Change Support in Process-Aware Information Systems - A Pattern-Based Analysis

In today's dynamic business world the economic success of an enterprise increasingly depends on its ability to react to changes in its environment in a quick and flexible way. Process-aware information systems (PAIS) offer promising perspectives in this respect and are increasingly employed for operationally supporting business processes. To provide effective business process support, flexible PAIS are needed which do not freeze existing business processes, but allow for loosely specified processes, which can be detailed during run-time. In addition, PAIS should enable authorized users to flexibly deviate from the predefined processes if required (e.g., by allowing them to dynamically add, delete, or move process activities) and to evolve business processes over time. At the same time PAIS must ensure consistency and robustness. The emergence of different process support paradigms and the lack of methods for comparing existing change approaches have made it difficult for PAIS engineers to choose the adequate technology. In this paper we suggest a set of changes patterns and change support features to foster the systematic comparison of existing process management technology with respect to process change support. Based on these change patterns and features, we provide a detailed analysis and evaluation of selected systems from both academia and industry. The identified change patterns and change support features facilitate the comparison of change support frameworks, and consequently will support PAIS engineers in selecting the right technology for realizing flexible PAIS. In addition, this work can be used as a reference for implementing more flexible PAIS

Deer Herd Management Using the Internet: A Comparative Study of California Targeted By Data Mining the Internet

An ongoing project to investigate the use of the internet as an information source for decision support identified the decline of the California deer population as a significant issue. Using Google Alerts, an automated keyword search tool, text and numerical data were collected from a daily internet search and categorized by region and topic to allow for identification of information trends. This simple data mining approach determined that California is one of only four states that do not currently report total, finalized deer harvest (kill) data online and that it is the only state that has reduced the amount of information made available over the internet in recent years. Contradictory information identified by the internet data mining prompted the analysis described in this paper indicating that the graphical information presented on the California Fish and Wildlife website significantly understates the severity of the deer population decline over the past 50 years. This paper presents a survey of how states use the internet in their deer management programs and an estimate of the California deer population over the last 100 years. It demonstrates how any organization can use the internet for data collection and discovery