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Wenny Rahayu

Other affiliations: Victoria University, Australia
Bio: Wenny Rahayu is an academic researcher from La Trobe University. The author has contributed to research in topics: Efficient XML Interchange & XML Schema Editor. The author has an hindex of 31, co-authored 275 publications receiving 5015 citations. Previous affiliations of Wenny Rahayu include Victoria University, Australia.


Papers
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Journal ArticleDOI
TL;DR: This paper provides an extensive survey of mobile cloud computing research, while highlighting the specific concerns in mobile cloud Computing, and presents a taxonomy based on the key issues in this area, and discusses the different approaches taken to tackle these issues.

1,671 citations

Journal ArticleDOI
Wenjin Yu1, Tharam S. Dillon1, Fahed Mostafa1, Wenny Rahayu1, Yuehua Liu1 
TL;DR: A big data ecosystem is presented for the implementation of fault detection and diagnosis in predictive maintenance with real industrial big data gathered directly from large-scale global manufacturing plants, aiming to provide a complete architecture which could be used in industrial IoT-based smart manufacturing in an industrial 4.0 system.
Abstract: Artificial intelligence, big data, machine learning, cloud computing, and Internet of Things (IoT) are terms which have driven the fourth industrial revolution. The digital revolution has transformed the manufacturing industry into smart manufacturing through the development of intelligent systems. In this paper, a big data ecosystem is presented for the implementation of fault detection and diagnosis in predictive maintenance with real industrial big data gathered directly from large-scale global manufacturing plants, aiming to provide a complete architecture which could be used in industrial IoT-based smart manufacturing in an industrial 4.0 system. The proposed architecture overcomes multiple challenges including big data ingestion, integration, transformation, storage, analytics, and visualization in a real-time environment using various technologies such as the data lake, NoSQL database, Apache Spark, Apache Drill, Apache Hive, OPC Collector, and other techniques. Transformation protocols, authentication, and data encryption methods are also utilized to address data and network security issues. A MapReduce-based distributed PCA model is designed for fault detection and diagnosis. In a large-scale manufacturing system, not all kinds of failure data are accessible, and the absence of labels precludes all the supervised methods in the predictive phase. Furthermore, the proposed framework takes advantage of some of the characteristics of PCA such as its ease of implementation on Spark, its simple algorithmic structure, and its real-time processing ability. All these elements are essential for smart manufacturing in the evolution to Industry 4.0. The proposed detection system has been implemented into the real-time industrial production system in a cooperated company, running for several years, and the results successfully provide an alarm warning several days before the fault happens. A test case involving several outages in 2014 is reported and analyzed in detail during the experiment section.

127 citations

Book
13 Oct 2008
TL;DR: This book targets the theoretical/conceptual details needed to form a base of understanding and then delivers information on development, implementations, and analytical modeling of parallel databases, including key information on new developments with grid databases.
Abstract: This book targets the theoretical/conceptual details needed to form a base of understanding and then delivers information on development, implementations, and analytical modeling of parallel databases. It includes key information on new developments with grid databases. Also uses a theoretical and practical balance to support in-depth study of parallel query processing offered by modern DBMS as well as hands on experience of parallel query algorithms development, implementation, and analysis.

122 citations

Posted Content
TL;DR: The state of the art in privacy issues and solutions for machine learning is surveyed and future research directions in this field are pointed out.
Abstract: The newly emerged machine learning (e.g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning (ML) is still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This paper surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.

119 citations

Journal ArticleDOI
TL;DR: The proposed approach does not need to divide the query path into segments, hence improving the overall query processing performance, and the applicability of the VCKNN approach to solve CKNN queries was verified.
Abstract: Digital ecosystems are formed by “digital organisms” in complex, dynamic, and interrelated ecosystems and utilize multiple technologies to provide cost-efficient digital services and value-creating activities. A distributed wireless mobile network that serves as the underlying infrastructure to digital ecosystems provides important applications to the digital ecosystems, two of which are mobile navigation and continuous mobile information services. Most information and query services in a mobile environment are continuous mobile query processing or continuous k nearest neighbor (CKNN), which finds the locations where interest points or interest objects change while mobile users are moving. These locations are known as “split nodes.” All of the existing works on CKNN divide the query path into segments, which is a segment of road separated by two intersections, and then, the process to find split nodes is applied to each segment. Since there are many segments (due to many intersections, obviously), processing each segment is naturally inefficient. In this paper, we propose an alternative solution to overcome this problem. We use the Voronoi diagram for CKNN [called Voronoi CKNN (VCKNN)]. Our proposed approach does not need to divide the query path into segments, hence improving the overall query processing performance. Our experiment verified the applicability of the VCKNN approach to solve CKNN queries.

115 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 1964
TL;DR: In this paper, the notion of a collective unconscious was introduced as a theory of remembering in social psychology, and a study of remembering as a study in Social Psychology was carried out.
Abstract: Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

5,690 citations

Journal ArticleDOI
TL;DR: The definition, characteristics, and classification of big data along with some discussions on cloud computing are introduced, and research challenges are investigated, with focus on scalability, availability, data integrity, data transformation, data quality, data heterogeneity, privacy, legal and regulatory issues, and governance.

2,141 citations

Proceedings ArticleDOI
10 Apr 2011
TL;DR: The design and implementation of CloneCloud is presented, a system that automatically transforms mobile applications to benefit from the cloud that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud.
Abstract: Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity with more powerful machines ranging from laptops and desktops to commercial clouds. This paper presents the design and implementation of CloneCloud, a system that automatically transforms mobile applications to benefit from the cloud. The system is a flexible application partitioner and execution runtime that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud. CloneCloud uses a combination of static analysis and dynamic profiling to partition applications automatically at a fine granularity while optimizing execution time and energy use for a target computation and communication environment. At runtime, the application partitioning is effected by migrating a thread from the mobile device at a chosen point to the clone in the cloud, executing there for the remainder of the partition, and re-integrating the migrated thread back to the mobile device. Our evaluation shows that CloneCloud can adapt application partitioning to different environments, and can help some applications achieve as much as a 20x execution speed-up and a 20-fold decrease of energy spent on the mobile device.

2,054 citations

Journal ArticleDOI
TL;DR: A general probable 5G cellular network architecture is proposed, which shows that D2D, small cell access points, network cloud, and the Internet of Things can be a part of 5G Cellular network architecture.
Abstract: In the near future, i.e., beyond 4G, some of the prime objectives or demands that need to be addressed are increased capacity, improved data rate, decreased latency, and better quality of service. To meet these demands, drastic improvements need to be made in cellular network architecture. This paper presents the results of a detailed survey on the fifth generation (5G) cellular network architecture and some of the key emerging technologies that are helpful in improving the architecture and meeting the demands of users. In this detailed survey, the prime focus is on the 5G cellular network architecture, massive multiple input multiple output technology, and device-to-device communication (D2D). Along with this, some of the emerging technologies that are addressed in this paper include interference management, spectrum sharing with cognitive radio, ultra-dense networks, multi-radio access technology association, full duplex radios, millimeter wave solutions for 5G cellular networks, and cloud technologies for 5G radio access networks and software defined networks. In this paper, a general probable 5G cellular network architecture is proposed, which shows that D2D, small cell access points, network cloud, and the Internet of Things can be a part of 5G cellular network architecture. A detailed survey is included regarding current research projects being conducted in different countries by research groups and institutions that are working on 5G technologies.

1,899 citations