Enterprise Collaboration Systems (ECS) have become substantial for computer-mediated communication and collaboration among employees in organisations. As ECS combine features from social media and traditional groupware, a growing number of organisations implement ECS to facilitate collaboration among employees. Consequently, ECS form the core of the digital workplace. Thus, the activity logs of ECS are particularly valuable since they provide a unique opportunity for observing and analysing collaboration in the digital workplace. Evidence from academia and practice demonstrates that there is no standardised approach for the analysis of ECS logs and that practitioners struggle with various barriers. Because current ECS analytics tools only provide basic features, academics and practitioners cannot leverage the full potential of the activity logs. As ECS activity logs are a valuable source for understanding collaboration in the digital workplace, new methods and metrics for their analysis are required. This dissertation develops Social Collaboration Analytics (SCA) as a method for measuring and analysing collaboration activities in ECS. To address the existing limitations in academia and practice and to contribute a method and structures for applying SCA in practice, this dissertation aims to answer two main research questions: 1. What are the current practices for measuring collaboration activities in Enterprise Collaboration Systems? 2. How can Social Collaboration Analytics be implemented in practice? By answering the research questions, this dissertation seeks to (1) establish a broad thematic understanding of the research field of SCA and (2) to develop SCA as a structured method for analysing ac-tivity logs of ECS. As part of the first research question, this dissertation documents the status quo of SCA in the academic literature and practice. By answering the second research question, this dissertation contributes the SCA framework (SCAF), which guides the practical application of SCA. SCAF is the main contribution of this dissertation. The framework was developed based on findings from an analysis of 86 SCA studies, results from 6 focus groups and results from a survey among 27 ECS user companies. The phases of SCAF were derived from a comparison of established process models for data mining and business intelligence. The eight phases of the framework contain detailed descriptions, working steps, and guiding questions, which provide a step by step guide for the application of SCA in practice. Thus, academics and practitioners can benefit from using the framework. The constant evaluation of the research outcomes in focus groups ensures both rigour and relevance. This dissertation employs a qualitative-dominant mixed-methods approach. As part of the university-industry collaboration initiative IndustryConnect, this research has access to more than 30 leading ECS user companies. Being built on a key case study and a series of advanced focus groups with representatives of user companies, this dissertation can draw from unique insights from practice as well as rich data with a longitudinal perspective.

Within the field of Business Process Management, business rules are commonly used to model company decision logic and govern allowed company behavior. An exemplary business rule in the financial sector could be for example: ”A customer with a mental condition is not creditworthy”. Business rules are usually created and maintained collaboratively and over time. In this setting, modelling errors can occur frequently. A challenging problem in this context is that of inconsistency, i.e., contradictory rules which cannot hold at the same time. For instance, regarding the exemplary rule above, an inconsistency would arise if a (second) modeller entered an additional rule: ”A customer with a mental condition is always creditworthy”, as the two rules cannot hold at the same time. In this thesis, we investigate how to handle such inconsistencies in business rule bases. In particular, we develop methods and techniques for the detection, analysis and resolution of inconsistencies in business rule bases

Regarding the rising amount of legal regulations, businesses should get the opportunity to use software to fulfill their Compliance Management with the usage of compliance pattern. These patterns are used to represent substantive and structural parts of the processes. This means companies can increase their efficiency and react to new regulations quickly to avoid possible violation which can lead to monetary losses or legal consequences. In the literature are many approaches that deal with compliance pattern but currently there does not exist any list with necessary compliance pattern that companies should face at (Delfmann and Hübers, 2015). The following bachelor thesis classifies 80 research contributions regarding their different approaches of compliance pattern. For that a systematic literature review was executed. As a result, the author developed a graphical classification context that provides an overview of connections between different compliance approaches. Furthermore, an appendix with 32 compliance patterns of the analyzed papers was developed that contains real-world patterns with the classification of the previous sections.

To construct a business process model manually is a highly complex and error-prone task which takes a lot of time and deep insights into the organizational structure, its operations and business rules. To improve the output of business analysts dealing with this process, different techniques have been introduced by researchers to support them during construction with helpful recommendations. These supporting recommendation systems vary in their way of what to recommend in the first place as well as their calculations taking place under the hood to recommend the most fitting element to the user. After a broad introduction into the field of business process modeling and its basic recommendation structures, this work will take a closer look at diverse proposals and descriptions published in current literature regarding implementation strategies to effectively and efficiently assist modelers during their business process model creation. A critical analysis of presentations in the selected literature will point out strengths and weaknesses of their approaches, studies and descriptions of those. As a result, the final concept matrix in this work will give a precise and helpful overview about the key features and recommendation methods used and implemented in previous research studies to pinpoint an entry into future works without the downsides already spotted by fellow researchers.

Business Process Querying (BPQ) is a discipline in the field of Business Process Man- agement which helps experts to understand existing process models and accelerates the development of new ones. Its queries can fetch and merge these models, answer questions regarding the underlying process, and conduct compliance checking in return. Many languages have been deployed in this discipline but two language types are dominant: Logic-based languages use temporal logic to verify models as finite state machines whereas graph-based languages use pattern matching to retrieve subgraphs of model graphs directly. This thesis aims to map the features of both language types to features of the other to identify strengths and weaknesses. Exemplarily, the features of Computational Tree Logic (CTL) and The Diagramed Modeling Language (DMQL) are mapped to one another. CTL explores the valid state space and thus is better for behavioral querying. Lacking certain structural features and counting mechanisms it is not appropriate to query structural properties. In contrast, DMQL issues structural queries and its patterns can reconstruct any CTL formula. However, they do not always achieve exactly the same semantic: Patterns treat conditional flow as sequential flow by ignoring its conditions. As a result, retrieved mappings are invalid process execution sequences, i.e. false positives, in certain scenarios. DMQL can be used for behavioral querying if these are absent or acceptable. In conclusion, both language types have strengths and are specialized for different BPQ use cases but in certain scenarios graph-based languages can be applied to both. Integrating the evaluation of conditions would remove the need for logic-based languages in BPQ completely.