Although several machine learning driven solutions are deemed to be effective at detecting data breaches, the recent proliferation in data breach incidents resulting from cyber attacks on computer networks demands an updated, thorough analysis of Machine Learning (ML) based data breach countermeasures to identify research gaps and guide future studies. In view of this, this study employs a systematic approach and draws insight from 89 research articles to classify machine learning based data breach countermeasures using eight criteria namely learning tasks, learning classifiers, datasets, feature engineering methods, multimodal approaches, pre-training approaches and performance. In classifying the studies, we: (a) propose a taxonomy of feature extraction and representation to classify studies using ten sub-criteria, (b) classify multimodal machine learning approaches used in the studies into three fusion sub-criteria: namely early fusion, intermediate fusion and late fusion, (c) show a comparison of studies based on pre-training techniques employed such as pre-text objective, learning model and data used in pre-training, (d) classify the datasets used in the study evaluation into two categories: real dataset and simulated dataset and (e) evaluate studies by detection performance and effectiveness against data breaches on unknown and obfuscated network traffic. To aid the literature identification, we analyse forty recent incidents and obtain prevalent cyber attack vectors of data breaches, which we present as the general workflow for data breaches due to cyber attacks. Finally, we highlight the research issues associated with existing ML-based data breach countermeasures and recommend future research directions.

Cyber-attacks on industrial applications, specifically, phishing and web attacks are the most common data breach vectors and, as such, have attracted significant research attention. To mitigate these types of attacks, many countermeasures based on machine learning (ML) have been proposed. Although ML-based countermeasures are reported to yield satisfactory detection performance on phishing and web attacks, they often require massive amounts of manually labelled email and web request data to build these countermeasures. The manual generation of labels, however, can be laborious, error-prone and infeasible to scale. To cope with the evolution of web attacks and phishing emails, methods which exploit the vast volumes of unlabelled email texts and web request data should be adopted. Recent studies have primarily employed sequential models such as BERT, to learn semantic contextual features from unlabelled email and HTTP request text. In this study, we take a step further by extracting complementary features from unlabelled email and web request data represented in two-dimensional structures. Our method applies computer vision-based transformations to these structured representations and employing a self-supervise learning approach, we pre-train a convolutional neural network model to recognise these transformations, enabling the model to learn syntactic structural features from unlabelled data. We then adopt concatenation as our ensemble strategy to combine contextual and syntactic features, yielding robust representation of email and HTTP request text, which we leverage in a downstream fully connected neural network model for phishing and web attack detection. Extensive experiments conducted on three phishing datasets including Nazario, Enron and Subhadeep’s email corpus, as well as the benchmark SR-BH web attack dataset show that, the proposed method outperforms baseline sequential models developed in this work, which rely solely on contextual representations for detecting phishing and web attacks.

Federated Learning (FL) is a machine learning paradigm that allows multiple local clients to collaboratively train a global model by sharing their model parameters instead of private data, thereby mitigating privacy leakage. However, recent studies have shown that gradient-based Data Reconstruction Attack (DRA) can still expose private information by exploiting model parameters from local clients. Existing privacy-preserving FL strategies provide some defense against these attacks, but at the cost of significantly reduced model accuracy. Moreover, the issue of client heterogeneity, particularly in Non-Identical and Independent Distributions (Non-IID) clients, further exacerbates these FL methods, resulting in drifted global models, slower convergence, and decreased performance. This study aims to address the two main challenges of FL: Non-IID data and client privacy through DRA. To this end, it leverages the lagrangian duality approach and incorporates a generator model to enable Knowledge Distillation (KD) among clients. By facilitating improved local model performance through inter-client knowledge transfer, the proposed method aims to simultaneously address the practical challenges commonly encountered by FL systems. Our study demonstrates a remarkable improvement in model accuracy, with KD boosting it by up to $15 \%$ on CIFAR-10 and MNIST classification tasks in Non-IID client settings. Furthermore, we propose an aggregation algorithm that inherently preserves client data privacy during the training phase, offering resilience against DRA.

In attribute-based access control (ABAC), access to resources depends on the specific attributes of the entity requesting access. Existing ABAC models primarily depend on local attribute authorities to define and confirm attributes, which makes it challenging to support access decisions cross-domains without introducing centralization. Centralized solutions often conflict with individual domains’ security, privacy, and control requirements and, if compromised for any reason, can impact access to large datasets across participating domains. This paper introduces a novel access control model for cross-domain environments that significantly reduces central control. Our decentralized ABAC (D-ABAC) model uses group signature techniques to exchange attribute information securely and privately within cross-domains. Each domain maintains its own policies and attribute authorities, reducing the need for global trust or centralization to mutual trust between attribute authorities. We further design and implement a proof-of-concept system to demonstrate the practical feasibility of our proposed system for the collaborative and secure sharing of healthcare data in cross-domain environments. The proposed system model enhances security, scalability, and privacy in cross-domain settings, making it suitable for sensitive environments such as healthcare.

Online Social Network (OSN) botnet attacks pose a growing threat to the cloud environment and reduce the services’ availability and reliability for users by launching distributed denial of service (DDoS) attacks on crucial servers in the cloud. These attacks involve the deployment of sophisticated botnets that exploit the interconnected nature of social networks to identify targets, exploit vulnerabilities, and launch attacks. The prevalence and impact of these botnet-driven attacks have recently been studied. Although the detection of these botnet attacks is still a challenging process, it remains crucial to gain a comprehensive understanding of and evaluate the best defense strategies against botnet attacks. This evaluation can be further utilized to formulate effective defense plans to mitigate the impact of such botnet attacks. In this paper, we first investigate the properties of OSN botnet attack stages that eventually lead to launching DDoS attacks toward a cloud system. Then, we formalize a defensive model using a sequential game model to analyze both the attacker’s and defenders’ best equilibrium strategies for the proposed botnet attack scenario. Moreover, we formulate optimal strategies for the defender against various attack strategies. Our experiments reveal the best defense strategies against various attack rates to maintain cloud functionality. Finally, we discuss possible countermeasures for these OSN botnet threats.

Satellite communications play an increasingly important role in a number of different industries with the rise of the Internet of Things (IoT). IoT-driven satellite (‘IoT-Satellite’ in short) networks have a number of vulnerabilities that can make them targets of common cyber attacks such as jamming and spoofing. These attacks can potentially be highly disruptive to the services they support. This paper presents a comprehensive survey of cyber threats and vulnerabilities with a focus on application areas in IoT-Satellite networks. Cyber threats include spoofing, jamming, malware and denial-of-service (DoS) attacks. Vulnerabilities in IoT-Driven satellite include deficits in encryption, access control and vulnerabilities in commercial off-the-shelf (COTS) parts. Subsequently, proposed in-depth solutions are also discussed in this paper. Proposed solutions include zero-trust security, software-defined networking, dynamic and context-based access control, blockchain and artificial intelligence (AI) approaches. While there are limited surveys specifically addressing IoT-driven satellite networks, we identify relevant studies and compare them with our work. Based on these findings we describe open research issues and potential future areas of research. The study suggests that further research is needed to develop a security framework for IoT-driven satellite networks, addressing prevalent cyber attacks and mitigating strategies.

Privacy breaches have become increasingly prevalent, exposing individuals to significant risks. These breaches can have far-reaching consequences, including identity theft and life-threatening situations. Several studies have analyzed data and privacy breaches and presented detection or prevention techniques to combat these breaches. However, because the number and type of breaches have significantly increased, these studies have become less relevant or outdated. Previous research on data and privacy breaches compared the techniques and results of various studies. However, none comprehensively analyzed the type of information and the level and severity of compromise that occurred after such breaches. In this survey, we examine the fundamental concepts of privacy and security and define the security incidents and data/privacy breaches. We propose a set of criteria to evaluate the published studies on privacy breaches. We thoroughly investigate the problem domains and security-related concerns considering six recent breach cases in Australia, elucidating the critical challenges and issues associated with privacy breaches. We comprehensively review and outline the trends and severity of security incidents and data/privacy breaches from 2020 to 2024. Additionally, we review the current state-of-the-art countermeasures to safeguard against these breaches. Finally, we identify an open research direction to develop an artificial intelligence (AI)-powered security framework. This framework aims to analyze cyber threats, characterize attackers’ behaviors, distinguish between legitimate and illegitimate privacy policies, and restrict access to individuals’ information. Overall, this survey will help organizations to reassess and update their security and privacy measures.

With the recent advances of the Internet of Things (IoT), innovative techniques, and concepts have emerged, such as digital twins and industrial 4.0. As one of the essential parts of a digital twin, IoT-based smart predictive maintenance (IoT-SPM) is a key enabling technology for smart manufacturing. This article introduces digital twins and their relationship to IoT-SPM and proposes a reference IoT-SPM, aiming to provide a comprehensive and systematic outlook for the IoT-SPM field. Thus, it can be used as a guide map for interested readers. To give a complete picture of the IoT-SPM ecosystem in industrial 4.0 systems, this article conducts an analysis from multiview perspectives, starting with the architecture, followed by platforms and component. The key components or requirements of an IoT-SPM ecosystem are identified and outlined, including the IoT and cyber–physical system (CPS) as the cornerstone technologies, IoT monitoring data as the base, big data platforms as the backbone, an upgraded computing paradigm as the catalyst, and machine learning-based data analysis as the main processor. This article also focuses on the issues surrounding IoT data when applying analytic models to a real-world industrial IoT system. Then, the current progresses relating IoT and IoT-SPM are depicted, and a research gap on IoT data quality is identified. In particular, regarding the identified IoT data quality problems, this article qualitatively evaluates and discusses the existing solutions. These discussions lead to several open research issues and future directions.

The Industrial Internet of Things (IIoT) enables intelligent predictive maintenance in smart manufacturing by incorporating IoT technologies, Big Data techniques, artificial intelligence, cloud computing, and other ever-developing enabling technologies. Although a large body of research has been conducted on IIoT based predictive maintenance, most work focuses on addressing only a part of the problem. However, predictive maintenance involves an ecosystem from ingesting data from sensors to displaying the results on a dashboard for engineers to visualize. With increasing requirements for real-time responses and privacy, integrating edge computing is no doubt a promising trend. This article proposes a complete and optimized IoT Big Data ecosystem embedded into a three-layer architecture for predictive maintenance applications. The proposed architecture consists of an edge layer, a cloud layer, and an application layer. The proposed edge infrastructure distributes the tasks effectively between the cloud layer and edge layer. On top of the architecture, different layers are integrated seamlessly to address reliability and scalability issues. In addition, an edge computing-assisted autoencoder is introduced and enabled by being deployed in a distributed manner to improve both performance and efficiency. For practical interest, we also provide an application programming interface-oriented implementation guideline for readers. To verify the proposed ecosystem, a real case study from industry is conducted to demonstrate the performance gain of edge computing-based Internet of Things systems in conjunction with the autoencoder-based deep learning technique.

With the rapid growth in the use of various smart digital sensors, the Internet of Things (IoT) is a swiftly growing technology, which has contributed significantly to Industry 4.0 and the promotion of IoT-based smart factories, which gives rise to the new challenges of big data analytics and the implementation of machine learning techniques. This article proposes a practical framework that combines IoT techniques, a data lake, data analysis, and cloud computing for manufacturing equipment health-state monitoring and diagnostics in smart manufacturing. It addresses all the required aspects in the realization of such a system and allows the seamless interchange of data and functionality. Due to the specific characteristics of IoT sensor data (low quality, redundant multisources, partial labeling), we not only provide a promising framework but also give detailed insights and pay considerable attention to data quality issues. In the proposed framework, an ingestion procedure is designed to manage data collection, data security, data transformation and data storage issues. To improve the quality of IoT big data, a high-noise feature filter is proposed for automated preliminary sensor selection to suppress noisy features, followed by a noisy data cleaning module to provide good quality data for unbiased diagnosis modeling. The proposed framework can achieve seamless integration between IoT big data ingestion from the physical factory and machine learning-based data analytics in the virtual systems. It is built on top of the Apache Spark processing engine, being capable of working in both big data and real-time environments. One case study has been conducted based on a four-stage syngas compressor from real industries, which won the Best Industry Application of IoT at the BigInsights Data & AI Innovation Awards. The experimental results demonstrate the effectiveness of both the proposed IoT-architecture and techniques to address the data quality issues.

The emergence of Industry 4.0 and the rapid advances in the Industrial Internet of Things (IIoT) have provided manufacturers with the ability to remotely monitor the process by deploying automatic fault detection in an IoT-based predictive maintenance system. However, the monitoring targets are now manufacturing plant-wide instead of being just a local area. Multiple types of faults are involved and the conventional centralized cloud computing-based IoT solutions always lead to a heavy burden on the network bandwidth due to the large amount of sensor data collected frequently that has to be transmitted to the central server and this leads to poor response time for the monitoring system. To address this problem, this article develops an artificial intelligence-assisted distributed system for manufacturing plant-wide predictive maintenance applications. The developed distributed system relies on the feature selection technique to identity an optimal feature subset for each type of fault and is enabled by deploying each independent model built on the obtained feature subset into different edge nodes. The distributed approach enables the data to be processed near the sensors, requiring less data to be transmitted to the central cloud server reducing network delay and delivering more accurate results. In addition, our proposed feature selection approach is especially designed to accommodate the characteristics of IIoT data such as the lack of labels. The effectiveness of the proposed method is validated using the widely used public Tennessee Eastman dataset.

Summary As a crowdsourcing map platform, OpenStreetMap (OSM) relies on public contributions to enhance its dataset where the contributors can create, modify or remove features from the maps or share their trajectory trips in the repository. The majority of the data provided in a crowdsourcing platform are manually created and reviewed to suit real‐world conditions, hence human perception is the key indicator to consider the correctness of the data. One of the data that is provided by crowdsourcing platform is public trajectory. Public trajectory data contains details of historical trips obtained from contributors' GPS logger devices that are embedded in mobile devices, wearable devices, satnavs, or vehicle GPS trackers to record the user's trajectory path. While public trajectory data can be used as an alternate data source for human movement analysis, this crowdsourced dataset is also prone to noise and inaccuracy which makes the preprocessing step an important phase prior of any processing step. In this article, we discuss the characteristics and the most common noise from crowdsourcing GPS trajectories and utilize a non‐map‐matching approach convex hull‐based reduction method to minimize spike noise, followed by granularity reduction to reduce the number of trajectory points while maintaining the nature of the trajectories.

In recent years many user-oriented and personalized computing technologies have been developed, in which users are immersed in a virtual world and surrounded by processing units. Such computing technologies require distributed signals to be collected, and perform intelligent analysis with data fusion depending on user preferences and the surrounding environment. In such human-oriented analysis, it is also necessary to consider different user preferences and even behavioral factors, that influence the final computing results. Development of user-oriented computing approaches are especially apparent in virtual reality and interactive technologies, multimedia, and decision-making systems, as well as user-oriented security protocols. Such human-oriented protocols allow the intelligent analysis of a great amount of information, perform analytics processes, extract meaning and manage systems in a secure manner. These subjects, as well as a number of others, such as personalized protocols for data analysis and security, computing approaches based on behavioral or perceptual factors, and bio-inspired technologies for knowledge extraction, will form the topics of this Special Issue on “Human oriented solutions for intelligent analysis, multimedia and communication systems” in the journal Concurrency and Computation: Practice and Experience. For this Special Issue eleven articles of particular interest were selected, which present the most interesting research activities and results within the subject matter of this special issue. The article “Towards human oriented solutions for deep semantic data analysis” by Ogiela and Snasel,1 presents novel solutions for efficient semantic analysis of data on the basis of cognitive reasoning and an assessment of marketing preferences registered in the course of the human perception process. Obtaining information from data on the basis of its interpreted meaning, with a view to determine individual preferences, makes it possible to designate the set of features on whose occurrence (or absence) attention is focused and those features whose occurrence has an impact on ‘interest’ within a given piece of information, product, service, and so forth. The approach presented is based on application of cognitive resonance processes implemented in cognitive information systems. The article entitled “A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme” by Park et al.2 presents studies on a method for processing analysis modules that can enrich result datasets with context information based on a data abstraction model. Data abstraction provides not only capabilities for context-aware systems and users to inspect the context at four levels from raw datasets to situational relationships, but also supports unified context levels for each entity that can be deployed at any location where systems deal with context to provide dedicated services. The article “Customer-oriented sales modeling strategy in a big data environment” by Chen et al.3 presents a new idea for a data mining technology application in business services. Different factors are analyzed, which may affect the profit of shopping malls in a big data environment and the most critical factors are found by data mining technology. This allows different sales promotion strategies to be provided for merchants to facilitate the expansion of sales. In management, small profits and quick returns is a popular sales strategy used by many shopping malls to increase turnover. The article “An online cognitive authentication and trust evaluation application programming interface for cognitive security gateway based on distributed massive Internet of Things network” by Chen et al.4 presents a new online cognitive authentication and trust evaluation API for CSG based on distributed massive IoT network. An online identity generation API is proposed, together with the modified EPC Class 1 Gen2 tag translator which is used to create both provider's as well as client's online identities. The article entitled “Dealing with Noise in Crowdsourced GPS Human Trajectory Logging Data” by Adhinugraha et al.5 presents new solutions for classifying the noise that might be found from public GPS traces. More than 5300 trajectories that started in the state of Victoria, Australia, were considered, and noise was classified into four types: spike noise, point noise, track noise, and logical noise. The authors tested the behavior of noise when processed with convex hull-based non-map-matching preprocessing methods to reduce spikes, followed by granularity reduction to reduce point density. In the article “An Effective Architecture of Digital Twin System to Support Human Decision Making and AI-Driven Autonomy” by Mostafa et al.6 a data analytic maturity model is presented, which consists of four phases with ordered activities. It shows that any data analytic project needs to be gradually developed from foundations to powerful AI algorithms. The effort and time spent on a routine will create an exponential increase in business value. The digital twin starts in phase two which immediately follows the event that the big data infrastructure is established. It is started by shallowly replicating the characteristics, features and states of its physical twin, and then dives deeper to copy its behaviors, which is achieved by AI technologies, typically machine learning models. The article “On the Undetectability of Payloads generated through Automatic Tools: a Human-oriented Approach” by Carpentieri et al.7 describes tools for the automatic generation of custom executable payloads. Such payloads typically enable to improve the interaction between the security experts and the asset under evaluation. However, due to the actions they take (i.e., remote access, privilege escalation, and so forth), these payloads are most likely classified as malicious by AVs, thus preventing their execution on a system. This article aims to provide the research community with an awareness of the possible security threats that can arise from automated tools commonly used in security assessment processes. The article entitled “QoS-aware Big Service Composition using Distributed Co-Evolutionary Algorithm” by Dutta et al.8 presents an efficient QoS-aware big service composition model using a distributed co-evolutionary algorithm in Spark. In the proposed model the authors designed a distributed NSGA-III for finding the optimal Pareto front and a distributed multi-objective algorithm to compare the solutions of NSGA-III. They also discuss the parallel implementation of distributed co-evolutionary algorithm that makes the algorithms faster and scalable to find the near-optimal solution. The article entitled “Probability and Topic-Based Data Transmission Protocol” by Saito et al.9 presents the MPSFC model to efficiently implement the IoT, where mobile fog nodes such as vehicles communicate with other nodes over wireless networks. Here, each fog node calculates output data on input data received from other fog nodes and forwards the output data to other fog nodes in the epidemic routing way. The authors proposed the TTLBDT and PTBDT protocols and compared them with the TBDT protocol. The article “Implementation and evaluation of the information flow control for the Internet of Things” by Nakamura et al.10 describes the OI protocol and evaluates the authorization process of the OI protocol in terms of the execution time. In the evaluation, the authors make clear the features of the execution time of authorization processes for GET, PUT, POST, and DELETE operations in the OI protocol. The OI protocol was implemented on a hybrid device realized in Raspberry Pi 3 Model B+. The article entitled “Chatbots: Security, Privacy, Data Protection and Social Aspects” by Hasal et al.11 presents all security aspects concerning communication with chatbots. It provides a review describing important steps in chatbot design techniques considering the chatbot security. It also defines possible security threats and vulnerabilities, and presents detailed methods which allow the development of a safe chatbot platform. We believe that the articles included in this Special Issue will have a great impact for future scientific research, and also contribute to the studies conducted by other researchers and practitioners, who work in the area of advanced information processing systems and computer security. We would like to express my sincere appreciation of the valuable contributions made by all authors. We'd like also to express our special thanks to Professor David W. Walker from the School of Computer Science and Informatics, Cardiff University, UK, Editor-in-Chief of Concurrency and Computation: Practice and Experience, for allowing the publication of this Special Issue, and for his great support throughout the entire publication process. "Data sharing not applicable to this article as no datasets were generated or analysed during the current study"

Over the last decade, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of continuous streaming data. A massive amount of IoT data will be generated in the future. Therefore, it is necessary to create more sophisticated frameworks to measure IoT data quality, considering relevant attributes such as the freshness, reliability and trustworthiness of IoT data. Existing data freshness models and frameworks mostly depend on the timestamp. However, the frequency of IoT data (e.g., data generated by sensors which is measured per millisecond or minute) needs to be considered, that is, IoT data can change frequently. We introduce a new model for measuring IoT data freshness. In our model, we define unreliable IoT data and discard them while considering fresh data. We introduce a formal approach to IoT data freshness including the underlying concepts and definitions. Using this formal approach, we propose an algorithm for the numerical calculation of the freshness attributes. We conduct several sets of experiments and demonstrate the feasibility of the proposed framework by quantifying the performance of the freshness measurement algorithm. We also demonstrate the capability of the framework to capture freshly generated IoT data through a software prototype and several case studies. Finally, we provide a roadmap for future research considering other IoT data quality attributes, such as reliability and trustworthiness.

In recent years, the Internet-of-Things (IoT)-oriented smart manufacturing has become a prominent solution in realizing evolutional digital transformation. Missing data are one of the biggest problems for data preprocessing in an IoT architecture, and it is crucial that missing values are recovered to improve the reliability of monitoring applications. However, due to the high-frequency collection of sensor data, missing data in IoT bring new challenges. Several methods have been developed to recover missing IoT data by utilizing data from sensors that are geographically close to the sensor which is responsible for the missing data, or from sensors which provide data that are highly correlated to the missing data. In IoT systems, because of the transmission of a large volume of data over networks, common mode failures need to be considered where a single event can lead to the loss of data from a large number of sensors. In this situation, it would be infeasible to recover missing data from other sensors. To address this issue, in this article, we focus on missing data imputation for large gaps in univariate time-series data and propose an iterative framework using multiple segmented gap iteration called Itr-MS-STLecImp to provide the most appropriate values. The gap is first segmented into several pieces to initialize the missing value imputation process and then, we iteratively run gap reconstruction and gap concatenation to obtain the final imputation results. We validate the proposed approach using sensor data collected from real manufacturing plants in Australia and the comparison results show that the proposed Itr-MS-STLecImp outperforms the state-of-the-art methods in terms of root-mean-square error. Under different gap-length conditions, the proposed approach consistently reduces the error rate more than the baseline algorithm, and the error reduction is greater when the lengths of the gaps increase, indicating that the performance is significantly improved. These analysis results further prove the effectiveness of the multiple segmentation of missing gaps and the iteration operation.

The emergence of the Industrial Internet of Things (IIoT) to enhance manufacturing and industrial processes allows data analysts to address significant problems such as predictive maintenance. For the purpose of accurate data analysis, cleansing noisy sensor data is one of the most fundamental and necessary steps. Without first removing the noise, the anomaly detection techniques are likely to give a large number of false positives. However, using traditional outlier detection methods directly for such analysis are not appropriate as both noise and significant anomalies might exist in the sensor data. This article introduces the new challenges and proposes a novel solution to address the issue of removing noise while preserving anomalies in the IIoT Data. It proposes an approach that measures both the rate of change and deviation to compute the noise score. It employs a sliding window technique to define the analysis unit of the contrast measure which is used in conjunction with statistical techniques. Extensive experiments demonstrate that the proposed approach outperforms the other state-of-the-art noise detection methods, providing a clean data set that preserves the anomalies on which one can effectively apply anomaly detection techniques.

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

Distributed deep learning systems (DDLS) train deep neural network models by utilizing the distributed resources of a cluster. Developers of DDLS are required to make many decisions to process their particular workloads in their chosen environment efficiently. The advent of GPU-based deep learning, the ever-increasing size of datasets, and deep neural network models, in combination with the bandwidth constraints that exist in cluster environments require developers of DDLS to be innovative in order to train high-quality models quickly. Comparing DDLS side-by-side is difficult due to their extensive feature lists and architectural deviations. We aim to shine some light on the fundamental principles that are at work when training deep neural networks in a cluster of independent machines by analyzing the general properties associated with training deep learning models and how such workloads can be distributed in a cluster to achieve collaborative model training. Thereby we provide an overview of the different techniques that are used by contemporary DDLS and discuss their influence and implications on the training process. To conceptualize and compare DDLS, we group different techniques into categories, thus establishing a taxonomy of distributed deep learning systems.

In today’s dynamic ICT environments, the ability to control users’ access to information resources and services has become ever important. On the one hand, it should provide flexibility to adapt to the users’ changing needs, while on the other hand, it should not be compromised. The user is often faced with different contexts and environments that may change the user’s information needs. To allow for this, it is essential to incorporate the dynamically changing context information into the access control policies to reflect different contexts and environments through the use of a new context-aware access control (CAAC) approach with both dynamic associations of user-role and role-permission capabilities. Our proposed CAAC framework differs from the existing access control frameworks in that it supports context-sensitive access control to information resources and dynamically re-evaluates the access control decisions when there are dynamic changes to the context. It uses the dynamic context information to specify the user-role and role-permission assignment policies. We first present a formal policy model for our framework, specifying CAAC policies. Using this model, we then introduce a policy ontology for modeling CAAC policies and a policy enforcement architecture which supports access to resources according to the dynamically changing context information. In addition, we demonstrate the feasibility of our framework by considering (i) the completeness, correctness and consistency of the ontology concepts through application to healthcare scenarios and (ii) the performance and usability testing of the framework when using desktop and mobile-based prototypes.

As mobile devices evolve to be powerful and pervasive computing tools, their usage also continues to increase rapidly. However, mobile device users frequently experience problems when running intensive applications on the device itself, or offloading to remote clouds, due to resource shortage and connectivity issues. Ironically, most users’ environments are saturated with devices with significant computational resources. This paper argues that nearby mobile devices can efficiently be utilised as a crowd-powered resource cloud to complement the remote clouds. Node heterogeneity, unknown worker capability, and dynamism are identified as essential challenges to be addressed when scheduling work among nearby mobile devices. We present a work-sharing model, called Honeybee, using an adaptation of the well-known work stealing method to load balance independent jobs among heterogeneous mobile nodes, able to accommodate nodes randomly leaving and joining the system. The overall strategy of Honeybee is to focus on short-term goals, taking advantage of opportunities as they arise, based on the concepts of proactive workers and opportunistic delegator. We evaluate our model using a prototype framework built using Android and implement two applications. We report speedups of up to four with seven devices and energy savings up to 71 percent witheight devices.

Human activity recognition using embedded mobile and embedded sensors is becoming increasingly important. Scaling up from individuals to groups, that is, Group Activity Recognition (GAR), has attracted significant attention recently. This article proposes a model and modeling language for GAR called GroupSense-L and a novel distributed middleware called GroupSense for mobile GAR. We implemented and tested GroupSense using smartphone sensors, smartwatch sensors, and embedded sensors in things, where we have a protocol for these different devices to exchange information required for GAR. A range of continuous group activities (from simple to fairly complex) illustrates our approach and demonstrates the feasibility of our model and richness of the proposed specialization. We then conclude with lessons learned for GAR and future work.

In role-based access control, roles are mostly organized in static hierarchies and users are authorized to play such roles in order to exercise the organizational functions. However, some of these roles cannot be organized in the same way in static hierarchies as the authorizations granted to such roles are strictly related to the dynamically changing contextual conditions (e.g., health profile information). Users need to satisfy these conditions in order to exercise the functions of such dynamic contextual roles. While several research works have been done in dynamic activation of static roles, no extensive research has been undertaken in the area of dynamic specification of contextual roles. This article makes a significant research contribution to the dynamic contextual role modeling and activation. We introduce both formal and ontology-based approaches in order to model the dynamic contextual roles and specify the context-aware access control policies by activating such dynamic roles at runtime. These contextual roles are equally important because of the demands of large-scale (pervasive) environments to control context-sensitive access to resources at different granularity levels with low processing overheads. We develop a software prototype to demonstrate the feasibility of our proposal and provide a walkthrough of the whole mechanism. Experimental results demonstrate the satisfactory performance of our proposed approach compared to our previous approach.

Many distributed deep learning systems have been published over the past few years, often accompanied by impressive performance claims. In practice these figures are often achieved in high performance computing (HPC) environments with fast InfiniBand network connections. For average deep learning practitioners this is usually an unrealistic scenario, since they cannot afford access to these facilities. Simple re-implementations of algorithms such as EASGD [1] for standard Ethernet environments often fail to replicate the scalability and performance of the original works [2] . In this paper, we explore this particular problem domain and present MPCA SGD, a method for distributed training of deep neural networks that is specifically designed to run in low-budget environments. MPCA SGD tries to make the best possible use of available resources, and can operate well if network bandwidth is constrained. Furthermore, MPCA SGD runs on top of the popular Apache Spark [3] framework. Thus, it can easily be deployed in existing data centers and office environments where Spark is already used. When training large deep learning models in a gigabit Ethernet cluster, MPCA SGD achieves significantly faster convergence rates than many popular alternatives. For example, MPCA SGD can train ResNet-152 [4] up to 5.3x faster than state-of-the-art systems like MXNet [5] , up to 5.3x faster than bulk-synchronous systems like SparkNet [6] and up to 5.3x faster than decentral asynchronous systems like EASGD [1] .

A domain ontology is an ontology that describes the fundamental knowledge of the domain, including the domain vocabulary, concepts, taxonomy, relations, properties, constraints, and axioms. The creation of a domain ontology may be done manually from scratch by domain experts or translated from existing knowledge sources. Our earlier work in [4] presented a framework on building an ontology by matching and merging existing domain ontologies. In this paper, we expand the framework to include different types of community knowledge representations in a common ontology. A common ontology is a domain ontology that is developed from community knowledge by gathering their commonality. To build a common ontology, we standardize different community data format using a schema mediation notation. We apply rule-based mapping and information extraction methodology, and we perform matching, clustering and merging, to collect common knowledge together. The evaluation shows that our proposed framework can build a valid and rich common ontology.

Despite1 the increase in drone popularity, still, there are numerous issues to be solved. But there are many beneficial applications that can be derived through the use of drones combined with other available, scalable and growing technologies such as mobile devices, smartwatches, and add-on electronic sensors. In this paper, we propose and investigate an approach to using drones acting as servers (edge computing style) in order to collect data, provide Internet access, and process data for mobile users.

Understanding the influence of a product is crucially important for making informed business decisions. This paper introduces a new type of skyline queries, called uncertain reverse skyline, for measuring the influence of a probabilistic product in uncertain data settings. More specifically, given a dataset of probabilistic products P and a set of customers C, an uncertain reverse skyline of a probabilistic product q retrieves all customers c ∈ C which include q as one of their preferred products. We present efficient pruning ideas and techniques for processing the uncertain reverse skyline query of a probabilistic product using R-Tree data index. We also present an efficient parallel approach to compute the uncertain reverse skyline and influence score of a probabilistic product. Our approach significantly outperforms the baseline approach derived from the existing literature. The efficiency of our approach is demonstrated by conducting experiments with both real and synthetic datasets.

Human activity recognition using mobile sensors is becoming increasingly important. Scaling up from individuals to groups, that is, Group Activity Recognition (GAR), has attracted significant attention recently. This paper investigates energy consumption for GAR and proposes a novel distributed middleware called GroupSense for mobile GAR. We implemented and tested GroupSense, which incorporates a protocol for the exchange of information required for GAR. We also investigated the battery drain of continuous activity recognition in a range of simple GAR scenarios. We then conclude with lessons learnt for GAR.

Ontology is widely used as a mean to represent and share common concepts and knowledge from a particular domain or specialisation. As a knowledge representation, the knowledge within an ontology must be able to evolve along with the recent changes and updates within the community practice. In this paper, we propose a new Ontology-based Information Extraction (OBIE) system that extends existing systems in order to enrich and validate an ontology. Our model enables the ontology to find related recent knowledge in the domain from communities, by exploiting their underlying knowledge as keywords. The knowledge extraction process uses ontology-based and pattern-based information extraction technique. Not only the extracted knowledge enriches the ontology, it also validates contradictory instance-related statements within the ontology that is no longer relevant to recent practices. We determine a confidence value during the enrichment and validation process to ensure the stability of the enriched ontology. We implement the model and present a case study in herbal medicine domain. The result of the enrichment and validation process shows promising results. Moreover, we analyse how our proposed model contributes to the achievement of a richer and stable ontology.

In a mobile Peer-to-Peer (P2P) environment, where inherent resource constraints (e.g. battery, bandwidth and computing power) exist, the notion of reliability and efficiency especially around communication of messages between peers is a crucial factor. In this paper, we introduce a trustworthy token-passing multi-point relays (TOP) data dissemination scheme for moving objects in wireless ad-hoc networks. The proposed scheme encompasses the trustworthy model and location-based scheduling technique capable of determining the most optimal schedule for the target peers to receive messages based on the location and mobility parameter. The performance of the proposed approach is compared against the existing state of techniques namely pure flooding and Trustworthiness-based Broadcast (TBB) scheme. The experimental evaluation includes a number of metrics, such as transmission cost, computational cost and message deliverability, of which the results showed promising performance of the proposed scheme.

Skyline queries play an important role in multi-criteria decision making applications of many areas. Given a dataset of objects, a skyline query retrieves data objects that are not dominated by any other data object in the dataset. Unlike standard skyline queries where the different aspects of data objects are compared directly, dynamic and reverse skyline queries adhere to the around-by semantics, which is realized by comparing the relative distances of the data objects w.r.t. a given query. Though, there are a number of works on parallelizing the standard skyline queries, only a few works are devoted to the parallel computation of dynamic and reverse skyline queries. This paper presents an efficient quad-tree based data indexing scheme, called Q+Tree, for parallelizing the computations of the dynamic and reverse skyline queries. We compare the performance of Q+Tree with an existing quad-tree based indexing scheme. We also present several optimization heuristics to improve the performance of both of the indexing schemes further. Experimentation with both real and synthetic datasets verifies the efficiency of the proposed indexing scheme and optimization heuristics.

Evolving intelligent system (EIS) is a machine learning algorithm, specifically designed to deal with learning from large data streams. Although the EIS research topic has attracted various contributions over the past decade, the issue of uncertainty, temporal system dynamic, and system order are relatively unexplored by existing studies. A novel EIS, namely evolving type-2 recurrent fuzzy neural network (eT2RFNN) is proposed in this paper. eT2RFNN features a novel recurrent network architecture, possessing double local recurrent connections. It generates a generalized interval type-2 fuzzy rule, where an interval type-2 multivariate Gaussian function constructs the rule premise, and the rule consequent is crafted by the nonlinear wavelet function. eT2RFNN adopts an open structure, where it can start learning process from scratch with an empty rule base. Fuzzy rules can be automatically generated according to degree of nonlinearity data stream conveys. It can performs a rule base simplification procedure by pruning and merging inactive, outdated and overlapping rules. eT2RFNN can deal with the high dimensionality problem, where an online dimensionality reduction method is integrated in the training process. The efficacy of the eT2RFNN has been numerically validated using two real-world data streams, where it provides high predictive accuracy, while retaining low complexity.

No abstract available.

This paper presents an object-based method for indexing recursive structured XML data and process branched queries efficiently. The proposed method is called Object-based Twig Query processing for Recursive data (OTQℜ). It is an extended approach of our existing work in [1] in order to handle recursion in XML data. Our motivation of extending OTQ to OTQℜ is to support many applications that require recursive data structure to be fully functional. OTQℜ is proposed to utilize semantics of XML data to efficiently process branched queries on recursive XML data. The experiments and evaluation are presented to cover variant evaluating points and the efficiency of our approach.

This paper presents the La Trobe e-Sanctuary concept, which aims to develop a cross-reality environment where a physical wildlife sanctuary is synchronised in real-time with its virtual counterpart. We outline the concept, research issues in realising this concept, components of our on-going project, and applications.

Summary In this paper, we introduce a new notion of Ontology as a Service, in which the ontology tailoring process serves as a service in the cloud. To illustrate Ontology as a Service, we propose sub‐ontology extraction and extension, whereby a sub‐ontology is extracted from the main ontology and is then extended to cover new concepts and relationships. We use a maximum extraction method to facilitate this. Unified Medical Language System meta‐thesaurus ontology is used as a walk‐through case study to illustrate our proposed methods. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Ontology as a formal representation of a domain knowledge has played an important role in a distributed environment whereby semantic interoperability is a major factor. In this paper, we particularly focus on a distributed ontology framework that utilizes Semantic Grid resources. The semantic representation in a machine‐understandable format (i.e., an ontology) is the backbone that enables interoperability between different user nodes in a semantic grid environment. However, the domain knowledge represented within an ontology is not static. From time to time, its concepts, properties and relationships need to be replaced or updated. Although many existing work have been focusing on how to utilize an ontology to support interoperability within a distributed environment, they often assume a rather static ontology. This paper focuses on formalizing and validating the process of ontology update, whereby sections of one ontologyO2are replaced by a subset extracted from another ontologyO1. In the first phase, a subsetS1is extracted from ontologyO1. Then in the second phase, the concepts in ontologyO2are replaced byS1. At the end of the process, the resulting ontologyO2must still be a valid ontology. A semantic completeness checking also needs to be conducted so that the updated ontologyO2is complete. A case study based on the Unified Medical Language System ontology from the medical informatics domain is presented. We use a semantic grid environment to build a framework for reusing, extracting and updating an ontology using a SOA. These allow the subset extracted from one ontology, to replace sections of another ontology, using shared resources in the semantic grid environment. A prototype of the framework is built using Web Services and a complexity evaluation measure is presented. The results of several simulations show ontology update in the semantic grid is a viable solution and can be further optimized. Copyright © 2012 John Wiley & Sons, Ltd.

With the proliferation of mobile devices, and their increasingly powerful embedded processors and storage, vast resources increasingly surround users. We have been investigating the concept of on-demand ad hoc forming of groups of nearby mobile devices in the midst of crowds to cooperatively perform computationally intensive tasks as a service to local mobile users, or what we call mobile crowd computing. As devices can vary in processing power and some can leave a group unexpectedly or new devices join in, there is a need for algorithms that can distribute work in a flexible manner and still work with different arrangements of devices that can arise in an ad hoc fashion. In this article, we first argue for the feasibility of such use of crowd-embedded computations using theoretical justifications and reporting on our experiments on Bluetooth-based proximity sensing. We then present a multilayered work-stealing style algorithm for distributing work efficiently among mobile devices and compare speedups attainable for different topologies of devices networked with Bluetooth, justifying a topology-flexible opportunistic approach. While our experiments are with Bluetooth and mobile devices, the approach is applicable to ecosystems of various embedded devices with powerful processors, networking technologies, and storage that will increasingly surround users.

The extensible Mark up Language (XML) is a meta language that is widely used to provide a non-proprietary universal format for sharing hierarchical data among different software systems and application domains. Moreover, many organizations and content providers have been publishing and sharing their information through XML and its standard schemas. In this context, it is extremely important when designing new schemas or enhancing current ones, there is a mechanism to ensure that the schemas will be well-designed versions. In this paper, we develop a tool that aids schema developers and standard groups to track XML schema changes, log them, and help in the enhancement of a particular schema version. We develop a schema monitoring tool called XSM, which efficiently stores and retrieves versioned XSDs and evaluates them based on the quality indicators defined for this purpose. The quality of delta changes in the schema versions is examined through a set of synthetic XSDs.

Ontology is used widely as a knowledge representation form. In the recent years, there is substantial growth of ontology utilization and development in different application domains that requires standardization and semantic interoperability. The increasing number in ontology development leads to overlapping in domain ontologies. Our approach called CODE (Common Ontology Development) is created to address the need for knowledge integration and reuse from distributed knowledge across multiple underlying ontologies. CODE is fully automated and it is a complete framework that aims to preserve source ontology knowledge. It uses natural language processing and scenario-based rules to ensure the accuracy of the integration mechanism. In addition, CODE can be used to merge more than two ontologies at the same time. To evaluate knowledge preservation and correctness in the proposed approach, SPARQL queries are used on the new integrated ontology and the results are verified against the source ontologies.

XML Schema standards often undergo several revisions to fit application requirements and business demands. In order to be successful, the development process of such standards must be collaborative allowing multiple users to work on the same schema. In this editing environment, the ability to merge branched versions of the schema is significant in certain situations. Using conventional three-way XML merging tools is not suitable for the purpose of merging XML Schema because the tree model of XML Schema is different from that of XML document.

Group Activity Recognition (GAR) is a challenging research area in context-aware computing which has attracted much attention recently. Many studies have been conducted in the field of activity recognition (AR) along with their applications in domains such as health, smart homes, daily living and life logging. However, still many open issues exist. Lack of an energy-efficient approach is one of the most vital issues in the context of AR. GAR work often suffers from energy consumption issues for the reason that, apart from AR process, there is the requirement to have more interaction among members of the group and a need to run more complex recognition processes. Moreover, almost all work in GAR are technology-oriented and assume that our real-life environment remains fixed once the system has been established, but this may not be the case. Hence, we propose a framework called Group Sense for GAR towards addressing these issues. Also, a relatively simple scheme for GAR, with a protocol for the exchange of information required for GAR, has been implemented, tested and evaluated. We then conclude with lessons learnt for GAR.

The increased deployment of the XML-based standard for representation and exchange in multi-disciplinary domains has enforced the need for a more effective way to deal with XML query processing. Since very limited attention has been given to the semantic nature of the XML data being processed, this paper focuses on a technique for XML query optimization, called Object-based Twig Query (OTQ), to utilize the semantic structure of the data being queried to process twig queries. A twig query, which is a type of query with multiple branches, requires complex processing due to the joins between multiple paths. Outperforms object-based data partitioning, which aims at leveraging the notion of frequently-accessed data subsets and putting these subsets together into adjacent partitions. It evaluates branched queries through two essential components: (i) OTQ indexing, which uses an object-based connection to construct its indices i.e. Schema index and Data index, and (ii) OTQ processing to produce the final results in optimal time. At the end of this paper, a set of experimental results for the proposed approach on arange of real and synthetic XML data, as well as a comparative study of a similar work in the area, is presented to demonstrate the effectiveness of OTQ optimization.

Summary form only given. With the development of global standards for data interchange in time critical domains such as air traffic control, transportation systems, and medical informatics, the general industry in these areas have started to move into a more data-centric operations and services. The main aim of the standards is to support integration and collaborative decision support systems that are operationally driven by the underlying data. The aim here is to develop an integration method for data that comes from different domains that operationally need to interact together. The work especially focuses on those domains that have temporal and spatial characteristics as their main properties. In this talk, recent efforts in large data integration, filtering, and visualisation will be presented. These integration efforts are often required to support real-time decision making processes in emergency situations, flight delays, and severe weather conditions. In particular, this work has been targeted for situations where current and predicted spatial-temporal data (moving objects and static objects) is an essential part of the decision making.

A participatory mobile sensing system is designed to enable clients to voluntarily collect environmental data using embedded sensors and a mobile device while going about their daily activities. Due to the spatio-temporal nature of the data, and the significant benefits of the data to the general public, it is necessary to employ an efficient and effective query processing model for the mobile clients to access the data that can be visualized via an interactive multimedia interface. This article introduces a unified on-demand and data broadcast model to serve queries in the context of a mobile sensing system. The contributions of this article include the following: (i) it presents a novel data structure and indexing method to support the system; (ii) it provides flexibility for the client to issue query using on-demand or broadcast channel according to the server load and broadcast schedule; (iii) it enables new data access and processing for the mobile client; and (iv) it is designed for a multiple channels/receivers environment in a 4G wireless network. The proposed model uses a holistic query processing approach for the mobile sensing system that offers substantial efficiency and autonomy for mobile clients when retrieving data. The results of the experiments undertaken affirm the effectiveness of its performance.

This paper introduces a novel clustering-based broadcast scheduling technique for mobile nearest-neighbor (NN) query processing in cyber physical systems. An efficient index structure is presented to guide mobile clients to the NN-objects. The proposed broadcast scheduling and indexing is aimed at minimizing query access time and energy consumption of the clients when retrieving NN-objects through wireless channels. We have experimentally studied the proposed scheme and its comparison with the state-of-the-art methods. The results suggest the efficacy of our proposed approach in offering minimum latency and energy consumption, which is critically important especially for resource-constrained wireless environments.

Due to the recent popularization of digital broadcasting systems, selective contents broadcasting depending on users' preferences with node relay-based web cast have attracted much attention. In node relay-based web cast, waiting time is reduced by receiving contents from several nodes. However, when a node that delivers contents disconnects from the network or reconnects to it while delivering contents, waiting time increases by changing a delivery schedule. In this paper, we propose a scheduling method considering reconnection on selective contents delivery with node relay-based web cast that relay data among nodes. Our proposed method reduces waiting time by restructuring the delivery schedule considering the disconnection and the reconnection of the node.

Moving objects databases are able to store and retrieve the movements of objects such as cars, people, animals, and vehicles. This data can be collected from GPS and Wi-Fi tracking systems, computer vision, or even the movements of virtual objects in simulations. Once we have this data, the next question is how can it be meaningfully interpreted, manipulated or queried? Moving objects datasets can appear chaotic when viewed in their entirety. This work aims to make moving object data accessible to analysts by way of a simple but powerful visual interface. To this end, we have formally defined a region-based filter that partitions moving object datasets. In practical terms, a filter is constructed, in real-time, by drawing and manipulating regions on a visualisation of a dataset. The partitions are then visualised to identify meaningful subsets of the data. We evaluate our prototype in terms of its ability to provide real-time feedback for varying types and quantities of filter regions. Finally, we evaluate the usefulness of the approach by exploring a possible scenario.

Due to the growing use of XML data format in global information, an effective XML data management system is needed. An Enabled XML DB is one of the recent widely accepted approaches to store XML documents. This ability coupled with the increase use of XML data in different areas have triggered the need for a better method to structure a large data in order to improve query performance. Issues concerning the ways to efficiently partition large XML documents into a more manageable form are yet to be addressed. At the same time, it is essential to ensure that the partitioning method maintains the preservation of XML data hierarchical structure. For this reason, this paper introduces OXDP that structures large XML data logically by partitioning them into object based XML components. An evaluation is shown to demonstrate the effectiveness of OXDP in XML partitioning which subsequently has the potential of improving query performance in Enabled XML DB environments.

XML has become a standard for the storage and exchange of data. The widespread use of XML has made queries executed over related, but distinct, XML data sources increasingly relevant. With the growing popularity of XML a wide variety of schemas may be applied to each document. In particular XML data that describes geographical/spatial information often needs to deal with a large number of complex elements and yet, during access and retrieval only a particular set of relevant information -- such as a local area -- is required. For this reason, we must search for ways to increase the performance of data processing and access. Parallelism is an attractive way in which to achieve this aim. With multi-processor or multi-core systems becoming the standard, the idea of parallelism is emerging as a significantly important concept. Methods for XML data processing have been designed and implemented with varying degrees of success. However, these approaches deliver datasets that can contain irrelevant information for the purposes of the user as they do not take the context into account while parsing. This can result in a decrease in the efficiency of the traversal of the data sets when querying. As such, this paper presents a framework the parallel processing of XML documents with a geographical context to filter and group spatial information. In this paper we propose an algorithm and a possible system implementation and identify a potential scale-up methodology.

Due to the recent popularization of digital webcast systems. close-range broadcasting using continuous media data, i.e. audio and video, has attracted great attention. For example, in a movie program, after a user watches interesting content such as a highlight scene, he/she will watch the main program continuously. In close-range broadcasting, the necessary bandwidth for continuously playing the two types of data increases. Conventional methods reduce the necessary bandwidth by producing an effective broadcast schedule for continuous media data. However, these methods do not consider the broadcast schedule for two types of continuous media data. When two types of continuous media data are scheduled, waiting time that occurs from finishing the highlight scene to starting the main scene may increase. In this paper, we propose a scheduling method to reduce the waiting time for close-range broadcasting. In our proposed method, by dividing two types of data and producing an effective broadcast schedule considering the available bandwidth, we can effectively reduce the waiting time.

Fast and accurate information exchange in the areas relating to spatial and temporal information, such as in aviation information management or disaster decision support systems, are of vital importance due to financial and more importantly safety implications. The problem that impedes rapid and correct decision-making is that information is often segregated in many different formats and domains, and integrating them has been recognized as one of the major problems. For example, in the aviation industry, weather data given to flight en-route has different formats and standards from those of the airport notification messages. The fact that messages are exchanged using different standards has been an inherent problem in data integration in many spatial temporal domains. The solution is to provide seamless data integration so that a sequence of information can be analyzed 'on the fly'.

Geographical information systems (GIS) and applications assist us in commuting, traveling and locating our points of interests. The efficient implementation and support of spatial queries in those systems is of particular interest and importance. The use of a Voronoi diagram has traditionally been applied to computational geometry. In this paper, we will show how a Voronoi diagram can be applied to support spatial queries in GIS systems, and in particular to reverse nearest neighbor (RNN) queries. An RNN query retrieves the set of interest objects having the query object as the nearest neighbor among other objects. Two cases of RNN queries are: monochromatic (MRNN) and bichromatic (BRNN). In the MRNN, the interest objects and the query object are of the same type, whereas in the BRNN they are of two different types. Due to the shortcomings of solutions for BRNN in the literature, we develop a new approach and algorithm, named the ‘2Vor BRNN algorithm’, for processing this query type in the context of the spatial network database (SNDB). Our novel approach extends the previous work and uses the ‘order-2 network Voronoi diagram’ to provide a more efficient solution for the BRNN. In addition, we experimentally confirm that the proposed algorithm outperforms the previous one in terms of memory used and response time.

A data warehouse is built as a layer on top of existing operational database systems. Once built, it has to be regularly updated (refreshed). Currently, most data warehouse approaches employ static refresh mechanisms whereby updates are based on a static timestamp, eg. once every day/week/quarter only. Whilst for some systems this might be adequate, others require a more rigorous approach ensuring that analysis is always 'up-to-date'. Static time interval for refreshing data warehouse is not adequate enough for systems with high update frequency. A real-time data warehouse incorporates operational data changes in real time. However, sometimes, it is often unnecessary or even inefficient to immediately refresh and send updates from the operational database into a data warehouse. In this paper, we propose a near real-time refresh mechanism that takes into consideration a number of measures: (i) Impact from record, (ii) Number of records affected, and (iii) Frequency Request Measure. The combination of these measures can accurately identify when the data warehouse needs to be strictly real-time, or near real-time (ie. right-time). Our experimentation shows that the proposed approach offers a significant benefit in terms of refresh operation cost in comparison to real-time warehousing, while at the same time still maintaining a high freshness level of the data warehouse.

This paper proposes a new global index structure and processing for location-dependent queries in mobile broadcast environments. The proposed scheme consists of two essential elements for addressing spatial queries in broadcast databases. These two elements are: (i) determine the client's location in relevant to the spatial model adopted by the broadcast scheme, and (ii) obtain the required object, which corresponds to the location of the client as determined by the model. The global index's concept will enhance the efficiency of the model. We explore the effectiveness of the proposed index scheme in single and multi channel environments. Performance comparisons with the earlier work through simulated-experiments have also been carried out and the results found have been promising.

Sub-ontology extraction provides a solution in dealing with large ontology. As time progresses, new discoveries and new ontology emerges making the ontology data much larger. Updating the sub-extracted ontology becomes a crucial task. Updating extracted sub-ontology can be done by performing the whole extraction process repeatedly from the base ontology. In contrast, our proposed approach is to develop an incremental update mechanism which can minimize the large communication and processing overhead in the former method. In this paper, we construct and describe a grid application service framework for updating extracted sub-ontology using the incremental update mechanism. The service can be accessed through a web portal. Functional verification and analysis provides viability of the framework.

Most query searches on road networks are either to find objects within a certain range (range search) or to find k nearest neighbors (kNN) on the actual road network map. In this paper, we propose a novel query, that is, path branch point (PBP). PBP can be defined as given a set of candidate interest objects and a pre-defined path starts from S and end at E, find a path which starts from S, via an interest point P and ends at E. This path should overlap with the user's ad hoc (pre-defined) path as much as possible with an acceptable distance increment. This is a novel query which is motivated by users' common requirements because most users have an ad hoc path in their daily travel and can tolerate a longer driving distance to some extent if they can drive on a familiar path. In this proposed approach, an Adjust Score is calculated for each path which is determined by overlapping distance and increased distance cost. Our experiment verifies the applicability of the proposed approach to solve the queries, which involves finding the optimal path branch points.

The semantic grid as the uniting concept of semantic web and grid computing, and its ontology technology provide an efficient data retrieval methodology in widely distributed semantic grid resources over the Internet. As the number of semantic grid resources increase, e.g., ontology servers, the amount of data increases and managing job workflows is becoming increasingly complex. This paper proposed a validation framework for sub-ontology extraction workflows in our proposed semantic grid computing environment. We considered two ontology optimization schemes and design its workflow models using Petri Net. The workflow for each model is described and validated. The results justify the feasibilities of our proposed validation framework of the aforementioned models which can be used as building blocks to model large and efficient sub-ontology extraction workflows in a semantic grid.

This tutorial will be based on the recently published book, High-Performance Parallel Database Processing and Grid Databases (John Wiley & Sons, 2008). The sizes of databases have seen exponential growth in the past and such growth is expected to accelerate in the future, with the steady drop in storage cost accompanied by a rapid increase in storage capacity. To effectively manage such volumes of data, it is necessary to allocate multiple resources to it, very often massively so. The processing of databases of such astronomical proportions requires an understanding of how high performance systems and parallelism work. Besides the massive volume of data in the database to be processed, some data has been distributed across the globe in a Grid environment. This important new book provides readers with a fundamental understanding of parallelism in data-intensive applications, and demonstrates how to develop faster capabilities to support them. It features not only the algorithms for database operations, but also quantitative analytical models, so that performance can be analyzed and evaluated more effectively.

Clustering as an intelligent technique for mining XML documents has been utilised as an excellent way of grouping the documents by their content or structure. A main step in many distance based XML clustering algorithms is to calculate pair-wise distances between documents; naturally, a time-efficient technique requests the pair-wise distances to be determined in a timely manner. In case of dynamic XML documents, the amount of changes between versions cannot be predicted. Therefore, in case of clustered dynamic XML documents, if changes were little or if they affected only some of the clustered documents, recalculating pair-wise distances every time would be highly redundant. In this paper we propose a time-efficient technique to reassess pair- wise distances between clustered dynamic XML documents which change in time, without performing redundant calculations but considering the previously known distances and the set of changes which might have affected the documents versions.

The need to extract knowledge from web data warehousing just 'in-time' for decision making has increased significantly. An efficient system that can generate up-to-date analysis and decision making of the ever changing web-based information will play a very important role in the current global market and society. The new era of business intelligence and web databases brings in new research and development issues whereby the efficient integration of various web data is needed and timely analysis of data resources are vital. Web Data Warehousing is a growing area that addresses the need for an efficient web data summary to support decision making and ensure the quality of web data analysis.

This paper presents a novel schema mediation approach, called XMiner, for mining mediated schemas from a set of XML schemas. XMiner addresses three main problems resulting from the heterogeneous source schemas: nesting discrepancy, backward paths and schema discrepancy. XMiner discovers frequent substructures using frequent subtree mining algorithms, and then constructs a mediated schemas. XMiner aims to preserve the hierarchical structure as the best as possible while avoiding information loss. XMiner exploits structural context, forward/backward paths, and label semantics for matching, mapping and merging frequent substructures. Experiments on real and synthetic datasets are reported to show that XMiner offers acceptable performance and quality for large-scale application scenarios.

This paper presents a service oriented architecture (SOA) approach to a distributed framework for reusing, extracting and extending (tailoring) large domain ontologies in the semantic grid environment. The conceptual level of the framework describes how sub-ontologies are tailored while the architectural level of the framework describes the components of the framework that allows the tailoring process happen in the semantic grid environment. A prototype of the framework and a complexity evaluation measure are also provided.

In a wireless environment, data broadcast paradigm has been recognized as an effective and scalable mechanism to disseminate frequently requested information to a large number of clients. This paper presents the development of a multiple entity types wireless broadcast database system. The broadcast system is designed to serve transitive queries or queries that access related data items belonging to different entity types. The broadcast data are retrieved from a central database server. We apply three different data broadcast schemes including: (i), filtering technique for mobile device to perform transitive queries and display the desired data from incoming multiple entity types broadcast data items; (ii), the index broadcasting scheme designed to predict the arrival of the desired data to appropriately execute power conserving mode; (iii), incorporate multiple channel environments to allow mobile device to tune into multiple broadcast channels to receive the desired data. The proposed model uses a share indices price context to demonstrate the effective use of these data broadcast schemes.

With increasing demands for a proper and efficient XML data storage, XML-enabled database (XEnDB) has emerged as one of the popular answers. It claims to combine the strengths and limit the shortcomings of the traditional database management systems and native XML database. The implication is more research need to be done for this database family. This paper focuses on the XML update management in XEnDB. Our aim is to preserve the conceptual semantic constraints in XML data during update operations. The constraints are classified and represented in SQL/XML schema. Then, we propose the update methodology that utilizes the proposed schema and implement the method in one of the current XEnDB products

GroupBy-Join queries in SQL are queries involving the group by clause joining several tables. In this paper, we describe three parallelization techniques for GroupBby-Join queries, particularly the queries where the group-by clause can be performed before the join operation. We subsequently call this query "GroupBy-Before-Join" queries. Performance evaluation of the three parallel processing methods is also carried out

This paper explores a distributed ontology framework for tailoring ontologies in the semantic grid environment. The framework is divided into five main categories: ontology processing, ontology location, ontology connection, users' connection and algorithm location. A number of possible scenarios are discussed following two case studies that indicate how the framework can be manipulated and used in various situations. This framework helps developers design tools for tailoring ontologies in the semantic grid environment.

XML data can be stored in different types of databases including object-relational databases (ORDB). Using ORDB, we get the benefit of relational maturity and the richness of object-oriented modeling. One modeling concept that can be captured is composition hierarchy, which is a special type of relationship that shows an exclusive existence-dependent "part-of" relationship. This type of relationship frequently occurs in XML data, yet very often when the data is stored in a database repository, the "part-of" relationship is either flattened or split into an entirely separate table. In this paper we propose a model to preserve composition type in XML data into ORDB using the concept of row types. We use the Semantic Network diagram to represent the composition hierarchy in XML data. The composition hierarchy is divided into three types, namely single row composition, multi rows composition, and multi level composition. Each of these composition types will then be transformed into storage in an ORDB environment.

The extensible markup language (XML) has emerged as the dominant standard in describing and exchanging data among heterogeneous data sources. The increasing presence of large volumes of data appearing in enterprise settings creates the need to investigate XML document warehouses (XDW) as a means of handling and analysing XML data for business intelligence. In our previous work, we proposed a conceptual modelling approach for the design and development of XDWs, with emphasis on capturing data warehouse requirements early in the design stage. To address this issue, in this paper, we explore a requirement engineering (RE) approach, namely the goal-oriented approach. We adopt and extend the notion of this approach and introduce the XDW requirement model. This focuses on deriving dimensions, as opposed to associating organizational objectives to the system functions, which is carried out by the traditional requirement engineering process.

The new era of semantic Web has enabled users to extract semantically relevant data from the Web. The backbone of the semantic Web is a shared uniform structure which defines how Web information is split up regardless of the implementation language or the syntax used to represent the data. This structure is known as an ontology. As information on the Web increases significantly in size, Web ontologies also tend to grow bigger, to such an extent that they become too large to be used in their entirety by any single application. This has stimulated our work in the area of sub-ontology extraction where each user may extract optimized sub-ontologies from an existing base ontology. Sub-ontologies are valid independent ontologies, known as materialized ontologies, that are specifically extracted to meet certain needs. Because of the size of the original ontology, the process of repeatedly iterating the millions of nodes and relationships to form an optimized sub-ontology can be very extensive. Therefore we have identified the need for a distributed approach to the extraction process. As ontologies are currently widely used, our proposed approach for distributed ontology extraction will play an important role in improving the efficiency of information retrieval.

The amount of text available in electronic form is increasing, especially since the rise of the Web. So too are the potential interconnections between concepts, given the advent of ontologies and other relationship based data sources. Text could be navigated using the structure from the ontologies, specifically, using dynamic taxonomies to navigate the is-a relationships. Dynamic taxonomies are rooted index structures that dynamically prune themselves in response to zoom requests. The use of dynamic taxonomies with existing ontologies, and in the medical field, is unexplored. This paper details the process of connecting index terms from a medical text database to a taxonomy extracted from an existing medical ontology.

SQL queries in the real world are replete with group-by and join operations. This type of queries is often known as "GroupBy-Join" queries. In some GroupBy-Join queries, it is desirable to perform group-by before join in order to achieve better performance. This subset of GroupBy-Join queries is called "GroupBy-Before-Join" queries. In this paper, we present a study on the parallelization of GroupBy-Before-Join queries, particularly by exploiting cluster architectures. From our study, we have learned that, in parallel query optimization, processing group-by operations as early as possible is not always desirable. On many occasions, performing data distribution first, before group-by, offers performance advantages. In this study, we also describe our cluster-based scheme.

The widespread use of the World Wide Web in recent years has opened up universal access to a vast number of information sources. An obstacle that affects the access to Web data is the lack of an information structure among and within Web pages. This raises a need for structured Web page management for efficient Web information searching. Our proposed structured Web page management is built in two stages: (i) HTML transformation to XML, and (ii) a navigation hierarchy. Also, we study how querying Web data can be accomplished in our structured Web page management, by which users may follow a navigation hierarchy to browse both inter-page and intra-page structures of the Web database and can specify queries for desired information.

With the increasing popularity of object-relational technology, it is becoming important to have a methodology which allows designers to exploit the great modelling power of object-oriented conceptual models (OOCMs) and yet which still facilitates implementation on relational database systems. This paper presents a practical solution for the implementation of different types of object-oriented association relationships, which include a wide range of collection types (i.e. sets, lists, arrays and bags), into relational database tables. The implementation strategies raise a number of constraints relating to data integrity in the database system. These constraints are associated with (i) normalisation of the resulting relational tables, and (ii) data integrity after insertion/deletion. In order to ensure completeness of the strategies, some techniques for the implementation of inverse traversal in association relationships are also provided. An example is used throughout this paper to demonstrate and evaluate the proposed method.

One of the differences between relational and object-oriented databases (OODB) is that attributes in OODB can be of a collection type (e.g. sets, lists, arrays, bags) as well as a simple type (e.g., integer, string). Consequently, explicit join queries in OODB may be based on collection attributes. One form of collection join queries in OODB is "collection-equi join queries", where the joins are based on collection attributes and the queries check for an equality of both collection operands. Our previous work (1997) describes "Parallel Double Sort-Merge" algorithm for collection-equi join queries, Since the publication, we realize that we have overlooked the complexity of collection merging in the algorithm. In this paper, we not only present alternative solutions relating to the collection merging problem, but also introduce a new algorithm called "Parallel Sort-Hash" algorithm. The two algorithms play an important role in parallel object-oriented query processing, due to their superiority over the conventional join methods through relational division and intersection operators.