In this demo paper, we present SPARK (Semantic Planning with Augmented Retrieval and Knowledge), an AI-driven web application that generates personalized itineraries with natural language input. By addressing the limitations of traditional routing systems and machine learning models that lack adaptability to user intent and real-time contexts, SPARK integrates a Neo4j [6] graph that built on OpenStreetMap [8] data with semantic analysis, custom route optimization, and retrieval-augmented generation. By using large language models (LLM), the system outputs travel planning with real-time and enriched data via external APIs. Also, SPARK dynamically adapts to personalized user queries and optimizes routes based on contextual relevance. As demonstrated in an urban scenario, this demo paper highlights the potential of combining graph-based retrieval and LLMs to deliver flexible and context-aware route planning. Meanwhile, our demo shows that the proposed system is scalable and deployable for travel guidance.

Professional Burnout Syndrome (PBS) among health-care professionals has been considered a threat to both staff well-being and patient safety, especially in a high-stress medical environment. While robotics and AI have been increasingly integrated into healthcare, their impact on PBS has not been explored in detail yet. Therefore, this paper proposes a real-time PBS detection framework for healthcare professionals using emotion recognition. This framework includes a deep learning-based emotion detection system for humanoid companion robots, which then correlates emotional trends with the Circumplex model to identify burnout risk. Our experimental evaluation results across five deep learning architectures, MobileNet, RegNetY, Swin Transformer, ConvNeXt V2, and EVA-02, show a highest accuracy of 74.05% on a public emotion dataset. These results demonstrate the feasibility of integrating such systems into healthcare workflows for early PBS warnings. Also, this work suggests a human-in-the-loop diagnostic model, where robotic emotion detection complements clinical expertise by providing proactive support, strengthening workforce resilience, and maintaining the quality of patient care.

Blockchain, as a trustless network, has provided diverse benefits for a wide range of application domains, such as enhancing data management in terms of data security, traceability, accountability, transparency, and decentralization. However, the detection of cybersecurity vulnerabilities in blockchain has initiated a debate on whether this inherently trustless technology needs further trust support or not. In this work, we explore mutual trust and trustless cooperation. First, we examine blockchain-assisted trust management to highlight the specific trustless trait of blockchain. Then, as consensus is an important component of blockchain technology, we examine the role of trust in evaluating the trustworthiness of peer participants in the blockchain consensus process and enhancing the growth of a consistent chain. Finally, we derive research findings in the promising cooperation between trustless and trust.

The concept of modernizing outdated systems in critical infrastructure through digital transformation has been a widely discussed topic nowadays. Following the transition of energy systems, the attention has now shifted towards digitalizing the water distribution systems. These systems are large-scale but outdated systems that frequently encounter various issues and upgrading them would enable easier to identify issues and provide smoother, more efficient service. However, this process requires cautious planning and guidance to ensure that the generated data is reliable, and the system remains operational during the transition. Hence, the primary objective of this paper is to propose a formal model based on ternary relational semantics that can guide the digital transformation of water distribution networks. The proposed model provides a flexible transformation process while making the system generate reliable data. Additionally, this paper demonstrates the application of the proposed model by developing a proof of concept based on a real-world scenario.

With the rapid increase of published open datasets, it is crucial to support the open data progress in smart cities while considering the open data quality. In the Czech Republic, and its National Open Data Catalogue (NODC), the open datasets are usually evaluated based on their metadata only, while leaving the content and the adherence to the recommended data structure to the sole responsibility of the data providers. The interoperability of open datasets remains unknown. This paper therefore aims to propose a novel content-aware quality evaluation framework that assesses the quality of open datasets based on five data quality dimensions. With the proposed framework, we provide a fundamental view on the interoperability-oriented data quality of Czech open datasets, which are published in NODC. Our evaluations find that domain-specific open data quality assessments are able to detect data quality issues beyond traditional heuristics used for determining Czech open data quality, increase their interoperability, and thus increase their potential to bring value for the society. The findings of this research are beneficial not only for the case of the Czech Republic, but also can be applied in other countries that intend to enhance their open data quality evaluation processes.

In social media, human-generated web data from real-world events have become exponentially complex due to the chaotic and spontaneous features of natural language. This may create an information overload for the information consumers, and in turn not easily digest a large amount of information in a limited time. To tackle this issue, we propose to use Complex Event Processing (CEP) and semantic web reasoners to disentangle the human-generated data and present users with only relevant and important data. However, one of the key obstacles is that the human-generated data can have no structured meaning sometimes even for the speaker, hindering the output of the CEP. Therefore, in order to adapt to the CEP inputs, we present two different techniques that allow for the discrimination and digestion of value of human-generated data. The first one relies on the Variable Sharing Property that was developed for relevance logics, while the second one is based on semantic equivalence and natural language processing. The results can be given to CEP for further semantic reasoning and generate digested information for users.

The fourth industrial revolution and the digital transformation, commonly known as Industry 4.0, is exponentially progressing in recent years. Connected computers, devices, and intelligent machines communicate with each other and interact with the environment to require only a minimum of human intervention. An important issue in Industry 4.0 is the evaluation of the quality of the process in terms of Key Performance Indicators (KPIs). Monte Carlo simulations can play an important role to improve the estimations. However, there is still a lack of clear workflow to conduct the Monte Carlo simulations to improve such estimations. This paper, therefore, proposes a simulation flow for conducting Monte Carlo methods comparison in Industry 4.0 applications. Based on the simulation flow, we compare Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) methods on the efficiency KPI of Smart Manufacturing data. The experimental results show the applicability of MC and MCMC with Industry 4.0 data and possible limitations of the two simulation methods.

With the rapid development of the Internet of Things (IoT), a large amount of data is exchanged between various communicating devices. Since the data should be communicated securely between the communicating devices, the network security is one of the dominant research areas for the 6LoWPAN IoT applications. Meanwhile, 6LoWPAN devices are vulnerable to attacks inherited from both the wireless sensor networks and the Internet protocols. Thus intrusion detection systems have become more and more critical and play a noteworthy role in improving the 6LoWPAN IoT networks. However, most intrusion detection systems focus on the attacked areas in the IoT networks instead of precisely on certain IoT nodes. This may lead more resources to further detect the compromised nodes or waste resources when detaching the whole attacked area. In this paper, we therefore proposed a new precisional detection strategy for 6LoWPAN Networks, named as PDS-6LoWPAN. In order to validate the strategy, we evaluate the performance and applicability of our solution with a thorough simulation by taking into account the detection accuracy and the detection response time.

For Point of Interest (POI) recommendations or touristic orienteering applications, it is essential to explore the user preference or potential interests to predict potential POIs or touristic routes. One of the state-of-the-art approaches for inferring the user’s interests is social sensing, which is based on implicit feedback and semantic similarities to extract the user preference. In this paper, we profile the user preferences by social sensing to exploit the similarity between users’ reviews and POI descriptions. The experiment is based on the "Yelp!" dataset and provides the labels associated with different businesses considered in the "Yelp!" Social Network. The preliminary results show that the proposed model can automatically estimate the user interests and improve the effectiveness of the user profiling procedure. The results also indicate that our proposed model can offer a foundational approach for ranking POIs and touristic routes.

With the recent developments of the Internet of Things and its integration in the web environment, the Web of Things and the real-time data submissions to Reasoners are enabled. However, the data that are fed to the Reasoners are often inconsistent. This can be possibly caused by the malfunction of certain Internet of Things device or by human errors. The data consistency issue is becoming more complex in the Web of Things network. This paper, therefore, proposes a new data processing model to tackle the inconsistent data, so that the processed data can be further used in Reasoners. The data processing model introduces an oversimplification of the Shramko-Wansing sixteen-valued trilattice, which is an extension of Belnap’s four-valued bilattice to assign the data classical truth-values. A preliminary implementation is demonstrated to validate the proposed model. The result shows that our model can avoid system collapse when contradictory outputs exist.

The open data are considered to be an important building block of Smart City services. Based on the data availability in the cities, companies are building innovative smart services to facilitate the city development. However, most of the practitioners are focusing only on the open data usage but paying little attention to the processes related to the data publication. Thus, there is a lack of understanding the whole process of open data life cycle such as before the data can be open, when they are published and when they need to be archived. Those phases in the open data life cycle are critical for assuring the data accuracy, availability and relevance because the data are analysed, changed, anatomized or generally processed during the whole life cycle. This also creates a set of research challenges for open data. Therefore, in the context of smart cities, this paper proposes to consider Open Data as a Service and identifies the research challenges along with the open data life cycle.

Nowadays, the development of data sharing technologies allows to involve more people to collaboratively contribute knowledge on the Web. The shared knowledge is usually represented as Collaborative Open Data (COD), for example, Wikipedia is one of the well-known sources for COD. The Wikipedia articles can be written in different languages, updated in real time, and originated from a vast variety of editors. However, COD also bring different data quality problems such as data inconsistency and low data objectiveness due to the crowd-based and dynamic nature. These data quality problems such as biased information may lead to sentimental changes or social impacts. This paper therefore proposes a new measurement model to assess the quality of COD. In order to evaluate the proposed model, A preliminary experiment is conducted with a large scale of Wikipedia articles to validate the applicability and efficiency of this proposed quality model in the real-world scenario.

Nowadays, a variety of Big Data architectures are emerging to organize the Big Data life cycle. While some of these architectures are proposed for general usage, many of them are proposed in a specific application domain such as smart cities, transportation, healthcare, and agriculture. There is, however, a lack of understanding of how and why Big Data architectures vary in different domains and how the Big Data architecture strategy in one domain may possibly advance other domains. Therefore, this paper surveys and compares the Big Data architectures in different application domains. It also chooses a representative architecture of each researched application domain to indicate which Big Data architecture from a given domain the researchers and practitioners may possibly start from. Next, a pairwise cross-domain comparison among the Big Data architectures is presented to outline the similarities and differences between the domain-specific architectures. Finally, the paper provides a set of practical guidelines for Big Data researchers and practitioners to build and improve Big Data architectures based on the knowledge gathered in this study.

With the development of Big Data applications in Smart Cities, various Big Data applications are proposed within the domain. These are however hard to test and prototype, since such prototyping requires big computing resources. In order to save the effort in building Big Data prototypes for Smart Cities, this paper proposes an enhanced sampling technique to obtain a coreset from Big Data while keeping the features of the Big Data, such as clustering structure and distribution density. In the proposed sampling method, for a given dataset and an e > 0, the method computes an e-coreset of the dataset. The e-coreset is then modified to obtain a sample set while ensuring the separation and balance in the set. Furthermore, by considering the representativeness of each sample point, our method can helps to remove noises and outliers. We believe that the coreset-based technique can be used to efficiently prototype and evaluate Big Data applications in the Smart City.

3D data have been widely used to represent complex data objects in different domains such as virtual reality, 3D printing or biological data analytics. Due to complexity of 3D data, it is usually featured as big 3D data. One of the typical big 3D data is the protein data, which can be used to visualize the protein structure in a 3D style. However, the 3D data also bring various data quality problems, which may cause the delay, inaccurate analysis results, even fatal errors for the critical decision making. Therefore, this paper proposes a novel big 3D data process model with specific consideration of 3D data quality. In order to validate this model, we conduct a case study for cleaning and analyzing the protein data. Our case study includes a comprehensive taxonomy of data quality problems for the 3D protein data and demonstrates the utility of our proposed model. Furthermore, this work can guide the researchers and domain experts such as biologists to manage the quality of their 3D protein data.

With the rapid development of the Big Data and Internet of Things (IoT), Big Data technologies have emerged as a key data analytics tool in IoT, in which, data clustering algorithms are considered as an essential component for data analysis. However, there has been limited research that addresses the challenges across Big Data and IoT and thus proposing a research agenda is important to clarify the research challenges for clustering Big Data in the context of IoT. By tackling this specific aspect - clustering algorithm in Big Data, this paper examines on Big Data technologies, related data clustering algorithms and possible usages in IoT. Based on our review, this paper identifies a set of research challenges that can be used as a research agenda for the Big Data clustering research. This research agenda aims at identifying and bridging the research gaps between Big Data clustering algorithms and IoT.

Traditional food recommender systems exploit items' ratings and descriptions in order to generate relevant recommendations for the users. While this data is important, it might not entirely capture the true users' preferences. In this paper, we analyse the performance of a food recommender that allows users to enter their preferences in the form of both ratings and tags, which are then used by a Matrix Factorization (MF) rating prediction model. The performed offline and online experiments have clarified the importance of user tags in comparison to content features. While item content contributes more to the quality of the prediction accuracy, user tags yields better ranking quality. Even more importantly, a live user study has revealed that a system variant, which leverages user tags in the prediction model and in the interface, achieves a significantly better user evaluation in terms of perceived effectiveness, choice satisfaction and choice difficulty.

A number of studies suggest that making correct decisions depends on high-quality information; how information quality affects decision-making is still not fully understood. Following the multi-dimensional view of information quality, this paper investigates the effects of information accuracy, completeness, and consistency on decision-making. Results show that information accuracy and completeness affect decision quality significantly. Although the effect of information consistency on decision quality appears to be non-significant, consistency of information may intensify the contribution of accuracy, indicating that information accuracy and consistency influence decision quality jointly.

When we evaluate the quality of recommender systems (RS), most approaches only focus on the predictive accuracy of these systems. Recent works suggest that beyond accuracy there is a variety of other metrics that should be considered when evaluating a RS. In this paper we focus on two crucial metrics in RS evaluation: coverage and serendipity. Based on a literature review, we first discuss both measurement methods as well as the trade-off between good coverage and serendipity. We then analyze the role of coverage and serendipity as indicators of recommendation quality, present novel ways of how they can be measured and discuss how to interpret the obtained measurements. Overall, we argue that our new ways of measuring these concepts reflect the quality impression perceived by the user in a better way than previous metrics thus leading to enhanced user satisfaction.