Abstract. Semantic Business Process Management (SBPM) has been proposed as an extension of BPM with Semantic Web and Semantic Web Services (SWS) technologies in order to increase and enhance the level of automation that can be achieved within the BPM life-cycle. In a nutshell, SBPM is based on the extensive and exhaustive conceptualization of the BPM domain so as to support reasoning during business processes modelling, composition, execution, and analysis, leading to important enhancements throughout the life-cycle of business processes. An important step of the BPM life-cycle is the analysis of the processes deployed in companies. This analysis provides feedback about how these processes are actually being executed (like common control-flow paths, performance measures, detection of bottlenecks, alert to approaching deadlines, auditing, etc). The use of semantic information can lead to dramatic enhancements in the state-of-the-art in analysis techniques. In this paper we present an outlook on the opportunities and challenges on semantic business process mining and monitoring, thus paving the way for the implementation of the next generation of BPM analysis tools. 1

Process mining aims at discovering new knowledge based on information hidden in event logs. Two important enablers for such analysis are powerful process mining techniques and the omnipresence of event logs in today's information systems. Most information systems supporting (structured) business processes (e.g. ERP, CRM, and workflow systems) record events in some form (e.g. transaction logs, audit trails, and database tables). Process mining techniques use event logs for all kinds of analysis, e.g., auditing, performance analysis, process discovery, etc. Although current process mining techniques/tools are quite mature, the analysis they support is somewhat limited because it is purely based on labels in logs. This means that these techniques cannot benefit from the actual semantics behind these labels which could cater for more accurate and robust analysis techniques. Existing analysis techniques are purely syntax oriented, i.e., much time is spent on filtering, translating, interpreting, and modifying event logs given a particular question. This paper presents the core building blocks necessary to enable semantic process mining techniques/tools. Although the approach is highly generic, we focus on a particular process mining technique and show how this technique can be extended and implemented in the ProM framework tool.

Abstract. Scenario-based analysis describes customer needs and focuses on different aspects of information systems. A scenario typically has several metrics which compute specific information about transaction data, organizational structures and configuration settings. The selection and configuration of metrics is not a trivial task and normally cannot be reused over different information systems. Therefore, this paper shows how semantics can aid this process. In fact, the proposed semantically aided analysis approach supports all the five phases of the scenario-based analysis process: (i) selection of metrics relevant to a given scenario, (ii) their configuration and (iii) execution, (iv) evaluation of returned results and (v) reporting of results. Our approach is illustrated by applying it to Reverse Business Engineering, a tool for scenario-based analysis commonly used by commercial ERP systems. However, the proposed approach is general enough to also be applied to other analysis techniques. 1

The D1.1 deliverable clarifies baseline, progress, and state of the art that CHOReOS will address. For each of the first four CHOReOS work packages, WP1 to WP4, this deliverable gives a precise definition of the state of the art, an indication of the envisaged progress beyond the state of the art by CHOReOS and the baseline for its research.

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