74,782 research outputs found
Extending Two-level Information Modeling to the Internet of Things
Interoperability is a major challenge for the Internet of Things (IoT). The real potential of the IoT lies in facilitating largescale sharing of high-quality context-rich information through systems-of-IoT-systems, rather than IoT systems that operate as isolated technology silos. Real large-scale interoperability requires layers of standards, and each layer addresses different interoperability challenges. The SensorThings API data model seeks to tackle data interoperability at the data and informational layers of IoT platforms. SensorThings API is aligned to the ISO/OGC O&M data standard, and like O&M it is semistructured. Semi-structured models allow for variance within implementations for different use-cases, which is both necessary and detrimental to systems interoperability. In this paper we propose that the SensorThings API data model should be defined as a set of archetypes, used to capture extensible domain concepts using a two-level modeling IoT systems design approach. Extending two-level modeling to the IoT using the SensorThings API as a base for domain concepts definition allows for a powerful framework to manage variance within systems implementation and maintaining semantic interoperability within systems-of-IoT-systems across diverse use-cases
Modeling IoT-aware Business Processes - A State of the Art Report
This research report presents an analysis of the state of the art of modeling
Internet of Things (IoT)-aware business processes. IOT links the physical world
to the digital world. Traditionally, we would find information about events and
processes in the physical world in the digital world entered by humans and
humans using this information to control the physical world. In the IoT
paradigm, the physical world is equipped with sensors and actuators to create a
direct link with the digital world. Business processes are used to coordinate a
complex environment including multiple actors for a common goal, typically in
the context of administrative work. In the past few years, we have seen
research efforts on the possibilities to model IoT- aware business processes,
extending process coordination to real world entities directly. This set of
research efforts is relatively small when compared to the overall research
effort into the IoT and much of the work is still in the early research stage.
To create a basis for a bridge between IoT and BPM, the goal of this report is
to collect and analyze the state of the art of existing frameworks for modeling
IoT-aware business processes.Comment: 42 page
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Process Mining of Programmable Logic Controllers: Input/Output Event Logs
This paper presents an approach to model an unknown Ladder Logic based
Programmable Logic Controller (PLC) program consisting of Boolean logic and
counters using Process Mining techniques. First, we tap the inputs and outputs
of a PLC to create a data flow log. Second, we propose a method to translate
the obtained data flow log to an event log suitable for Process Mining. In a
third step, we propose a hybrid Petri net (PN) and neural network approach to
approximate the logic of the actual underlying PLC program. We demonstrate the
applicability of our proposed approach on a case study with three simulated
scenarios
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
- …