Workflow technology has spread over the wide areas which require process control (e.g. logistics and e-business) or resource coordination (e.g. cooperative work and grid computing). Among various types of workflow, we introduce a case of ad-hoc workflow process in a Korean telecom company. Since such a service process is generally accompanied with customer's participation, the procedure and state are flexibly changed and sometimes capricious to cope with customer's request and operator's unexpected situation. In case of network service provisioning or problem shooting processes, customers often request the changes of their service types or visit appointments, which result in flexible and adaptive management of the process instances. In this paper, we present a novel approach to workflow modeling based on modified ECA rules (named P-ECA) for the purpose of ad-hoc workflow process modeling. The rule-based workflow modeling is comprehensible to engineers and can be implemented in programs at ease; therefore it is expected that it can be widely adopted for the ad-hoc and adaptive workflow modeling which requires dynamic changes of its states by internal or external events.

A variety of ubiquitous computing devices, such as radio frequency identification (RFID) and wireless sensor network (WSN), are generating huge and significant events that should be rapidly processed for business excellence. In this paper, we describe how complex event processing (CEP) technology can be applied to ubiquitous process management based on context-awareness. To address the issue, we propose a method for context-aware event processing using event processing language (EPL) statement. Specifically, the semantics of a situation drive the transformation of EPL statement templates into executable EPL statements. The proposed method is implemented in the domain of ubiquitous cold chain logistics management. With the proposed method, context-aware event processing can be realized to enhance business performance and excellence in ubiquitous computing environments.

As the recent growth of online social network services such as Facebook and Twitter, people are able to easily share information with each other by writing posts or commenting for another's posts. In this paper, we firstly suggest a method of discovering information flows of posts on Facebook and their underlying contexts by incorporating process mining and text mining techniques. Based on comments collected from Facebook, the experiment results illustrate how the proposed method can be applied to analyze information flows and contexts of posts on social network services.

Information diffusion analysis in social networks is of significance since it enables us to deeply understand dynamic social interactions among users. In this paper, we introduce approaches to discovering information diffusion process in social networks based on process mining. Process mining techniques are applied from three perspectives: social network analysis, process discovery and community recognition. We then present experimental results by using a real-life social network data. The proposed techniques are expected to employ as new analytical tools in online social networks such as blog and wikis for company marketers, politicians, news reporters and online writers.

Ubiquitous technologies such as sensor network and RFID have enabled companies to realize more rapid and agile manufacturing and service systems. In this paper, we addresses how the huge amount of real-time events coming from these devices can be filtered and integrated to business process such as manufacturing, logistics, and supply chain process. In particular, we focus on complex event processing of sensor and RFID events in order to integrate them to business rules in business activities. We also illustrate a ubiquitous event processing system, named ueFilter, which helps to filter and aggregate sensor event, to detect event patterns from sensors and RFID by means of event pattern languages (EPL), and trigger event-condition-action (ECA) in logistics processes.

Smart card payment systems provide a convenient billing mechanism for public transportation providers and passengers. In this paper, a smart card-based transit log is used to reveal functionally related regions in a city, which are called zones. To discover significant zones based on the transit log data, two algorithms, minimum spanning trees and agglomerative hierarchical clustering, are extended by considering the additional factors of geographical distance and adjacency. The hierarchical spatial geocoding system, called Geohash, is adopted to merge nearby bus stops to a region before zone discovery. We identify different urban zones that contain functionally interrelated regions based on passenger trip data stored in the smart card-based transit log by manipulating the level of abstraction and the adjustment parameters.