Knowledge-sharing in forums is an integral part of many MOOCs (Massive Open Online Courses).However, forum usage for knowledge-sharing in MOOCs is often inadequate.This study adopts a mixed-methods approach to investigate problems behind MOOC learners' problematic forum participation and propose real-time sharing-quality-monitoring mechanisms to mitigate the problems.We explore different designs and implementation of computerised nudges to enhance knowledge contribution, considering challenges such as vast data, user aversion to AI monitoring, and complex user interactions.Through testing graphical (Model A), numerical (Model B), and textual message (Model C) interface designs, we found that graphical and numerical designs were most effective in improving performance.However, Model C received conflicting judgments, with some users feeling controlled by the AI while others found algorithmic guidance valuable.Our findings shed light on leveraging computerised nudges for meaningful contributions and address concerns related to AI monitoring.The complex nature of user interactions, behaviours, and the abundance of data present significant challenges that require innovative approaches.This study contributes to understanding the issues in MOOC forum participation and provides insights into effective computerised nudges.We discuss directions for refining the current design, emphasising the need for more design science research in this domain.

Abstract The exponential growth of textual data in the digital era underlines the pivotal role of Knowledge Graphs (KGs) in effectively storing, managing, and utilizing this vast reservoir of information. Despite the copious amounts of text available on the web, a significant portion remains unstructured, presenting a substantial barrier to the automatic construction and enrichment of KGs. To address this issue, we introduce an enhanced Doc‐KG model, a sophisticated approach designed to transform unstructured documents into structured knowledge by generating local KGs and mapping these to a target KG, such as Wikidata. Our model innovatively leverages syntactic information to extract entities and predicates efficiently, integrating them into triples with improved accuracy. Furthermore, the Doc‐KG model's performance surpasses existing methodologies by utilizing advanced algorithms for both the extraction of triples and their subsequent identification within Wikidata, employing Wikidata's Unified Resource Identifiers for precise mapping. This dual capability not only facilitates the construction of KGs directly from unstructured texts but also enhances the process of identifying triple mentions within Wikidata, marking a significant advancement in the domain. Our comprehensive evaluation, conducted using the renowned WebNLG benchmark dataset, reveals the Doc‐KG model's superior performance in triple extraction tasks, achieving an unprecedented accuracy rate of 86.64%. In the domain of triple identification, the model demonstrated exceptional efficacy by mapping 61.35% of the local KG to Wikidata, thereby contributing 38.65% of novel information for KG enrichment. A qualitative analysis based on a manually annotated dataset further confirms the model's excellence, outshining baseline methods in extracting high‐fidelity triples. This research embodies a novel contribution to the field of knowledge extraction and management, offering a robust framework for the semantic structuring of unstructured data and paving the way for the next generation of KGs.

Enterprise and public Knowledge Graphs (KGs) are known to be incomplete. Methods for automatic completion, sometimes by rule learning, scale well. While previous rule-based methods learn closed (non-existential) rules, we introduce Open Path (OP) rules that are constrained existential rules. We present a novel algorithm, OPRL, for learning OP rules. Closed rules complete a KG by answering queries of unclear origin, usually derived from a holdback test set in experimental settings. However, OP rules can generate relevant queries for KG completion. OPRL generates queries even when there is no closed rule to answer the query, or when the correct answer is a missing entity that is not present in the KG. For OPRL to scale well, we propose a novel embedding-based fitness function to efficiently estimate rule quality. Additionally, we introduce a novel, efficient vector computation to formally assess rule quality. We evaluate OPRL using adaptations of Freebase, YAGO2, Wikidata, and a synthetic Poker KG. We find that OPRL mines hundreds of accurate rules from massive KGs with up to 8 M facts. The OP rules generate queries with precision as high as 98% and recall of 62% on a complete KG, demonstrating the first solution for active knowledge graph completion.

Extraction of categorised named entities from text is a complex task given the availability of a variety of Named Entity Recognition (NER) models and the unstructured information encoded in different source document formats. Processing the documents to extract text, identifying suitable NER models for a task, and obtaining statistical information is important in data analysis to make informed decisions. This paper presents\footnoteThe manuscript follows guidelines to showcase a demonstration that introduces an overview of how the toolkit works: input document set, initial settings, processing, and output set. The input document set is artificial in order to show various toolkit capabilities. TNNT, a toolkit that automates the extraction of categorised named entities from unstructured information encoded in source documents, using diverse state-of-the-art (SOTA) Natural Language Processing (NLP) tools and NER models.TNNT integrates 21 different NER models as part of a Knowledge Graph Construction Pipeline (KGCP) that takes a document set as input and processes it based on the defined settings, applying the selected blocks of NER models to output the results. The toolkit generates all results with an integrated summary of the extracted entities, enabling enhanced data analysis to support the KGCP, and also, to aid further NLP tasks.

Gesture Elicitation Studies (GES) are a widely used empirical method to develop gesture vocabularies, interaction models and methods for gesture-based systems in different contexts. While GES show great promise to identify user-defined gestures, there are inherent problems with current methods used for GES. Especially during the ongoing pandemic, it has been nearly impossible to conduct in-person, in-lab GES, while ensuring the safety and well-being of the participants, and complying with social distancing regulations. Further, with prevailing experiment designs, increasing the number of participants is time consuming, while in-lab environments also limit ecological validity. This study explores an intuitive way of conducting self-guided GES using immersive Virtual Reality (VR), utilizing its capability to simulate various contexts to enhance ecological validity. We present a methodology and a tool set that use an immersive VR environment to conduct ecologically valid GES (as a use case) while requiring minimal involvement by the investigator. We evaluate our method using the case of a smart home environment and measure participant acceptance and discuss opportunities and challenges involved in this method. We believe that this study will help HCI research to move forward with participatory design research, even when lab experiments are difficult to conduct.

MOTIVATION: Searching for precise terms and terminological definitions in the biomedical data space is problematic, as researchers find overlapping, closely related and even equivalent concepts in a single or multiple ontologies. Search engines that retrieve ontological resources often suggest an extensive list of search results for a given input term, which leads to the tedious task of selecting the best-fit ontological resource (class or property) for the input term and reduces user confidence in the retrieval engines. A systematic evaluation of these search engines is necessary to understand their strengths and weaknesses in different search requirements. RESULT: We have implemented seven comparable Information Retrieval ranking algorithms to search through ontologies and compared them against four search engines for ontologies. Free-text queries have been performed, the outcomes have been judged by experts and the ranking algorithms and search engines have been evaluated against the expert-based ground truth (GT). In addition, we propose a probabilistic GT that is developed automatically to provide deeper insights and confidence to the expert-based GT as well as evaluating a broader range of search queries. CONCLUSION: The main outcome of this work is the identification of key search factors for biomedical ontologies together with search requirements and a set of recommendations that will help biomedical experts and ontology engineers to select the best-suited retrieval mechanism in their search scenarios. We expect that this evaluation will allow researchers and practitioners to apply the current search techniques more reliably and that it will help them to select the right solution for their daily work. AVAILABILITY: The source code (of seven ranking algorithms), ground truths and experimental results are available at https://github.com/danielapoliveira/bioont-search-benchmark.

The Semantic Web provides access to an increasing amount of structured information in a wide variety of domains. Information overload due to the large amount of structured data is as much a problem as on the traditional Web. To solve this problem, ample research has been proposed on Semantic Web data retrieval techniques and after more than a decade of research in this domain it is now reasonable to consider the questions: is the field of Semantic Web data retrieval making progress? What are the directions that have been taken? and what are some of the promising significant directions to pursue future research? To answer these questions, we review the state-of-the-art Semantic Web data retrieval techniques and define a taxonomy of these techniques to classify the ongoing research and find potential future research directions.

Cloud computing is the latest computing paradigm that transparently delivers Information and Communication Technology resources as services, freeing the users of Cloud applications from dealing with low-level implementation and system administration details. Cloud provides the promise of on-demand access to affordable large-scale computing (e.g., multi-core CPUs, GPUs, and clusters of GPUs), storage (such as disks), and software (e.g., databases, application servers, and data processing frameworks) resources without substantial up-front investment. Cloud resources are hosted in large datacenters, often referred to as virtualized data farms, operated by companies such as Amazon, Apple, GoGrid, and Microsoft. While the growing ubiquity of Cloud computing is having a significant impact in many applications domains, there are still significant problems that exist with regard to efficient provisioning and delivery of applications using its Information and Communication Technology resources. These barriers are due to resource uncertainties 1 that have degradable effect on the run-time Quality of Service (e.g., access latency and number of requests being successfully served per second) of software applications deployed in the Cloud. There are many reasons for such uncertainties including (i) unpredictable application workload types (enterprise, scientific, and streaming big data analytics), (ii) fluctuations in resource capacity demands (i.e., bandwidth, memory, storage, and CPU), (iii) abrupt failures (e.g., failure of a network link), (iv) stochastic access patterns (e.g., number of end-users and their geo-location), (v) heterogeneity in device types (e.g., mobile phone, laptop, and smart TV), (v) heterogeneous resource types and their providers, and (vi) heterogeneity in data types (3D images, videos, audios, text, etc.) and network types (e.g., wired and wireless). These Cloud resource uncertainties need to be managed optimally to maintain contractual requirements defined in Service-Level Agreements (SLAs) that underlie most Cloud computing contracts. Basically, SLAs are legal documents (paper and/or electronic) that encode the nature and scope of QoS parameters (e.g., ensure availability 99.99% and ensure web application server latency to be less than 100 ms). To tackle uncertainties, recent research and industry efforts 2 have focused on developing monitoring techniques and frameworks that can assist cloud providers and application owners in (i) keeping their resources and applications operating at peak efficiency, (ii) detecting variations in resource and application performance, (iii) accounting the SLA violations of certain QoS parameters, and (iv) tracking the leave and join operations of cloud resources due to failures and other dynamic configuration changes. The rest of this editorial note is organized as follows: Section 2 gives a brief overview of the research and development work carried out for monitoring application QoS over cloud resources; Section 3 summarizes the research contributions that were accepted for this special issue; Section 3 concludes the paper with some future remarks. In last 20 years, a large body of research has focused on developing tools and techniques for monitoring the QoS status of resources and applications over distributed systems (e.g., grids, clusters, and clouds). Some QoS monitoring techniques have been investigated and implemented in computational grids, such as Network Weather Service (NWS) 3, which monitors the network and computing resource QoS and periodically forecast the QoS in a future arrival of a application workload. The current version of NWS gathers the operating system level metrics such as available CPU percentage, available non-paged memory, and TCP/IP Performance. Other monitoring tools 4, 5 that were popular in grid and cluster computing era included R-GMA, Hawkeye, Ganglia, MDS-I, and MDS-II. Aforementioned monitoring techniques and tools were designed for managing static system configuration, where numbers of hardware and software resource types were assumed to remain constant over lifecycle of an application. In other words, these tools did not consider the issue of auto scaling and de-scaling primitives supported by virtualized cloud resources. These tools were only concerned about monitoring the QoS parameters for the hardware resources (CPU, storage, and network), while being completely agnostic to application-specific QoS parameters and SLA requirements. The performance of these tools was optimized for monitoring the QoS of only one type of application (e.g., high performance computing application). On the other hand, in cloud computing datacenters, multiple application instances can be multiplexed and co-allocated on single physical resource. Clearly, the monitoring tools developed in grid and cluster computing era (while being innovative and useful) is not suitable to tackle the challenges on cloud computing environments and hosted application types. Current cloud resource and application QoS monitoring frameworks (e.g., Amazon CloudWatch 6, Azure Fabric Controller) typically monitor the entire virtual machine (VM, a software implementation of a physical CPU resource) as a black box and lacks ability to inter-operate across cloud datacenters managed by different providers (e.g., Amazon, Microsoft, GoGrid, and CA). This means that QoS of software resources (e.g., web server, and database server) contained in the application stack is not properly monitored and managed. While frameworks such as Monitis 7 and Nimsoft 8 overcome the aforementioned limitations of CloudWatch and Fabric Controller, they lack ability to monitor and enforce application-specific QoS requirements. Further, all of the aforementioned frameworks lack ability to predict and detect faults before they occur. Some of the recent research works 9 have also focused on applying large-scale data and pattern mining to the QoS monitoring history and event log data. Authors in 10 evaluated the prediction capability of Support Vector Machine, Neural Network, and Linear Regression techniques for learning the QoS behavior of cloud hosted applications. To predict the CPU usage of VMs, authors in 11 applied Markov Chain model. Authors in 12 applied prediction techniques such as Moving Average, Auto Regression, Neural Networks, Support Vector Machines, and Gene Expression Programming for predictive VM QoS monitoring and provisioning. Most of these techniques focused on monitoring and predicting QoS of VMs rather than individual application components. Further, these approaches did not reason about the interplay of QoS parameters and SLA requirements across multiple layers (software as a service, platform as a service, and infrastructure as a service) of cloud application stack. In this special issue, we present seven articles that tackle several aspects of the aforementioned resource uncertainties for monitoring QoS of applications hosted on Cloud resources. In particular, Ryckbosch and Diwan propose a Temporal Pattern Analyzer system in their paper 13 Analyzing Performance Traces Using Temporal Formulas that uses formulas in linear-temporal logic extended with variables to analyze traces to investigate long-tail performance problems at Google and reduce the manual labor involved in analyzing traces. The technique is applied on user request logs, which contain events at each stage of processing of a user request to Gmail. The authors show that the system can scale to large traces, a prerequisite considering that Gmail produces a million or more events a second. Two of the case studies presented in the paper have directly contributed to improving the performance of Google. Cao et al. also use execution trace information, in this case, CPU load traces and propose 14 a novel method for CPU load prediction for cloud environment based on a dynamic ensemble model to obtain better performances. The ensemble model proposed consists of two layers, a predictor optimization layer that can continuously incorporate new predictor instances and remove those ones with a poor performance and an ensemble layer that is responsible for producing the final prediction based on the results of multiple predictor instances. The four papers are all concerned with monitoring Cloud applications, ranging from a model and language to define design-time adaption techniques in the paper 15 by Inzinger et al. on a Generic Event-Based Monitoring and Adaptation Methodology for Heterogeneous Distributed Systems, to better visualization techniques in the monitoring process in the paper 16 A Novel Monitoring Mechanism by Event Trigger for Hadoop System Performance Analysis by Chang et al., to adapting to failed application service in a distributed environment by introducing fault avoidance service that can be called instead of the failed service by Gülcü et al. in their paper 17 Fault Masking as a Service, to a feature-based high availability mechanism that monitors data streams for a quantile feature in the paper 18 by Ding et al. on a Feature-based High Availability Mechanism for Quantile Tasks in Real-time Data Stream Processing. In particular, Inzinger et al. present 15 a novel domain-specific language termed MONINA that allows specification of system components and their monitoring and adaptation-relevant behavior for controlling Cloud systems. The authors propose a mechanism for optimal deployment of the defined control operators onto available computing resources by monitoring the cloud environment with complex-event processing queries and adapt to problems by condition action rules performed on top of a distributed knowledge base. Chang et al. propose 16 a system called Event Trigger that provides an automatic recording mechanism on the Hadoop Cloud Computing system, to check the system performance at every static time interval, and compares the variation. The performance parameters are collected during the system monitoring process and are applied onto an easy-understandable visual graph for users to adjust the hardware deployment in order to refine the Hadoop system. Gülcü et al. propose 17 an approach to prevent the occurrence of errors that result from the unavailability of partner services in the first place. They introduce a fault avoidance service to which composite services can register at will. After registration, this fault avoidance service periodically checks the partner links, detects unavailable partner services, and updates the composite service with available alternatives. Thus, in case of a partner service error, the composite service will have been updated before attempting an ill-destined request. Ding et al. focus 18 on the monitoring of data streams on the quantile tasks, a typical summary-oriented operation for aggregation, and propose a feature-based high availability mechanism to reduce related overhead and latency. With the help of a monitor module, the quantile feature is maintained incrementally through histogram synopsis over a time-based sliding window. Consequently, failed tasks can be recovered precisely with a high probability in an efficient way. Finally, the special issue is rounded off by a paper 19 on Design and Implementation of Task Scheduling Strategies for Massive Remote Sensing Data Processing Across Multiple Data Centers by Zhang et al. that proposes scheduling strategies for data processing workflows. In particular, they propose scheduling strategies in massive remote sensing data processing to reduce the total task execution time. The authors divided the data processing workflows into two categories, namely, Bag of Tasks applications that consist of a large number of independent tasks and Direct Acyclic Graph applications that contain a large number of interdependent tasks. They propose two strategies to deal with issues in either of the two categories, a Partitioning Group based on Hypergraph algorithm that partitions data into several groups to minimize the amount of sharing data transferring and an Optimized Task Tree strategy to find the key workflow path, which would be endowed with a high priority in the execution. This special issue presents, through these seven papers, several techniques that can dynamically predict and capture the relationship between an application performance targets, current hardware resource allocation, and changes in workload patterns, in order to adjust resource configuration at design-time and run-time. More work in this area is rapidly emerging, further improving the availability of massively distributed Cloud applications. This will further improve the economies of scale of Cloud applications making Cloud Computing an even more compelling paradigm in comparison to traditional in-house hosted applications. Application QoS monitoring will continue to remain an important research area for cloud-based systems. More tangible efforts are needed for developing monitoring tools and techniques that can specify, reason, and monitor QoS related to a variety of application (enterprise, scientific, and streaming big data analytics) and cloud datacenter types (private and public). Further, research should also aim to correlate events with data from many different sources (e.g., holiday schedules, job schedules, and trends from social media about application usage sentiment) in order to predict how external events can impact an application QoS. In this special issue, we have selected research papers that aim to address some of these challenges. We hope that the readers will find the articles of this special issue informative and useful.

The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud-based infrastructure services (e.g. storage, network, CPU). The outcomes of this will be decision support tools and techniques, which will automate and map users' specified application requirements to Cloud service configurations.

The Cloud infrastructure services landscape advances steadily leaving users in the agony of choice. As a result, Cloud service dentification and discovery remains a hard problem due to different service descriptions, nonstandardised naming conventions and heterogeneous types and features of Cloud

In this work we construct partial order plans from a pool of atomic services described in OWL-S. We make extensions to Partial Order Planning to allow multiple conditional effects in action definitions. The purpose is to handle the uncertain behavior of Web services with incomplete initial information. We post-process the partial order plan to auto-generate a workflow model. We developed a method to identify a subset of workflow patterns from the solution plan to create a workflow diagram.

Workflow models have been used and refined for years to execute processes within organisations. To deal with collaborative processes (choreographies) these internal workflow models have to be aligned with the external behaviour advertised through Web service interfaces. However, traditional workflow management systems (WfMS) do not offer this functionality. Simply sharing and merging process models is often not possible, because workflow management lacks a widely accepted standard theory for workflow models.Multiple research and standardisation efforts to integrate different workflow theories have been proposed over the years. XPDL is the most widely used standard for process model interchange and supported by over 80 systems.However, XPDL also lacks the possibility to relate a workflow model to its possible choreography interface abstractions.To remedy this situation, we propose to abstract the XPDL model to a higher-level model, perform the integration and the compaction algorithms at that level and then ground it back to the desired choreography models. We develop and use an integrated ontology which is based on the XPDL standard for this purpose. To facilitate the abstraction and grounding, we present a mapping procedure to automatically translate XPDL and BPMN workflow models into this ontology. After translation, these models are annotated with a parameterised role model and other collaborative properties. We present a compaction procedure that automatically maps the annotated models into external choreography interfaces that expose only the relevant information for a particular partner collaboration. Our procedure is agnostic with respect to the target choreography model. We demonstrate our approach using WSMO choreographies which enables us to automatically generate interface models from any WfMSs that supports XPDL export.

Object-oriented programming is the current mainstream programming paradigm but existing RDF APIs are mostly triple-oriented. Traditional techniques for bridging a similar gap between relational databases and object-oriented programs cannot be applied directly given the different nature of Semantic Web data, for example in the semantics of class membership, inheritance relations, and object conformance to schemas.

In this paper we introduce our multi metamodel process ontology (m3po), which is based on various existing reference models and languages from the workflow and choreography domain. This ontology allows the extraction of arbitrary choreography interface descriptions from arbitrary internal workflow models. We also report on an initial validation: we translate an IBM Websphere MQ Workflow model into the m3po ontology and then extract an Abstract BPEL model from the ontology.

Web services offer an interoperability model that abstracts from the idiosyncrasies of specific implementations; they were introduced to address the increasing need for seamless interoperability between systems in the business-to-business domain. We analyse the requirements from this domain and show that to fully address interoperability demands we need to make use of semantic descriptions of Web services. We therefore introduce the Web service execution environment (WSMX), at software system that enables the creation and execution of semantic Web services based on the Web service modelling ontology. Providers can use it to register and offer their services and requesters can use it to dynamically discover and invoke relevant services. WSMX allows a requester to discover, mediate and invoke Web services in order to carry out its tasks, based on services available on the Internet.

Summary form only given. The emerging concept of semantic Web services aims at more sophisticated Web Service technologies: on basis of semantic description frameworks, intelligent mechanisms are envisioned for discovery, composition, and contracting of Web services. The tutorial explains the current state of the art in semantic Web services on basis of the Web service modeling ontology WSMO and related initiatives. Commencing from the vision and arising challenges for semantic Web services, the tutorial in detail explains the specifications of recent frameworks for semantic Web services and presents the Web service execution environment WSMX as the WSMO reference implementation. The tutorial consists of three main sections that subsequently provide a complete overview of semantic Web services and the latest status of WSMO. The tutorial addresses academic as well as industrial researches and developers that are working with Web services and are interested in semantic Web services.