18,170 research outputs found
Quality-optimized predictive analytics
On-line statistical and machine learning analytic tasks over large-
scale contextual data streams coming from e.g., wireless sensor networks, Inter-
net of Things environments, have gained high popularity nowadays due to their
significance in knowledge extraction, regression and classification tasks, and,
more generally, in making sense from large-scale streaming data. The quality
of the received contextual information, however, impacts predictive analytics
tasks especially when dealing with uncertain data, outliers data, and data con-
taining missing values. Low quality of received contextual data significantly
spoils the progressive inference and on-line statistical reasoning tasks, thus,
bias is introduced in the induced knowledge, e.g., classification and decision
making. To alleviate such situation, which is not so rare in real time contextual
information processing systems, we propose a progressive time-optimized data
quality-aware mechanism, which attempts to deliver contextual information
of high quality to predictive analytics engines by progressively introducing a
certain controlled delay. Such a mechanism progressively delivers high qual-
ity data as much as possible, thus eliminating possible biases in knowledge
extraction and predictive analysis tasks. We propose an analytical model for
this mechanism and show the benefits stem from this approach through com-
prehensive experimental evaluation and comparative assessment with quality-
unaware methods over real sensory multivariate contextual data
Edge-centric inferential modeling & analytics
This work contributes to a real-time, edge-centric inferential modeling and analytics methodology introducing the fundamental mechanisms for (i) predictive models update and (ii) diverse models selection in distributed computing. Our objective in edge-centric analytics is the time-optimized model caching and selective forwarding at the network edge adopting optimal stopping theory, where communication overhead is significantly reduced as only inferred knowledge and sufficient statistics are delivered instead of raw data obtaining high quality of analytics. Novel model selection algorithms are introduced to fuse the inherent models' diversity over distributed edge nodes to support inferential analytics tasks to end-users/analysts, and applications in real-time. We provide statistical learning modeling and establish the corresponding mathematical analyses of our mechanisms along with comprehensive performance and comparative assessment using real data from different domains and showing its benefits in edge computing
Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm
Industry 4.0 aims at achieving mass customization at a
mass production cost. A key component to realizing this is accurate
prediction of customer needs and wants, which is however a
challenging issue due to the lack of smart analytics tools. This
paper investigates this issue in depth and then develops a predictive
analytic framework for integrating cloud computing, big data
analysis, business informatics, communication technologies, and
digital industrial production systems. Computational intelligence
in the form of a cluster k-means approach is used to manage
relevant big data for feeding potential customer needs and wants
to smart designs for targeted productivity and customized mass
production. The identification of patterns from big data is achieved
with cluster k-means and with the selection of optimal attributes
using genetic algorithms. A car customization case study shows
how it may be applied and where to assign new clusters with
growing knowledge of customer needs and wants. This approach
offer a number of features suitable to smart design in realizing
Industry 4.0
Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments
Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment
- …