ORCID Profile
0000-0002-4408-1952
Current Organisation
Macquarie University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Information Systems | Database Management | Interorganisational Information Systems and Web Services | Pattern Recognition and Data Mining | Conceptual Modelling | Artificial Intelligence and Image Processing | Global Information Systems | Information Systems Development Methodologies | Distributed Computing | Data mining and knowledge discovery | Conceptual Modelling | Graph social and multimedia data | Web Technologies (excl. Web Search) | Database systems | Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence) | Data management and data science | Database Management
ANZSRC Socio-Economic Objective (SEO)
Information Processing Services (incl. Data Entry and Capture) | Application Tools and System Utilities | Information processing services | Application tools and system utilities | Computer software and services not elsewhere classified | Application Software Packages (excl. Computer Games) | Expanding Knowledge in the Information and Computing Sciences | Computer Software and Services not elsewhere classified | Information Services not elsewhere classified | Expanding Knowledge in Technology | Electronic Information Storage and Retrieval Services |
Related Links
Publications
Business Collaboration Development: A Case Study in Capital Market
Publisher: IEEE
Date: 10-2006
DOI: 10.1109/EDOC.2006.19
Using time-sensitive interactions to improve topic derivation in twitter
Publisher: Springer Science and Business Media LLC
Date: 06-10-2017
Fair-SRS: A Fair Session-based Recommendation System
Publisher: ACM
Date: 11-02-2022
PB-Worker: A Novel Participating Behavior-based Worker Ability Model for General Tasks on Crowdsourcing Platforms
Publisher: IEEE
Date: 10-2020
Incremental Graph Computation: Anchored Vertex Tracking in Dynamic Social Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
REFS 2010 - Fourth IEEE International Workshop on Requirements Engineering for Services
Publisher: IEEE
Date: 07-2010
Towards policy driven context aware differentiated services design and development
Publisher: Informa UK Limited
Date: 11-2008
Supporting Differentiated ServicesWith Configurable Business Processes
Publisher: IEEE
Date: 07-2007
iProcess: Enabling IoT Platforms in Data-Driven Knowledge-Intensive Processes
Publisher: Springer International Publishing
Date: 2018
REFS 2012: The Sixth IEEE International Workshop on Requirements Engineering for Services
Publisher: IEEE
Date: 07-2012
Evolutionary Community Detection in Dynamic Social Networks
Publisher: IEEE
Date: 07-2019
A Policy Driven Authorization Control Framework for Business Collaboration
Publisher: IEEE
Date: 07-2007
Advertising services in the Internet marketplace
Publisher: IEEE Comput. Soc
Date: 2000
Message from the CPSCom2015 Chairs
Publisher: IEEE
Date: 12-2015
ReInCre: Enhancing Collaborative Filtering Recommendations by Incorporating User Rating Credibility
Publisher: Springer Singapore
Date: 2020
Detecting the evolving community structure in dynamic social networks
Publisher: Springer Science and Business Media LLC
Date: 23-10-2019
The capability matching of Web services
Publisher: IEEE Comput. Soc
Date: 2002
A rule driven approach for developing adaptive service oriented business collaboration
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11596141_6
An Optimal Online Semi-connected PLA Algorithm with Maximum Error Bound (Extended Abstract)
Publisher: IEEE
Date: 04-2023
Separating Execution and Data Management: A Key to Business-Process-as-a-Service (BPaaS)
Publisher: Springer International Publishing
Date: 2014
Landscaping the information space of large multi-database networks
Publisher: Elsevier BV
Date: 03-2001
A survey of recent methods on deriving topics from Twitter
Publisher: Springer Science and Business Media LLC
Date: 09-01-2020
Reconnecting the Estranged Relationships: Optimizing the Influence Propagation in Evolving Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Multi-View Fusion with Extreme Learning Machine for Clustering
Publisher: Association for Computing Machinery (ACM)
Date: 30-09-2019
DOI: 10.1145/3340268
Abstract: Unlabeled, multi-view data presents a considerable challenge in many real-world data analysis tasks. These data are worth exploring because they often contain complementary information that improves the quality of the analysis results. Clustering with multi-view data is a particularly challenging problem as revealing the complex data structures between many feature spaces demands discriminative features that are specific to the task and, when too few of these features are present, performance suffers. Extreme learning machines (ELMs) are an emerging form of learning model that have shown an outstanding representation ability and superior performance in a range of different learning tasks. Motivated by the promise of this advancement, we have developed a novel multi-view fusion clustering framework based on an ELM, called MVEC. MVEC learns the embeddings from each view of the data via the ELM network, then constructs a single unified embedding according to the correlations and dependencies between each embedding and automatically weighting the contribution of each. This process exposes the underlying clustering structures embedded within multi-view data with a high degree of accuracy. A simple yet efficient solution is also provided to solve the optimization problem within MVEC. Experiments and comparisons on eight different benchmarks from different domains confirm MVEC’s clustering accuracy.
Identify Authorization Control Requirement in Business Collaboration
Publisher: IEEE
Date: 07-2008
DOI: 10.1109/SCC.2008.75
From Data-centric Business Processes to Enterprise Process Frameworks
Publisher: IEEE
Date: 10-2017
DOI: 10.1109/EDOC.2017.11
AI-enabled processes
Publisher: Springer International Publishing
Date: 2022
Intelligent Data Collaboration in Heterogeneous-device IoT Platforms
Publisher: Association for Computing Machinery (ACM)
Date: 21-06-2021
DOI: 10.1145/3427912
Abstract: The merging boundaries between edge computing and deep learning are forging a new blueprint for the Internet of Things (IoT). However, the low-quality of data in many IoT platforms, especially those composed of heterogeneous devices, is hindering the development of high-quality applications for those platforms. The solution presented in this article is intelligent data collaboration, i.e., the concept of deep learning providing IoT with the ability to adaptively collaborate to accomplish a task. Here, we outline the concept of intelligent data collaboration in detail and present a mathematical model in general form. To demonstrate one possible case where intelligent data collaboration would be useful, we prepared an implementation called adaptive data cleaning (ADC), designed to filter noisy data out of temperature readings in an IoT base station network. ADC primarily consists of a denoising autoencoder LSTM for predictions and a four-level data processing mechanism to perform the filtering. Comparisons between ADC and a maximum slop method show ADC with the lowest false error and the best filtering rates.
User Interface Derivation Based on Role-Enriched Business Process Model
Publisher: Springer International Publishing
Date: 2016
GroExpert: A Novel Group-Aware Experts Identification Approach in Crowdsourcing
Publisher: Springer International Publishing
Date: 2019
Resolving Conflicts among Unbalanced Multi-Source Data When Multi-Value Objects Exist
Publisher: IEEE
Date: 02-05-2022
SOAC Engine: A System to Manage Composite Web Service Authorization
Publisher: Springer Berlin Heidelberg
Date: 2011
A Change Analysis Tool for Service-Based Business Processes
Publisher: Springer Berlin Heidelberg
Date: 2011
ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence
Publisher: IEEE
Date: 07-2023
The Semantic Web and Web Services
Publisher: Elsevier BV
Date: 06-2006
Incorporating user rating credibility in recommender systems
Publisher: Elsevier BV
Date: 10-2023
Time-sensitive topic derivation in Twitter
Publisher: Springer International Publishing
Date: 2015
Tackling the challenges of service composition in e-marketplaces
Publisher: IEEE Comput. Soc
Date: 2002
Collaborative business transactions management: issues and challenges
Publisher: IEEE
Date: 2005
DOI: 10.1109/SCC.2005.37
SeGA: A Mediator for Artifact-Centric Business Processes
Publisher: Springer Berlin Heidelberg
Date: 2012
User Interface Derivation for Business Processes
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Message from the Workshop Chairs
Publisher: IEEE
Date: 07-2009
Towards Unconditional Anonymity: Privacy Enforcement Model in Web Services
Publisher: IEEE
Date: 09-2008
A Study on the Retweeting Behaviour of Marketing Microblogs with High Retweets in Sina Weibo
Publisher: IEEE
Date: 10-2015
DOI: 10.1109/CBD.2015.14
A User-Oriented Taxi Ridesharing System with Large-Scale Urban GPS Sensor Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Service components for managing the life-cycle of service compositions
Publisher: Elsevier BV
Date: 04-2004
An Optimal Online Semi-connected PLA Algorithm with Maximum Error Bound
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
ProDiff: A Process Difference Detection Method Based on Hierarchical Decomposition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Enhancing Trust Prediction in Attributed Social Networks with Self-Supervised Learning
Publisher: Springer Nature Singapore
Date: 2023
Space Reduction for Contextual Transaction Trust Computation in E-Commerce and E-Service Environments
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/SCC.2015.97
Adaptive Development and Management of Business Collaborations
Publisher: IGI Global
Date: 2010
DOI: 10.4018/978-1-60566-669-3.CH011
Abstract: The IT infrastructure of organizations must be agile and dynamic in order to respond quickly to the new business models and requirements. This has led to an increasing demand from in idual organizations for corporate business services that can easily adapt to changes through business collaboration. Popular solutions for business collaboration development and management do not properly cater for the specification of new collaborations nor do they facilitate the management of existing ones. In this book chapter we present a rule based approach for collaboration development and management. The proposed approach allows organizations to capture the requirements for their business collaborations in an explicit, manageable and uniform manner in the form of rules. These rules can then be used to drive and constrain the development and management of needed business collaboration models. Practical feasibility of the approach is demonstrated in the context of a complex insurance claim scenario using prototype tooling.
Heterogeneous Graph Neural Network via Knowledge Relations for Fake News Detection
Publisher: ACM
Date: 10-07-2023
Building enterprise mashups
Publisher: Elsevier BV
Date: 05-2011
personality2vec: Enabling the Analysis of Behavioral Disorders in Social Networks
Publisher: ACM
Date: 20-01-2020
Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks
Publisher: ACM
Date: 27-02-2023
eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks
Publisher: Association for Computing Machinery (ACM)
Date: 07-03-2022
DOI: 10.1145/3474379
Abstract: With the development of e-commerce, fraud behaviors have been becoming one of the biggest threats to the e-commerce business. Fraud behaviors seriously damage the ranking system of e-commerce platforms and adversely influence the shopping experience of users. It is of great practical value to detect fraud behaviors on e-commerce platforms. However, the task is non-trivial, since the adversarial action taken by fraudsters. Existing fraud detection systems used in the e-commerce industry easily suffer from performance decay and can not adapt to the upgrade of fraud patterns, as they take already known fraud behaviors as supervision information to detect other suspicious behaviors. In this article, we propose a competitive graph neural networks (CGNN)-based fraud detection system (eFraudCom) to detect fraud behaviors at one of the largest e-commerce platforms, “Taobao” 1 . In the eFraudCom system, (1) the competitive graph neural networks (CGNN) as the core part of eFraudCom can classify behaviors of users directly by modeling the distributions of normal and fraud behaviors separately (2) some normal behaviors will be utilized as weak supervision information to guide the CGNN to build the profile for normal behaviors that are more stable than fraud behaviors. The algorithm dependency on fraud behaviors will be eliminated, which enables eFraudCom to detect fraud behaviors in presence of the new fraud patterns (3) the mutual information regularization term can maximize the separability between normal and fraud behaviors to further improve CGNN. eFraudCom is implemented into a prototype system and the performance of the system is evaluated by extensive experiments. The experiments on two Taobao and two public datasets demonstrate that the proposed deep framework CGNN is superior to other baselines in detecting fraud behaviors. A case study on Taobao datasets verifies that CGNN is still robust when the fraud patterns have been upgraded.
A change management framework for service based business process
Publisher: ACM
Date: 31-01-2017
Service deployment for virtual enterprises
Publisher: IEEE Comput. Soc
Date: 2001
Incorporating tweet relationships into topic derivation
Publisher: Springer Singapore
Date: 2016
An Approach Towards User Interface Derivation from Business Process Model
Publisher: Springer Singapore
Date: 2016
CoTrRank: Trust Ranking on Twitter
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Online Active Learning for Drifting Data Streams
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Peer-Based Relay Scheme of Collaborative Filtering for Research Literature
Publisher: Springer Berlin Heidelberg
Date: 2011
Preservation of Integrity Constraints by Workflow
Publisher: Springer Berlin Heidelberg
Date: 2011
REFS 2011 - The Fourth IEEE International Workshop on Service Science and Systems
Publisher: IEEE
Date: 07-2011
Tracking the Evolution of Clusters in Social Media Streams
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Bootstrapping Trust and Reputation for Web Services
Publisher: IEEE
Date: 09-2012
DOI: 10.1109/CEC.2012.16
Probabilistic QoS Aggregations for Service Composition
Publisher: Association for Computing Machinery (ACM)
Date: 20-05-2016
DOI: 10.1145/2876513
Abstract: In this article, we propose a comprehensive approach for Quality of Service (QoS) calculation in service composition. Differing from the existing work on QoS aggregations that represent QoS as single values, discrete values with frequencies, or standard statistical distributions, the proposed approach has the capability to handle any type of QoS probability distribution. A set of formulae and algorithms are developed to calculate the QoS of a composite service according to four identified basic patterns as sequential, parallel, conditional, and loop. We demonstrate that the proposed QoS calculation method is much more efficient than existing simulation methods. It has a high scalability and builds a solid foundation for real-time QoS analysis and prediction in service composition. Experiment results are provided to show the effectiveness and efficiency of the proposed method.
From Appearance to Essence
Publisher: Association for Computing Machinery (ACM)
Date: 11-09-2020
DOI: 10.1145/3411749
Abstract: Truth discovery has been widely studied in recent years as a fundamental means for resolving the conflicts in multi-source data. Although many truth discovery methods have been proposed based on different considerations and intuitions, investigations show that no single method consistently outperforms the others. To select the right truth discovery method for a specific application scenario, it becomes essential to evaluate and compare the performance of different methods. A drawback of current research efforts is that they commonly assume the availability of certain ground truth for the evaluation of methods. However, the ground truth may be very limited or even impossible to obtain, rendering the evaluation biased. In this article, we present CompTruthHyp , a generic approach for comparing the performance of truth discovery methods without using ground truth. In particular, our approach calculates the probability of observations in a dataset based on the output of different methods. The probability is then ranked to reflect the performance of these methods. We review and compare 12 representative truth discovery methods and consider both single-valued and multi-valued objects. The empirical studies on both real-world and synthetic datasets demonstrate the effectiveness of our approach for comparing truth discovery methods.
CoBTx-Net: A model for business collaboration reliability verification
Publisher: Springer Science and Business Media LLC
Date: 23-05-2008
Establishing and maintaining compatibility in service oriented business collaboration
Publisher: ACM Press
Date: 2005
FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance
Publisher: IEEE
Date: 12-2021
Towards a Trust Management Enabled Identity Metasystem
Publisher: IGI Global
Date: 04-2010
Abstract: Existing identity metasystems provide enabling tools to manage, select, and control of digital identities but they have not provided the support of trust management that should cover how trust requirements associated with digital identities are modeled, how runtime conditions for trust are evaluated, and how the results of trust evaluation are consumed by systems/applications. In this paper, the authors propose an approach toward a trust management enabled identity metasystem that covers the analysis of trust requirements and the development of trust management system in a consistent manner. The proposed trust management architecture extends the existing identity metasystems by introducing computing components for carrying out typical trust management tasks associated with digital identities. The computing components in proposed architecture provide intelligent services for these tasks. The proposed high level architecture targets the automation of the development of the trust management layer for digital identities.
A Rule Driven Approach for Developing Adaptive Service Oriented Business Collaboration
Publisher: IEEE
Date: 09-2006
DOI: 10.1109/SCC.2006.14
Matrix Inter-joint Factorization-A New Approach for Topic Derivation in Twitter
Publisher: IEEE
Date: 06-2015
Message from the MDA4SOA07 Chairs
Publisher: IEEE
Date: 2007
Deriving Topics in Twitter by Exploiting Tweet Interactions
Publisher: IEEE
Date: 06-2015
Functional Impairment of Human Resident Cardiac Stem Cells by the Cardiotoxic Antineoplastic Agent Trastuzumab
Publisher: Oxford University Press (OUP)
Date: 04-2012
Abstract: Trastuzumab (TZM), a monoclonal antibody against the ERBB2 protein, increases survival in ERBB2-positive breast cancer patients. Its clinical use, however, is limited by cardiotoxicity. We sought to evaluate whether TZM cardiotoxicity involves inhibition of human adult cardiac-derived stem cells, in addition to previously reported direct adverse effects on cardiomyocytes. To test this idea, we exposed human cardiosphere-derived cells (hCDCs), a natural mixture of cardiac stem cells and supporting cells that has been shown to exert potent regenerative effects, to TZM and tested the effects in vitro and in vivo. We found that ERBB2 mRNA and protein are expressed in hCDCs at levels comparable to those in human myocardium. Although clinically relevant concentrations of TZM had no effect on proliferation, apoptosis, or size of the c-kit-positive hCDC subpopulation, in vitro assays demonstrated diminished potential for cardiogenic differentiation and impaired ability to form microvascular networks in TZM-treated cells. The functional benefit of hCDCs injected into the border zone of acutely infarcted mouse hearts was abrogated by TZM: infarcted animals treated with TZM + hCDCs had a lower ejection fraction, thinner infarct scar, and reduced capillary density in the infarct border zone compared with animals that received hCDCs alone (n = 12 per group). Collectively, these results indicate that TZM inhibits the cardiomyogenic and angiogenic capacities of hCDCs in vitro and abrogates the morphological and functional benefits of hCDC transplantation in vivo. Thus, TZM impairs the function of human resident cardiac stem cells, potentially contributing to TZM cardiotoxicity.
Yank Your Data Out of My Engine: A New Approach to Workflow System Design
Publisher: IEEE
Date: 09-2015
SOAC-Net: A Model to Manage Service-Based Business Process Authorization
Publisher: IEEE
Date: 06-2012
DOI: 10.1109/SCC.2012.37
Understand Collaborative Authorization Policies: Models and Specifications
Publisher: IEEE
Date: 2009
Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2022
Abstract: Point-of-interest (POI) recommendations can help users explore attractive locations, which is playing an important role in location-based social networks (LBSNs). In POI recommendations, the results are largely impacted by users' preferences. However, the existing POI methods model user and location almost separately, which cannot capture users' personal and dynamic preferences to location. In addition, they also ignore users' acceptance to distance/time of location. To overcome the limitations of the existing methods, we first introduce Knowledge Graph with temporal information (known as TKG) into POI recommendation, including both user and location with timest s. Then, based on TKG, we propose a Spatial-Temporal Graph Convolutional Attention Network (STGCAN), a novel network that learns users' preferences on TKG by dynamically capturing the spatial-temporal neighbourhoods. Specifically, in STGCAN, we construct receptive fields on TKG to aggregate neighbourhoods of user and location respectively at each timest . And we measure the spatial-temporal interval as users' acceptance to distance/time with self-attention. Experiments on three real-world datasets demonstrate that the proposed model outperforms the state-of-the-art POI recommendation approaches.
QoSDIST: A QoS Probability Distribution Estimation Tool for Web Service Compositions
Publisher: IEEE
Date: 12-2010
Enforcing Business Collaboration Consistency in Business Transaction Net
Publisher: IEEE
Date: 07-2008
Deep Multi-Attributed-View Graph Representation Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Develop Service Oriented Finance Business Processes: A Case Study in Capital Market
Publisher: IEEE
Date: 09-2006
DOI: 10.1109/SCC.2006.43
What and with whom? Identifying topics in Twitter through both interactions and text
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Web service componentization
Publisher: Association for Computing Machinery (ACM)
Date: 10-2003
Abstract: Developing a framework for analyzing service composition reuse and specialization.
An evolutionary approach to materialized views selection in a data warehouse environment
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2001
DOI: 10.1109/5326.971656
Contract-First Design Techniques for Building Enterprise Web Services
Publisher: IEEE
Date: 07-2009
DOI: 10.1109/ICWS.2009.91
Managing Changes for Service Based Business Processes
Publisher: IEEE
Date: 12-2010
E-BAG 2008 Workshop PC Chairs’ Message
Publisher: Springer Berlin Heidelberg
Date: 2008
Web Services, E-Business, and the Semantic Web
Publisher: Springer Berlin Heidelberg
Date: 2002
ICS-Assist: Intelligent Customer Inquiry Resolution Recommendation in Online Customer Service for Large E-Commerce Businesses
Publisher: Springer International Publishing
Date: 2020
Web Information Systems Engineering
Publisher: Springer Singapore
Date: 2020
Hyperthyroidism Progress Prediction with Enhanced LSTM
Publisher: Springer International Publishing
Date: 2020
A Comprehensive Survey of the Key Technologies and Challenges Surrounding Vehicular Ad Hoc Networks
Publisher: Association for Computing Machinery (ACM)
Date: 08-06-2021
DOI: 10.1145/3451984
Abstract: Vehicular ad hoc networks ( VANETs ) and the services they support are an essential part of intelligent transportation. Through physical technologies, applications, protocols, and standards, they help to ensure traffic moves efficiently and vehicles operate safely. This article surveys the current state of play in VANETs development. The summarized and classified include the key technologies critical to the field, the resource-management and safety applications needed for smooth operations, the communications and data transmission protocols that support networking, and the theoretical and environmental constructs underpinning research and development, such as graph neural networks and the Internet of Things. Additionally, we identify and discuss several challenges facing VANETs, including poor safety, poor reliability, non-uniform standards, and low intelligence levels. Finally, we touch on hot technologies and techniques, such as reinforcement learning and 5G communications, to provide an outlook for the future of intelligent transportation systems.
Collaborative Business Transactions Management: Issues and Challenges
Publisher: IEEE
Date: 2005
DOI: 10.1109/ICWS.2005.34
A Comprehensive Survey on Community Detection With Deep Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Minimum Entropy Principle Guided Graph Neural Networks
Publisher: ACM
Date: 27-02-2023
Finding and Analyzing Principal Features for Measuring User Influence on Twitter
Publisher: IEEE
Date: 03-2015
An Ethical Multi-Stakeholder Recommender System Based on Evolutionary Multi-Objective Optimization
Publisher: IEEE
Date: 11-2020
An Artifact-Centric Approach to Dynamic Modification of Workflow Execution
Publisher: Springer Berlin Heidelberg
Date: 2011
Incorporating tie strength in robust social recommendation
Publisher: IEEE
Date: 06-2015
A rule based approach to the service composition life-cycle
Publisher: IEEE Comput. Soc
Date: 2003
A Comprehensive Survey on Collaborative Data-access Enablers in the IIoT
Publisher: Association for Computing Machinery (ACM)
Date: 15-09-2023
DOI: 10.1145/3612918
Abstract: The scope of the Industrial Internet of Things (IIoT) has stretched beyond manufacturing to include energy, healthcare, transportation and all that tomorrow’s smart cities will entail. The realm of IIoT includes smart sensors, actuators, programmable logic controllers, distributed control systems (DCS), embedded devices, supervisory control and data acquisition systems - all produced by manufacturers for different purposes and with different data structures and formats designed according to different standards, and made to follow different protocols. In this sea of incompatibility, how can we flexibly acquire these heterogeneous data, and how can we uniformly structure them to suit thousand of different applications? In this article, we survey the four pillars of information science that enable collaborative data access in an IIoT - standardization, data acquisition, data fusion, and scalable architecture - to provide an up-to-date audit of current research in the field. Here, standardization in IIoT relies on standards and technologies to make things communicative data acquisition attempts to transparently collect data through plug-and-play architectures, reconfigurable schemes, or hardware expansion data fusion refers to the techniques and strategies for overcoming heterogeneity in data formats and sources and scalable architecture provides basic techniques to support heterogeneous requirements. The paper also concludes with an overview of the frontier researches and emerging technologies for supporting or challenging data access from the aspects of 5G, machine learning, blockchain, and semantic web.
Service Change Analyzer: An Enabling Tool for Change Management in Service-Based Business Processes
Publisher: IEEE
Date: 10-2011
QoS analysis for web service compositions based on probabilistic QoS
Publisher: Springer Berlin Heidelberg
Date: 2011
dFDA-VeD: A dynamic future demand aware vehicle dispatching system
Publisher: ACM
Date: 07-12-2021
On the Specification and Negotiation of Quality of Service for Collaborative Services
Publisher: IEEE
Date: 09-2008
DOI: 10.1109/EDOC.2008.38
Analyzing QoS for Web Service Compositions by QoSDIST
Publisher: Springer Berlin Heidelberg
Date: 2012
QoS Analysis in Service Oriented Computing
Publisher: Springer New York
Date: 04-09-2013
BTx-Net: A Token Based Dynamic Model for Supporting Consistent Collaborative Business Transactions
Publisher: IEEE
Date: 2007
DOI: 10.1109/SCC.2007.33
Message from the REFS 2008 Workshop Organizers
Publisher: IEEE
Date: 2008
PD-SRS: Personalized Diversity for a Fair Session-Based Recommendation System
Publisher: Springer Nature Switzerland
Date: 2022
Provisioning Web Services from Resource Constrained Mobile Devices
Publisher: IEEE
Date: 07-2010
ARA-Assessor: Application-Aware Runtime Risk Assessment for Cloud-Based Business Continuity
Publisher: Springer International Publishing
Date: 2017
A Tree-Based Reliability Analysis for Fault-Tolerant Web Services Composition
Publisher: Springer International Publishing
Date: 2017
Data Product-Oriented Services for Data Ecosystem
Publisher: IEEE
Date: 07-2023
Introduction to the Special Issue on Advanced Graph Mining on the Web: Theory, Algorithms, and Applications: Part 1
Publisher: Association for Computing Machinery (ACM)
Date: 22-05-2023
DOI: 10.1145/3579360
Bridging the gap between business and IT in service oriented business collaboration
Publisher: IEEE
Date: 2005
DOI: 10.1109/SCC.2005.115
Towards Graph-level Anomaly Detection via Deep Evolutionary Mapping
Publisher: ACM
Date: 04-08-2023
FDA-VeD: A future-demand-aware vehicle dispatching service
Publisher: IEEE
Date: 11-2020
Multi-truth discovery while being aware of unbalanced data distribution
Publisher: IEEE
Date: 18-06-2023
eRiskCom: an e-commerce risky community detection platform
Publisher: Springer Science and Business Media LLC
Date: 17-01-2022
Genetic Algorithm for Materialized View Selection in Data Warehouse Environments
Publisher: Springer Berlin Heidelberg
Date: 1999
AtNE-Trust: Attributed Trust Network Embedding for Trust Prediction in Online Social Networks
Publisher: IEEE
Date: 11-2020
Design Methodology for Service-Based Data Product Sharing and Trading
Publisher: Springer International Publishing
Date: 2021
Exploiting Users’ Rating Behaviour to Enhance the Robustness of Social Recommendation
Publisher: Springer International Publishing
Date: 2017
Change impact analysis for service based business processes
Publisher: IEEE
Date: 12-2010
Query Data Inconsistency for Business Processes
Publisher: IEEE
Date: 07-2019
A Contrastive Learning Framework for Dual-Target Cross-Domain Recommendation
Publisher: ACM
Date: 26-10-2023
MGNN: Mutualistic Graph Neural Network for Joint Friend and Item Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
A Trust and Reputation Model Based on Bayesian Network for Web Services
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/ICWS.2010.36
Deep Learning for Community Detection: Progress, Challenges and Opportunities
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics. However, the classic methods of community detection, such as spectral clustering and statistical inference, are falling by the wayside as deep learning techniques demonstrate an increasing capacity to handle high-dimensional graph data with impressive performance. Thus, a survey of current progress in community detection through deep learning is timely. Structured into three broad research streams in this domain – deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.
Evolving materialized views in data warehouse
Publisher: IEEE
Date: 1999
QoS probability distribution estimation for web services and service compositions
Publisher: IEEE
Date: 12-2010
QoS analysis for web service compositions with complex structures
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2013
DOI: 10.1109/TSC.2012.7
Social Trust Network Embedding
Publisher: IEEE
Date: 11-2019
Learning Influence Probabilities and Modelling Influence Diffusion in Twitter
Publisher: ACM
Date: 13-05-2019
DeepTrust: A Deep User Model of Homophily Effect for Trust Prediction
Publisher: IEEE
Date: 11-2019
GUEST EDITORS' INTRODUCTION
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2004
KPMCF: A Learning Model for Measuring Social Relationship Strength
Publisher: Springer Berlin Heidelberg
Date: 2013
Rule-based business collaboration development and management
Publisher: Inderscience Publishers
Date: 2007
Time Based QoS Modeling and Prediction for Web Services
Publisher: Springer Berlin Heidelberg
Date: 2011
A Capability Description Language Design for Data Products
Publisher: ACM
Date: 18-06-2023
Deep graph level anomaly detection with contrastive learning
Publisher: Springer Science and Business Media LLC
Date: 18-11-2022
DOI: 10.1038/S41598-022-22086-3
Abstract: Graph level anomaly detection (GLAD) aims to spot anomalous graphs that structure pattern and feature information are different from most normal graphs in a graph set, which is rarely studied by other researchers but has significant application value. For instance, GLAD can be used to distinguish some different characteristic molecules in drug discovery and chemical analysis. However, GLAD mainly faces the following three challenges: (1) learning more comprehensive graph level representations to differ normal graphs and abnormal graphs, (2) designing an effective graph anomaly evaluation paradigm to capture graph anomalies from the local and global graph perspectives, (3) overcoming the number imbalance problem of normal and abnormal graphs. In this paper, we combine graph neural networks and contrastive learning to build an end-to-end GLAD framework for solving the three challenges above. We aim to design a new graph level anomaly evaluation way, which first utilizes the contrastive learning strategy to enhance different level representations of normal graphs from node and graph levels by a graph convolution autoencoder with perturbed graph encoder. Then, we evaluate the error of them with corresponding representations of the generated reconstruction graph to detect anomalous graphs. Extensive experiments on ten real-world datasets from three areas, such as molecular, protein and social network anomaly graphs, show that our model can effectively detect graph level anomaly from the majority and outperform existing advanced methods.
Business Process Management Workshops
Publisher: Springer Berlin Heidelberg
Date: 2009
Relation Based Service Networks for Reliable Service Selection
Publisher: IEEE
Date: 07-2009
DOI: 10.1109/CEC.2009.19
The Joint effects of tweet content similarity and tweet interactions for topic derivation
Publisher: IEEE
Date: 06-2017
Authorization in cross-border eHealth systems
Publisher: Springer Science and Business Media LLC
Date: 26-07-2011
Predicting e-book ranking based on the implicit user feedback
Publisher: Springer Science and Business Media LLC
Date: 14-04-2018
Individual Interest and Trust Driving Collective Intelligence Awareness for Social Recommendation
Publisher: IEEE
Date: 07-2018
Influence maximization on twitter: A mechanism for effective marketing campaign
Publisher: IEEE
Date: 05-2017
BPEL4RBAC: An Authorisation Specification for WS-BPEL
Publisher: Springer Berlin Heidelberg
Date: 2008
Modeling data for business processes
Publisher: IEEE
Date: 03-2014
A hybrid feature selection method for predicting user influence on Twitter
Publisher: Springer International Publishing
Date: 2015
A fairness-aware multi-stakeholder recommender system
Publisher: Springer Science and Business Media LLC
Date: 22-09-2021
Anytime Monte Carlo
Publisher: Cambridge University Press (CUP)
Date: 2021
DOI: 10.1017/DCE.2021.6
Abstract: Monte Carlo algorithms simulates some prescribed number of s les, taking some random real time to complete the computations necessary. This work considers the converse: to impose a real-time budget on the computation, which results in the number of s les simulated being random. To complicate matters, the real time taken for each simulation may depend on the s le produced, so that the s les themselves are not independent of their number, and a length bias with respect to compute time is apparent. This is especially problematic when a Markov chain Monte Carlo (MCMC) algorithm is used and the final state of the Markov chain—rather than an average over all states—is required, which is the case in parallel tempering implementations of MCMC. The length bias does not diminish with the compute budget in this case. It also occurs in sequential Monte Carlo (SMC) algorithms, which is the focus of this paper. We propose an anytime framework to address the concern, using a continuous-time Markov jump process to study the progress of the computation in real time. We first show that for any MCMC algorithm, the length bias of the final state’s distribution due to the imposed real-time computing budget can be eliminated by using a multiple chain construction. The utility of this construction is then demonstrated on a large-scale SMC $ {}^2 $ implementation, using four billion particles distributed across a cluster of 128 graphics processing units on the Amazon EC2 service. The anytime framework imposes a real-time budget on the MCMC move steps within the SMC $ {}^2 $ algorithm, ensuring that all processors are simultaneously ready for the res ling step, demonstrably reducing idleness to due waiting times and providing substantial control over the total compute budget.
Change impact analysis in service-based business processes
Publisher: Springer Science and Business Media LLC
Date: 02-11-2011
Maximizing the Effectiveness of Advertising Campaigns on Twitter
Publisher: IEEE
Date: 06-2017
TiCoBTx-Net: A Model to Manage Temporal Consistency of Service-Oriented Business Collaboration
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2012
DOI: 10.1109/TSC.2011.5
CoRank
Publisher: Springer International Publishing
Date: 2018
Knowledge Graph Enhanced Heterogeneous Graph Neural Network for Fake News Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
C-DeepTrust: A Context-Aware Deep Trust Prediction Model in Online Social Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Personal-Hosting RESTful Web Services for Social Network Based Recommendation
Publisher: Springer Berlin Heidelberg
Date: 2011
Universal Artifacts
Publisher: Association for Computing Machinery (ACM)
Date: 29-03-2016
DOI: 10.1145/2886104
Abstract: In most BPM systems (a.k.a. workflow systems), the data for process execution is scattered across databases for enterprise, auxiliary local data stores within the BPM systems, and even file systems (e.g., specification of process models). The interleaving nature of data management and BP execution and the lack of a coherent conceptual data model for all data needed for execution make it hard for (1) providing Business-Process-as-a-Service (BPaaS) and (2) effective support for collaboration between business processes. The primary reason is that an enormous effort is required for maintaining both the engines and the data for the client applications. In particular, different modeling languages and different BPM systems make process interoperation one of the toughest challenges. In this article, we formulate a concept of a “universal artifact,” which extends artifact-centric models by capturing all needed data for a process instance throughout its execution. A framework called SeGA based on universal artifacts is developed to support separation of data and BP execution, a key principle for BPM systems. We demonstrate in this article that SeGA is versatile enough to fully facilitate not only executions of in idual processes (to support BPaaS) but also various collaboration models. Moreover, SeGA reduces the complexity in runtime management including runtime querying, constraints enforcement, and dynamic modification upon collaboration across possibly different BPM systems.
ServiceCom: A tool for service composition reuse and specialization
Publisher: IEEE Comput. Soc
Date: 2003
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
MARVEL, a four-telescope array for high-precision radial-velocity monitoring
Publisher: SPIE
Date: 13-12-2020
DOI: 10.1117/12.2560895
DAliM: Machine Learning Based Intelligent Lucky Money Determination for Large-Scale E-Commerce Businesses
Publisher: Springer International Publishing
Date: 2018
A Three-Layer Attentional Framework Based on Similar Users for Dual-Target Cross-Domain Recommendation
Publisher: Springer Nature Switzerland
Date: 2023
Managing Conflict of Interest in Service Composition
Publisher: Springer Berlin Heidelberg
Date: 2010
QoS Analysis and Service Selection for Composite Services
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/SCC.2010.16
DAGAD: Data Augmentation for Graph Anomaly Detection
Publisher: IEEE
Date: 11-2022
CoTrRank: Trust Evaluation of Users and Tweets
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: Trust evaluation of people and information on Twitter is critical for maintaining a healthy online social environment. How to evaluate the trustworthiness of users and tweets becomes a challenging question. In this demo, we show how our proposed CoTrRank approach deal with this problem. This approach models users and tweets in two coupled networks and calculate their trust values in different trust spaces. In particular, our solution provides a configurable way when mapping the calculated raw evidences to the trust values. The CoTrRank demo system has an interactive interface to show how our proposed approach produces more effective and adaptive trust evaluation results comparing with baseline methods.
SOAC: A Conceptual Model for Managing Service-Oriented Authorization
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/SCC.2010.13
Let’s CoRank: trust of users and tweets on social networks
Publisher: Springer Science and Business Media LLC
Date: 14-07-2020
On the Specification of Payment Requirements for Collaborative Services
Publisher: IEEE
Date: 07-2008
DOI: 10.1109/SCC.2008.62
Learning Social Relationship Strength via Matrix Co-Factorization with Multiple Kernels
Publisher: Springer Berlin Heidelberg
Date: 2013
Security Policy Specification and Integration in Business Collaboration
Publisher: IEEE
Date: 2007
DOI: 10.1109/SCC.2007.96
Describing capabilities of Internet data sources for information discovery and sharing
Publisher: IEEE Comput. Soc
Date: 2001
Probability Distribution-Based QoS Analysis for Web Service Composition
Publisher: Springer Berlin Heidelberg
Date: 2011
Introduction to the Special Issue with Best Papers from BPM 2008 Workshops
Publisher: Wiley
Date: 06-04-2010
DOI: 10.1002/SMR.464
TWLR: A Novel Truth Inference Approach based on Worker Representations for Crowdsourcing in the Low Redundancy Situation
Publisher: IEEE
Date: 09-2021
AppNet: A Large-Scale Multi-layer Heterogeneous Complex App Network for Intelligent Program Search
Publisher: Springer International Publishing
Date: 2019
GUEST EDITORS' INTRODUCTION
Publisher: World Scientific Pub Co Pte Lt
Date: 03-2012
Correctness Verification for Service-Based Business Processes
Publisher: IEEE
Date: 06-2017
DOI: 10.1109/ICWS.2017.90
An Approach towards Task Abstraction and Aggregation in Business Processes
Publisher: IEEE
Date: 06-2017
DOI: 10.1109/ICWS.2017.91
WorP: A Novel Worker Performance Prediction Model for General Tasks on Crowdsourcing Platforms
Publisher: IEEE
Date: 09-2021
A Tool for QoS Probability Distribution Estimation of Web Service Compositions
Publisher: Springer Berlin Heidelberg
Date: 2010
iCOP
Publisher: Springer International Publishing
Date: 2019
TReAT: A Tool for Analyzing Relations Between Tasks in a Process
Publisher: Springer International Publishing
Date: 2019
CoBTx-Net: A Model for Reliability Verification of Collaborative Business Transaction
Publisher: Springer Berlin Heidelberg
Date: 2008
QoS Analysis for Web Service Composition
Publisher: IEEE
Date: 2009
DOI: 10.1109/SCC.2009.28
Isheets: A spreadsheet-based machine learning development platform for data-driven process analytics
Publisher: Springer International Publishing
Date: 2019
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