Smart City Data Modelling using Semantic Web Technologies

Stakeholders in Open Source Software (OSS) projects need to determine whether a project is likely to sustain for a sufficient period of time in order to justify their investments into this project. In an OSS project context, there are typically several data sources and OSS processes relevant for determining project health indicators. However, even within one project these data sources often are technically and/or semantically heterogeneous, which makes data collection and analysis tedious and error prone. In this paper, the authors propose and evaluate a framework for OSS data analysis (FOSSDA), which enables the efficient collection, integration, and analysis of data from heterogeneous sources. Major results of the empirical studies are: (a) the framework is useful for integrating data from heterogeneous data sources effectively and (b) project health indicators based on integrated data analyses were found to be more accurate than analyses based on individual non-integrated data sources.

In the last years, enterprises increase their effort to collect large amounts of data from many heterogeneous data sources and store it in modern architectures like data lakes. However, this approach faces different drawbacks for finding and understanding data sources. Ontology-Based Data Access (OBDA) originating from the Semantic Web enables a homogeneous access to the data sources by using a mapping, called semantic model, between a data source and a target ontology. However, OBDA requires a detailed ontology, which is usually created by ontology engineers and domain experts resulting along with high effort for designing and maintaining. To overcome these limitations, we develop an approach consisting of a knowledge graph, which features an internal growing ontology and linked data-source specific semantic models. The ontology continuously evolves on-demand based on newly added data sources along with their corresponding semantic models, which are created by domain experts. To ensure the knowledge graph's stability, we develop an intuitive user-oriented assistant and combine it with a semi-supervised evolving strategy that assists the user with the help of external knowledge bases. We evaluate accuracy and usability of our approach by conducting a user study with a heterogeneous group of participants that define semantic models upon pre-defined data sets. The results show that semantic models become more objective and consistent with our provided user assistant and thus lead to a knowledge graph with higher interconnectivity and stability.

ObjectiveBiomedical research increasingly relies on the integration of information from multiple heterogeneous data sources. Despite the fact that structural and terminological aspects of interoperability are interdependent and rely on a common set of requirements, current efforts typically address them in isolation. We propose a unified ontology-based knowledge framework to facilitate interoperability between heterogeneous sources, and investigate if using the LexEVS terminology server is a viable implementation method.Materials and methodsWe developed a framework based on an ontology, the general information model (GIM), to unify structural models and terminologies, together with relevant mapping sets. This allowed a uniform access to these resources within LexEVS to facilitate interoperability by various components and data sources from implementing architectures.ResultsOur unified framework has been tested in the context of the EU Framework Program 7 TRANSFoRm project, where it was used to achieve data integration in a retrospective diabetes cohort study. The GIM was successfully instantiated in TRANSFoRm as the clinical data integration model, and necessary mappings were created to support effective information retrieval for software tools in the project.ConclusionsWe present a novel, unifying approach to address interoperability challenges in heterogeneous data sources, by representing structural and semantic models in one framework. Systems using this architecture can rely solely on the GIM that abstracts over both the structure and coding. Information models, terminologies and mappings are all stored in LexEVS and can be accessed in a uniform manner (implementing the HL7 CTS2 service functional model). The system is flexible and should reduce the effort needed from data sources personnel for implementing and managing the integration.

Data sourcing or integration is inevitable in current business scenario. Major issues of data sourcing from heterogeneous data sources are lack of semantic richness and deprived querying. To overcome these issues, an Ontology Based Data federation using Object Relational Database (OBDF-ORDB) architecture has been proposed and implemented. This OBDF-ORDB architecture consists of semantic layer and transformation & query layer. In semantic layer the ontology used to create local and global schema to enrich the semantics. In transformation & querying layer, Object Relational Database (ORDB) is used for storing the local ontology, global ontology to improve storage, maintenance and retrieval. The transformation rule engine proposed for the architecture converts and stores the local ontology and global ontology from flat OWL file to ORDB. The user queries are passed to ORDB for result extraction. To analyze the performance of the OBDF-ORDB architecture E-shopping application is selected. Experimental results shows that the proposed OBDF-ORDB architecture is relatively better than the traditional data access and ontology based data access in recall and response time. It is observed that the recall mechanism in OBDF-ORDB architecture has been improved by 25% compared to traditional data federation and response time is reduced by 15% compared to ontology based data federation.

Purpose of ReviewThe complexity of the data collected for sleep research is increasing, and the focal point of sleep research is dependent on a higher number of data sources. Data collected for sleep studies often includes both subjective and objective measurements of sleep quality and is gathered over a more extended period, e.g., for weeks, months, or even years. However, this variety and volume of data make it challenging and time-consuming for researchers to utilize. Therefore, sophisticated data structures are necessary to utilize data in sleep research.Recent FindingsThis paper explores how heterogeneous data sources can be represented in a homogeneous database design. The following research questions drove our work: (i) How can we represent sleep data from heterogeneous sources in a homogenous digital platform database? and (ii) How can a data source pipeline transform various data sources into a homogeneous data format?SummaryThis paper’s main contributions are conceptualizing the design and development of a homogeneous database and digital platform architecture and a data source pipeline that fits well for sleep research in particular and healthcare research in general.

• Offers a new vision on automated data harmonization in Dataspace (DS) systems. • Introduces LLM-based methods for scalable DS ingestion of heterogeneous datasources. • Presents a system with Harmonizer, Transformer, Evaluator components for ingestion. • Demonstrated an automated data ingestion prototype using LLM agents. • Validates the system with healthcare use case harmonizing heterogeneous data sources. Dataspaces (DS) enable stakeholders to collaborate on innovative, data-driven services by integrating data across domains. However, the realization and adoption of DS remain challenging due to domain-specific heterogeneity at the system, service, and data levels. While system and service-level heterogeneity can often be addressed through standards, data-level heterogeneity, namely data structures and semantics variations, remains challenging. To effectively ingest data into the DS, two communication endpoints must correctly interpret each other’s data models, therefore, DS ecosystems rely on “harmonization”, the process of generating a unified target data model from heterogeneous sources and transforming incoming data accordingly. Currently, harmonization and transformation are performed manually whenever new data sources are integrated. This is time-consuming, costly, and difficult to scale, posing a critical barrier to the realization and adoption of DS in practice. This study proposes a novel methodology for automated data harmonization during ingestion into DS ecosystems. The approach integrates harmonization, transformation, and human-in-the-loop evaluation within an automated system powered by modern LLM-based AI agents. These agents address data-level heterogeneity and generate harmonized target data models, representing a substantial departure from current manually-handled data harmonization. The system is validated through a healthcare use case, demonstrating its practical feasibility for harmonization during data ingestion into the DS. Overall, this work provides a foundational step toward seamless, efficient, and scalable data integration in DS. By automating data harmonization, it delivers substantial value to industry digital solutions as well as domains where data heterogeneity persists, including IoT or Big Data platforms.

In the domains of Finance and Economics, interacting with large amounts of data from heterogeneous sources is a common and critical task for both academic researchers and industrial practitioners. As the Big Data trend is sweeping through academia, enterprises and governments with large amounts of data from various new sources, it results in tremendous potential opportunities to extract values from it, such as automatically discovering novel patterns which would be impossible with small samples from a single data source. Meanwhile, complicated by the connectivity and interdependence of the world's markets, corporations and financial instruments, significant challenges have been posted for efficiently managing relevant datasets which would be used to facilitate scientific discoveries and support business decisions. This tutorial demonstrates an approach to address the data interchange challenges by illustrating how the Semantic Web technologies can be used in interacting with financial and economic data. We first introduce an incremental data organization model based on the Resource Description Framework (RDF), and then show the processes of data collecting, adding structures as well as domain knowledge and linking across different data sources. In keeping with CIFEr's practical spirit, this tutorial gives participants hand-on experience of using SPARQL to query large datasets in RDF, and analyzing the results with R through examples such as retrieving textual data from the New York Times and studying corporations' lobbying behaviors.

The two driving forces for designing dimensional models of data warehouse are data sources and business requirements. Medical data sources are characterized by too many data types, large amount of data, lack of associations among data, mixed dictionary tables, etc., which result in lacking uniform representation and effective organization among a large number of concepts. These features may lead to serious semantic heterogeneity among different data sources. Medical data warehouse design involves a wide range of business requirements, while the users' perspectives are different and the expression of semantic heterogeneity is substantial. So how to obtain and integrate these business requirements is a challenge. Aiming at the characteristics of medical data sources and business requirements, this paper proposes an ontology-based medical data warehouse dimensional modeling method. Experiment shows that this method can not only optimize the data warehouse requirements analysis process, but also effectively eliminate the semantic heterogeneity in data sources and business requirements, thereby greatly improving the efficiency of medical data warehouse dimensional modeling.

Entity alignment is the problem of identifying which entities in a data source refer to the same real-world entity in the others. Identifying entities across heterogeneous data sources is paramount to many research fields, such as data cleaning, data integration, information retrieval and machine learning. The aligning process is not only overwhelmingly expensive for large data sources since it involves all tuples from two or more data sources, but also need to handle heterogeneous entity attributes. In this paper, we propose an unsupervised approach, called EnAli, to match entities across two or more heterogeneous data sources. EnAli employs a generative probabilistic model to incorporate the heterogeneous entity attributes via employing exponential family, handle missing values, and also utilize the locality sensitive hashing schema to reduce the candidate tuples and speed up the aligning process. EnAli is highly accurate and efficient even without any ground-truth tuples. We illustrate the performance of EnAli on re-identifying entities from the same data source, as well as aligning entities across three real data sources. Our experimental results manifest that our proposed approach outperforms the comparable baseline.

In the AI era, high-value targeted injection attacks and defences based on the semantic layer of Large Language Models will become the main battlefield for security confrontations. Ultimately, any form of artificial information warfare boils down to a battle at the semantic level. This involves using information technology to attack the semantic layer and, consequently, the human brain. Specifically, the goal is to launch targeted attacks on the brains of specific decision-making groups within society, thereby undermining human social decision-making mechanisms. The ultimate goal is to maximize value output in the fields of political economy, religion, and ideology, including wealth and power, with minimal investment in information technology. This paper uses the pyramid model perspective to unify the information security confrontation protocol stack, including biological intelligence, human intelligence, and artificial intelligence. It begins by analysing the characteristics and explainable of AI models, and feasible means of their multi-dimensions offensive and defensive mechanisms, proposing an open engineering practice strategy that leverages semantic layer gaming between LLMs. This strategy involves targeted training set contamination at the semantic layer and penetration induction through social networks. At the end of this article, expands the contamination of training set data sources to the swarm oscillating environment in human-machine sociology and ethical confrontation, then discusses attacks targeting the information cocoon of individuals or communities and extends the interaction mechanism between humans and LLMs and GPTs above the semantic layer to the evolution dynamics of a Fractal Pyramid Model.

Smart cities emerge in computer science as a topic to cover how the technology of information and communication can be used in the urban centers to monitor its dynamics and allow the improvement of services for the citizens. In these urban centers, different methodologies are used in order to collect data and provide them to applications. These data come from several heterogeneous sources, thus there is an effort to integrate and standardize them before their use. Also, a significant amount of this data has spatio-temporal annotations, which may be used to analyze the city dynamics, such as the mobility flow. Due to these characteristics of the data generated in urban centers, and also the possibilities brought by their use and analyses, this work presents a novel approach to collect, integrate and perform some analysis tasks in mobility data from smart cities. Thus, the SMAFramework can analyze mobility patterns based on a Multi-Aspect Graph (MAG) data structure. To show the potential of the framework, it is proposed a method to analyze the saptio-temporal correlation between data from two different data sources in the same city. Real data collected from social media and a taxi system of the city of New York are used to evaluate this method. The obtained results allowed to understand some of the applicabilities of the framework and also provided some insights on how to use the framework to resolve specific problems when analyzing mobility in urban environments.

Municipalities now collect large amounts of data that could be potentially used to guide road users and public transport users in their decision-making. However, these data sources are heterogeneous and a semantic layer, expressed in an ontology, is proposed to match data sources, traveler needs, and the context of their journeys in order to support decision-making.

In this paper, we focus on the problem in information integration systems of obtaining data from an heterogeneous data source accurately and effectively. XML is used as data exchange format of the wrapper. We design the wrapper architecture based on the conversion and management of the views as the bridge from global schema to local schema of various data sources. Our wrapper has two main subsystems, the data extract subsystem and query executor subsystem. The former is for loading data for the cache in the mediator when changes more than threshold are detected, and the latter is for answering the query from the mediator. The architecture adapts to the data and schema change of the data source and could answer the query of the mediator effectively. Considering the wrapper may run in the environment without control, the process in the wrapper should be simple enough. The storage in the wrapper itself should be as small as possible and the storage of data source could be used. The detail of the module query rewrite, view management, query merge, result wrap and schema change detect are discussed. The behavior of the wrapper during the query process in the wrapper is discussed with a running example. The security strategy, especially in the distance that the wrapper runs in autonomic data source, is also introduced in this paper.

Stimulated by the arrival of the big data era, various and heterogeneous data sources such as data in social networks, mobile devices and sensor data for users have emerged, mirroring characteristics and preferences of data owners. These data sources are often used to construct user profiles so as to facilitate personalized services like recommendations or personalized data access. In the context of second language learning, learner data involve learning logs, standard test results, and individual learning preferences and styles. Given its attribute of reflecting the characteristics of learners, such data can be exploited to build the learner profiles. However, these data sources possibly include noises or bias, and hence influence the reliability of the correspondingly constructed learner profiles. Consequently, the inaccurate profiles may result in ineffective learning tasks that are generated by e-Learning systems. To tackle this issue, it is significant and critical to evaluate the accuracy of learner profiles. In a response to this call, we propose a novel metric named “profile mean square error” to examine the accuracy of learner profiles founded upon diverse sources. We also demonstrate how to construct various learner profiles though applying different data sources such as learning logs, standard test results, and personal learning preferences in e-Learning systems and pedagogical activities. Moreover, we conduct an experimental study among some second language learners, the results of which illustrate that the most accurate profiles are generated from multiple data sources if they are integrated in a rational way.

Selection of genes that are differentially expressed and critical to a particular biological process has been a major challenge in post-array analysis. Recent development in bioinformatics has made various data sources available such as mRNA and miRNA expression profiles, biological pathway and gene annotation, etc. Efficient and effective integration of multiple data sources helps enrich our knowledge about the involved samples and genes for selecting genes bearing significant biological relevance. In this work, we studied a novel problem of multi-source gene selection: given multiple heterogeneous data sources (or data sets), select genes from expression profiles by integrating information from various data sources. We investigated how to effectively employ information contained in multiple data sources to extract an intrinsic global geometric pattern and use it in covariance analysis for gene selection. We designed and conducted experiments to systematically compare the proposed approach with representative methods in terms of statistical and biological significance, and showed the efficacy and potential of the proposed approach with promising findings.