Refine
Year of publication
- 2019 (4) (remove)
Document Type
- Bachelor Thesis (4) (remove)
Keywords
- BPM (1)
- Business Process Modeling (1)
- Empfehlungssystem (1)
- Probabilistic finite automata (1)
- Recommender System (1)
Institute
- Institut für Wirtschafts- und Verwaltungsinformatik (4) (remove)
The main goal of this paper is to ascertain, if neural networks (especially LSTM) are helpful in predicting processes by making predictions as accurately as possible.
TensorFlow is the used framework in Python to build recurrent neural networks. Two networks are built, whereby one is used for training and the other one for prediction.
Used datasets contain several processes with several events each. With those processes, the network ist trained and afterwards, the parameters are saved. The network for prediction uses these parameters to make predictions.
The neural network is able to make clear predictions about subsequent events. Even branches can be predicted.
When developed further, integration in other programs is possible. It is recommended to use unique names for the events or to rename them.
The Internet of Things is still one of the most relevant topics in the field of economics and research powered by the increasing demand of innovative services. Cost reductions in manufacturing of IoT hardware and the development of completely new communication ways has led to the point of bil-lions of devices connected to the internet. But in order to rule this new IoT landscape a standardized solution to conquer these challenges must be developed, the IoT Architecture.
This thesis examines the structure, purpose and requirements of IoT Architecture Models in the global IoT landscape and proposes an overview across the selected ones. For that purpose, a struc-tured literature analysis on this topic is conducted within this thesis, including an analysis on three existing research approaches trying to frame this topic and a tool supported evaluation of IoT Archi-tecture literature with over 200 accessed documents.
Furthermore, a coding of literature with the help of the specialised coding tool ATLAS.ti 8 is conduct-ed on 30 different IoT Architecture Models. In a final step these Architecture Models are categorized and compared to each other showing that the environment of IoT and its Architectures gets even more complex the further the research goes.
Social Network of Business Objects (SoNBO) is a concept for aggregating information distributed in he-terogeneous system landscapes and making it available via a single user interface. The central idea is to understand company information as a network (graph). There is already a SoNBO-Explorer which integrates the information of a customer relationship management system (CRM system). The challenge in configuring such an application is to identify the corporate network and thus find out how the stored data is linked within the company. A tool that can visualize the corporate network is helpful for this. In this thesis a selfdeveloped tool (SoNBO-Graph-App) is presented as a prototype, which realizes this visualization. With this application the configuration of the network in the SoNBO Explorer consisting of the merged data can be supported by carrying out that configuration on a graphical level. The prototype is connected to two different databases of a Customer Relationship Management (CRM) system and allows the aggregation of these data so that it is displayed as a graph in an overview. This gives the user a better insight and understanding of the relationship between the different data. This work is part of the longterm research project SoNBO, whose goal is a concept for the integration of information from different business application systems.
The goal of this thesis is to create a recommender system (RS) for business processes, based on the existing ProM plugin RegPFA. To accomplish this task, firstly an interface must be created that sets up and expands a database receiving probabilistic finite automata (PFA) created by RegPFA in tsml format as input. Secondly, a Java program must be designed that uses said database to recommend the process elements that are most likely to follow a given sequence of process elements.