An iterative approach to synthesize business process templates from compliance rules

Companies have to adhere to compliance requirements. The compliance analysis of business operations is typically a joint effort of business experts and compliance experts. Those experts need to create a common understanding of business processes to effectively conduct compliance management. In this paper, we present a technique that aims at supporting this process. We argue that process templates generated out of compliance requirements provide a basis for negotiation among business and compliance experts. We introduce a semi-automated and iterative approach to the synthesis of such process templates from compliance requirements expressed in Linear Temporal Logic (LTL). We show how generic constraints related to business process execution are incorporated and present criteria that point at underspecification. Further, we outline how such underspecification may be resolved to iteratively build up a complete specification. For the synthesis, we leverage existing work on process mining and process restructuring. However, our approach is not limited to the control-flow perspective, but also considers direct and indirect data-flow dependencies. Finally, we elaborate on the application of the derived process templates and present an implementation of our approach.     

Controlled violation of temporal process constraints – Models,algorithms and results

While there has been much work on modeling and analysis of temporal constraints in workflows in the context of many real-world applications, there has not been much work on managing violations of temporal constraints. In real-time workflows, such as in medical processes and emergency situations, and also in logistics, finance and in other business processes with deadlines some violations are unavoidable. Here we introduce the notion of controlled violations as the ability to monitor a running process and develop an approach based on constraint satisfaction to determine the best schedule for its completion in a way so as to minimize the total penalty from the violations. The violations are evaluated in terms of metrics like number of violations, delay in process completion, and penalty of weighted violations. We also relate our work to the concept of controllability in literature and show how it can be checked using our method. Finally, we analyze the properties of our approach and also offer a proposal for implementation.     

Conformance checking and diagnosis for declarative business process models in data-aware scenarios

A business process (BP) consists of a set of activities which are performed in coordination in an organizational and technical environment and which jointly realize a business goal. In such context, BP management (BPM) can be seen as supporting BPs using methods, techniques, and software in order to design, enact, control, and analyze operational processes involving humans, organizations, applications, and other sources of information. Since the accurate management of BPs is receiving increasing attention, conformance checking, i.e., verifying whether the observed behavior matches a modelled behavior, is becoming more and more critical. Moreover, declarative languages are more frequently used to provide an increased flexibility. However, whereas there exist solid conformance checking techniques for imperative models, little work has been conducted for declarative models. Furthermore, only control-flow perspective is usually considered although other perspectives (e.g., data) are crucial. In addition, most approaches exclusively check the conformance without providing any related diagnostics. To enhance the accurate management of flexible BPs, this work presents a constraint-based approach for conformance checking over declarative BP models (including both control-flow and data perspectives). In addition, two constraint-based proposals for providing related diagnosis are detailed. To demonstrate both the effectiveness and the efficiency of the proposed approaches, the analysis of different performance measures related to a wide diversified set of test models of varying complexity has been performed.     

Range and Roots: Two common patterns for specifying and propagating counting and occurrence constraints

We propose Range and Roots which are two common patterns useful for specifying a wide range of counting and occurrence constraints. We design specialised propagation algorithms for these two patterns. Counting and occurrence constraints specified using these patterns thus directly inherit a propagation algorithm. To illustrate the capabilities of the Range and Roots constraints, we specify a number of global constraints taken from the literature. Preliminary experiments demonstrate that propagating counting and occurrence constraints using these two patterns leads to a small loss in performance when compared to specialised global constraints and is competitive with alternative decompositions using elementary constraints.     

工作流柔性角色解析研究与实现

在活动分配管理过程中能够实现多大限度的适应性和柔性,是影响一个工作流管理系统的性能的重要方面。针对工作流的活动分配的适应性需求,提出一种融合负载平衡,实例级上下文环境的柔性角色解析方法,从而提高了工作流的适应性。     

Generalised arc consistency for the AllDifferent constraint: An empirical survey

The AllDifferent constraint is a crucial component of any constraint toolkit, language or solver, since it is very widely used in a variety of constraint models. The literature contains many different versions of this constraint, which trade strength of inference against computational cost. In this paper, we focus on the highest strength of inference, enforcing a property known as generalised arc consistency (GAC). This work is an analytical survey of optimizations of the main algorithm for GAC for the AllDifferent constraint. We evaluate empirically a number of key techniques from the literature. We also report important implementation details of those techniques, which have often not been described in published papers. We pay particular attention to improving incrementality by exploiting the strongly-connected components discovered during the standard propagation process, since this has not been detailed before. Our empirical work represents by far the most extensive set of experiments on variants of GAC algorithms for AllDifferent. Overall, the best combination of optimizations gives a mean speedup of 168 times over the same implementation without the optimizations.     

Disjunctions, independence, refinements

An important question in constraint satisfaction is how to restrict the problem to ensure tractability (since the general problem is NP-hard). The use of disjunctions has proven to be a useful method for constructing tractable constraint classes from existing classes; the well-known ‘max-closed’ and ‘ORD-Horn’ constraints are examples of tractable classes that can be constructed this way. Three sufficient conditions (the guaranteed satisfaction property, 1-independence and 2-independence) that each ensure the tractability of constraints combined by disjunctions have been proposed in the literature. We show that these conditions are both necessary and sufficient for tractability in three different natural classes of disjunctive constraints. This suggests that deciding this kind of property is a very important task when dealing with disjunctive constraints. We provide a simple, automatic method for checking the 1-independence property—this method is applicable whenever the consistency of the constraints under consideration can be decided by path-consistency. Our method builds on a connection between independence and refinements (which is a way of reducing one constraint satisfaction problem to another.)     

基于资源约束的CAPP系统

在分析目前的专用型 ＣＡＰＰ和通用型 ＣＡＰＰ的优缺点的基础上,建立了基于资源约束的 ＣＡＰＰ模型,该模型可在用户所建的资源子系统约束下,进行工艺评价,同时在车间环境下动态生成工艺规划和加工参数,能适应目前制造业的柔性化趋势。     

基于工作流技术的软件过程定义和追踪机制

工作流作为一种过程监控技术得到了越来越广泛的关注和认同。论文论述了基于工作流技术实现软件过程的定义及其追踪和监控的概念模型和基本实现方案,给出了软件过程定义的元模型,探讨了在软件过程定义以及监控中涉及到的软件过程三个层面———软件过程、软件具体过程以及软件具体过程实例的概念及其相互关系。     

Distributed AEC databases for collaborative design

The Architecture, Engineering and Construction (AEC) design process for a facility involves participation of many design specialists. These participants are architects, engineers (structural, mechanical and electrical) and contractors, who may be independent design professionals or design teams within an organization. From the viewpoint of information processing, two characteristic features distinguish the AEC design process from many other design domains. Firstly, there is a massive volume of design data involved in the design of each of its component specialties. Secondly, the specialization of the disciplines themselves warrant substantial autonomy. For design automation, this autonomy should be realized without sacrificing the collaborative nature of the multidisciplinary AEC design process. We propose autonomous AEC databases to deal with the first issue, and a global constraint maintenance mechanism for the second. Autonomous design databases can support the existing local applications in architectural, structural and mechanical engineering, and construction domains. However, a set of inter-disciplinary constraints needs to be enforced to ensure spatial and functional consistency of the design. A global constraint checking mechanism frees designers from the burden of keeping track of various design changes that may result in cross-functional conflicts. In this paper, we discuss the relevant issues for constraint management on distributed AEC databases. Although specific AEC examples will be used, the presentation is general enough to be applicable to other design domains, such as VLSI and manufacturing.     

Workflow simulation for operational decision support

Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of workflow management. To do this we exploit not only the workflow’s design, but also use logged data describing the system’s observed historic behavior, and incorporate information extracted about the current state of the workflow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for different scenarios. The approach is supported by a practical toolset which combines and extends the workflow management system YAWL and the process mining framework ProM.     

Abstracting soft constraints: Framework, properties, examples

Soft constraints are very flexible and expressive. However, they are also very complex to handle. For this reason, it may be reasonable in several cases to pass to an abstract version of a given soft constraint problem, and then to bring some useful information from the abstract problem to the concrete one. This will hopefully make the search for a solution, or for an optimal solution, of the concrete problem, faster.In this paper we propose an abstraction scheme for soft constraint problems and we study its main properties. We show that processing the abstracted version of a soft constraint problem can help us in finding good approximations of the optimal solutions, or also in obtaining information that can make the subsequent search for the best solution easier.We also show how the abstraction scheme can be used to devise new hybrid algorithms for solving soft constraint problems, and also to import constraint propagation algorithms from the abstract scenario to the concrete one. This may be useful when we don't have any (or any efficient) propagation algorithm in the concrete setting.     

一种改进的约束关联规则挖掘算法

在约束关联规则挖掘过程中,影响交互的制约因素是挖掘算法的执行时间。为了提高挖掘过程的交互性,文章提出一种基于两阶段的约束关联规则挖掘算法。算法利用已挖掘的关联规则,实现约束关联规则的挖掘过程。在算法实现的过程中对关联规则集存储结构进行了优化,并扩展了类SQL查询语句。实验结果表明,由于在约束条件挖掘的过程中不需要再对数据库进行挖掘处理,节省了大量的用户时间,因此算法是有效的。     

Models for Global Constraint Applications

In this paper we give an overview of some industrial applications built using global constraints. We look at three systems from different application domains and show the core models used to express their constraints. We also consider different search strategies that have been applied and discuss some of the application aspects.     

Optimal Graph Constraint Reduction for Symbolic Layout Compaction

The compaction problem in VLSI layout can be formulated as a linear program. To reduce the execution time and memory usage in compaction, it is important to reduce the size of the linear program. Since most constraints in compaction are derived directly or indirectly from physical separation and electrical connectivity requirements which can be expressed in the form of graph constraints, we study the graph constraint reduction problem. That is the problem of producing, for a given system of graph constraints, an equivalent system with the fewest graph constraints. After observing that the problem as previously formulated is NP-hard and overrestrictive in that the maximum possible reduction is not always attainable, we propose a new formulation in which the maximum possible reduction is guaranteed. We further present a polynomial-time algorithm for the new formulation. Received September 13, 1994; revised December 4, 1995.     

An optimal coarse-grained arc consistency algorithm

The use of constraint propagation is the main feature of any constraint solver. It is thus of prime importance to manage the propagation in an efficient and effective fashion. There are two classes of propagation algorithms for general constraints: fine-grained algorithms where the removal of a value for a variable will be propagated to the corresponding values for other variables, and coarse-grained algorithms where the removal of a value will be propagated to the related variables. One big advantage of coarse-grained algorithms, like AC-3, over fine-grained algorithms, like AC-4, is the ease of integration when implementing an algorithm in a constraint solver. However, fine-grained algorithms usually have optimal worst case time complexity while coarse-grained algorithms do not. For example, AC-3 is an algorithm with non-optimal worst case complexity although it is simple, efficient in practice, and widely used. In this paper we propose a coarse-grained algorithm, AC2001/3.1, that is worst case optimal and preserves as much as possible the ease of its integration into a solver (no heavy data structure to be maintained during search). Experimental results show that AC2001/3.1 is competitive with the best fine-grained algorithms such as AC-6. The idea behind the new algorithm can immediately be applied to obtain a path consistency algorithm that has the best-known time and space complexity. The same idea is then extended to non-binary constraints.     

Revisiting decomposition analysis of geometric constraint graphs

Geometric problems defined by constraints can be represented by geometric constraint graphs whose nodes are geometric elements and whose arcs represent geometric constraints. Reduction and decomposition are techniques commonly used to analyze geometric constraint graphs in geometric constraint solving.In this paper we first introduce the concept of deficit of a constraint graph. Then we give a new formalization of the decomposition algorithm due to Owen. This new formalization is based on preserving the deficit rather than on computing triconnected components of the graph and is simpler. Finally we apply tree decompositions to prove that the class of problems solved by the formalizations studied here and other formalizations reported in the literature is the same.     

Prototype selection algorithms for distributed learning

Distributed learning from data is one of the typical tasks solved by distributed data-mining techniques and is seen as a fundamental computational problem. One of the approaches suitable for distributed learning is to select, by data reduction, relevant local patterns, called also prototypes, from geographically distributed databases. Next, locally selected prototypes can be moved to other sites and merged into the global knowledge model. The paper presents three agent-based population learning algorithms for distributed learning. The proposed algorithms are based on agent collaborations in distributed prototype selection processes and on agent collaborations when the learning global model is created. The basic property of the presented algorithms is that the prototypes are selected by agent-based population learning algorithm from data clusters induced at distributed sites. The main goal of the paper is to empirically compare how the way of inducing such clusters can influence the distributed learning performance. The paper investigates the agent-based population learning algorithms used to solve distributed data reduction and gives a brief discussion of the procedures for clusters initialization. Finally, computational experiment results are shown.     

On learning dual classifiers for better data classification

Instance selection aims at filtering out noisy data (or outliers) from a given training set, which not only reduces the need for storage space, but can also ensure that the classifier trained by the reduced set provides similar or better performance than the baseline classifier trained by the original set. However, since there are numerous instance selection algorithms, there is no concrete winner that is the best for various problem domain datasets. In other words, the instance selection performance is algorithm and dataset dependent. One main reason for this is because it is very hard to define what the outliers are over different datasets. It should be noted that, using a specific instance selection algorithm, over-selection may occur by filtering out too many ‘good’ data samples, which leads to the classifier providing worse performance than the baseline. In this paper, we introduce a dual classification (DuC) approach, which aims to deal with the potential drawback of over-selection. Specifically, performing instance selection over a given training set, two classifiers are trained using both a ‘good’ and ‘noisy’ sets respectively identified by the instance selection algorithm. Then, a test sample is used to compare the similarities between the data in the good and noisy sets. This comparison guides the input of the test sample to one of the two classifiers. The experiments are conducted using 50 small scale and 4 large scale datasets and the results demonstrate the superior performance of the proposed DuC approach over the baseline instance selection approach.