194,749 research outputs found
Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making
The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study
Specification-Driven Predictive Business Process Monitoring
Predictive analysis in business process monitoring aims at forecasting the
future information of a running business process. The prediction is typically
made based on the model extracted from historical process execution logs (event
logs). In practice, different business domains might require different kinds of
predictions. Hence, it is important to have a means for properly specifying the
desired prediction tasks, and a mechanism to deal with these various prediction
tasks. Although there have been many studies in this area, they mostly focus on
a specific prediction task. This work introduces a language for specifying the
desired prediction tasks, and this language allows us to express various kinds
of prediction tasks. This work also presents a mechanism for automatically
creating the corresponding prediction model based on the given specification.
Differently from previous studies, instead of focusing on a particular
prediction task, we present an approach to deal with various prediction tasks
based on the given specification of the desired prediction tasks. We also
provide an implementation of the approach which is used to conduct experiments
using real-life event logs.Comment: This article significantly extends the previous work in
https://doi.org/10.1007/978-3-319-91704-7_7 which has a technical report in
arXiv:1804.00617. This article and the previous work have a coauthor in
commo
Model-driven performance evaluation for service engineering
Service engineering and service-oriented architecture as an
integration and platform technology is a recent approach to software systems integration. Software quality aspects such as performance are of central importance for the integration of heterogeneous, distributed service-based systems. Empirical performance evaluation is a process of
measuring and calculating performance metrics of the implemented software. We present an approach for the empirical, model-based performance evaluation of services and service compositions in the context of model-driven service engineering. Temporal databases theory is utilised
for the empirical performance evaluation of model-driven developed service systems
Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud
With the advent of cloud computing, organizations are nowadays able to react
rapidly to changing demands for computational resources. Not only individual
applications can be hosted on virtual cloud infrastructures, but also complete
business processes. This allows the realization of so-called elastic processes,
i.e., processes which are carried out using elastic cloud resources. Despite
the manifold benefits of elastic processes, there is still a lack of solutions
supporting them.
In this paper, we identify the state of the art of elastic Business Process
Management with a focus on infrastructural challenges. We conceptualize an
architecture for an elastic Business Process Management System and discuss
existing work on scheduling, resource allocation, monitoring, decentralized
coordination, and state management for elastic processes. Furthermore, we
present two representative elastic Business Process Management Systems which
are intended to counter these challenges. Based on our findings, we identify
open issues and outline possible research directions for the realization of
elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and
P. Hoenisch (2015). Elastic Business Process Management: State of the Art and
Open Challenges for BPM in the Cloud. Future Generation Computer Systems,
Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00
- …