Conferences related to IEEE Transactions on Knowledge and Data Engineering

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2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) is a premier international forum for scientists and researchers to present the state of the art of data mining and intelligent methods inspired from nature, particularly biological, linguistic, and physical systems, with applications to computers, circuits, systems, control, robotics, communications, and more.

  • 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

    International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) is a premier international forum for scientists and researchers to present the state of the art of data mining and intelligent methods inspired from nature, particularly biological, linguistic, and physical systems, with applications to computers, circuits, systems, control, robotics, communications, and more.

  • 2016 12th International Conference on Natural Computation and 13th Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

    International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) is a premier international forum for scientists and researchers to present the state of the art of data mining and intelligent methods inspired from nature, particularly biological, linguistic, and physical systems, with applications to computers, circuits, systems, control, robotics, communications, and more.

  • 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy sets, Bioinformatics and Bio-Medical Informatics, Genomics, Proteomics, Big Data, Databases and Applications, Semi-Structured/Unstructured Data Mining, Multimedia Mining, Web and Text Data Mining, Graphic Model Discovery, Data Warehousing and OLAP, Pattern Recognition and Diagnostics, etc..

  • 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy sets, rough sets, Statistical methods, Parallel/ Distributed data mining, KDD Process and human interaction, Knowledge management, Knowledge visualization, Reliability and robustness, Knowledge Discovery in Specific Domains, High dimensional data, Temporal data, Data streaming, Scientific databases, Semi-structured/unstructured data, Multimedia, Text, Web and the Internet, Graphic model discovery, Software warehouse and software mining, Data engineering, Communications and networking, Software engineering, Distributed systems and computer hardware

  • 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy sets, rough sets, Statistical methods, Parallel/ Distributed data mining, KDD Process and human interaction, Knowledge management, Knowledge visualization, Reliability and robustness, Knowledge Discovery in Specific Domains, High dimensional data, Temporal data, Data streaming, Scientific databases, Semi-structured/unstructured data, Multimedia, Text, Web and the Internet, Graphic model discovery, Software warehouse and software mining, Data engineering, Communications and networking, Software engineering, Distributed systems and computer hardware, etc.

  • 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy theory and foundations; stability of fuzzy systems; fuzzy methods and algorithms; fuzzy image, speech and signal processing; multimedia; fuzzy hardware and architectures; data mining.

  • 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy theory and foundations; stability of fuzzy systems; fuzzy methods and algorithms; fuzzy image, speech and signal processing; multimedia; fuzzy hardware and architectures; data mining.

  • 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD is an international forum on fuzzy systems and knowledge discovery. Specific topics include fuzzy theory and foundations; stability of fuzzy systems; fuzzy methods and algorithms; fuzzy image, speech and signal processing; multimedia; fuzzy hardware and architectures; data mining.

  • 2007 International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

    FSKD '07 covers all aspects of fuzzy systems and knowledge discovery, including recent theoretical advances and interesting applications, for example, fuzzy theory and models, mathematical foundation of fuzzy systems, fuzzy image/signal processing, fuzzy control and robotics, fuzzy hardware and architectures, fuzzy systems and the internet, fuzzy optimization and modeling, fuzzy decision and support, classification, clustering, statistical methods, knowledge etc.


2018 IEEE 34th International Conference on Data Engineering (ICDE)

The annual ICDE conference addresses research issues and state of the art in designing, building, managing, and evaluating advanced data systems and industrial applications.


2018 IEEE 8th International Advance Computing Conference (IACC)

The scope of the conference is the analysis, design, implementation, deployment and evaluation of advanced topics of computing. It aims to provide a high profile, leading edge forum for researchers, engineers, standard developers and students to showcase their latest research activities, techniques and experiences in the areas of computing.

  • 2017 IEEE 7th International Advance Computing Conference (IACC)

    The scope of the conference is the analysis, design, implementation, deployment andevaluation of advanced topics of computing. It aims to provide a high profile, leading edge forumfor researchers, engineers, standard developers and students to showcase their latest researchactivities, techniques and experiences in the areas of computing.

  • 2016 IEEE 6th International Conference on Advanced Computing (IACC)

    The scope of the conference is the analysis, design, implementation, deployment andevaluation of advanced topics of computing. It aims to provide a high profile, leading edge forumfor researchers, engineers, standard developers and students to showcase their latest researchactivities, techniques and experiences in the areas of computing.

  • 2015 IEEE International Advance Computing Conference (IACC)

    The scope of the conference is the analysis, design, implementation, deployment and evaluation of advanced topics of computing. It aims to provide a high profile, leading edge forum for researchers, engineers, standard developers and students to showcase their latest research activities, techniques and experiences in the areas of computing.

  • 2014 IEEE International Advance Computing Conference (IACC)

    Conference Scope consists of topics of interest from Advance Computing that includes Algorithms, High Performance Computing, Databases and data structures.

  • 2013 IEEE International Advance Computing Conference (IACC)

    In Computer Science the problem complexity and size is increasing. The Projects are becomingincreasingly difficult and huge in volume. Therefore the area of advance computing techniques in terms of efficientalgorithms and reliable computing technologies is becoming of utmost importance.

  • 2010 IEEE 2nd International Advance Computing Conference (IACC 2010)

    Organizations and Institutions are competing to take leads in different areas of advance computing. Due to increasing complexity and size of problems the importance of this area has grown tremendously. The problems which were out of bounds to the computer scientists are now being solved using advance computing technologies. It has opened the flood gates for new research and innovations. In this endeavor we have taken an initiative to provide a common platform for all who are involved in this field.

  • 2009 IEEE International Advance Computing Conference (IACC 2009)

    In Computer Science the problem complexity and size is increasing. The Projects are becomingincreasingly difficult and huge in volume. Therefore the area of advance computing techniques in terms of efficient algorithms and reliable computing technologies is becoming of utmostimportance. This conference will work towards bridging the gap regarding the solution of the latest problems and the available techniques and technologies.


2018 International Conference on Management Science and Engineering (ICMSE)

Management Science and Engineering


2017 IEEE 14th International Conference on e-Business Engineering (ICEBE)

New IT breakthrough always brings the evolution of e-business in wide spectrum, e.g. innovative business model, new marketing and sales channel, rapid sense-and-response, etc. How to adapt the changing computing paradigm and adopt new IT technologies for keeping competitive is a great challenge for modern enterprises. Based on the essential complexities in e-business, ICEBE 2017 invites an extensive coverage of system, software, service, business and combinations of the aforementioned etc.

  • 2016 IEEE International Conference on e-Business Engineering/Service-Oriented Computing and Applications (ICEBE/SOCA)

    E-Business, Service-Oriented Architecture, Cloud-based E-Business, Data Analytics for IoT Applications

  • 2015 IEEE 12th International Conference on e-Business Engineering (ICEBE)

    New IT breakthrough always brings the evolution of e-business in wide spectrum, e.g. innovative business model, new marketing and sales channel, rapid sense-and-response, etc. How to adapt the changing computing paradigm and adopt new IT technologies for keeping competitive is a great challenge for modern enterprises. Based on the essential complexities in e-business, ICEBE 2015 invites an extensive coverage of system, software, service, business and combinations of the aforementioned etc.

  • 2014 IEEE 11th International Conference on e-Business Engineering (ICEBE)

    ICEBE 2014 is a prestigious conference since 2003. As a flagship event sponsored by IEEE Technical Committee on Business Informatics and Systems (TCBIS, formerly known as TC on Electronic Commerce), ICEBE is a high-quality international forum for researchers, engineers and business specialists to exchange the cutting-edge ideas, findings and experiences of e-business.New IT breakthrough always brings the evolution of e-business in wide spectrum, e.g. innovative business model, new marketing and sales channel, rapid sense-and-response, etc. How to adapt the changing computing paradigm and adopt new IT technologies for keeping competitive is a great challenge for modern enterprises. Based on the essential complexities in e-business, ICEBE 2014 invites an extensive coverage of system, software, service, business and combinations of the aforementioned etc.

  • 2013 IEEE 10th International Conference on e-Business Engineering (ICEBE)

    e-Commerce Platforms, Models and Applications, Workflows and Transactions in e-Business, Management and Engineering of IT Enabled Services, Requirement Analysis and Modeling, Dependability and Performance, Business Performance Management, Data and Knowledge Engineering, Mobile and Pervasive Commerce Security, Privacy in e-Commerce, Open Source Technologies in E-Commerce/Business/Services, Cloud Computing, Industry Experience and Applications, IoT

  • 2012 IEEE 9th International Conference on e-Business Engineering (ICEBE)

    E-Commerce Platforms, Models and Applications, Workflows and Transactions in E-Business, Management and Engineering of IT Enabled Services, Requirement Analysis and Modeling, Dependability and Performance, Business Performance Management, Data and Knowledge Engineering, Mobile and Pervasive Commerce Security, Privacy in E-Commerce, Open Source Technologies in E-Commerce/Business/Services, Cloud Computing, Industry Experience and Applications, IoT

  • 2011 IEEE 8th International Conference on e-Business Engineering (ICEBE)

    IoT, E-Commerce Platforms, Models and Applications, Workflows and Transactions in E-Business, Management and Engineering of IT Enabled Services, Requirement Analysis and Modeling, Dependability and Performance, Business Performance Management, Data and Knowledge Engineering, Mobile and Pervasive Commerce Security, Privacy in E-Commerce, Open Source Technologies in E-Commerce/Business/Services, Cloud Computing, Industry Experience and Applications.

  • 2010 IEEE 7th International Conference on e-Business Engineering (ICEBE)

    E-Commerce Platforms, Models and Applications, Workflows and Transactions in E-Business, Management and Engineering of IT Enabled Services, Requirement Analysis and Modeling, Dependability and Performance, Business Performance Management, Data and Knowledge Engineering, Mobile and Pervasive Commerce Security, Privacy in E-Commerce, Open Source Technologies in E-Commerce/Business/Services, Cloud Computing, Industry Experience and Applications

  • 2009 IEEE International Conference on e-Business Engineering (ICEBE)

    The main themes of this year s conference will be enabling integrated e -business systems through servicing and collaboration , which is distributed in the following program tracks: - Software engineering for e -business - Data and knowledge management - Service engineering - Integration and collaboration - Security, privacy and open sources - Mobile and pervasive commerce - Industrial experiences and applications

  • 2008 IEEE International Conference on e-Business Engineering (ICEBE)

    E-Commerce Platforms, Models and Applications, Workflows and Transactions in E-Business, Management and Engineering of IT-Enabled Services, Requirement Analysis and Modeling of E-Business Systems, Dependability and Performance of E-Business Systems, Business Performance Management, Data and Knowledge Engineering for E-Business, Mobile and Pervasive Commerce Security, Privacy in E-Commerce, Open Source Technologies in E-Commerce/Business/Services

  • 2007 IEEE International Conference on e-Business Engineering (ICEBE)

    E-Commerce Platforms, Models and Applications, Workflows and Transactions in E-Business, Management and Engineering of IT-Enabled Services, Requirement Analysis and Modeling of E-Business Systems, Dependability and Performance of E-Business Systems, Business Performance Management, Data and Knowledge Engineering for E-Business, Mobile and Pervasive Commerce Security, Privacy in E-Commerce, Open Source Technologies in E-Commerce/Business/Services

  • 2006 IEEE Conference on e-Business Engineering (ICEBE)

  • 2005 IEEE Conference on e-Business Engineering (ICEBE)


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Periodicals related to IEEE Transactions on Knowledge and Data Engineering

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


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Most published Xplore authors for IEEE Transactions on Knowledge and Data Engineering

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Xplore Articles related to IEEE Transactions on Knowledge and Data Engineering

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Correction to 'Ontological versus knowledge engineering'

IEEE Transactions on Knowledge and Data Engineering, 1989

The author previously cited an incorrect publisher (see ibid., vol.1, p.84-8, 1989). The correct publisher information is: Reading, MA: Addison Wesley.<<ETX>>


Errata Corrige on "Modeling and Computing Ternary Projective Relations between Regions"

IEEE Transactions on Knowledge and Data Engineering, 2013

We report a corrected version of the algorithms to compute ternary projective relations between regions appeared in E. Clementini and R. Billen, "Modeling and computing ternary projective relations between regions," IEEE Transactions on Knowledge and Data Engineering, vol. 18, pp. 799-814, 2006.


SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis

IEEE Transactions on Knowledge and Data Engineering, 2011

In this article, we provide a new technique for temporal data mining which is based on classification rules that can easily be understood by human domain experts. Basically, time series are decomposed into short segments, and short- term trends of the time series within the segments (e.g., average, slope, and curvature) are described by means of polynomial models. Then, the ...


Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning

IEEE Transactions on Knowledge and Data Engineering, None

Predicting flows (e.g. the traffic of vehicles, crowds and bikes), consisting of the in-out traffic at a node and transitions between different nodes, in a spatio-temporal network plays an important role in transportation systems. However, this is a very challenging problem, affected by multiple complex factors, such as spatial correlations between different locations, temporal correlations among different time intervals, and ...


Time series classification using Gaussian mixture models of reconstructed phase spaces

IEEE Transactions on Knowledge and Data Engineering, 2004

A new signal classification approach is presented that is based upon modeling the dynamics of a system as they are captured in a reconstructed phase space. The modeling is done using full covariance Gaussian mixture models of time domain signatures, in contrast with current and previous work in signal classification that is typically focused on either linear systems analysis using ...


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Educational Resources on IEEE Transactions on Knowledge and Data Engineering

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IEEE-USA E-Books

  • Correction to 'Ontological versus knowledge engineering'

    The author previously cited an incorrect publisher (see ibid., vol.1, p.84-8, 1989). The correct publisher information is: Reading, MA: Addison Wesley.<<ETX>>

  • Errata Corrige on "Modeling and Computing Ternary Projective Relations between Regions"

    We report a corrected version of the algorithms to compute ternary projective relations between regions appeared in E. Clementini and R. Billen, "Modeling and computing ternary projective relations between regions," IEEE Transactions on Knowledge and Data Engineering, vol. 18, pp. 799-814, 2006.

  • SwiftRule: Mining Comprehensible Classification Rules for Time Series Analysis

    In this article, we provide a new technique for temporal data mining which is based on classification rules that can easily be understood by human domain experts. Basically, time series are decomposed into short segments, and short- term trends of the time series within the segments (e.g., average, slope, and curvature) are described by means of polynomial models. Then, the classifiers assess short sequences of trends in subsequent segments with their rule premises. The conclusions gradually assign an input to a class. As the classifier is a generative model of the processes from which the time series are assumed to originate, anomalies can be detected, too. Segmentation and piecewise polynomial modeling are done extremely fast in only one pass over the time series. Thus, the approach is applicable to problems with harsh timing constraints. We lay the theoretical foundations for this classifier, including a new distance measure for time series and a new technique to construct a dynamic classifier from a static one, and demonstrate its properties by means of various benchmark time series, for example, Lorenz attractor time series, energy consumption in a building, or ECG data.

  • Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning

    Predicting flows (e.g. the traffic of vehicles, crowds and bikes), consisting of the in-out traffic at a node and transitions between different nodes, in a spatio-temporal network plays an important role in transportation systems. However, this is a very challenging problem, affected by multiple complex factors, such as spatial correlations between different locations, temporal correlations among different time intervals, and external factors (like events and weather). In addition, the flow at a node (called node flow) and transitions between nodes (edge flow) mutually influence each other. To address these issues, we propose a multitask deep-learning framework that simultaneously predicts the node flow and edge flow throughout a spatio- temporal network. Using fully convolutional networks, our approach designs two sophisticated models for predicting node flow and edge flow respectively. Two models are connected by coupling their latent representations of middle layers, and trained together. The external factors are also integrated into the framework through a gating fusion mechanism. In the edge flow prediction model, we employ an embedding component to deal with the sparse transitions between nodes. We evaluate our method based on the taxicab data in Beijing and New York City.Experimental results show advantages of our method beyond 11 baselines, such as ConvLSTM, CNN, and Markov Random Field.

  • Time series classification using Gaussian mixture models of reconstructed phase spaces

    A new signal classification approach is presented that is based upon modeling the dynamics of a system as they are captured in a reconstructed phase space. The modeling is done using full covariance Gaussian mixture models of time domain signatures, in contrast with current and previous work in signal classification that is typically focused on either linear systems analysis using frequency content or simple nonlinear machine learning models such as artificial neural networks. The proposed approach has strong theoretical foundations based on dynamical systems and topological theorems, resulting in a signal reconstruction, which is asymptotically guaranteed to be a complete representation of the underlying system, given properly chosen parameters. The algorithm automatically calculates these parameters to form appropriate reconstructed phase spaces, requiring only the number of mixtures, the signals, and their class labels as input. Three separate data sets are used for validation, including motor current simulations, electrocardiogram recordings, and speech waveforms. The results show that the proposed method is robust across these diverse domains, significantly outperforming the time delay neural network used as a baseline.

  • Discovery of inexact concepts from structural data

    Concept discovery in structural data requires the identification of repetitive substructures in the data. A method for discovering substructures in data using an inexact graph match is described. An implementation of the authors' SUBDUE system that employs an inexact graph match to discover substructures which occur often in the data, but not always in the same form, is described. This inexact substructure discovery can be used to formulate fuzzy concepts, compress the data description, and discover interesting structures in data that are found either in an identical or in a slightly convoluted form. Examples from the domains of scene analysis and chemical compound analysis demonstrate the benefits of the inexact discovery technique.<<ETX>>

  • Graph Pattern Matching: A Join/Semijoin Approach

    Due to rapid growth of the Internet and new scientific/technological advances, there exist many new applications that model data as graphs, because graphs have sufficient expressiveness to model complicated structures. The dominance of graphs in real-world applications demands new graph processing techniques to access large data graphs effectively and efficiently. In this paper, we study a graph pattern matching problem, which is to find all patterns in a large data graph that match a user-given graph pattern. We propose new two- step R-join (reachability join) algorithms with a filter step (R-semijoin) and a fetch step (R-join) by utilizing a new cluster-based join index with graph codes in a relational database context. We also propose two optimization approaches to further optimize sequences of R-joins/R-semijoins. The first approach is based on R-join order selection followed by R-semijoin enhancement, and the second approach is to interleave R-joins with R-semijoins. We conducted extensive performance studies, and confirm the efficiency of our proposed new approaches.

  • Temporal semantic assumptions and their use in databases

    Data explicitly stored in a temporal database are often associated with certain semantic assumptions. Each assumption can be viewed as a way of deriving implicit information from explicitly stored data. Rather than leaving the task of deriving (possibly infinite) implicit data to application programs, as is the case currently, it is desirable that this be handled by the database management system. To achieve this, the paper formalizes and studies two types of semantic assumptions: point based and interval based. The point based assumptions include those assumptions that use interpolation methods over values at different time instants, while the interval based assumptions include those that involve the conversion of values across different time granularities. The paper presents techniques on: (1) how assumptions on specific sets of attributes can be automatically derived from the specification of interpolation and conversion functions; and (2) given the representation of assumptions, how a user query can be converted into a system query such that the answer of this system query over the explicit data is the same as that of the user query over the explicit and the implicit data. To precisely illustrate concepts and algorithms, the paper uses a logic based abstract query language. The paper also shows how the same concepts can be applied to concrete temporal query languages.

  • Continuous K-Means Monitoring with Low Reporting Cost in Sensor Networks

    In this paper, we study an interesting problem: continuously monitoring k-means clustering of sensor readings in a large sensor network. Given a set of sensors whose readings evolve over time, we want to maintain the k-means of the readings continuously. The optimization goal is to reduce the reporting cost in the network, that is, let as few sensors as possible report their current readings to the data center in the course of maintenance. To tackle the problem, we propose the reading reporting tree, a hierarchical data collection, and analysis framework. Moreover, we develop several reporting cost-effective methods using reading reporting trees in continuous k-means monitoring. First, a uniform sampling method using a reading reporting tree can achieve good quality approximation of k-means. Second, we propose a reporting threshold method which can guarantee the approximation quality. Last, we explore a lazy approach which can reduce the intermediate computation substantially. We conduct a systematic simulation evaluation using synthetic data sets to examine the characteristics of the proposed methods.

  • Security of Tzeng's time-bound key assignment scheme for access control in a hierarchy

    Tzeng (2002) proposed a time-bound cryptographic key assignment scheme for access control in a partial-order hierarchy. In this paper, we show that Tzeng's scheme is insecure against the collusion attack whereby three users conspire to access some secret class keys that they should not know according to Tzeng's scheme.



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