Wei Wang

Bio: Wei Wang is an academic researcher from IBM. The author has contributed to research in topics: Supply chain & Supply chain management. The author has an hindex of 11, co-authored 41 publications receiving 393 citations. Previous affiliations of Wei Wang include Tsinghua University.

Trees Weighting Random Forest Method for Classifying High-Dimensional Noisy Data

Abstract: Random forest is an excellent ensemble learning method, which is composed of multiple decision trees grown on random input samples and splitting nodes on a random subset of features. Due to its good classification and generalization ability, random forest has achieved success in various domains. However, random forest will generate many noisy trees when it learns from the data set that has high dimension with many noise features. These noisy trees will affect the classification accuracy, and even make a wrong decision for new instances. In this paper, we present a new approach to solve this problem through weighting the trees according to their classification ability, which is named Trees Weighting Random Forest (TWRF). Here, Out-Of-Bag, which is the training data subset generated by Bagging and not involved in building decision tree, is used to evaluate the tree. For simplicity, we choose the accuracy as the index that notes tree’s classification ability and set it as the tree’s weight. Experiments show that TWRF has better performance than the original random forest and other traditional methods, such as C45, Naive Bayes and so on.

A Comparison of Business Process Modeling Methods

Abstract: Business process modeling is the basis of business process management. The target of business process modeling is to get an abstract representation of the actual business processes. Although there are many business modeling methods, no well established modeling standard is available in this area. This paper reviews major business process modeling methods. Important aspects of these methods are discussed, including meta-model, graphical notation, serial representation, and tool support.

IBM SmartSCOR - a SCOR based supply chain transformation platform through simulation and optimization techniques

Abstract: Identified as a strategic area, supply chain transformation plays a critical role in today's IBM business. In this paper, we introduce an effort in IBM Research Division named SmartSCOR, which provides a comprehensive framework and methodology for On-Demand SCM problem-solving based on the cross-industry process standard Supply Chain Operations Reference (SCOR) model and a variety of simulation/optimization techniques. SmartSCOR sees transformation in two different levels, from supply chain strategy design/redesign to supply chain process improvement. Supply chain strategy design/redesign transforms a supply chain in a fundamental manner by means of manufacturing and distribution network reconfiguration, value chain integration, etc. Supply chain process improvement helps align the underlying business processes to strategy setting and get them streamlined. The two levels interact with each other and result in a profound while smooth transformation. SmartSCOR has been successfully applied in two supply chain transformation projects for validation and hardening.

A Sampling-Based Stochastic Winner Determination Model for Truckload Service Procurement

Abstract: Many large shippers procure truckload (TL) service from carriers via a combinatorial auction. In order to determine the winners of the auction, they need to solve a combinatorial optimization problem known as winner determination problem (WDP). In practice, shippers must resolve the WDP under shipment volume uncertainty due to limited information of future demands. In this paper, we propose a sampling-based two-stage stochastic programming approach to solve WDP under shipment volume uncertainty. We propose a refined formulation of deterministic WDPs in which shortage in shipments and the associated penalty cost are explicitly modeled. We demonstrate that the refined model is more general and more feasible under uncertainty than the benchmarks. Theoretical results pertaining to problem feasibility are derived and their insights to TL service procurement are provided. We propose a sampling-based solution approach called Monte Carlo Approximation (MCA) and use numerical tests to show that MCA is numerically tractable for solving moderately sized instances of TL service procurement. Finally, we verify via Monte Carlo simulation that the solution to our proposed stochastic WDP yields lower procurement cost than the solution to the deterministic WDP.

A Novel Approach for Process Mining Based on Event Types

Abstract: Process mining aims at distilling useful knowledge from the execution logs of process models. It has become a vivid research area in recent years. In this paper, a novel approach for process mining based on two event types, i.e., START and COMPLETE, is proposed. Information about the start and completion of tasks can be used to explicitly detect parallelism. The algorithm presented in this paper overcomes some of the limitations of existing algorithms such as the a-algorithm (e.g., short-loops) and therefore enhances the applicability of process mining in practical situations. Based on the completeness of the given event log and the behavior theory of Petri nets, the correctness of the algorithm can be proved theoretically.

Out of the Crisis

Abstract: According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

The prom framework: a new era in process mining tool support

Abstract: Under the umbrella of buzzwords such as “Business Activity Monitoring” (BAM) and “Business Process Intelligence” (BPI) both academic (e.g., EMiT, Little Thumb, InWoLvE, Process Miner, and MinSoN) and commercial tools (e.g., ARIS PPM, HP BPI, and ILOG JViews) have been developed. The goal of these tools is to extract knowledge from event logs (e.g., transaction logs in an ERP system or audit trails in a WFM system), i.e., to do process mining. Unfortunately, tools use different formats for reading/storing log files and present their results in different ways. This makes it difficult to use different tools on the same data set and to compare the mining results. Furthermore, some of these tools implement concepts that can be very useful in the other tools but it is often difficult to combine tools. As a result, researchers working on new process mining techniques are forced to build a mining infrastructure from scratch or test their techniques in an isolated way, disconnected from any practical applications. To overcome these kind of problems, we have developed the ProM framework, i.e., an “pluggable” environment for process mining. The framework is flexible with respect to the input and output format, and is also open enough to allow for the easy reuse of code during the implementation of new process mining ideas. This paper introduces the ProM framework and gives an overview of the plug-ins that have been developed.

Business process management (BPM) standards: a survey

Abstract: Purpose – In the last two decades, a proliferation of business process management (BPM) modeling languages, standards and software systems has given rise to much confusion and obstacles to adoption. Since new BPM languages and notation terminologies were not well defined, duplicate features are common. This paper seeks to make sense of the myriad BPM standards, organising them in a classification framework, and to identify key industry trends.Design/methodology/approach – An extensive literature review is conducted and relevant BPM notations, languages and standards are referenced against the proposed BPM Standards Classification Framework, which lists each standard's distinct features, strengths and weaknesses.Findings – The paper is unaware of any classification of BPM languages. An attempt is made to classify BPM languages, standards and notations into four main groups: execution, interchange, graphical, and diagnosis standards. At the present time, there is a lack of established diagnosis standards. I...

Mining process models with non-free-choice constructs

Abstract: Process mining aims at extracting information from event logs to capture the business process as it is being executed. Process mining is particularly useful in situations where events are recorded but there is no system enforcing people to work in a particular way. Consider for example a hospital where the diagnosis and treatment activities are recorded in the hospital information system, but where health-care professionals determine the "careflow." Many process mining approaches have been proposed in recent years. However, in spite of many researchers' persistent efforts, there are still several challenging problems to be solved. In this paper, we focus on mining non-free-choice constructs, i.e., situations where there is a mixture of choice and synchronization. Although most real-life processes exhibit non-free-choice behavior, existing algorithms are unable to adequately deal with such constructs. Using a Petri-net-based representation, we will show that there are two kinds of causal dependencies between tasks, i.e., explicit and implicit ones. We propose an algorithm that is able to deal with both kinds of dependencies. The algorithm has been implemented in the ProM framework and experimental results shows that the algorithm indeed significantly improves existing process mining techniques.