12,229 research outputs found
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
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
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent âdevicesâ, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew âcognitive devicesâ are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
Real-time analytics that requires integration and aggregation of
heterogeneous and distributed streaming and static data is a typical task in
many industrial scenarios such as diagnostics of turbines in Siemens. OBDA
approach has a great potential to facilitate such tasks; however, it has a
number of limitations in dealing with analytics that restrict its use in
important industrial applications. Based on our experience with Siemens, we
argue that in order to overcome those limitations OBDA should be extended and
become analytics, source, and cost aware. In this work we propose such an
extension. In particular, we propose an ontology, mapping, and query language
for OBDA, where aggregate and other analytical functions are first class
citizens. Moreover, we develop query optimisation techniques that allow to
efficiently process analytical tasks over static and streaming data. We
implement our approach in a system and evaluate our system with Siemens turbine
data
Data Science and Analytics in Industrial Maintenance: Selection, Evaluation, and Application of Data-Driven Methods
Data-driven maintenance bears the potential to realize various benefits based on multifaceted data assets generated in increasingly digitized industrial environments. By taking advantage of modern methods and technologies from the field of data science and analytics (DSA), it is possible, for example, to gain a better understanding of complex technical processes and to anticipate impending machine faults and failures at an early stage. However, successful implementation of DSA projects requires multidisciplinary expertise, which can rarely be covered by individual employees or single units within an organization. This expertise covers, for example, a solid understanding of the domain, analytical method and modeling skills, experience in dealing with different source systems and data structures, and the ability to transfer suitable solution approaches into information systems. Against this background, various approaches have emerged in recent years to make the implementation of DSA projects more accessible to broader user groups. These include structured procedure models, systematization and modeling frameworks, domain-specific benchmark studies to illustrate best practices, standardized DSA software solutions, and intelligent assistance systems.
The present thesis ties in with previous efforts and provides further contributions for their continuation. More specifically, it aims to create supportive artifacts for the selection, evaluation, and application of data-driven methods in the field of industrial maintenance. For this purpose, the thesis covers four artifacts, which were developed in several publications. These artifacts include (i) a comprehensive systematization framework for the description of central properties of recurring data analysis problems in the field of industrial maintenance, (ii) a text-based assistance system that offers advice regarding the most suitable class of analysis methods based on natural language and domain-specific problem descriptions, (iii) a taxonomic evaluation framework for the systematic assessment of data-driven methods under varying conditions, and (iv) a novel solution approach for the development of prognostic decision models in cases of missing label information.
Individual research objectives guide the construction of the artifacts as part of a systematic research design. The findings are presented in a structured manner by summarizing the results of the corresponding publications. Moreover, the connections between the developed artifacts as well as related work are discussed. Subsequently, a critical reflection is offered concerning the generalization and transferability of the achieved results. Thus, the thesis not only provides a contribution based on the proposed artifacts; it also paves the way for future opportunities, for which a detailed research agenda is outlined.:List of Figures
List of Tables
List of Abbreviations
1 Introduction
1.1 Motivation
1.2 Conceptual Background
1.3 Related Work
1.4 Research Design
1.5 Structure of the Thesis
2 Systematization of the Field
2.1 The Current State of Research
2.2 Systematization Framework
2.3 Exemplary Framework Application
3 Intelligent Assistance System for Automated Method Selection
3.1 Elicitation of Requirements
3.2 Design Principles and Design Features
3.3 Prototypical Instantiation and Evaluation
4 Taxonomic Framework for Method Evaluation
4.1 Survey of Prognostic Solutions
4.2 Taxonomic Evaluation Framework
4.3 Exemplary Framework Application
5 Method Application Under Industrial Conditions
5.1 Conceptualization of a Solution Approach
5.2 Prototypical Implementation and Evaluation
6 Discussion of the Results
6.1 Connections Between Developed Artifacts and Related Work
6.2 Generalization and Transferability of the Results
7 Concluding Remarks
Bibliography
Appendix I: Implementation Details
Appendix II: List of Publications
A Publication P1: Focus Area Systematization
B Publication P2: Focus Area Method Selection
C Publication P3: Focus Area Method Selection
D Publication P4: Focus Area Method Evaluation
E Publication P5: Focus Area Method ApplicationDatengetriebene Instandhaltung birgt das Potential, aus den in Industrieumgebungen vielfĂ€ltig anfallenden Datensammlungen unterschiedliche Nutzeneffekte zu erzielen. Unter Verwendung von modernen Methoden und Technologien aus dem Bereich Data Science und Analytics (DSA) ist es beispielsweise möglich, das Verhalten komplexer technischer Prozesse besser nachzuvollziehen oder bevorstehende MaschinenausfĂ€lle und Fehler frĂŒhzeitig zu erkennen. Eine erfolgreiche Umsetzung von DSA-Projekten erfordert jedoch multidisziplinĂ€res Expertenwissen, welches sich nur selten von einzelnen Personen bzw. Einheiten innerhalb einer Organisation abdecken lĂ€sst. Dies umfasst beispielsweise ein fundiertes DomĂ€nenverstĂ€ndnis, Kenntnisse ĂŒber zahlreiche Analysemethoden, Erfahrungen im Umgang mit verschiedenen Quellsystemen und Datenstrukturen sowie die FĂ€higkeit, geeignete LösungsansĂ€tze in Informationssysteme zu ĂŒberfĂŒhren. Vor diesem Hintergrund haben sich in den letzten Jahren verschiedene AnsĂ€tze herausgebildet, um die DurchfĂŒhrung von DSA-Projekten fĂŒr breitere Anwendergruppen zugĂ€nglich zu machen. Dazu gehören strukturierte Vorgehensmodelle, Systematisierungs- und Modellierungsframeworks, domĂ€nenspezifische Benchmark-Studien zur Veranschaulichung von Best Practices, Standardlösungen fĂŒr DSA-Software und intelligente Assistenzsysteme.
An diese Arbeiten knĂŒpft die vorliegende Dissertation an und liefert weitere Artefakte, um insbesondere die Selektion, Evaluation und Anwendung datengetriebener Methoden im Bereich der industriellen Instandhaltung zu unterstĂŒtzen. Insgesamt erstreckt sich die Abhandlung auf vier Artefakte, die in einzelnen Publikationen erarbeitet wurden. Dies umfasst (i) ein umfangreiches Systematisierungsframework zur Beschreibung zentraler AusprĂ€gungen wiederkehrender Datenanalyseprobleme im Bereich der industriellen Instandhaltung, (ii) ein textbasiertes Assistenzsystem, welches ausgehend von natĂŒrlichsprachlichen und domĂ€nenspezifischen Problembeschreibungen eine geeignete Klasse von Analysemethoden vorschlĂ€gt, (iii) ein taxonomisches Evaluationsframework zur systematischen Bewertung von datengetriebenen Methoden unter verschiedenen Rahmenbedingungen sowie (iv) einen neuartigen Lösungsansatz zur Entwicklung von prognostischen Entscheidungsmodellen im Fall von eingeschrĂ€nkter Informationslage.
Die Konstruktion der Artefakte wird durch einzelne Forschungsziele im Rahmen eines systematischen Forschungsdesigns angeleitet. Neben der Darstellung der einzelnen ForschungsbeitrĂ€ge unter Bezugnahme auf die erzielten Ergebnisse der dazugehörigen Publikationen werden auch die Verbindungen zwischen den entwickelten Artefakten beleuchtet und ZusammenhĂ€nge zu angrenzenden Arbeiten hergestellt. Zudem erfolgt eine kritische Reflektion der Ergebnisse hinsichtlich ihrer Verallgemeinerung und Ăbertragung auf andere Rahmenbedingungen. Dadurch liefert die vorliegende Abhandlung nicht nur einen Beitrag anhand der erzeugten Artefakte, sondern ebnet auch den Weg fĂŒr fortfĂŒhrende Forschungsarbeiten, wofĂŒr eine detaillierte Forschungsagenda erarbeitet wird.:List of Figures
List of Tables
List of Abbreviations
1 Introduction
1.1 Motivation
1.2 Conceptual Background
1.3 Related Work
1.4 Research Design
1.5 Structure of the Thesis
2 Systematization of the Field
2.1 The Current State of Research
2.2 Systematization Framework
2.3 Exemplary Framework Application
3 Intelligent Assistance System for Automated Method Selection
3.1 Elicitation of Requirements
3.2 Design Principles and Design Features
3.3 Prototypical Instantiation and Evaluation
4 Taxonomic Framework for Method Evaluation
4.1 Survey of Prognostic Solutions
4.2 Taxonomic Evaluation Framework
4.3 Exemplary Framework Application
5 Method Application Under Industrial Conditions
5.1 Conceptualization of a Solution Approach
5.2 Prototypical Implementation and Evaluation
6 Discussion of the Results
6.1 Connections Between Developed Artifacts and Related Work
6.2 Generalization and Transferability of the Results
7 Concluding Remarks
Bibliography
Appendix I: Implementation Details
Appendix II: List of Publications
A Publication P1: Focus Area Systematization
B Publication P2: Focus Area Method Selection
C Publication P3: Focus Area Method Selection
D Publication P4: Focus Area Method Evaluation
E Publication P5: Focus Area Method Applicatio
Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing
Drilling activities in the oil and gas industry have been reported over
decades for thousands of wells on a daily basis, yet the analysis of this text
at large-scale for information retrieval, sequence mining, and pattern analysis
is very challenging. Drilling reports contain interpretations written by
drillers from noting measurements in downhole sensors and surface equipment,
and can be used for operation optimization and accident mitigation. In this
initial work, a methodology is proposed for automatic classification of
sentences written in drilling reports into three relevant labels (EVENT,
SYMPTOM and ACTION) for hundreds of wells in an actual field. Some of the main
challenges in the text corpus were overcome, which include the high frequency
of technical symbols, mistyping/abbreviation of technical terms, and the
presence of incomplete sentences in the drilling reports. We obtain
state-of-the-art classification accuracy within this technical language and
illustrate advanced queries enabled by the tool.Comment: 7 pages, 14 figures, technical repor
- âŠ