Nour Assy

A Configurable Resource Allocation for Multi-tenant Process Development in the Cloud

Cloud computing has become an important infrastructure for outsourcing service-based business processes in a multi-tenancy way. Configurable process models enable the sharing of a reference process among different tenants that can be customized according to specific needs. While concepts for specifying the control flow of such processes are well understood, there is a lack of support for cloud-specific resource configuration where different allocation alternatives need to be explicitly defined. In this paper, we address this research gap by extending configurable process models with the required configurable cloud resource allocation. Our proposal allows different tenants to customize the selection of the needed resources taking into account two important properties elasticity and shareability. Our prototypical implementation demonstrates the feasibility and the results of our experiments highlight the effectiveness of our approach.

An Automated Approach for Assisting the Design of Configurable Process Models

With the intention of design by reuse, configurable process models provide a way to model variability in reference models that need to be configured according to specific needs. The design of configurable process models is a well known complex and error-prone task. Thus, many approaches have been proposed to automate their design by merging existing process models into configurable reference models. However, the complexity introduced by such approaches remains an open issue. The designer ends up with one model that integrates a family of process variants making the process design and update a complex task. In this work, we propose to assist the design of configurable process models with configurable process fragments. Concretely, we present an algorithm for extracting, clustering and merging process fragments around a particular activity to construct a configurable fragment. The approach has been implemented as an extension of the Signavio Process Editor and evaluated against a large collection of process models. Experimental results show that our approach is efficient and produces comprehensible configurable fragments.

Assisting Business Process Design with Configurable Process Fragments

In recent years, many approaches have been proposed to facilitate business process design. They attempted to measure the similarity between business processes, merge business process models, mine event logs or recommend activities. In this paper, we present a merging approach that also aims at facilitating business process design. However, instead of merging business process models, we merge process fragments around a particular activity to construct a consolidated fragment for each activity. This consolidated fragment is presented as a configurable sub-process which allows process designers to overview the interactions of an activity and configure them to create business process variants according to particular requirements. The approach has been implemented as an application and tested against a large collection of business process models taken from different domains. Experimental results show that our approach produces concise and efficient configurable fragments.

Extracting Configuration Guidance Models from Business Process Repositories

Configurable process models are gaining a great importance for the design and development of reusable business processes. As these processes tend to be very complex, their configuration becomes a difficult task. Therefore, many approaches propose to build decision support systems to assist users selecting desirable configuration choices. Nevertheless, these systems are to a large extent manually created by domain experts, which is a time-consuming and tedious task. In addition, relying solely on the expert knowledge is not only error-prone, but also challengeable. In this paper, we propose to learn from past experience in process configuration in order to automatically extract a configuration guidance model. Instead of starting from scratch, a configuration guidance model assists analysts creating business-driven support systems.

Mining Configurable Process Fragments for Business Process Design

As business requirements become increasingly challenging in today’s fast changing environments, cross-organizational collaboration gains more and more attention for a successful business process design. Since many organizations may work on similar processes with some variations, configurable reference models have been proposed as a key aspect for a flexible process design. However, the complexity introduced by such models remains an open issue. The designer ends up with one model that integrates a family of process variants making the process design and update a complex task. In this work, we propose to assist the designer with configurable process fragments. However, instead of building the configurable process fragment from existing process models, we propose to use event logs as input. Such recorded executions capture the real behavior of processes which cannot be derived from their designed models. Then, using these logs we derive guidelines that direct the configuration of the resulted fragment. Our approach has been implemented as a plugin in the ProM framework and tested using a collection of event logs.

A semantic framework for configurable business process as a service in the cloud

With the advent of Cloud Computing, new opportunities for Business Process Outsourcing services have emerged. Business Process as a Service (BPaaS), a new cloud service model, has recently gained a great importance for outsourcing cloud-based business processes constructed for multi-tenancy. In such a multi-tenant environment, using configurable business process models enables the sharing of a reference process among different tenants that can be customized according to specific needs. With a large choice of configurable process modeling languages, different providers may deliver configurable processes with common functionalities but different representations which makes the process discovery and configuration a manual tedious task. This in turn creates cloud silos and vendors lock-in with non-reusable configurable BPaaS models. Therefore, with the aim of enabling the interoperability between multiple BPaaS providers, we propose in this paper a semantic framework for BPaaS configurable models. Taking advantage of Semantic Web technologies and data mining techniques, our framework allows for (1) an ontology-based high level abstract representation of BPaaS configurable models enriched with configuration guidelines and (2) an automated approach for extracting the configuration guidelines from existing process repositories. To show the feasibility and effectiveness of our approach, we extend Signavio with our semantic framework and conduct experiments on a dataset from SAP reference model.

Configuration Rule Mining for Variability Analysis in Configurable Process Models

With the intention of design by reuse, configurable process models provide a way to model variability in reference models that need to be configured according to specific needs. Recently, the increasing adoption of configurable process models has resulted in a large number of configured process variants. Current research activities are successfully investigating the design and configuration of configurable process models. However, a little attention is attributed to analyze the way they are configured. Such analysis can yield useful information in order to help organizations improving the quality of their configurable process models. In this paper, we introduce configuration rule mining, a frequency-based approach for supporting the variability analysis in configurable process models. Basically, we propose to enhance configurable process models with configuration rules that describe the interrelationships between the frequently selected configurations. These rules are extracted from a large collection of process variants using association rule mining techniques. To show the feasibility and effectiveness of our approach, we conduct experiments on a dataset from SAP reference model.

A Framework to Support Behavioral Design Pattern Detection from Software Execution Data.

The detection of design patterns provides useful insights to help understanding not only the code but also the design and architecture of the underlying software system. Most existing design pattern detection approaches and tools rely on source code as input. However, if the source code is not available (e.g., in case of legacy software systems) these approaches are not applicable anymore. During the execution of software, tremendous amounts of data can be recorded. This provides rich information on the runtime behavior analysis of software. This paper presents a general framework to detect behavioral design patterns by analyzing sequences of the method calls and interactions of the objects that are collected in software execution data. To demonstrate the applicability, the framework is instantiated for three well-known behavioral design patterns, i.e., observer, state and strategy patterns. Using the open-source process mining toolkit ProM, we have developed a tool that supports the whole detection process. We applied and validated the framework using software execution data containing around 1000.000 method calls generated from both synthetic and open-source software systems.

A general framework to detect behavioral design patterns

This paper presents a general framework to detect behavioral design patterns by combining source code and execution data. The framework has been instantiated for the observer, state and strategy patterns to demonstrate its applicability. By experimental evaluation, we show that our combined approach can guarantee a higher precision and recall than purely static approaches. In addition, our approach can discover all missing roles and return complete pattern instances that cannot be supported by existing approaches.

Component interface identification and behavioral model discovery from software execution data

Restructuring an object-oriented software system into a component-based one allows for a better understanding of the system and facilitates its future maintenance. A component-based architecture structures a software system in terms of its components and interactions where each component refers to a set of classes. To represent the architectural interaction, each component provides a set of interfaces. Existing interface identification approaches are mostly structure-oriented rather than function-oriented. In this paper, we propose an approach to identify interfaces of a component according to the functional interaction information that is recorded in the software execution data. In addition, we also discover the contract (represented as a behavioral model) for each identified interface by using process mining techniques to help understand how each interface actually works. All proposed approaches have been implemented in the open source process mining toolkit ProM. Using a set of software execution data containing more than 650.000 method calls generated from three software systems, we evaluate our approach against three existing interface identification approaches. The empirical evaluation demonstrates that our approach can discover more functionally consistent interfaces which facilitate the reconstruction of architectural models with higher quality.