ORCID Profile
0000-0001-6739-4145
Current Organisations
E O Lawrence Berkeley National Laboratory
,
University of Queensland
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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 | Global Information Systems | Global Information Systems | Database Management | Conceptual Modelling | Information Storage, Retrieval And Management | Interorganisational Information Systems | Data Format not elsewhere classified | Decision Support And Group Support Systems | Information Systems Development Methodologies | Conceptual Modelling | Web Technologies (excl. Web Search) | Pattern Recognition and Data Mining | Data Structures | Computer-Human Interaction | Logics And Meanings Of Programs | Research, Science and Technology Policy | Business Information Systems |
ANZSRC Socio-Economic Objective (SEO)
Electronic Information Storage and Retrieval Services | Information processing services | Application tools and system utilities | Application Tools and System Utilities | Computer software and services not elsewhere classified | Information Processing Services (incl. Data Entry and Capture) | Expanding Knowledge in the Information and Computing Sciences | Information Services not elsewhere classified | Technological and Organisational Innovation | Expanding Knowledge in Technology | Other
Related Links
Publications
Effect of Linked Rules on Business Process Model Understanding
Publisher: Springer International Publishing
Date: 2017
MCIS2010 Workshop Organizers’ Message
Publisher: Springer Berlin Heidelberg
Date: 2010
Explainable Artificial Intelligence in education
Publisher: Elsevier BV
Date: 2022
Incorporating Explainable Learning Analytics to Assist Educators with Identifying Students in Need of Attention
Publisher: ACM
Date: 06-2022
A Relevant Portion of an Ontology: Defining a System of ED Rules Using a Part-Whole Relationship
Publisher: IEEE
Date: 05-2008
DOI: 10.1109/AMS.2008.28
Storing and Processing Massive Trajectory Data on SAP HANA
Publisher: Springer International Publishing
Date: 2015
A web-based approach to data imputation
Publisher: Springer Science and Business Media LLC
Date: 24-10-2013
A Spatial-Temporal Topic Model for the Semantic Annotation of POIs in LBSNs
Publisher: Association for Computing Machinery (ACM)
Date: 25-07-2016
DOI: 10.1145/2905373
Abstract: Semantic tags of points of interest (POIs) are a crucial prerequisite for location search, recommendation services, and data cleaning. However, most POIs in location-based social networks (LBSNs) are either tag-missing or tag-incomplete. This article aims to develop semantic annotation techniques to automatically infer tags for POIs. We first analyze two LBSN datasets and observe that there are two types of tags, category-related ones and sentimental ones, which have unique characteristics. Category-related tags are hierarchical, whereas sentimental ones are category-aware. All existing related work has adopted classification methods to predict high-level category-related tags in the hierarchy, but they cannot apply to infer either low-level category tags or sentimental ones. In light of this, we propose a latent-class probabilistic generative model, namely the spatial-temporal topic model (STM), to infer personal interests, the temporal and spatial patterns of topics/semantics embedded in users’ check-in activities, the interdependence between category-topic and sentiment-topic, and the correlation between sentimental tags and rating scores from users’ check-in and rating behaviors. Then, this learned knowledge is utilized to automatically annotate all POIs with both category-related and sentimental tags in a unified way. We conduct extensive experiments to evaluate the performance of the proposed STM on a real large-scale dataset. The experimental results show the superiority of our proposed STM, and we also observe that the real challenge of inferring category-related tags for POIs lies in the low-level ones of the hierarchy and that the challenge of predicting sentimental tags are those with neutral ratings.
On Using Workflow Technology to Model eLearning Process
Publisher: IEEE
Date: 2008
Modeling Control Objectives for Business Process Compliance
Publisher: Springer Berlin Heidelberg
Date: 2007
Workshop Organizers’ Message
Publisher: Springer Berlin Heidelberg
Date: 2009
QUALITY OF SERVICE IN FLEXIBLE WORKFLOWS THROUGH PROCESS CONSTRAINTS
Publisher: Springer Netherlands
Date: 2006
Modelling Learners in Crowdsourcing Educational Systems
Publisher: Springer International Publishing
Date: 2020
Avoiding Inconsistency in User Preferences for Data Quality Aware Queries
Publisher: Springer Berlin Heidelberg
Date: 2010
Linking contracts, processes and services: an event-driven approach
Publisher: IEEE
Date: 09-2006
DOI: 10.1109/SCC.2006.64
Open data: Quality over quantity
Publisher: Elsevier BV
Date: 06-2017
Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation
Publisher: Association for Computing Machinery (ACM)
Date: 10-10-2016
DOI: 10.1145/2873055
Abstract: Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting places, especially when users travel out of town. However, the extreme sparsity of a user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, the Topic-Region Model (TRM) , to simultaneously discover the semantic, temporal, and spatial patterns of users’ check-in activities, and to model their joint effect on users’ decision making for selection of POIs to visit. To demonstrate the applicability and flexibility of TRM, we investigate how it supports two recommendation scenarios in a unified way, that is, hometown recommendation and out-of-town recommendation. TRM effectively overcomes data sparsity by the complementarity and mutual enhancement of the erse information associated with users’ check-in activities (e.g., check-in content, time, and location) in the processes of discovering heterogeneous patterns and producing recommendations. To support real-time POI recommendations, we further extend the TRM model to an online learning model, TRM-Online, to track changing user interests and speed up the model training. In addition, based on the learned model, we propose a clustering-based branch and bound algorithm (CBB) to prune the POI search space and facilitate fast retrieval of the top- k recommendations. We conduct extensive experiments to evaluate the performance of our proposals on two real-world datasets, including recommendation effectiveness, overcoming the cold-start problem, recommendation efficiency, and model-training efficiency. The experimental results demonstrate the superiority of our TRM models, especially TRM-Online, compared with state-of-the-art competitive methods, by making more effective and efficient mobile recommendations. In addition, we study the importance of each type of pattern in the two recommendation scenarios, respectively, and find that exploiting temporal patterns is most important for the hometown recommendation scenario, while the semantic patterns play a dominant role in improving the recommendation effectiveness for out-of-town users.
Business Process and Rule Integration Approaches - An Empirical Analysis
Publisher: Springer International Publishing
Date: 2018
Processing Group Nearest Group Query
Publisher: IEEE
Date: 03-2009
ST-SAGE
Publisher: Association for Computing Machinery (ACM)
Date: 20-04-2017
DOI: 10.1145/3011019
Abstract: With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important mobile application, especially when users travel away from home. However, this type of recommendation is very challenging compared to traditional recommender systems. A user may visit only a limited number of spatial items, leading to a very sparse user-item matrix. This matrix becomes even sparser when the user travels to a distant place, as most of the items visited by a user are usually located within a short distance from the user’s home. Moreover, user interests and behavior patterns may vary dramatically across different time and geographical regions. In light of this, we propose ST-SAGE, a spatial-temporal sparse additive generative model for spatial item recommendation in this article. ST-SAGE considers both personal interests of the users and the preferences of the crowd in the target region at the given time by exploiting both the co-occurrence patterns and content of spatial items. To further alleviate the data-sparsity issue, ST-SAGE exploits the geographical correlation by smoothing the crowd’s preferences over a well-designed spatial index structure called the spatial pyramid . To speed up the training process of ST-SAGE, we implement a parallel version of the model inference algorithm on the GraphLab framework. We conduct extensive experiments the experimental results clearly demonstrate that ST-SAGE outperforms the state-of-the-art recommender systems in terms of recommendation effectiveness, model training efficiency, and online recommendation efficiency.
On Understanding Data Worker Interaction Behaviors
Publisher: ACM
Date: 25-07-2020
Applying Graph Reduction Techniques for Identifying Structural Conflicts in Process Models
Publisher: Springer International Publishing
Date: 1999
On Improving Learning Outcomes Through Sharing of Learning Experiences
Publisher: IEEE
Date: 07-2010
Towards a Software Architecture for Neurophysiological Experiments
Publisher: Springer International Publishing
Date: 24-10-2019
To Integrate or Not to Integrate – The Business Rules Question
Publisher: Springer International Publishing
Date: 2016
Data Quality Aware Queries in Collaborative Information Systems
Publisher: Springer Berlin Heidelberg
Date: 2009
Report on the 1st Workshop on Human-in-the-Loop Data Curation (HIL-DC 2022) at CIKM 2022
Publisher: Association for Computing Machinery (ACM)
Date: 12-2022
Abstract: We report on the First Workshop on Human-in-the-loop Data Curation (HIL-DC), which was co-located with the ACM International Conference on Information and Knowledge Management (CIKM) 2022. Data curation, which may include annotation, cleaning, transformation, integration, etc., is a critical step to provide adequate assurances on the quality of analytics and machine learning results. Current approaches include manual, automated, and hybrid human-machine methods to data curation. However, this topic remains relatively unstudied, so our main aim for organizing this workshop was to bring together a group of people from both industry and academia with an interest in the topic, in order to arrive at a shared roadmap for the future. Through a program that included two keynotes, seven peer-reviewed papers, and six lightening talks, we have made initial steps towards a common understanding and shared research agenda for this timely and important topic. Date: 21 October, 2022. Website: hilworkshops.github.io/hil-dc2022/.
Compliance aware business process design
Publisher: Springer Berlin Heidelberg
Date: 2008
The Leadership of Co-Production in Health and Social Care Integration in Scotland
Publisher: Cambridge University Press (CUP)
Date: 03-02-2022
DOI: 10.1017/S0047279421000799
Abstract: The involvement of citizens in the production and creation of public services has become a central tenet for administrations internationally. In Scotland, co-production has underpinned the integration of health and social care via the Public Bodies (Joint Working) (Scotland) Act 2014. We report on a qualitative study that examined the experiences and perspectives of local and national leaders in Scotland on undertaking and sustaining co-production in public services. By adopting a meso and macro perspective, we interviewed senior planning officers from eight health and social care partnership areas in Scotland and key actors in national agencies. The findings suggest that an overly complex Scottish governance landscape undermines the sustainability of co-production efforts. As part of a COVID-19 recovery, both the implementation of meaningful co-production and coordinated leadership for health and social care in Scotland need to be addressed, as should the development of evaluation capacities of those working across health and social care boundaries so that co-production can be evaluated and report to inform the future of the integration agenda.
Defining Adaptation Constraints for Business Process Variants
Publisher: Springer Berlin Heidelberg
Date: 2009
Data management of AI-powered education technologies: Challenges and opportunities
Publisher: University of South Australia Library
Date: 2023
DOI: 10.59453/XLUD7002
Specification and validation of process constraints for flexible workflows
Publisher: Elsevier BV
Date: 07-2005
On the Optimal Robot Routing Problem in Wireless Sensor Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2007
On the Design Issues of Wireless Sensor Networks for Telemetry
Publisher: IEEE
Date: 2005
Workshop PC Chairs’ Message
Publisher: Springer Berlin Heidelberg
Date: 2007
DEI Perspectives in Information Technology Education
Publisher: ACM
Date: 04-06-2023
The Journey to Business Process Compliance
Publisher: IGI Global
Date: 2009
DOI: 10.4018/978-1-60566-288-6.CH020
Abstract: It is a typical scenario that many organisations have their business processes specified independently of their business obligations (which includes contractual obligations to business partners, as well as obligations a business has to fulfil against regulations and industry standards). This is because of the lack of guidelines and tools that facilitate derivation of processes from contracts but also because of the traditional mindset of treating contracts separately from business processes. This chapter will provide a solution to one specific problem that arises from this situation, namely the lack of mechanisms to check whether business processes are compliant with business contracts. The chapter begins by defining the space for business process compliance and the eco-system for ensuring that process are compliant. The key point is that compliance is a relationship between two sets of specifications: the specifications for executing a business process and the specifications regulating a business. The central part of the chapter focuses on a logic based formalism for describing both the semantics of normative specifications and the semantics of compliance checking procedures.
Handbook of Data Quality
Publisher: Springer Berlin Heidelberg
Date: 2013
A UML profile for perdurant ontology of domain interlocking Institutional Worlds
Publisher: Inderscience Publishers
Date: 2010
Flexible Provenance Tracing
Publisher: IGI Global
Date: 04-2011
Abstract: The description of the origins of a piece of data and the transformations by which it arrived in a database is termed the data provenance. The importance of data provenance has already been widely recognized in database community. The two major approaches to representing provenance information use annotations and inversion. While annotation is metadata pre-computed to include the derivation history of a data product, the inversion method finds the source data based on the situation that some derivation process can be inverted. Annotations are flexible to represent erse provenance metadata but the complete provenance data may outsize data itself. Inversion method is concise by using a single inverse query or function but the provenance needs to be computed on-the-fly. This paper proposes a new provenance representation which is a hybrid of annotation and inversion methods in order to achieve combined advantage. This representation is adaptive to the storage constraint and the response time requirement of provenance inversion on-the-fly.
Development and Adoption of an Adaptive Learning System
Publisher: ACM
Date: 26-02-2020
The biggest business process management problems to solve before we die
Publisher: Elsevier BV
Date: 04-2023
Exploring Data Literacy Levels in the Crowd – the Case of COVID-19
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 31-05-2022
DOI: 10.1609/ICWSM.V16I1.19395
Abstract: During global health crises, the use of data becomes critical to control the spread of infections, to inform the general public and to foster safe behaviors. The ability of people to read and understand data (i.e., data literacy) has the potential to affect human behaviors. In this paper, we study non-expert human subjects' ability to make accurate interpretations of complex pandemic data visualizations designed for general public consumption. We present them with popular plots and graphs that have been shown by traditional and social media, and ask them to answer questions to assess their data literacy at three levels: extracting information, finding relationships among data, and expanding or predicting information. Our results show the presence of variance in interpretations and reveal insights into how messages communicated through data may be perceived differently by different people. We also highlight the importance of designing communication strategies that ensure the spread of the right message through data.
Instance optimal query processing in spatial networks
Publisher: Springer Science and Business Media LLC
Date: 04-09-2008
A performance study on large-scale data analytics using disk-based and in-memory database systems
Publisher: IEEE
Date: 02-2017
A Pattern-Based Framework for Addressing Data Representational Inconsistency
Publisher: Springer International Publishing
Date: 2016
Special Issue on BPM 2009 Workshops
Publisher: Wiley
Date: 30-03-2011
DOI: 10.1002/SMR.538
Preface GRCIS 2011
Publisher: Springer Berlin Heidelberg
Date: 2011
Managing change and time in dynamic workflow processes
Publisher: World Scientific Pub Co Pte Lt
Date: 03-2000
DOI: 10.1142/S0218843000000077
Abstract: Business environments have become exceedingly dynamic and competitive in recent times. This dynamism is manifested in the form of changing process requirements and time constraints. Workflow technology is currently one of the most promising fields of research in business process automation. However, workflow systems to date do not provide the flexibility necessary to support the dynamic nature of business processes. In this paper we primarily discuss the issues and challenges related to managing change and time in workflows representing dynamic business processes. We also present an analysis of workflow modifications and provide feasibility considerations for the automation of this process.
Data Quality
Publisher: Association for Computing Machinery (ACM)
Date: 22-02-2018
Abstract: We outline a call to action for promoting empiricism in data quality research. The action points result from an analysis of the landscape of data quality research. The landscape exhibits two dimensions of empiricism in data quality research relating to type of metrics and scope of method. Our study indicates the presence of a data continuum ranging from real to synthetic data, which has implications for how data quality methods are evaluated. The dimensions of empiricism and their inter-relationships provide a means of positioning data quality research, and help expose limitations, gaps and opportunities.
Exploiting Hierarchies for Efficient Detection of Completeness in Stream Data
Publisher: Springer International Publishing
Date: 2016
Intelligent learning analytics dashboards: automated drill-down recommendations to support teacher data exploration
Publisher: Society for Learning Analytics Research
Date: 03-11-2021
Abstract: Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on predictive analytics. While predictive models have been successful in many domains, there is an increasing realization of the inadequacies of using predictive models in decision-making tasks that affect in iduals without human oversight. In this paper, we employ a suite of state-of-the-art algorithms, from the online analytics processing, data mining, and process mining domains, to present an alternative human-in-the-loop AI method to enable educators to identify, explore, and use appropriate interventions for subpopulations of students with the highest deviation in performance or learning process compared to the rest of the class. We demonstrate an application of our proposed approach in an existing learning analytics dashboard (LAD) and explore the recommended drill-downs in a course with 875 students. The demonstration provides an ex le of the recommendations from real course data and shows how recommendations can lead the user to interesting insights. Furthermore, we demonstrate how our approach can be employed to develop intelligent LADs.
A Business Process Driven Approach to Manage Data Dependency Constraints
Publisher: Springer Berlin Heidelberg
Date: 2011
A Behavioural Analysis of Metadata Use in Evaluating the Quality of Repurposed Data
Publisher: Springer International Publishing
Date: 2022
A Methodology for Improving Business Process Performance through Positive Deviance
Publisher: IGI Global
Date: 04-2013
Abstract: The importance of process improvement and the role that best practice reference models play in the achievement of process improvement are both well recognized. Best practice reference models are generally created by experts who are external to the organisation. However, best practices can be implicitly derived from the work practices of actual workers within the organisation, especially when there is opportunity for variance within the work, i.e. there may be different approaches to achieve the same process goal. In this paper, the authors propose to support improvement of process performance intrinsically by utilizing the experiences and knowledge of business process users to inform and improve the current practices. The proposed methodology is inspired by the theory of positive deviance. By utilizing a multiple criteria decision making approach and Shannon’s entropy method of information theory in determining useful information from uncertain data within execution log of business process, the authors are able to define the “best” and most suitable previous practices as a recommendation that fits with the current competence/experience levels of in iduals. The authors demonstrate that the proposed method is capable to generate meaningful recommendations from large data sets and effectively facilitating learning within organisation leading to process performance improvement.
Responsible Process Mining - A Data Quality Perspective
Publisher: Springer International Publishing
Date: 2019
Managing Regulatory Compliance in Business Processes
Publisher: Springer Berlin Heidelberg
Date: 10-04-2014
Emerging Challenges in Information Systems Research for Regulatory Compliance Management
Publisher: Springer International Publishing
Date: 2010
Extending a class of continuous estimation of distribution algorithms to dynamic problems
Publisher: Springer Science and Business Media LLC
Date: 30-11-2007
Discovering Data Quality Problems
Publisher: Springer Science and Business Media LLC
Date: 22-07-2019
On the Discovery of Preferred Work Practice Through Business Process Variants
Publisher: Springer Berlin Heidelberg
Date: 2007
Governance, risk and compliance: Applications in information systems
Publisher: Springer Science and Business Media LLC
Date: 02-08-2011
Empowering learners for the age of artificial intelligence
Publisher: Elsevier BV
Date: 2023
Making Sense of Spatial Trajectories
Publisher: ACM
Date: 17-10-2015
Experience Driven Process Improvement
Publisher: Springer Berlin Heidelberg
Date: 2011
Socio-Economic Diversity in Human Annotations
Publisher: ACM
Date: 26-06-2022
Translating business contract into compliant business processes
Publisher: IEEE
Date: 10-2006
DOI: 10.1109/EDOC.2006.64
Does Evidence from Peers Help Crowd Workers in Assessing Truthfulness?
Publisher: ACM
Date: 25-04-2022
Utilising Learnersourcing to Inform Design Loop Adaptivity
Publisher: Springer International Publishing
Date: 2020
A Data-Driven Analysis of Behaviors in Data Curation Processes
Publisher: Association for Computing Machinery (ACM)
Date: 07-02-2023
DOI: 10.1145/3567419
Abstract: Understanding how data workers interact with data, and various pieces of information related to data preparation, is key to designing systems that can better support them in exploring datasets. To date, however, there is a paucity of research studying the strategies adopted by data workers as they carry out data preparation activities. In this work, we investigate a specific data preparation activity, namely data quality discovery , and aim to (i) understand the behaviors of data workers in discovering data quality issues, (ii) explore what factors (e.g., prior experience) can affect their behaviors, as well as (iii) understand how these behavioral observations relate to their performance. To this end, we collect a multi-modal dataset through a data-driven experiment that relies on the use of eye-tracking technology with a purpose-designed platform built on top of iPython Notebook. The experiment results reveal that: (i) ‘copy–paste–modify’ is a typical strategy for writing code to complete tasks (ii) proficiency in writing code has a significant impact on the quality of task performance, while perceived difficulty and efficacy can influence task completion patterns and (iii) searching in external resources is a prevalent action that can be leveraged to achieve better performance. Furthermore, our experiment indicates that providing s le code within the system can help data workers get started with their task, and surfacing underlying data is an effective way to support exploration. By investigating data worker behaviors prior to each search action, we also find that the most common reasons that trigger external search actions are the need to seek assistance in writing or debugging code and to search for relevant code to reuse. Based on our experiment results, we showcase a systematic approach to select from the top best code snippets created by data workers and assemble them to achieve better performance than the best in idual performer in the dataset. By doing so, our findings not only provide insights into patterns of interactions with various system components and information resources when performing data curation tasks, but also build effective and efficient data curation processes through data workers’ collective intelligence.
Model Driven Distribution of Collaborative Business Processes
Publisher: IEEE
Date: 09-2006
DOI: 10.1109/SCC.2006.71
Charting the Design and Analytics Agenda of Learnersourcing Systems
Publisher: ACM
Date: 12-04-2021
On Sensor Network Segmentation for Urban Water Distribution Monitoring
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11610113_104
Providing Flexible Tradeoff for Provenance Tracking
Publisher: Springer Berlin Heidelberg
Date: 2011
Business process and rule integration approaches—An empirical analysis of model understanding
Publisher: Elsevier BV
Date: 02-2022
Sampling dirty data for matching attributes
Publisher: ACM
Date: 06-06-2010
An Approach for Optimizing Relational Provenance Storage
Publisher: China Science Publishing & Media Ltd.
Date: 28-10-2011
Factors influencing effective use of big data: A research framework
Publisher: Elsevier BV
Date: 2020
Joint Modeling of User Check-in Behaviors for Point-of-Interest Recommendation
Publisher: ACM
Date: 17-10-2015
Beyond rigidity – dynamic process lifecycle support
Publisher: Springer Science and Business Media LLC
Date: 22-04-2009
SQLator
Publisher: Association for Computing Machinery (ACM)
Date: 28-06-2004
Abstract: SQL (Structured Query Language) is one of the essential topics in foundation databases courses in higher education. Due to its apparent simple syntax, learning to use the full power of SQL can be a very difficult activity. In this paper, we introduce SQLator, which is a web-based interactive tool for learning SQL. SQLator's key function is the evaluate function, which allows a user to evaluate the correctness of his/her query formulation. The evaluate engine is based on complex heuristic algorithms. The tool also provides instructors the facility to create and populate database schemas with an associated pool of SQL queries. Currently it hosts two databases with a query pool of 300+ across the two databases. The pool is ided into 3 categories according to query complexity. The SQLator user can perform unlimited executions and evaluations on query formulations and/or view the solutions. The SQLator evaluate function has a high rate of success in evaluating the user's statement as correct (or incorrect) corresponding to the question. We will present in this paper, the basic architecture and functions of SQLator. We will further discuss the value of SQLator as an educational technology and report on educational outcomes based on studies conducted at the School of Information Technology and Electrical Engineering, The University of Queensland.
Efficient Retrieval of Top-K Most Similar Users from Travel Smart Card Data
Publisher: IEEE
Date: 07-2014
DOI: 10.1109/MDM.2014.38
Research and Practice in Data Quality
Publisher: Springer Berlin Heidelberg
Date: 2008
Leveraging multi-aspect time-related influence in location recommendation
Publisher: Springer Science and Business Media LLC
Date: 02-05-2018
A Compliance Management Ontology: Developing Shared Understanding through Models
Publisher: Springer International Publishing
Date: 2012
Enterprise Integration Architecture for Harmonized Messaging
Publisher: IGI Global
Date: 2007
DOI: 10.4018/978-1-59904-189-6.CH026
Abstract: Integrating business processes across disparate systems of partner organizations is known to be one of the biggest challenges facing enterprise systems development. Recent developments in service technologies and advanced middleware solutions, together with efforts toward standard interfaces, have helped overcome some of the difficulties. However, managing the rules that govern the interactions between cross-organizational business processes is still under developed. In this chapter, we present an approach for enterprise integration facilitated through a rule based messaging technology. In particular, we will present insights into rule specification, verification, and execution for such an enterprise integration architecture.
Compliance Aware Business Process Design
Publisher: Springer Berlin Heidelberg
Date: 2008
Approximate keyword search in semantic trajectory database
Publisher: IEEE
Date: 04-2015
Variational Counterfactual Prediction under Runtime Domain Corruption
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
A Roadmap for Research in Business Process Compliance
Publisher: Springer Berlin Heidelberg
Date: 2011
Making sense of trajectory data: A partition-and-summarization approach
Publisher: IEEE
Date: 04-2015
Exploiting Structural Similarity for Automatic Information Extraction from Lists
Publisher: Springer Berlin Heidelberg
Date: 2013
WebPut: Efficient Web-Based Data Imputation
Publisher: Springer Berlin Heidelberg
Date: 2012
Integrating design thinking and agile approaches in analytics development: The case of Aginic
Publisher: SAGE Publications
Date: 25-05-2023
DOI: 10.1177/20438869231178035
Abstract: Drawing on the story of Aginic and its educational analytics platform edPortal, this teaching case study examines how applying agile methods and design thinking to analytics has helped unlock significant value for Aginic, its clients and the education sector overall. It describes key factors driving the successful integration of agile values and design approaches, allowing students to gain a deep understanding on how such integration can facilitate the development of innovative data analytics products.
Detecting Regulatory Compliance for Business Process Models through Semantic Annotations
Publisher: Springer Berlin Heidelberg
Date: 2009
A Framework for Utilizing Preferred Work Practice for Business Process Evolution
Publisher: Springer Netherlands
Date: 2007
Modifying Li+ and Anion Diffusivities in Polyacetal Electrolytes: A Pulsed-Field-Gradient NMR Study of Ion Self-Diffusion
Publisher: American Chemical Society (ACS)
Date: 29-06-2021
Data centric research at the University of Queensland
Publisher: Association for Computing Machinery (ACM)
Date: 17-10-2013
Measurement of Compliance Distance in Business Processes
Publisher: Informa UK Limited
Date: 09-10-2008
Open Learner Models for Multi-activity Educational Systems
Publisher: Springer International Publishing
Date: 2021
Investigative Queries in Sensor Networks
Publisher: Springer Berlin Heidelberg
Date: 2007
Transformation of SBVR Compliant Business Rules to Executable FCL Rules
Publisher: Springer Berlin Heidelberg
Date: 2010
Data Quality - The Key Success Factor for Data Driven Engineering
Publisher: IEEE
Date: 09-2007
DOI: 10.1109/NPC.2007.176
Managing Process Variants as an Information Resource
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11841760_34
Compliance management ontology – a shared conceptualization for research and practice in compliance management
Publisher: Springer Science and Business Media LLC
Date: 03-02-2016
ORange: Objective-Aware Range Query Refinement
Publisher: IEEE
Date: 07-2014
DOI: 10.1109/MDM.2014.48
Data Quality in Web Information Systems
Publisher: Springer Berlin Heidelberg
Date: 2008
Framework for Business Process and Rule Integration: A Case of BPMN and SBVR
Publisher: Springer Berlin Heidelberg
Date: 2011
Efficient provenance storage for relational queries
Publisher: ACM
Date: 29-10-2012
Data management of AI-powered education technologies: Challenges and opportunities
Publisher: University of South Australia Library
Date: 2023
DOI: 10.59453//XLUD7002
Abstract: The use of AI-powered educational technologies (AI-EdTech) offers a range of advantages to students, instructors, and educational institutions. While much has been achieved, several challenges in managing the data underpinning AI-EdTech are limiting progress in the field. This paper outlines some of these challenges and argues that data management research has the potential to provide solutions that can enable responsible and effective learner-supporting, teacher-supporting, and institution-supporting AI-EdTech. Our hope is to establish a common ground for collaboration and to foster partnerships among educational experts, AI developers and data management researchers in order to respond effectively to the rapidly evolving global educational landscape and drive the development of AI-EdTech.
Human-in-the-loop Regular Expression Extraction for Single Column Format Inconsistency
Publisher: ACM
Date: 30-04-2023
Employing Peer Review to Evaluate the Quality of Student Generated Content at Scale: A Trust Propagation Approach
Publisher: ACM
Date: 08-06-2021
EISA: An Efficient Information Theoretical Approach to Value Segmentation in Large Databases
Publisher: Springer International Publishing
Date: 2014
Big data quality - whose problem is it?
Publisher: IEEE
Date: 05-2016
Sensemaking in Dual Artefact Tasks – The Case of Business Process Models and Business Rules
Publisher: Springer International Publishing
Date: 2020
Identifying Cohorts: Recommending Drill-Downs Based on Differences in Behaviour for Process Mining
Publisher: Springer International Publishing
Date: 2020
Preface
Publisher: Springer Science and Business Media LLC
Date: 04-2023
Incorporating Training, Self-monitoring and AI-Assistance to Improve Peer Feedback Quality
Publisher: ACM
Date: 06-2022
Advances in Multimedia Sensor Networks for Health-Care and Related Applications
Publisher: SAGE Publications
Date: 06-2015
DOI: 10.1155/2015/596096
FORMAL CONSIDERATIONS OF RULE-BASED MESSAGING FOR BUSINESS PROCESS INTEGRATION
Publisher: Informa UK Limited
Date: 03-2006
Handling dynamic schema change in process models
Publisher: IEEE Comput. Soc
Date: 2000
Context-Aware Attention-Based Data Augmentation for POI Recommendation
Publisher: IEEE
Date: 04-2019
Collaborative Generative Adversarial Network for Recommendation Systems
Publisher: IEEE
Date: 04-2019
On Group Nearest Group Query Processing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
A Survey of Comparative Business Process Modeling Approaches
Publisher: Springer Berlin Heidelberg
Date: 2007
Information Resilience: the nexus of responsible and agile approaches to information use
Publisher: Springer Science and Business Media LLC
Date: 16-01-2022
DOI: 10.1007/S00778-021-00720-2
Abstract: The appetite for effective use of information assets has been steadily rising in both public and private sector organisations. However, whether the information is used for social good or commercial gain, there is a growing recognition of the complex socio-technical challenges associated with balancing the erse demands of regulatory compliance and data privacy, social expectations and ethical use, business process agility and value creation, and scarcity of data science talent. In this vision paper, we present a series of case studies that highlight these interconnected challenges, across a range of application areas. We use the insights from the case studies to introduce Information Resilience, as a scaffold within which the competing requirements of responsible and agile approaches to information use can be positioned. The aim of this paper is to develop and present a manifesto for Information Resilience that can serve as a reference for future research and development in relevant areas of responsible data management.
On the role of human and machine metadata in relevance judgment tasks
Publisher: Elsevier BV
Date: 03-2023
Special Issue of DASFAA 2018
Publisher: Springer Science and Business Media LLC
Date: 17-11-2018
On managing business processes variants
Publisher: Elsevier BV
Date: 07-2009
Personalized Courses Recommendation Functionality for Flex-eL
Publisher: IEEE
Date: 07-2007
Assessing the Quality of Student-Generated Content at Scale: A Comparative Analysis of Peer-Review Models
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
A Data Quality Control Method for Seafloor Observatories: The Application of Observed Time Series Data in the East China Sea
Publisher: MDPI AG
Date: 10-08-2018
DOI: 10.3390/S18082628
Abstract: With the construction and deployment of seafloor observatories around the world, massive amounts of oceanographic measurement data were gathered and transmitted to data centers. The increase in the amount of observed data not only provides support for marine scientific research but also raises the requirements for data quality control, as scientists must ensure that their research outcomes come from high-quality data. In this paper, we first analyzed and defined data quality problems occurring in the East China Sea Seafloor Observatory System (ECSSOS). We then proposed a method to detect and repair the data quality problems of seafloor observatories. Incorporating data statistics and expert knowledge from domain specialists, the proposed method consists of three parts: a general pretest to preprocess data and provide a router for further processing, data outlier detection methods to label suspect data points, and a data interpolation method to fill up missing and suspect data. The autoregressive integrated moving average (ARIMA) model was improved and applied to seafloor observatory data quality control by using a sliding window and cleaning the input modeling data. Furthermore, a quality control flag system was also proposed and applied to describe data quality control results and processing procedure information. The real observed data in ECSSOS were used to implement and test the proposed method. The results demonstrated that the proposed method performed effectively at detecting and repairing data quality problems for seafloor observatory data.
Building Data Curation Processes with Crowd Intelligence
Publisher: Springer International Publishing
Date: 2020
Semi-supervised Clustering with Deep Metric Learning
Publisher: Springer International Publishing
Date: 2019
Towards a Methodology for Deriving Contract-Compliant Business Processes
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11841760_29
Jointly Modeling Heterogeneous Temporal Properties in Location Recommendation
Publisher: Springer International Publishing
Date: 2017
DataOps-4G: On Supporting Generalists in Data Quality Discovery
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Location Oriented Phrase Detection in Microblogs
Publisher: Springer International Publishing
Date: 2014
Incorporating AI and learning analytics to build trustworthy peer assessment systems
Publisher: Wiley
Date: 18-05-2022
DOI: 10.1111/BJET.13233
Abstract: Peer assessment has been recognised as a sustainable and scalable assessment method that promotes higher‐order learning and provides students with fast and detailed feedback on their work. Despite these benefits, some common concerns and criticisms are associated with the use of peer assessments (eg, scarcity of high‐quality feedback from peer student‐assessors and lack of accuracy in assigning a grade to the assessee) that raise questions about their trustworthiness. Consequently, many instructors and educational institutions have been anxious about incorporating peer assessment into their teaching. This paper aims to contribute to the growing literature on how AI and learning analytics may be incorporated to address some of the common concerns associated with peer assessment systems, which in turn can increase their trustworthiness and adoption. In particular, we present and evaluate our AI‐assisted and analytical approaches that aim to (1) offer guidelines and assistance to student‐assessors during in idual reviews to provide better feedback, (2) integrate probabilistic and text analysis inference models to improve the accuracy of the assigned grades, (3) develop feedback on reviews strategies that enable peer assessors to review the work of each other, and (4) employ a spot‐checking mechanism to assist instructors in optimally overseeing the peer assessment process. What is already known about this topic Engaging students in peer assessment has been demonstrated to have various benefits. However, there are some common concerns associated with employing peer assessment that raise questions about their trustworthiness as an assessment item. What this paper adds Methods and processes on how AI and learning analytics may be incorporated to address some of the common concerns associated with peer assessment systems, which in turn, can increase their trustworthiness and adoption. Implications for practice Presentation of a systematic approach for development, deployment and evaluation of AI and analytics approaches in peer assessment systems.
Prologue: Research and Practice in Data Quality Management
Publisher: Springer Berlin Heidelberg
Date: 2013
Analyzing process models using graph reduction techniques
Publisher: Elsevier BV
Date: 04-2000
Modelling User Behavior Dynamics with Embeddings
Publisher: ACM
Date: 19-10-2020
Guidelines for Business Rule Modeling Decisions
Publisher: Informa UK Limited
Date: 14-04-2017
A framework for data quality aware query systems
Publisher: Elsevier BV
Date: 12-2014
Compliance checking between business processes and business contracts
Publisher: IEEE
Date: 10-2006
DOI: 10.1109/EDOC.2006.22
On Capturing Exceptions in Workflow Process Models
Publisher: Springer London
Date: 2000
Finding the Optimal Path in 3D Spaces Using EDAs – The Wireless Sensor Networks Scenario
Publisher: Springer Berlin Heidelberg
Date: 2007
Evie - A developer toolkit for encoding service interaction patterns
Publisher: Springer Science and Business Media LLC
Date: 06-06-2008
Modelling Learners in Adaptive Educational Systems: A Multivariate Glicko-based Approach
Publisher: ACM
Date: 12-04-2021
Evie – A Developers Toolkit for Encoding Service Interaction Patterns
Publisher: Springer Berlin Heidelberg
Date: 2008
Active Duplicate Detection
Publisher: Springer Berlin Heidelberg
Date: 2010
Recommending Insightful Drill-Downs Based on Learning Processes for Learning Analytics Dashboards
Publisher: Springer International Publishing
Date: 2020
Discovering interpretable geo-social communities for user behavior prediction
Publisher: IEEE
Date: 05-2016
Evaluating the quality of learning resources: a Learnersourcing approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Efficient Aggregate Farthest Neighbour Query Processing on Road Networks
Publisher: Springer International Publishing
Date: 2014
Facilitating Business Process Improvement through Personalized Recommendation
Publisher: Springer Berlin Heidelberg
Date: 2011
SPORE: A sequential personalized spatial item recommender system
Publisher: IEEE
Date: 05-2016
Neurophysiological Measurements in Higher Education: A Systematic Literature Review
Publisher: Springer Science and Business Media LLC
Date: 16-06-2021
Related Organisations
E O Lawrence Berkeley National Laboratory
Location: United States of America
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