(Extended Abstract)

We present efficiency models to evaluate data warehouse operations and analyze a preliminary data set using one of the models. The macro model represents the efficiency of operating a collection of data warehouses for both refresh processing and query production over a monthly horizon. The micro models represent the efficiency of daily operations an individual data warehouse for either refresh processing or query production. The variables in the models include traditional resource consumption (labor usage and computing budgets), system usage measures (usage time, users, and queries), data quality measures (timeliness and availability), and size measures (logical size, physical size, change data amounts, and data sources). We report on the evaluation of a preliminary data set for the refresh efficiency model using input-oriented Data Envelopment Analysis. We compare the efficiency of refresh processing in hypothetical data warehouses using constant returns, variable returns, and fuzzy DEA

A Framework for Integration of Knowledge Management and Business Process Management

Recently, several attempts have been made to introduce the process concept to knowledge management (KM) or the knowledge concept to business process management (BPM) in order to combine the advantages of the two approaches. However, clear description about their interrelationship or a comprehensive framework to combine them has not been provided. This paper explores how KM and BPM can complement each other and proposes a framework to integrate the two paradigms. The concept of process knowledge proposed by this paper focuses on the importance of business processes as knowledge, which is overlooked by existing KM or BPM research efforts. The paper proposes a framework that combines and extends the functionalities of existing knowledge management systems (KMSs) and business process management systems (BPMSs) by identifying the functionalities required to manage process knowledge from the lifecycle perspective of both knowledge management and business process management. A prototype system is also presented to demonstrate the feasibility of the proposed framework.clos

AN XML-BASED PROCESS REPOSITORY AND PROCESS QUERY LANGUAGE FOR INTEGRATED PROCESS MANAGEMENT

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AN INTEGRATED FRAMEWORK FOR PROCESS KNOWLEDGE MANAGEMENT

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Integrated Process Management: New Paradigm for Business Process Management

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An integration architecture for knowledge management systems and business process management systems

Recently, interests in the notion of process-oriented knowledge management (PKM) from academia and industry have been significantly increased. Comprehensive research and development requirements along with a cogent framework, however, have not been proposed for integrating knowledge management (KM) and business process management (BPM), which were proposed independently. Based on a comprehensive framework that reflects lifecycle requirements of both KM and BPM, this paper proposes an architecture for integrating knowledge management systems (KMSs) and business process management systems (BPMSs) to combine the advantages of the two paradigms. The paper first defines the concept of process knowledge and classifies it into three types. Then, it suggests how the functionalities of existing KMSs and BPMSs must be extended to support the three types of process knowledge while satisfying the lifecycle requirements of both knowledge and business processes. The architecture, which is comprehensive since it is derived from the extended requirements from the lifecycle perspective, will provide a basis for research and development of process-oriented knowledge management systems. A prototype system is presented to demonstrate the feasibility of the proposed architecture.close286

SQL/M UNIFIED RELATIONAL AND OBJECT-ORIENTED MULTIDATABASE LANGUAGE

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How Much to Aggregate: Learning Adaptive Node-Wise Scales on Graphs for Brain Networks

Brain connectomes are heavily studied to characterize early symptoms of various neurodegenerative diseases such as Alzheimer’s Disease (AD). As the connectomes over different brain regions are naturally represented as a graph, variants of Graph Neural Networks (GNNs) have been developed to identify topological patterns for disease early diagnosis. However, existing GNNs heavily rely on the fixed local structure given by an initial graph as they aggregate information from a direct neighborhood of each node. Such an approach overlooks useful information from further nodes, and multiple layers for node aggregations have to be stacked across the entire graph which leads to an over-smoothing issue. In this regard, we propose a flexible model that learns adaptive scales of neighborhood for individual nodes of a graph to incorporate broader information from appropriate range. Leveraging an adaptive diffusion kernel, the proposed model identifies desirable scales for each node for feature aggregation, which leads to better prediction of diagnostic labels of brain networks. Empirical results show that our method outperforms well-structured baselines on Alzheimer’s Disease Neuroimaging Initiative (ADNI) study for classifying various stages towards AD based on the brain connectome and relevant node-wise features from neuroimages.1

A BUSINESS PROCESS SIMULATION FRAMEWORK INCORPORATING THE EFFECTS OF ORGANIZATIONAL STRUCTURE

Organizations constantly change their business processes and/or organizational structure to innovate and adapt to the rapidly changing environment. Business process simulation is one of the most popular methodologies for more effectively predicting the effects of process and organizational redesign. Most existing approaches, however, consider only business processes and not organizational structures that can significantly affect business process performance. This study presents a framework for incorporating the effects of organizational structure into business process simulation. Further, it demonstrates how to use and analyze the proposed model. Finally, a case study of the Korean prosecutor's office is presented to illustrate the importance and feasibility of the proposed approach, which will enable a more precise prediction of the changes caused by process and organizational redesignopen