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A key aspect of the development of Smart Cities involves the efficient and effective management of resources to improve liveability. Achieving this requires large volumes of sensors strategically deployed across urban areas. In many cases, however, it is not feasible to install devices in remote and inaccessible areas, resulting in incomplete data coverage. In such situations, citizens can often play a crucial role in filling this data collection gap. A popular complimentary science to traditional sensor-based data collection is to design Citizen Science (CS) activities in collaboration with citizens and local communities. Such activities are also designed with a feedback loop where the Citizens benefit from their participation by gaining a greater sense of awareness of their local issues while also influencing how the activities can align best with their local contexts. The participation and engagement of citizens are vital and yet often a real challenge in ensuring the long-term continuity of CS projects. In this paper, we explore engagement factors, factors that help keeping engagement high, in technology-centric CS projects where technology is a key enabler to support CS activities. We outline a literature review of exploring and understanding various motivational and engagement factors that influence the participation of citizens in technology-driven CS activities. Based on this literature, we present a mobile-based flood monitoring citizen science application aimed at supporting data collection activities in a real-world CS project as part of an EU project. We discuss the results of a user evaluation of this app, and finally discuss our findings within the context of citizens’ engagement.
Muhammad Ali; Bhupesh Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Sydney Simpson. Using Citizen Science to Complement IoT Data Collection: A Survey of Motivational and Engagement Factors in Technology-Centric Citizen Science Projects. IoT 2021, 2, 275 -309.
AMA StyleMuhammad Ali, Bhupesh Mishra, Dhavalkumar Thakker, Suvodeep Mazumdar, Sydney Simpson. Using Citizen Science to Complement IoT Data Collection: A Survey of Motivational and Engagement Factors in Technology-Centric Citizen Science Projects. IoT. 2021; 2 (2):275-309.
Chicago/Turabian StyleMuhammad Ali; Bhupesh Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Sydney Simpson. 2021. "Using Citizen Science to Complement IoT Data Collection: A Survey of Motivational and Engagement Factors in Technology-Centric Citizen Science Projects." IoT 2, no. 2: 275-309.
This paper presents a long-term study on how the public engage with discussions around citizen science and crowdsourcing topics. With progress in sensor technologies and IoT, our cities and neighbourhoods are increasingly sensed, measured and observed. While such data are often used to inform citizen science projects, it is still difficult to understand how citizens and communities discuss citizen science activities and engage with citizen science projects. Understanding these engagements in greater depth will provide citizen scientists, project owners, practitioners and the generic public with insights around how social media can be used to share citizen science related topics, particularly to help increase visibility, influence change and in general and raise awareness on topics. To the knowledge of the authors, this is the first large-scale study on understanding how such information is discussed on Twitter, particularly outside the scope of individual projects. The paper reports on the wide variety of topics (e.g., politics, news, ecological observations) being discussed on social media and a wide variety of network types and the varied roles played by users in sharing information in Twitter. Based on these findings, the paper highlights recommendations for stakeholders for engaging with citizen science topics.
Suvodeep Mazumdar; Dhavalkumar Thakker. Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks. Future Internet 2020, 12, 210 .
AMA StyleSuvodeep Mazumdar, Dhavalkumar Thakker. Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks. Future Internet. 2020; 12 (12):210.
Chicago/Turabian StyleSuvodeep Mazumdar; Dhavalkumar Thakker. 2020. "Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks." Future Internet 12, no. 12: 210.
Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.
Dhavalkumar Thakker; Bhupesh Kumar Mishra; Amr Abdullatif; Suvodeep Mazumdar; Sydney Simpson. Explainable Artificial Intelligence for Developing Smart Cities Solutions. Smart Cities 2020, 3, 1353 -1382.
AMA StyleDhavalkumar Thakker, Bhupesh Kumar Mishra, Amr Abdullatif, Suvodeep Mazumdar, Sydney Simpson. Explainable Artificial Intelligence for Developing Smart Cities Solutions. Smart Cities. 2020; 3 (4):1353-1382.
Chicago/Turabian StyleDhavalkumar Thakker; Bhupesh Kumar Mishra; Amr Abdullatif; Suvodeep Mazumdar; Sydney Simpson. 2020. "Explainable Artificial Intelligence for Developing Smart Cities Solutions." Smart Cities 3, no. 4: 1353-1382.
In XML databases, the indexing process is based on a labelling or numbering scheme and generally used to label an XML document to perform an XML query using the path node information. Moreover, a labelling scheme helps to capture the structural relationships during query processing without the need to access the physical document. Two of the main problems for labelling XML schemes are duplicated labels, and cost efficiency regarding labelling time and size. This research presents a novel dynamic XML labelling scheme, called the Pentagonal Scheme, in which data are represented as ordered XML nodes with relationships between them. The update of these nodes from large-scale XML documents has been widely investigated and represents a challenging research problem, as it means relabelling a whole tree. Our algorithms provide an efficient dynamic XML labelling scheme to support data updates without duplicating labels or relabelling old nodes. Our work evaluates the labelling process in terms of size and time, and evaluates the labelling scheme’s ability to handle several insertions in XML documents. The findings indicate that the Pentagonal scheme shows a better initial labelling time performance than compared schemes. Particularly when using large size XML datasets. Moreover, it efficiently supports random skewed updates, has fast calculations and uncomplicated implementations to thus handle updates efficiently. In addition, the comparable evaluation of the query response time and relationships in Pentagonal scheme can be efficiently performed without presenting any extra cost. It was for this reason that our labelling scheme achieved the goal of this research.
Ebtesam Taktek; Dhavalkumar Thakker. Pentagonal scheme for dynamic XML prefix labelling. Knowledge-Based Systems 2020, 209, 106446 .
AMA StyleEbtesam Taktek, Dhavalkumar Thakker. Pentagonal scheme for dynamic XML prefix labelling. Knowledge-Based Systems. 2020; 209 ():106446.
Chicago/Turabian StyleEbtesam Taktek; Dhavalkumar Thakker. 2020. "Pentagonal scheme for dynamic XML prefix labelling." Knowledge-Based Systems 209, no. : 106446.
The traditional linear economy using a take‐make‐dispose model is resource intensive and has adverse environmental impacts. Circular economy (CE) which is regenerative and restorative by design is recommended as the business model for resource efficiency. While there is a need for businesses and organisations to switch from linear to CE, there are several challenges that needs addressing such as business models and the criticism of CE projects often being small scale. Technology can be an enabler toward scaling up CE; however, the prime challenge is to identify technologies that can allow predicting, tracking and proactively monitoring product's residual value to motivate businesses to pursue circularity decisions. In this paper, we propose an IoT‐enabled decision support system (DSS) for CE business model that effectively allows tracking, monitoring, and analysing products in real time with the focus on residual value. The business model is implemented using an ontological model. This model is complemented by a semantic decision support system. The semantic ontological model, first of its kind, is evaluated for technical compliance. We applied DSS and the ontological model in a real‐world use case and demonstrate viability and applicability of our approach.
Julius Sechang Mboli; Dhavalkumar Thakker; Jyoti Mishra. An Internet of Things-enabled decision support system for circular economy business model. Software: Practice and Experience 2020, 1 .
AMA StyleJulius Sechang Mboli, Dhavalkumar Thakker, Jyoti Mishra. An Internet of Things-enabled decision support system for circular economy business model. Software: Practice and Experience. 2020; ():1.
Chicago/Turabian StyleJulius Sechang Mboli; Dhavalkumar Thakker; Jyoti Mishra. 2020. "An Internet of Things-enabled decision support system for circular economy business model." Software: Practice and Experience , no. : 1.
Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms.
Bhupesh Kumar Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Daniel Neagu; Marian Gheorghe; Sydney Simpson. A novel application of deep learning with image cropping: a smart city use case for flood monitoring. Journal of Reliable Intelligent Environments 2020, 6, 51 -61.
AMA StyleBhupesh Kumar Mishra, Dhavalkumar Thakker, Suvodeep Mazumdar, Daniel Neagu, Marian Gheorghe, Sydney Simpson. A novel application of deep learning with image cropping: a smart city use case for flood monitoring. Journal of Reliable Intelligent Environments. 2020; 6 (1):51-61.
Chicago/Turabian StyleBhupesh Kumar Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Daniel Neagu; Marian Gheorghe; Sydney Simpson. 2020. "A novel application of deep learning with image cropping: a smart city use case for flood monitoring." Journal of Reliable Intelligent Environments 6, no. 1: 51-61.
Tunnel maintenance requires complex decision making, which involves pathology diagnosis and risk assessment, to ensure full safety while optimising maintenance and repair costs. A Decision Support System (DSS) can play a key role in this process by supporting the decision makers in identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key aspect is to identify which spatial stretches within a tunnel contain pathologies of similar kinds within neighbouring tunnel segments. This paper presents PADTUN, a novel intelligent decision support system that assists with pathology diagnosis and assessment of tunnels with respect to their disorders and diagnosis influencing factors. It utilises semantic web technologies for knowledge capture, representation, and reasoning. The core of PADTUN is a family of ontologies which represent the main concepts and relations associated with pathology assessment, and capture the decision process concerning tunnel maintenance. Tunnel inspection data is linked to these ontologies to take advantage of inference capabilities offered by semantic technologies. In addition, an intelligent mechanism is presented which exploits abstraction and inference capabilities. Thus PADTUN provides the world’s first semantically based intelligent DSS for tunnel maintenance. PADTUN was developed by an interdisciplinary team of tunnel experts and knowledge engineers in real-world settings offered by the NeTTUN EU Project. An evaluation of the PADTUN system is performed using real-world tunnel data and diagnosis tasks. We show how the use of semantic technologies allows addressing the complex issues of tunnel pathology inferencing, aiding in, and matching transportation experts’ expectations of decision support. The methodology is applicable to any linear transport structures, offering intelligent ways to aid with complex decision processes related to diagnosis and maintenance.
Vania Dimitrova; Muhammad Owais Mehmood; Dhavalkumar Thakker; Bastien Sage-Vallier; Joaquin Valdes; Anthony G. Cohn. An ontological approach for pathology assessment and diagnosis of tunnels. Engineering Applications of Artificial Intelligence 2020, 90, 103450 .
AMA StyleVania Dimitrova, Muhammad Owais Mehmood, Dhavalkumar Thakker, Bastien Sage-Vallier, Joaquin Valdes, Anthony G. Cohn. An ontological approach for pathology assessment and diagnosis of tunnels. Engineering Applications of Artificial Intelligence. 2020; 90 ():103450.
Chicago/Turabian StyleVania Dimitrova; Muhammad Owais Mehmood; Dhavalkumar Thakker; Bastien Sage-Vallier; Joaquin Valdes; Anthony G. Cohn. 2020. "An ontological approach for pathology assessment and diagnosis of tunnels." Engineering Applications of Artificial Intelligence 90, no. : 103450.
Rose Yemson; Savas Konur; Dhavalkumar Thakker. A Novel Semantic Complex Event Processing Framework for Streaming Processing. Proceedings of the 9th International Conference on the Internet of Things 2019, 32 .
AMA StyleRose Yemson, Savas Konur, Dhavalkumar Thakker. A Novel Semantic Complex Event Processing Framework for Streaming Processing. Proceedings of the 9th International Conference on the Internet of Things. 2019; ():32.
Chicago/Turabian StyleRose Yemson; Savas Konur; Dhavalkumar Thakker. 2019. "A Novel Semantic Complex Event Processing Framework for Streaming Processing." Proceedings of the 9th International Conference on the Internet of Things , no. : 32.
Modern Early Warning Systems (EWS) rely on scientific methods to analyse a variety of Earth Observation (EO) and ancillary data provided by multiple and heterogeneous data sources for the prediction and monitoring of hazard events. Furthermore, through social media, the general public can also contribute to the monitoring by reporting warning signs related to hazardous events. However, the warning signs reported by people require additional processing to verify the possibility of the occurrence of hazards. Such processing requires potential data sources to be discovered and accessed. However, the complexity and high variety of these data sources makes this particularly challenging. Moreover, sophisticated domain knowledge of natural hazards and risk management are also required to enable dynamic and timely decision making about serious hazards. In this paper we propose a data integration and analytics system which allows social media users to contribute to hazard monitoring and supports decision making for its prediction. We prototype the system using landslides as an example hazard. Essentially, the system consists of background knowledge about landslides as well as information about data sources to facilitate the process of data integration and analysis. The system also consists of an interactive agent that allows social media users to report their observations. Using the knowledge modelled within the system, the agent can raise an alert about a potential occurrence of landslides and perform new processes using the data sources suggested by the knowledge base to verify the event.
Jedsada Phengsuwan; Nipun Balan Thekkummal; Teja Shah; Phil James; Dhavalkumar Thakker; Rui Sun; Divya Pullarkatt; T. Hemalatha; Maneesha Vinodini Ramesh; Rajiv Ranjan. Context-Based Knowledge Discovery and Querying for Social Media Data. 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) 2019, 307 -314.
AMA StyleJedsada Phengsuwan, Nipun Balan Thekkummal, Teja Shah, Phil James, Dhavalkumar Thakker, Rui Sun, Divya Pullarkatt, T. Hemalatha, Maneesha Vinodini Ramesh, Rajiv Ranjan. Context-Based Knowledge Discovery and Querying for Social Media Data. 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI). 2019; ():307-314.
Chicago/Turabian StyleJedsada Phengsuwan; Nipun Balan Thekkummal; Teja Shah; Phil James; Dhavalkumar Thakker; Rui Sun; Divya Pullarkatt; T. Hemalatha; Maneesha Vinodini Ramesh; Rajiv Ranjan. 2019. "Context-Based Knowledge Discovery and Querying for Social Media Data." 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) , no. : 307-314.
Modern early warning system (EWS) requires sophisticated knowledge of the natural hazards, the urban context and underlying risk factors to enable dynamic and timely decision making (e.g., hazard detection, hazard preparedness). Landslides are a common form of natural hazard with a global impact and closely linked to a variety of other hazards. EWS for landslides prediction and detection relies on scientific methods and models which requires input from the time series data, such as the earth observation (EO) and urban environment data. Such data sets are produced by a variety of remote sensing satellites and Internet of things sensors which are deployed in the landslide prone areas. To this end, the automatic discovery of potential time series data sources has become a challenge due to the complexity and high variety of data sources. To solve this hard research problem, in this paper, we propose a novel ontology, namely Landslip Ontology, to provide the knowledge base that establishes relationship between landslide hazard and EO and urban data sources. The purpose of Landslip Ontology is to facilitate time series data source discovery for the verification and prediction of landslide hazards. The ontology is evaluated based on scenarios and competency questions to verify the coverage and consistency. Moreover, the ontology can also be used to realize the implementation of data sources discovery system which is an essential component in EWS that needs to manage (store, search, process) rich information from heterogeneous data sources.
Jedsada Phengsuwan; Tejal Shah; Philip James; Dhavalkumar Thakker; Stuart Barr; Rajiv Ranjan. Ontology-based discovery of time-series data sources for landslide early warning system. Computing 2019, 102, 745 -763.
AMA StyleJedsada Phengsuwan, Tejal Shah, Philip James, Dhavalkumar Thakker, Stuart Barr, Rajiv Ranjan. Ontology-based discovery of time-series data sources for landslide early warning system. Computing. 2019; 102 (3):745-763.
Chicago/Turabian StyleJedsada Phengsuwan; Tejal Shah; Philip James; Dhavalkumar Thakker; Stuart Barr; Rajiv Ranjan. 2019. "Ontology-based discovery of time-series data sources for landslide early warning system." Computing 102, no. 3: 745-763.
In recent years, deep learning has been increasingly used for several applications such as object analysis, feature extraction and image classification. This paper explores the use of deep learning in a flood monitoring application in the context of an EC-funded project, Smart Cities and Open Data REuse (SCORE). IoT sensors for detecting blocked gullies and drainages are notoriously hard to build, hence we propose a novel technique to utilise deep learning for building an IoT-enabled smart camera to address this need. In our work, we apply deep leaning to classify drain blockage images to develop an effective image classification model for different severity of blockages. Using this model, an image can be analysed and classified in number of classes depending upon the context of the image. In building such model, we explored the use of filtering in terms of segmentation as one of the approaches to increase the accuracy of classification by concentrating only into the area of interest within the image. Segmentation is applied in data pre-processing stage in our application before the training. We used crowdsourced publicly available images to train and test our model. Our model with segmentation showed an improvement in the classification accuracy.
Bhupesh Kumar Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Sydney Simpson; Daniel Neagu. Using Deep Learning for IoT-enabled Camera: A Use Case of Flood Monitoring. 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT) 2019, 235 -240.
AMA StyleBhupesh Kumar Mishra, Dhavalkumar Thakker, Suvodeep Mazumdar, Sydney Simpson, Daniel Neagu. Using Deep Learning for IoT-enabled Camera: A Use Case of Flood Monitoring. 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT). 2019; ():235-240.
Chicago/Turabian StyleBhupesh Kumar Mishra; Dhavalkumar Thakker; Suvodeep Mazumdar; Sydney Simpson; Daniel Neagu. 2019. "Using Deep Learning for IoT-enabled Camera: A Use Case of Flood Monitoring." 2019 10th International Conference on Dependable Systems, Services and Technologies (DESSERT) , no. : 235-240.
Dhavalkumar Thakker; Daniel Schwabe; Roberto García; Kouji Kozaki; Marco Brambilla; Vania Dimitrova. A note on intelligent exploration of semantic data. Semantic Web 2019, 10, 525 -527.
AMA StyleDhavalkumar Thakker, Daniel Schwabe, Roberto García, Kouji Kozaki, Marco Brambilla, Vania Dimitrova. A note on intelligent exploration of semantic data. Semantic Web. 2019; 10 (3):525-527.
Chicago/Turabian StyleDhavalkumar Thakker; Daniel Schwabe; Roberto García; Kouji Kozaki; Marco Brambilla; Vania Dimitrova. 2019. "A note on intelligent exploration of semantic data." Semantic Web 10, no. 3: 525-527.
With the advancement of disruptive new technologies, there has been a considerable focus on personalisation as an important component in nurturing users' engagement. In the context of smart cities, Internet of Things (IoT) offer a unique opportunity to help empower citizens and improve societies' engagement with their governments at both micro and macro levels. This study aims to examine the role of perceived value of IoT in improving citizens' engagement with public services. A survey of 313 citizens in the UK, engaging in various public services, enabled through IoT, found that the perceived value of IoT is strongly influenced by empowerment, perceived usefulness and privacy related issues resulting in significantly affecting their continuous use intentions. The study offers valuable insights into the importance of perceived value of IoT-enabled services, while at the same time, providing an intersectional perspective of UK citizens towards the use of disruptive new technologies in the public sector.
Ramzi El-Haddadeh; Vishanth Weerakkody; Mohamad Osmani; Dhaval Thakker; Kawaljeet Kaur Kapoor. Examining citizens' perceived value of internet of things technologies in facilitating public sector services engagement. Government Information Quarterly 2018, 36, 310 -320.
AMA StyleRamzi El-Haddadeh, Vishanth Weerakkody, Mohamad Osmani, Dhaval Thakker, Kawaljeet Kaur Kapoor. Examining citizens' perceived value of internet of things technologies in facilitating public sector services engagement. Government Information Quarterly. 2018; 36 (2):310-320.
Chicago/Turabian StyleRamzi El-Haddadeh; Vishanth Weerakkody; Mohamad Osmani; Dhaval Thakker; Kawaljeet Kaur Kapoor. 2018. "Examining citizens' perceived value of internet of things technologies in facilitating public sector services engagement." Government Information Quarterly 36, no. 2: 310-320.
Ebtesam Taktek; Dhavalkumar Thakker; Daniel Neagu. Comparison between Range-based and Prefix Dewey Encoding. Proceedings of the 14th International Conference on Web Information Systems and Technologies 2018, 364 -368.
AMA StyleEbtesam Taktek, Dhavalkumar Thakker, Daniel Neagu. Comparison between Range-based and Prefix Dewey Encoding. Proceedings of the 14th International Conference on Web Information Systems and Technologies. 2018; ():364-368.
Chicago/Turabian StyleEbtesam Taktek; Dhavalkumar Thakker; Daniel Neagu. 2018. "Comparison between Range-based and Prefix Dewey Encoding." Proceedings of the 14th International Conference on Web Information Systems and Technologies , no. : 364-368.
Rajiv Ranjan; Dhavalkumar Thakker; Armin Haller; Rajkumar Buyya. A note on exploration of IoT generated big data using semantics. Future Generation Computer Systems 2017, 76, 495 -498.
AMA StyleRajiv Ranjan, Dhavalkumar Thakker, Armin Haller, Rajkumar Buyya. A note on exploration of IoT generated big data using semantics. Future Generation Computer Systems. 2017; 76 ():495-498.
Chicago/Turabian StyleRajiv Ranjan; Dhavalkumar Thakker; Armin Haller; Rajkumar Buyya. 2017. "A note on exploration of IoT generated big data using semantics." Future Generation Computer Systems 76, no. : 495-498.
It is our great pleasure to welcome you to the UMAP 2017 LBR, Demo, and TOR Track, the 25th User Modelling, Adaptation and Personalization, held in Bratislava, Slovakia organized between the July 9-12th, 2017. This track of UMAP wraps three categories: (i) Demos, which showcase research prototypes and commercially available products of UMAP-based systems, (ii) Late-breaking Results, which contain original and unpublished accounts of innovative research ideas, preliminary results, industry showcases, and system prototypes, addressing both the theory and practice of UMAP and (iii) Theory, Opinion and Reflection (TOR). TOR is an additional category introduced in this edition of UMAP. Papers in this category critically look at ongoing research topics, reflect on persistent or fleeting trends in the field and offer blue sky future agendas for UMAP research. A novelty related to the TOR category is the presentation format. TOR papers will be presented in the form of an interactive campfire session in a discussion corner during the poster session. In total, we received 13 LBR, 6 TOR, and 4 Demo submissions out of which 8, 3 and 3 were deemed of high quality by the reviewers. Further 6 LBR papers were accepted from the main conference.
Marko Tkalcic; Dhavalkumar Thakker. UMAP'17 Late-Breaking Results, Demonstration and Theory, Opinion & Reflection Papers Chairs' Preface & Organization. Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization 2017, 1 -3.
AMA StyleMarko Tkalcic, Dhavalkumar Thakker. UMAP'17 Late-Breaking Results, Demonstration and Theory, Opinion & Reflection Papers Chairs' Preface & Organization. Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. 2017; ():1-3.
Chicago/Turabian StyleMarko Tkalcic; Dhavalkumar Thakker. 2017. "UMAP'17 Late-Breaking Results, Demonstration and Theory, Opinion & Reflection Papers Chairs' Preface & Organization." Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization , no. : 1-3.
The growing number of available data graphs in the form of RDF Linked Data enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they interact with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by users who are not domain experts. We propose a new navigation support approach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cognitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two application domains - semantic browsing (Music) and semantic search (Careers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts.
Marwan Al-Tawil; Vania Dimitrova; Dhavalkumar Thakker; Alexandra Poulovassilis. Evaluating Knowledge Anchors in Data Graphs Against Basic Level Objects. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 3 -22.
AMA StyleMarwan Al-Tawil, Vania Dimitrova, Dhavalkumar Thakker, Alexandra Poulovassilis. Evaluating Knowledge Anchors in Data Graphs Against Basic Level Objects. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():3-22.
Chicago/Turabian StyleMarwan Al-Tawil; Vania Dimitrova; Dhavalkumar Thakker; Alexandra Poulovassilis. 2017. "Evaluating Knowledge Anchors in Data Graphs Against Basic Level Objects." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 3-22.
The domain of cultural variations in interpersonal communication is becoming increasingly important in various areas, including human–human interaction (e.g., business settings) and human–computer interaction (e.g., during simulations, or with social robots). User-generated content (UGC) in social media can provide an invaluable source of culturally diverse viewpoints for supporting the understanding of cultural variations. However, discovering and organizing UGC is notoriously challenging and laborious for humans, especially in ill-defined domains such as culture. This calls for computational approaches to automate the UGC sensemaking process by using tagging, linking, and exploring. Semantic technologies allow automated structuring and qualitative analysis of UGC, but are dependent on the availability of an ontology representing the main concepts in a specific domain. For the domain of cultural variations in interpersonal communication, no ontological model exists. This paper presents the first such ontological model, called AMOn+, which defines cultural variations and enables tagging culture-related mentions in textual content. AMOn+ is designed based on a novel interdisciplinary approach that combines theoretical models of culture with crowdsourced knowledge (DBpedia). An evaluation of AMOn+ demonstrated its fitness-for-purpose regarding domain coverage for annotating culture-related concepts mentioned in text corpora. This ontology can underpin computational models for making sense of UGC.
Dhavalkumar Thakker; Stan Karanasios; Emmanuel Blanchard; Lydia Lau; Vania Dimitrova. Ontology for cultural variations in interpersonal communication: Building on theoretical models and crowdsourced knowledge. Journal of the Association for Information Science and Technology 2017, 68, 1411 -1428.
AMA StyleDhavalkumar Thakker, Stan Karanasios, Emmanuel Blanchard, Lydia Lau, Vania Dimitrova. Ontology for cultural variations in interpersonal communication: Building on theoretical models and crowdsourced knowledge. Journal of the Association for Information Science and Technology. 2017; 68 (6):1411-1428.
Chicago/Turabian StyleDhavalkumar Thakker; Stan Karanasios; Emmanuel Blanchard; Lydia Lau; Vania Dimitrova. 2017. "Ontology for cultural variations in interpersonal communication: Building on theoretical models and crowdsourced knowledge." Journal of the Association for Information Science and Technology 68, no. 6: 1411-1428.
A large number of cloud middleware platforms and tools are deployed to support a variety of internet‐of‐things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases integration and cooperation and can also lead to innovative use of the data. Multi‐cloud, privacy‐aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics‐as‐a‐service providers. There is a lack of both architectural blueprints that can support such diverse, multi‐cloud environments and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data‐processing architecture that utilises semantics at all the levels of IoT stack in multi‐cloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads. Copyright © 2016 John Wiley & Sons, Ltd.
Prem Prakash Jayaraman; Charith Perera; Dimitrios Georgakopoulos; Schahram Dustdar; Dhavalkumar Thakker; Rajiv Ranjan. Analytics-as-a-service in a multi-cloud environment through semantically-enabled hierarchical data processing. Software: Practice and Experience 2016, 47, 1139 -1156.
AMA StylePrem Prakash Jayaraman, Charith Perera, Dimitrios Georgakopoulos, Schahram Dustdar, Dhavalkumar Thakker, Rajiv Ranjan. Analytics-as-a-service in a multi-cloud environment through semantically-enabled hierarchical data processing. Software: Practice and Experience. 2016; 47 (8):1139-1156.
Chicago/Turabian StylePrem Prakash Jayaraman; Charith Perera; Dimitrios Georgakopoulos; Schahram Dustdar; Dhavalkumar Thakker; Rajiv Ranjan. 2016. "Analytics-as-a-service in a multi-cloud environment through semantically-enabled hierarchical data processing." Software: Practice and Experience 47, no. 8: 1139-1156.
The recent growth of the Web of Data has brought to the fore the need to develop intelligent means to support user exploration through big data graphs. To be effective, approaches for data graph exploration should take into account the utility from a user's point of view. We have been investigating knowledge utility -- how useful the trajectories in a data graph are for expanding users' knowledge. Following the theory for meaningful learning, according to which new knowledge is developed starting from familiar entities (anchors) and expanding to new and unfamiliar entities, we propose here an approach to identify knowledge anchors in a data graph. Our approach is underpinned by the Cognitive Science notion of basic level objects in domain taxonomies. Several metrics for extracting knowledge anchors in a data graph, and the corresponding algorithms, are presented. The metrics performance is examined, and a hybridization approach that combines the strengths of each metric is proposed.
Marwan Al-Tawil; Vania Dimitrova; Dhavalkumar Thakker; Brandon Bennett. Identifying Knowledge Anchors in a Data Graph. Proceedings of the 27th ACM Conference on Hypertext and Social Media 2016, 189 -194.
AMA StyleMarwan Al-Tawil, Vania Dimitrova, Dhavalkumar Thakker, Brandon Bennett. Identifying Knowledge Anchors in a Data Graph. Proceedings of the 27th ACM Conference on Hypertext and Social Media. 2016; ():189-194.
Chicago/Turabian StyleMarwan Al-Tawil; Vania Dimitrova; Dhavalkumar Thakker; Brandon Bennett. 2016. "Identifying Knowledge Anchors in a Data Graph." Proceedings of the 27th ACM Conference on Hypertext and Social Media , no. : 189-194.