Pascal Hirmer

FlexMash – Flexible Data Mashups Based on Pattern-Based Model Transformation

Today, the ad-hoc processing and integration of data is an important issue due to fast growing IT systems and an increased connectivity of the corresponding data sources. The overall goal is deriving high-level information based on a huge amount of low-level data. However, an increasing amount of data leads to high complexity and many technical challenges. Especially non-IT expert users are overburdened with highly complex solutions such as Extract-Transform-Load processes. To tackle these issues, we need a means to abstract from technical details and provide a flexible execution of data processing and integration scenarios. In this paper, we present an approach for modeling and pattern-based execution of data mashups based on Mashup Plans, a domain-specific mashup model that has been introduced in previous work. This non-executable model can be mapped onto different executable ones depending on the use case scenario. The concepts introduced in this paper were presented during the Rapid Mashup Challenge at the International Conference on Web Engineering 2015. This paper presents our approach, the scenario that was implemented for this challenge, as well as the encountered issues during its preparation.

The Social Factory: Connecting People, Machines and Data in Manufacturing for Context-Aware Exception Escalation

Manufacturing environments are socio-technical systems where people have to interact with machines to achieve a common goal. The goal of the fourth industrial revolution is to improve their flexibility for mass customization and rapidly changing production conditions. As a contribution towards this goal, we introduce the Social Factory: a social network with a powerful analytics backend to improve the connection between the persons working in the production environment, the manufacturing machines, and the data that is created in the process. We represent machines, people and chatbots for information provisioning as abstract users in the social network. We enable natural language based communication between them and provide a rich knowledge base and automated problem solution suggestions. Access to complex production environments thus becomes intuitive, cooperation among users improves and problems are resolved more easily.

Automating the Provisioning and Configuration of Devices in the Internet of Things

The Internet of Things benefits from an increasing number of interconnected technical devices. This has led to the existence of so-called smart environments, which encompass one or more devices sensing, acting, and automatically performing different tasks to enable their self-organization. Smart environments are divided into two parts: the physical environment and its digital representation, oftentimes referred to as digital twin. However, the automated binding and monitoring of devices of smart environments are still major issues. In this article we present a method and system architecture to cope with these challenges by enabling (i) easy modeling of sensors, actuators, devices, and their attributes, (ii) dynamic device binding based on their type, (iii) the access to devices using different paradigms, and (iv) the monitoring of smart environments in regard to failures or changes. We furthermore provide a prototypical implementation of the introduced approach.

Extended Techniques for Flexible Modeling and Execution of Data Mashups

Today, a multitude of highly-connected applications and information systems hold, consume and produce huge amounts of heterogeneous data. The overall amount of data is even expected to dramatically increase in the future. In order to conduct, e.g., data analysis, visualizations or other value-adding scenarios, it is necessary to integrate specific, relevant parts of data into a common source. Due to oftentimes changing environments and dynamic requests, this integration has to support ad-hoc and flexible data processing capabilities. Furthermore, an iterative and explorative trial-and-error integration based on different data sources has to be possible. To cope with these requirements, several data mashup platforms have been developed in the past. However, existing solutions are mostly non-extensible, monolithic systems or applications with many limitations regarding the mentioned requirements. In this paper, we introduce an approach that copes with these issues (i) by the introduction of patterns to enable decoupling from implementation details, (ii) by a cloud-ready approach to enable availability and scalability, and (iii) by a high degree of flexibility and extensibility that enables the integration of heterogeneous data as well as dynamic (un-)tethering of data sources. We evaluate our approach using runtime measurements of our prototypical implementation.

A situation-aware workflow modelling extension

The automation of business processes is of vital importance for organizations to speed up their business and to lower costs. Due to emerging technologies in the field of Internet of Things, changing situations can be recognized automatically, which provides the basis for an automated adaptation of process executions in order to react to changing circumstances. Although approaches exist that enable creating self-adapting workflows, a systematic modelling approach that supports the specification of situational dependencies directly in workflow models is missing. In this paper, we tackle this issue by presenting a modelling extension called SitME that defines (i) an extensible Situation Event type, (ii) the concept of Situational Scopes, and (iii) a visual notation. As the introduced extension is language-independent, we apply the approach to BPEL to validate its practical feasibility.

TOSCA4Mashups: enhanced method for on-demand data mashup provisioning

Nowadays, the amount of data increases tremendously. Extracting information and generating knowledge from this data is a great challenge. To cope with this issue – oftentimes referred to as big data problem – we need effective means for efficient data integration, data processing, and data analysis. To enable flexible, explorative and ad-hoc data processing, several data mashup approaches and tools have been developed in the past. One of these tools is FlexMash – a data mashup tool developed at the University of Stuttgart. By offering domain-specific graphical modeling as well as a pattern-based execution, FlexMash enables usage by a wide range of users, both domain experts and technical experts. The core idea of FlexMash is a flexible execution of data mashups using different, user-requirement-dependent execution components. In this paper, we present a new approach for on-demand, automated provisioning of these components in a cloud computing environment using the Topology and Orchestration Specification for Cloud Applications. This enables many advantages for mashup execution such as scalability, availability and cost savings.

Dynamic Ontology-Based Sensor Binding

In recent years, the Internet of Things gains more and more attention through cheap hardware devices and, consequently, an increased interconnection of them. These devices equipped with sensors and actuators form the foundation for so called smart environments that enable monitoring as well as self-organization. However, an efficient sensor registration, binding, and sensor data provisioning is still a major issue for the Internet of Things. Usually, these steps can take up to days or even weeks due to a manual configuration and binding by sensor experts that furthermore have to communicate with domain-experts that define the requirements, e.g. the types of sensors, for the smart environments. In previous work, we introduced a first vision of a method for automated sensor registration, binding, and sensor data provisioning. In this paper, we further detai l and extend this vision, e.g., by introducing optimization steps to enhance efficiency as well as effectiveness. Furthermore, the approach is evaluated through a prototypical implementation.

FlexMash 2.0 – Flexible Modeling and Execution of Data Mashups

In recent years, the amount of data highly increases through cheap hardware, fast network technology, and the increasing digitization within most domains. The data produced is oftentimes heterogeneous, dynamic and originates from many highly distributed data sources. Deriving information and, as a consequence, knowledge from this data can lead to a higher effectiveness for problem solving and thus higher profits for companies. However, this is a great challenge – oftentimes referred to as Big Data problem. The data mashup tool FlexMash, developed at the University of Stuttgart, tackles this challenge by offering a means for integration and processing of heterogeneous, dynamic data sources. By doing so, FlexMash focuses on (i) an easy means to model data integration and processing scenarios by domain-experts based on the Pipes and Filters pattern, (ii) a flexible execution based on the user’s non-functional requirements, and (iii) high extensibility to enable a generic approach. A first version of this tool was presented during the ICWE Rapid Mashup Challenge 2015. In this article, we present the new version FlexMash 2.0, which introduces new features such as cloud-based execution and human interaction during runtime. These concepts have been presented during the ICWE Rapid Mashup Challenge 2016.

Towards Interactive Data Processing and Analytics - Putting the Human in the Center of the Loop.

Today, it is increasingly important for companies to evaluate data and use the information contained. In practice, this is however a great challenge, especially for domain users that lack the necessary technical knowledge. However, analyses prefabricated by technical experts do not provide the necessary flexibility and are oftentimes only implemented by the IT department if there is sufficient demand. Concepts like Visual Analytics or Self-Service Business Intelligence involve the user in the analysis process and try to reduce the technical requirements. However, these approaches either only cover specific application areas or they do not consider the entire analysis process. In this paper, we present an extended Visual Analytics process, which puts the user at the center of the analysis. Based on a use case scenario, requirements for this process are determined and, later on, a possible application for this scenario is discussed that emphasizes the benefits of our approach.

Customization and provisioning of complex event processing using TOSCA

In many Internet of Things scenarios, a large amount of sensor data is continuously produced and exchanged among devices in smart environments. Handling these data causes great challenges since their processing must occur in a timely manner. Furthermore, the exchanged data must be kept at a minimum in order to efficiently use network resources of constrained devices. Complex event processing (CEP) is an established approach frequently employed to address these challenges. There are many existing means to automatically provision CEP systems, such as Docker or Ansible. However, the resulting instances are generic and need customization to be applied to Internet of Things scenarios. More precisely, data sources and sinks need to be bound and queries need to be defined. This customization is a tedious task when conducted manually. To cope with these issues, we present an approach based on the Topology and Orchestration Specification for Cloud Applications standard, which enables the customization and provisioning of CEP systems including all required data sources and sinks, as well as queries to process the data.