routine clinical conditions but with patients selected on the basis of their being likely to comply. Following in-patient detoxification, 53 male alcohol-dependent patients admitted to the Alcohol Treatment Centre at Woodbridge Hospital, Singapore, were enrolled in a 12-week, placebo-controlled trial of naltrexone hydrochloride (50 mg/day). Subjects were randomized on a 2:1 basis, with 35 receiving naltrexone and 18 receiving placebo. Analyses identified that a higher percentage of naltrexone patients completed the study (40% vs. 22%). In the study non-completers, the dropout rate due to drinking relapse was also lower in the naltrexone group (9% vs. 43%). Of the 39 patients for whom drinking status over the trial could be ascertained, fewer naltrexone-treated patients drank (33% vs. 53%). Alcohol craving scores also showed a selective and distinct reduction in the naltrexone-treated group. Results suggest that naltrexone may be an effective and safe aid to treatment of alcohol dependent patients in Asian patients, for whom the aims are to reduce alcohol craving and drinking reinstatement, but where compliance is likely to be low.

Fitting mixtures of Kent distributions to aid in joint set identification

When examining a rock mass, joint sets and their orientations can play a significant role with regard to how the rock mass will behave. To identify joint sets present in the rock mass, the orientation of individual fracture planer can be measured on exposed rock faces and the resulting data can be examined for heterogeneity. In this article, the expectation-maximization algorithm is used to lit mixtures of Kent component distributions to the fracture data to aid in the identification of joint sets. An additional uniform component is also included in the model to accommodate the noise present in the data.