Even for a conservative estimate, 80% of enterprise data reside in unstructured files, stored in data lakes that accommodate heterogeneous formats. Classical search engines can no longer meet information seeking needs, especially when the task is to browse and explore for insight formulation. In other words, there are no obvious search keywords to use. Knowledge graphs, due to their natural visual appeals that reduce the human cognitive load, become the winning candidate for heterogeneous data integration and knowledge representation. In this paper, we introduce Docs2KG, a novel framework designed to extract multimodal information from diverse and heterogeneous unstructured documents, including emails, web pages, PDF files, and Excel files. Dynamically generates a unified knowledge graph that represents the extracted key information, Docs2KG enables efficient querying and exploration of document data lakes. Unlike existing approaches that focus on domain-specific data sources or pre-designed schemas, Docs2KG offers a flexible and extensible solution that can adapt to various document structures and content types. The proposed framework unifies data processing supporting a multitude of downstream tasks with improved domain interpretability. Docs2KG is publicly accessible at https://docs2kg.ai4wa.com, and a demonstration video is available at https://docs2kg.ai4wa.com/Video.
This paper studies policy evaluation with multiple data sources, especially in scenarios that involve one experimental dataset with two arms, complemented by a historical dataset generated under a single control arm. We propose novel data integration methods that linearly integrate base policy value estimators constructed based on the experimental and historical data, with weights optimized to minimize the mean square error (MSE) of the resulting combined estimator. We further apply the pessimistic principle to obtain more robust estimators, and extend these developments to sequential decision making. Theoretically, we establish non-asymptotic error bounds for the MSEs of our proposed estimators, and derive their oracle, efficiency and robustness properties across a broad spectrum of reward shift scenarios. Numerical experiments and real-data-based analyses from a ridesharing company demonstrate the superior performance of the proposed estimators.
Biodiversity data are substantially increasing, spurred by technological advances and community (citizen) science initiatives. To integrate data is, likewise, becoming more commonplace. Open science promotes open sharing and data usage. Data standardization is an instrument for the organization and integration of biodiversity data, which is required for complex research projects and digital twins. However, just like with an actual instrument, there is a learning curve to understanding the data standards field. Here we provide a guide, for data providers and data users, on the logistics of compiling and utilizing biodiversity data. We emphasize data standards, because they are integral to data integration. Three primary avenues for compiling biodiversity data are compared, explaining the importance of research infrastructures for coordinated long-term data aggregation. We exemplify the Biodiversity Digital Twin (BioDT) as a case study. Four approaches to data standardization are presented in terms of the balance between practical constraints and the advancement of the data standards field. We aim for this paper to guide and raise awareness of the existing issues related to data standardization, and especially how data standards are key to data interoperability, i.e., machine accessibility. The future is promising for computational biodiversity advancements, such as with the BioDT project, but it rests upon the shoulders of machine actionability and readability, and that requires data standards for computational communication.
Work zone is one of the major causes of non-recurrent traffic congestion and road incidents. Despite the significance of its impact, studies on predicting the traffic impact of work zones remain scarce. In this paper, we propose a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms, and introduce a novel deep learning model to predict the traffic speed and incident likelihood during planned work zone events. The proposed model transforms traffic patterns into 2D space-time images for both model input and output and employs an attention-based multi-context convolutional encoder-decoder architecture to capture the spatial-temporal dependencies between work zone events and traffic variations. Trained and validated on four years of archived work zone traffic data from Maryland, USA, the model demonstrates superior performance over baseline models in predicting traffic speed, incident likelihood, and inferred traffic attributes such as queue length and congestion timings (i.e., start time and duration). Specifically, the proposed model outperforms the baseline models by reducing the prediction error of traffic speed by 5% to 34%, queue length by 11% to 29%, congestion timing by 6% to 17%, and increasing the accuracy of incident predictions by 5% to 7%. Consequently, this model offers substantial promise for enhancing the planning and traffic management of work zones.
Urban heat islands, defined as specific zones exhibiting substantially higher temperatures than their immediate environs, pose significant threats to environmental sustainability and public health. This study introduces a novel machine-learning model that amalgamates data from the Sentinel-3 satellite, meteorological predictions, and additional remote sensing inputs. The primary aim is to generate detailed spatiotemporal maps that forecast the peak temperatures within a 24-hour period in Turin. Experimental results validate the model's proficiency in predicting temperature patterns, achieving a Mean Absolute Error (MAE) of 2.09 degrees Celsius for the year 2023 at a resolution of 20 meters per pixel, thereby enriching our knowledge of urban climatic behavior. This investigation enhances the understanding of urban microclimates, emphasizing the importance of cross-disciplinary data integration, and laying the groundwork for informed policy-making aimed at alleviating the negative impacts of extreme urban temperatures.
Entity matching (EM) is a critical task in data integration, aiming to identify records across different datasets that refer to the same real-world entities. Traditional methods often rely on manually engineered features and rule-based systems, which struggle with diverse and unstructured data. The emergence of Large Language Models (LLMs) such as GPT-4 offers transformative potential for EM, leveraging their advanced semantic understanding and contextual capabilities. This vision paper explores the application of LLMs to EM, discussing their advantages, challenges, and future research directions. Additionally, we review related work on applying weak supervision and unsupervised approaches to EM, highlighting how LLMs can enhance these methods.
Paired single-cell sequencing technologies enable the simultaneous measurement of complementary modalities of molecular data at single-cell resolution. Along with the advances in these technologies, many methods based on variational autoencoders have been developed to integrate these data. However, these methods do not explicitly incorporate prior biological relationships between the data modalities, which could significantly enhance modeling and interpretation. We propose a novel probabilistic learning framework that explicitly incorporates conditional independence relationships between multi-modal data as a directed acyclic graph using a generalized hierarchical variational autoencoder. We demonstrate the versatility of our framework across various applications pertinent to single-cell multi-omics data integration. These include the isolation of common and distinct information from different modalities, modality-specific differential analysis, and integrated cell clustering. We anticipate that the proposed framework can facilitate the construction of highly flexible graphical models that can capture the complexities of biological hypotheses and unravel the connections between different biological data types, such as different modalities of paired single-cell multi-omics data. The implementation of the proposed framework can be found in the repository https://github.com/kuijjerlab/CAVACHON.
Traditional time series forecasting methods predominantly rely on historical data patterns, neglecting external interventions that significantly shape future dynamics. Through control-theoretic analysis, we show that the implicit "self-stimulation" assumption limits the accuracy of these forecasts. To overcome this limitation, we propose an Intervention-Aware Time Series Forecasting (IATSF) framework explicitly designed to incorporate external interventions. We particularly emphasize textual interventions due to their unique capability to represent qualitative or uncertain influences inadequately captured by conventional exogenous variables. We propose a leak-free benchmark composed of temporally synchronized textual intervention data across synthetic and real-world scenarios. To rigorously evaluate IATSF, we develop FIATS, a lightweight forecasting model that integrates textual interventions through Channel-Aware Adaptive Sensitivity Modeling (CASM) and Channel-Aware Parameter Sharing (CAPS) mechanisms, enabling the model to adjust its sensitivity to interventions and historical data in a channel-specific manner. Extensive empirical evaluations confirm that FIATS surpasses state-of-the-art methods, highlighting that forecasting improvements stem explicitly from modeling external interventions rather than increased model complexity alone.
The increasing demand for sustainable energy solutions has driven the integration of digitalized buildings into the power grid, leveraging Internet-of-Things (IoT) technologies to enhance energy efficiency and operational performance. Despite their potential, effectively utilizing IoT point data within deep-learning frameworks presents significant challenges, primarily due to its inherent heterogeneity. This study investigates the diverse dimensions of IoT data heterogeneity in both intra-building and inter-building contexts, examining their implications for predictive modeling. A benchmarking analysis of state-of-the-art time series models highlights their performance on this complex dataset. The results emphasize the critical need for multi-modal data integration, domain-informed modeling, and automated data engineering pipelines. Additionally, the study advocates for collaborative efforts to establish high-quality public datasets, which are essential for advancing intelligent and sustainable energy management systems in digitalized buildings.
Consumers wish to choose sustainable accommodation for their travels, and in the case of corporations, may be required to do so. Yet accommodation marketplaces provide no meaningful capability for sustainable choice: typically CO2 estimates are provided that are identical for all accommodation of the same type across an entire country. We propose a decision support system that enables real choice of sustainable accommodation. We develop a data-driven address-specific metric called EcoGrade, which integrates government approved datasets and uses interpolation where data is sparse. We validate the metric on 10,000 UK addresses in 10 cities, showing the match of our interpolations to reality is statistically significant. We show how the metric has been embedded into a decision support system for a global accommodation marketplace and tested by real users over several months with positive user feedback. In the EU, forty percent of final energy consumption is from buildings. We need to encourage all building owners to make their accommodation more efficient. The rental sector is one area where change can occur rapidly, as rented accommodation is renovated frequently. We anticipate our decision support system using EcoGrade will encourage this positive change.
This systematic review assessed the current state and future prospects of artificial intelligence (AI) in schizophrenia rehabilitation management. We reviewed 61 studies on AI-related data types, feature engineering methods, algorithmic models, and evaluation metrics published from 2012-2024. The review categorizes AI applications into the following key application areas: symptom monitoring, medication management, risk management, functional training, and psychosocial support. Findings indicate that supervised machine learning techniques, particularly for symptom monitoring and relapse risk management, remain the predominant approaches, effectively leveraging structured data while incorporating interpretable algorithms. This study underscores the potential of AI in transforming long-term management strategies for schizophrenia, offering valuable insights into improving the quality of life of patients. Future research should focus on expanding data sources through multimodal data integration, exploring deep learning models, and integrating AI-driven interventions into training tasks to fully capitalize on AI's potential in schizophrenia rehabilitation.
Drought is a complex environmental phenomenon that affects millions of people and communities all over the globe and is too elusive to be accurately predicted. This is mostly due to the scalability and variability of the web of environmental parameters that directly/indirectly causes the onset of different categories of drought. Since the dawn of man, efforts have been made to uniquely understand the natural indicators that provide signs of likely environmental events. These indicators/signs in the form of indigenous knowledge system have been used for generations. The intricate complexity of drought has, however, always been a major stumbling block for accurate drought prediction and forecasting systems. Recently, scientists in the field of agriculture and environmental monitoring have been discussing the integration of indigenous knowledge and scientific knowledge for a more accurate environmental forecasting system in order to incorporate diverse environmental information for a reliable drought forecast. Hence, in this research, the core objective is the development of a semantics-based data integration middleware that encompasses and integrates heterogeneous data models of local indigenous knowledge and sensor data towards an accurate drought forecasting system for the study areas. The local indigenous knowledge on drought gathered from the domain experts is transformed into rules to be used for performing deductive inference in conjunction with sensors data for determining the onset of drought through an automated inference generation module of the middleware. The semantic middleware incorporates, inter alia, a distributed architecture that consists of a streaming data processing engine based on Apache Kafka for real-time stream processing; a rule-based reasoning module; an ontology module for semantic representation of the knowledge bases.
Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the analysis of diverse data types, namely gene expression data. While gene expression data can provide valuable insights, challenges arise from the fact that the sample sizes in expression datasets are usually limited, and the data from different datasets with different gene expressions cannot be easily combined. This work proposes a novel approach to address these challenges by integrating multiple gene expression datasets and domain-specific knowledge using knowledge graphs, a unique tool for biomedical data integration. KG embedding methods are then employed to generate vector representations, serving as inputs for a classifier. Experiments demonstrated the efficacy of our approach, revealing improvements in diabetes prediction when integrating multiple gene expression datasets and domain-specific knowledge about protein functions and interactions.
Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by leveraging large and heterogeneous data, are often constrained by their size and data integration, limiting their effectiveness in addressing the full range of Earth system prediction challenges. To overcome these limitations, we introduce the Oak Ridge Base Foundation Model for Earth System Predictability (ORBIT), an advanced vision transformer model that scales up to 113 billion parameters using a novel hybrid tensor-data orthogonal parallelism technique. As the largest model of its kind, ORBIT surpasses the current climate AI foundation model size by a thousandfold. Performance scaling tests conducted on the Frontier supercomputer have demonstrated that ORBIT achieves 684 petaFLOPS to 1.6 exaFLOPS sustained throughput, with scaling efficiency maintained at 41% to 85% across 49,152 AMD GPUs. These breakthroughs establish new advances in AI-driven climate modeling and demonstrate promise to significantly improve the Earth system predictability.
The creation of high-quality ontologies is crucial for data integration and knowledge-based reasoning, specifically in the context of the rising data economy. However, automatic ontology matchers are often bound to the heuristics they are based on, leaving many matches unidentified. Interactive ontology matching systems involving human experts have been introduced, but they do not solve the fundamental issue of flexibly finding additional matches outside the scope of the implemented heuristics, even though this is highly demanded in industrial settings. Active machine learning methods appear to be a promising path towards a flexible interactive ontology matcher. However, off-the-shelf active learning mechanisms suffer from low query efficiency due to extreme class imbalance, resulting in a last-mile problem where high human effort is required to identify the remaining matches. To address the last-mile problem, this work introduces DualLoop, an active learning method tailored to ontology matching. DualLoop offers three main contributions: (1) an ensemble of tunable heuristic matchers, (2) a short-term learner with a novel query strategy adapted to highly imbalanced data, and (3) long-term learners to explore potential matches by creating and tuning new heuristics. We evaluated DualLoop on three datasets of varying sizes and domains. Compared to existing active learning methods, we consistently achieved better F1 scores and recall, reducing the expected query cost spent on finding 90% of all matches by over 50%. Compared to traditional interactive ontology matchers, we are able to find additional, last-mile matches. Finally, we detail the successful deployment of our approach within an actual product and report its operational performance results within the Architecture, Engineering, and Construction (AEC) industry sector, showcasing its practical value and efficiency.
The joint utilization of diverse data sources for medical imaging segmentation has emerged as a crucial area of research, aiming to address challenges such as data heterogeneity, domain shift, and data quality discrepancies. Integrating information from multiple data domains has shown promise in improving model generalizability and adaptability. However, this approach often demands substantial computational resources, hindering its practicality. In response, knowledge distillation (KD) has garnered attention as a solution. KD involves training light-weight models to emulate the behavior of more resource-intensive models, thereby mitigating the computational burden while maintaining performance. This paper addresses the pressing need to develop a lightweight and generalizable model for medical imaging segmentation that can effectively handle data integration challenges. Our proposed approach introduces a novel relation-based knowledge framework by seamlessly combining adaptive affinity-based and kernel-based distillation through a gram matrix that can capture the style representation across features. This methodology empowers the student model to accurately replicate the feature representations of the teacher model, facilitating robust performance even in the face of domain shift and data heterogeneity. To validate our innovative approach, we conducted experiments on publicly available multi-source prostate MRI data. The results demonstrate a significant enhancement in segmentation performance using lightweight networks. Notably, our method achieves this improvement while reducing both inference time and storage usage, rendering it a practical and efficient solution for real-time medical imaging segmentation.
Bromochloro alkanes (BCAs) have been manufactured for use as flame retardants for decades and preliminary environmental risk screening suggests they are likely to behave similarly to polychlorinated alkanes (PCAs), subclasses of which are restricted as Stockholm Convention Persistent Organic Pollutants (POPs). BCAs have rarely been studied in the environment, though some evidence suggests they may migrate from treated-consumer materials into indoor dust, resulting in human exposure via inadvertent ingestion. In this study, BCA-C14 mixture standards were synthesized and used to validate an analytical method. This method relies on chloride-enhanced liquid chromatography-electrospray ionization-Orbitrap-high resolution mass spectrometry (LC-ESI-Orbitrap-HRMS) and a novel CP-Seeker integration software package for homologue detection and integration. Dust sample preparation via ultrasonic extraction, acidified silica clean-up and fractionation on neutral silica cartridges was found to be suitable for BCAs, with absolute recovery of individual homologues averaging 66 to 78% and coefficients of variation $\le$10% in replicated spiking experiments (n=3). In addition, a total of 59 indoor dust samples from six countries including Australia (n=10), Belgium (n=10), Colombia (n=10), Japan (n=10), Thailand (n=10) and the United States of America (n=9) were analysed for BCAs. BCAs were detected in seven samples from the USA, with carbon chain lengths of C8, C10, C12, C14, C16, C18, C24 to C28, C30 and C31 observed overall, though not detected in samples from any other countries. Bromination of detected homologues in the indoor dust samples ranged from Br1-4 as well as Br7, while chlorine numbers ranged from Cl2-11. BCA-C18 were the most frequently detected, observed in each of the USA samples, while the most prevalent halogenation degrees were homologues of Br2 and Cl4-5. Broad estimations of BCA concentrations in the dust samples indicated that levels may approach those of other flame retardants in at least some instances. These findings suggest that development of quantification strategies and further investigation of environmental occurrence and health implications are needed.
This study aims to develop a framework for multicriteria analysis to evaluate alternatives for sustainable corn agricultural area planning, considering the integration of ecological, economic, and social aspects as pillars of sustainability. The research method uses qualitative and quantitative approaches to integrate ecological, economic, and social aspects in the multicriteria analysis. The analysis involves land evaluation, subcriteria identification, and data integration using Multidimensional Scaling and Analytical Hierarchy Process methods to prioritize developing sustainable corn agricultural areas. Based on the results of the RAP-Corn analysis, it indicates that the ecological dimension depicts less sustainability. The AHP results yield weight distribution and highly relevant scores that describe tangible preferences. Priority directions are grouped as strategic steps toward achieving the goals of sustainable corn agricultural area planning.
Given the uncertainty surrounding how existing explainability methods for autonomous vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is imperative to determine the contexts requiring explanations and suitable interaction strategies. A comprehensive review becomes crucial to assess the alignment of current approaches with the varied interests and expectations within the AV ecosystem. This study presents a review to discuss the complexities associated with explanation generation and presentation to facilitate the development of more effective and inclusive explainable AV systems. Our investigation led to categorising existing literature into three primary topics: explanatory tasks, explanatory information, and explanatory information communication. Drawing upon our insights, we have proposed a comprehensive roadmap for future research centred on (i) knowing the interlocutor, (ii) generating timely explanations, (ii) communicating human-friendly explanations, and (iv) continuous learning. Our roadmap is underpinned by principles of responsible research and innovation, emphasising the significance of diverse explanation requirements. To effectively tackle the challenges associated with implementing explainable AV systems, we have delineated various research directions, including the development of privacy-preserving data integration, ethical frameworks, real-time analytics, human-centric interaction design, and enhanced cross-disciplinary collaborations. By exploring these research directions, the study aims to guide the development and deployment of explainable AVs, informed by a holistic understanding of user needs, technological advancements, regulatory compliance, and ethical considerations, thereby ensuring safer and more trustworthy autonomous driving experiences.
Gastric cancer (GC) is a prevalent malignancy worldwide, ranking as the fifth most common cancer with over 1 million new cases and 700 thousand deaths in 2020. Locally advanced gastric cancer (LAGC) accounts for approximately two-thirds of GC diagnoses, and neoadjuvant chemotherapy (NACT) has emerged as the standard treatment for LAGC. However, the effectiveness of NACT varies significantly among patients, with a considerable subset displaying treatment resistance. Ineffective NACT not only leads to adverse effects but also misses the optimal therapeutic window, resulting in lower survival rate. However, existing multimodal learning methods assume the availability of all modalities for each patient, which does not align with the reality of clinical practice. The limited availability of modalities for each patient would cause information loss, adversely affecting predictive accuracy. In this study, we propose an incomplete multimodal data integration framework for GC (iMD4GC) to address the challenges posed by incomplete multimodal data, enabling precise response prediction and survival analysis. Specifically, iMD4GC incorporates unimodal attention layers for each modality to capture intra-modal information. Subsequently, the cross-modal interaction layers explore potential inter-modal interactions and capture complementary information across modalities, thereby enabling information compensation for missing modalities. To evaluate iMD4GC, we collected three multimodal datasets for GC study: GastricRes (698 cases) for response prediction, GastricSur (801 cases) for survival analysis, and TCGA-STAD (400 cases) for survival analysis. The scale of our datasets is significantly larger than previous studies. The iMD4GC achieved impressive performance with an 80.2% AUC on GastricRes, 71.4% C-index on GastricSur, and 66.1% C-index on TCGA-STAD, significantly surpassing other compared methods.