Jianwei Yin

An Efficient Recommendation Method for Improving Business Process Modeling

In modern commerce, both frequent changes of custom demands and the specialization of the business process require the capacity of modeling business processes for enterprises effectively and efficiently. Traditional methods for improving business process modeling, such as workflow mining and process retrieval, still requires much manual work. To address this, based on the structure of a business process, a method called workflow recommendation technique is proposed in this paper to provide process designers with support for automatically constructing the new business process that is under consideration. In this paper, with the help of the minimum depth-first search (DFS) codes of business process graphs, we propose an efficient method for calculating the distance between process fragments and select candidate node sets for recommendation purpose. In addition, a recommendation system for improving the modeling efficiency and accuracy was implemented and its implementation details are discussed. At last, based on both synthetic and real-world datasets, we have conducted experiments to compare the proposed method with other methods and the experiment results proved its effectiveness for practical applications.

A MapReduce-Based Architecture for Rule Matching in Production System

Production system which accepts the facts and draws conclusions by repeatedly matching facts with rules plays an important role of improving the business by providing agility and flexibility. However, rule matching in production is badly time-consuming, and single computer limits the improvement for current matching algorithm. To address these problems, we proposed a MapReduce-based architecture to implement the distributed and parallel matching in different computers running with Rete algorithm. The architecture would benefit production system in performance, large scale of rules and facts are for special. This paper firstly formalizes some definitions for an accurate description, then not only discusses the details of implementation for different stages of the architecture but also shows the high efficiency through the experiment. At the end, we mention some complex factors which will be considered in the future for better performance.

A Stochastic Control Approach to Maximize Profit on Service Provisioning for Mobile Cloudlet Platforms

The recent emergence of mobile cloud computing has enabled mobile users to offload computing tasks from mobile devices to nearby cloudlets, so as to reduce energy consumption and improve application performance. In this paper, we consider the problem of maximizing the profit of the cloudlets’ managing platform that receives computing requests from mobile users and fulfils these requests by leveraging computing service of participating cloudlets. However, it is very challenging to maximize the operating profit for such a managing platform, due to unpredictable arrival of user requests, dynamic participation of mobile cloudlets, and complexity in computing resource allocations. Based on the Lyapunov optimization technique combined with the technique of weight perturbation, we introduce a new stochastic control algorithm that makes online decisions on computing request admission and dispatching, computing service purchasing, and computing resource allocation. Different from traditional techniques, this algorithm does not require any statistical knowledge of relevant system dynamics, and is efficient for implementation in practice. Theoretical analysis and simulation results have demonstrated both the profit optimality and the system stability achieved by the proposed control algorithm.

Enterprise Pattern: integrating the business process into a unified enterprise model of modern service company

Modern service company (MSC), the enterprise involving special domains, such as the financial industry, information service industry and technology development industry, depends heavily on information technology. Modelling of such enterprise has attracted much research attention because it promises to help enterprise managers to analyse basic business strategies (e.g. the pricing strategy) and even optimise the business process (BP) to gain benefits. While the existing models proposed by economists cover the economic elements, they fail to address the basic BP and its relationship with the economic characteristics. Those proposed in computer science regardless of achieving great success in BP modelling perform poorly in supporting the economic analysis. Therefore, the existing approaches fail to satisfy the requirement of enterprise modelling for MSC, which demands simultaneous consideration of both economic analysing and business processing. In this article, we provide a unified enterprise modelling approach named Enterprise Pattern (EP) which bridges the gap between the BP model and the enterprise economic model of MSC. Proposing a language named Enterprise Pattern Description Language (EPDL) covering all the basic language elements of EP, we formulate the language syntaxes and two basic extraction rules assisting economic analysis. Furthermore, we extend Business Process Model and Notation (BPMN) to support EPDL, named BPMN for Enterprise Pattern (BPMN4EP). The example of mobile application platform is studied in detail for a better understanding of EPDL.

Mobility-Aware Service Composition in Mobile Communities

The advances in mobile technologies enable mobile devices to perform tasks that are traditionally run by personal computers as well as provide services to the others. Mobile users can form a service sharing community within an area by using their mobile devices. This paper highlights several challenges involved in building such service compositions in mobile communities when both service requesters and providers are mobile. To deal with them, we first propose a mobile service provisioning architecture named a mobile service sharing community and then propose a service composition approach by utilizing the Krill-Herd algorithm. To evaluate the effectiveness and efficiency of our approach, we build a simulation tool. The experimental results demonstrate that our approach can obtain superior solutions as compared with current standard composition methods in mobile environments. It can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

Mapping Elements with the Hungarian Algorithm: An Efficient Method for Querying Business Process Models

Efficient query processing over a large amount of business process models is important for managing the business process model repository. The structural similarity between two process models is considered as the main measurement for ranking the process models for a given search model. Current business process query methods are inefficient since too many expensive computations of the graph edit distance are involved for constructing the elements mapping as well as deriving the structural similarity. To address this, using Petri-net as the modelling method, this paper presents the Hungarian algorithm based query method, where we firstly define the context similarity for a pair of place nodes that are from different process models by taking into account both the common paths and common transitions, then transform the elements (e.g., The transitions and the places) mapping to classical assignment problem that can be solved by Hungarian algorithm efficiently. In this way, we can save a lot of time for searching the best combination of elements mapping. Finally, we use the common method of the graph edit distance to measure the structural similarity based on the found best combination of elements mapping.

Querying Similar Process Models based on the Hungarian Algorithm

The structural similarity between two process models is usually considered as the main measurement for ranking the process models for a given query model. Current process query methods are inefficient since too many expensive computations of the graph edit distance are involved. To address this issue, using Petri-net as the modeling method, this paper presents the Hungarian algorithm based similarity query method. Unlike previous work where the non-task nodes (i.e., place nodes in the Petri-net) were lightly studied or even ignored, we think these non-task nodes also play an essential role in measuring the structural similarity between process models. First, we extract the context for each place and define the similarity for a pair of place nodes that are from different process models from two perspectives: commonality and the graph edit distance. Then, the place mapping is transformed to classical assignment problem that can be solved by Hungarian algorithm efficiently. Furthermore, we propose a new process similarity measurement on the basis of the place similarity. The extensive experimental evaluation shows that our Hungarian based methods outperform the baseline algorithm in both retrieval quality and query response time.

Workload Classification Model for Specializing Virtual Machine Operating System

There is growing demand on strategies to help cloud computing utilize its scale adaptiveness and cost effectiveness advantages. Previous operating systems(OS) are designed to suit all, leading to that virtual machines with different workloads use indiscriminate processing platform. However, there are conflicts between generality and performance, limited resource utilization and low processing efficiency of common OS penalize system performance. Therefore, we design four kinds of OS optimization strategies corresponding to four primary classes of workloads: CPU-Intensive, Memory-Intensive, I/O-Intensive and Network-Intensive. In this paper, we propose a Feedback-Based Workload Classification(FBWC) model which contains metrics collector, data preprocessor, Training Set Refresh Support Vector Machine(TSRSVM) classifier, decision maker and operating system tuner to classify workloads into appropriate class. TSRSVM combines support vectors of origin training set and correctly classified testing set together as new training set to get higher classification accuracy and efficiency. Comprehensive experiments compared with K Nearest Neighbors(KNN) and SVM demonstrate effectiveness of FBWC model and TSRSVM classification algorithm. Performance comparison between common virtual machine and the tuned one shows high degree performance improvement by OS specialization.

An efficient MapReduce-based rule matching method for production system

Production systems based on knowledge rules have been widely used for reasoning both in industry and academia. However, rule matching in production system is time-consuming too much and it always incur the system crash when the massive knowledge exceeds the limitations of memory and computing capacity of one single computer. The advent of cloud computing-a new on-demand computing model brings us an inspiring perspective to address this problem. In this paper, a MapReduce-based rule matching method was proposed. It decomposes the task of rule matching and maps subtasks to different computers in a distributed and parallel computing environment, and gets the final matching result after reduce phase. An experimental evaluation shows the high efficiency of the method. A MapReduce based architecture for production system and its prototype implementation are presented and studied.Task allocation strategies including sub rules and facts are presented.Rule decomposition for map phase is studied.Redundant mechanism is introduced for credibility and stability.

Social Network Supported Process Recommender System

Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced.