Fine-grained Data Research Articles

Capturing patterns of argumentation elements, encountered challenges, and social regulation in collaborative argumentation: an epistemic network analysis study

ABSTRACT Collaborative argumentation is a crucial learning activity affected by various factors, including the use of argumentation elements, encountered challenges, and social regulation. Few studies examined how these factors collectively impact argumentation outcomes based on fine-grained process data. This research employed Chi-squared tests and epistemic network analysis (ENA) to reveal the different frequencies and patterns of these factors in 4 high- and 4 low-performing groups. The data were derived from a 90-minute argumentation task involving 55 undergraduates. The Chi-squared test revealed the frequency differences, indicating that high-performing groups tended to use strong claims, whereas low-performing groups typically used weak counterclaims/rebuttals and encountered more socioemotional challenges. The ENA results further illustrated the differences in the co-occurrences of these factors. In low-performing groups, weak counterclaims/rebuttals intertwined with socioemotional and cognitive challenges. Although students attempted to use socioemotional regulations to address challenges, their efforts were ineffective due to the tense atmosphere. Conversely, high-performing groups successfully alternated between cognitive and socioemotional regulations to formulate strong claims and overcome challenges. These findings highlight the advantages of using ENA to analyze and compare patterns of factors and suggest the need to design educational tools to support effective social regulations.

Machine learning for smart water distribution systems: exploring applications, challenges and future perspectives

The advancements of the Internet of Things and Low-Power Wide-Area Network technology will accelerate in the next future the adoption of smart meters in water distribution systems, enabling the collection of a huge amount of fine-grained data. How to turn massive smart meter data into actionable knowledge will be the key point to limit water wastage and promote efficient and sustainable distribution. Although the collection of data worldwide is currently limited, the potential future impact of exploiting data-driven and machine learning methods is increasingly recognized in research and industry, as shown by many scientific works published in recent years. In particular, the interest in deep learning for smart water distribution systems is increasing, motivated by the ability to learn intricate patterns from big data. This work aims to provide an overview of the current research and identify challenges for future directions by conducting an application-oriented survey. Specifically, by analysing data characteristics and operational targets, we propose a new taxonomy that helps structure properly the macro-areas of water management into infrastructure analysis, demand analysis and water quality monitoring. Existing methods are discussed for each application under these three stages. In addition, we also discuss potential research directions, such as federated learning, incremental learning, probabilistic modeling and explainability and address broad issues like data availability and implications for privacy.

Keeping up with the Joneses? Perceptions of mobility and opportunity within local knowledge economies across Germany

Abstract Recent work on the rise of the knowledge economy has identified the importance of economic context in shaping individual perceptions and preferences. Combining novel survey and locally fine-grained data for the case of Germany, we analyze the association between local knowledge economies and individual perceptions of social mobility and economic opportunity. We find that individual perceptions of mobility, access to affordable housing, and employment security are negatively associated with thriving local knowledge economies. This finding is partly at odds with accounts of ‘aspirational voters’ being the beneficiaries of local economic growth. Rather, we interpret our findings as supporting the ‘status anxiety’ hypothesis, which attributes negative mobility perceptions with competition for scarce economic and social resources. In time, these perceptions can become fertile ground for the development of populist attitudes and vote choices.

An ontology-driven knowledge graph for tourism information management

Background Lao Tzu, the ancient Chinese philosopher, had famously said, “a journey of a thousand miles begins with a single step”. The modern ecosystem of tourism is extremely multi-faceted and multi-context dependent, so much so that tourists, planning on an excursion, have an entire range of queries from as basic as “Where to eat?” to as complex as “List all the eating establishments which are suitable for groups and also provide children menu”. Such a list becomes even more complicated due to the intermixing of different perceptions, languages, cultures and economies in which tourists are enmeshed in. Accordingly, such increasing complexity in questions from tourists necessitate the need to provide specialized and fine-grained data and information services which are beyond the Information and communications technology (ICT) infrastructures in vogue in tourism today. Methods In this paper, we propose and advocate the use of, via a theoretically and implementationally well-founded methodology, the Artificial Intelligence (AI) backed technology of ontology-driven Knowledge Graphs, to provide fine-grained information services to tourists at any scale and at any level of complexity. Results and Conclusions The methodology is validated by developing a fully populated tourism-specific ontology-driven knowledge graph having 4264 RDF statements and ready for reuse, extension and exploitation as a part of any of the current ICT infrastructure in tourism sector.

Clustering moves: Spatial network analysis in residential mobility

In the past sixty years, network analysis has become a valuable analytic tool in the study of interpersonal relationships and also in the construction of spatial relationships within urban geographies. Researchers have applied network analysis in a wide array of spatial applications; however, the power of network analysis has yet to be applied to understanding patterns in residential mobility. Residential movement patterns take place over longer time scales and potentially greater distances than daily journeys or commutes, but equally create networks connecting places and people. This research demonstrates how applying network analysis to residential moves may lend insight to open questions in the study of residential mobility, using fine-grained data on residential moves held by the city of Zurich, Switzerland. By creating a weighted directed network linking locations on a 400 meter coordinate grid with residents moving between them, the work demonstrates that network analysis can reveal geographies that overwrite the political boundaries commonly used to categorize residential spaces in the city. The new geographies produced by this method capture a wholistic picture of moving behaviour independent of reported residential choices. By tracking completed moves rather than stated aims, the uncovered mobility geographies sidestep the common categorisation of voluntary versus structurally determined residential choices, aggregating accumulated choices to demonstrate some of the geographic specificities of residential moving behaviour.

Enabling per-file data recovery from ransomware attacks via file system forensics and flash translation layer data extraction

AbstractRansomware attacks are increasingly prevalent in recent years. Crypto-ransomware corrupts files on an infected device and demands a ransom to recover them. In computing devices using flash memory storage (e.g., SSD, MicroSD, etc.), existing designs recover the compromised data by extracting the entire raw flash memory image, restoring the entire external storage to a good prior state. This is feasible through taking advantage of the out-of-place updates feature implemented in the flash translation layer (FTL). However, due to the lack of “file” semantics in the FTL, such a solution does not allow a fine-grained data recovery in terms of files. Considering the file-centric nature of ransomware attacks, recovering the entire disk is mostly unnecessary. In particular, the user may just wish a speedy recovery of certain critical files after a ransomware attack. In this work, we have designed $$\textsf{FFRecovery}$$ FFRecovery , a new ransomware defense strategy that can support fine-grained per file data recovery after the ransomware attack. Our key idea is that, to restore a file corrupted by the ransomware, we (1) restore its file system metadata via file system forensics, and (2) extract its file data via raw data extraction from the FTL, and (3) assemble the corresponding file system metadata and the file data. Another essential aspect of $$\textsf{FFRecovery}$$ FFRecovery is that we add a garbage collection delay and freeze mechanism into the FTL so that no raw data will be lost prior to the recovery and, additionally, the raw data needed for the recovery can be always located. A prototype of $$\textsf{FFRecovery}$$ FFRecovery has been developed and our experiments using real-world ransomware samples demonstrate the effectiveness of $$\textsf{FFRecovery}$$ FFRecovery . We also demonstrate that $$\textsf{FFRecovery}$$ FFRecovery has negligible storage cost and performance impact.

Towards secure and fine-grained data sharing over cloud platform

Towards secure and fine-grained data sharing over cloud platform

Collapse of an insular bird species driven by a decrease in rainfall

Arid island environments harbour a unique biota characterised to have adaptive features that enable them to thrive in such harsh habitats. However, our understanding of how anthropogenic climate change compromises the biodiversity and sustainability of these ecosystems is greatly unknown. Here we used fine-grained field data to evaluate the effects of extreme weather on the population size, distribution, and habitat preferences of an endemic bird species inhabiting an arid Atlantic island, across two temporal windows spanning approximately 20years (2005-2024). Population size declined sharply (63%-70%) between periods, according to a distance-based sampling design and a habitat suitability modelling approach, with the number of individuals estimated in 2024 being 4650 (CI 95%: 3600-5950) and 4150 (CI 95%: 3600-4800) respectively. The density of this species in 2024 was reduced by approximately three times compared to the previous study period. The results revealed that in 2024 a larger island area (246km2 and 514km2) was necessary to encompass 50% and 75% of all individuals of this species, respectively, compared to the previous period (195km2 and 434km2). Such a population collapse was associated with the decrease in rainfall on the island, which is closely related to the reproductive success in this species. We recorded a pattern of continuous decrease in rainfall since 2005-2006, which included several extremely dry years immediately before the 2024 study. We also found strong evidence for the population decline of other native bird species of the same foraging guild. Overall, our data emphasizes the significance of recurrent and extreme climatic events on arid island bird species in driving population declines and reducing their distribution ranges; and highlights how fragile such unique taxa can be under longer dry periods, with uncertain consequences for their future viability.

BFFDT: Blockchain-Based Fair and Fine-Grained Data Trading Using Proxy Re-Encryption and Verifiable Commitment

BFFDT: Blockchain-Based Fair and Fine-Grained Data Trading Using Proxy Re-Encryption and Verifiable Commitment

Fine-grained data deletion supporting dynamic data insertion for cloud storage

Fine-grained data deletion supporting dynamic data insertion for cloud storage

Singular-plural verb stem alternation: uncovering global and local drivers of typological variation

Abstract In this paper, we present a comparative concept for singular-plural verb stem alternation (Sg-Pl alternation) and a systematic global survey of its presence as well as its syntactic and semantic attributes, with a denser sample in the Americas. We show that Sg-Pl alternation is fairly common, occurring in about one in three languages, although its geographic distribution is highly uneven. Fine-grained data collected on the verbs targeted by the phenomenon reveal distinct global and local drivers. General semantic and syntactic properties are fairly consistent at the global level and reveal the relevance of the spatial configuration of the Figure (Motion Event), while at the local level highly specific semantics are shared across neighboring languages. This work will be of interest to both fieldworkers and typologists interested in distinguishing between the various causal factors for the development of typological properties.

PMGraph: Accelerating Concurrent Graph Queries over Streaming Graphs

There are usually a large number of concurrent graph queries (CGQs) requirements in streaming graphs. However, existing graph processing systems mainly optimize a single graph query in streaming graphs or CGQs in static graphs. They have a large number of redundant computations and expensive memory access overhead, and cannot process CGQs in streaming graphs efficiently. To address these issues, we propose PMGraph , a software-hardware collaborative accelerator for efficient processing of CGQs in streaming graphs. First, PMGraph centers on fine-grained data, selects graph queries that meet the requirements through vertex data, and utilizes the similarity between different graph queries to merge the same vertices they need to process to address the problem of a large amount of repeated access to the same data by different graph queries in CGQs, thereby reducing memory access overhead. Furthermore, it adopts the update strategy that regularizes the processing order of vertices in each graph query according to the order of the vertex dependence chain, consequently effectively reducing redundant computations. Second, we propose a CGQs-oriented scheduling strategy to increase the data overlap when different graph queries are processed, thereby further improving the performance. Finally, PMGraph prefetches the vertex information according to the global active vertex set Frontier of all graph queries, hiding the memory access latency. It also provides prefetching for the same vertices that need to be processed by different graph queries, reducing the memory access overhead. Compared with the state-of-the-art concurrent graph query software systems Kickstarter-C and Tripoline, PMGraph achieves average speedups of 5.57× and 4.58×, respectively. Compared with the state-of-the-art hardware accelerators Minnow, HATS, LCCG, and JetStream, PMGraph achieves the speedup of 3.65×, 3.41×, 1.73×, and 1.38× on average, respectively. Experimental results show that our proposed PMGraph outperforms the state-of-the-art concurrent graph processing systems and hardware accelerators.

Data Deduplication Based on Content Locality of Transactions to Enhance Blockchain Scalability

Blockchain is a promising infrastructure for the internet and digital economy, but it has serious scalability problems, that is, long block synchronization time and high storage cost. Conventional coarse-grained data deduplication schemes (block or file level) are proved to be ineffective on improving the scalability of blockchains. Based on comprehensive analysis on typical blockchain workloads, we propose two new locality concepts (economic and argument locality) and a novel fine-grained data deduplication scheme (transaction level) named Alias-Chain. Specifically, Alias-Chain replaces frequently used data, for example, smart contract arguments, with much shorter aliases to reduce the block sizes, which results in both shorter synchronization time and lower storage cost. Furthermore, to solve the potential consistency issue in Alias-Chain, we propose two complementary techniques: one is generating aliases from history blocks with high consistency, and the other is speeding up the generation of aliases via a specific algorithm. Our simulation results show: (1) the average transfer and SC-call transaction (a transaction used to call the smart contracts in the blockchain) sizes can be significantly reduced by up to 11.03% and 79.44% in native Ethereum, and up to 39.29% and 81.84% in Ethereum optimized by state-of-the-art techniques; and (2) the two complementary techniques well address the inconsistency risk with very limited impact on the benefit of Alias-Chain. Prototyping-based experiments are further conducted on a testbed consisting of up to 3200 miners. The results demonstrate the effectiveness and efficiency of Alias-Chain on reducing block synchronization time and storage cost under typical real-world workloads.

Successful Precipitation Downscaling Through an Innovative Transformer-Based Model

In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model’s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model.

Abstract Su806: Enhancing Cardiac Arrest Registries and Research – A Framework for Handling Defibrillator Data

Background: Cardiac arrest is the most time-sensitive medical condition, and survival probability is highly dependent on early, high-quality cardiopulmonary resuscitation (CPR) measures. Resuscitation registries are established tools for continuous quality improvement and serve as a source for research. Yet they lack comprehensive information on the quality of CPR measures themselves—such as the depth, rate, and release of chest compressions—and are limited in the temporal resolution of these measures. However, data on both are contained in the recordings of defibrillators. Aims: To develop a framework for healthcare providers to gain immediate case-specific feedback, reduce documentation efforts and make fine-grained information accessible for research. Approach: An open-source Python module named CPRDAT (Kern et al. Zenodo, https://doi.org/10.5281/zenodo.10057964) was designed. This module is capable of importing and extracting data from various defibrillators as well as data recorded by VitalRecorder ( VitalDB , Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Korea) into a uniform data object. Methods were implemented to automatically process, analyze, and aggregate these time-series of physiological signals, with particular attention to CPR. To reduce manual annotation workload, classification algorithms were developed. Plotting routines were developed to display the signals interactively in web applications, allowing healthcare providers to interact with the data through zooming, scaling, shifting, concatenating, merging and labelling. Results: The module was implemented into the German Resuscitation Registry, offering users a manufacturer-agnostic, data-driven, case-specific debriefing tool. Extracted information on CPR measures is stored in the registry. The framework, able to manage multiple time-series data sources, was further implemented in other settings, such as the analysis of laboratory data of a CPR swine model. Conclusions: The new module dramatically enhances the accessibility of physiologic time-series recordings. More fine-grained data will provide deeper insights into processes and CPR physiology. Finally, the open-source Python module abolishes barriers in research.

EADC: An Efficient Anonymous Data Collection Scheme with Blockchain in Internet of Things.

The integration of smart contracts (SCs) within blockchain technology represents a pivotal direction in the evolution of the Internet of Things (IoT), enabling decentralization and enhancing user trust in the system. However, ensuring data privacy is a fundamental challenge that must be addressed during the deployment of these SCs. Many scholars have adopted data aggregation to protect privacy, but these methods are difficult to achieve fine-grained data collection. To this end, this paper proposes an efficient anonymous data collection (EADC) scheme suitable for the IoT environment. The scheme combines matrix algorithms with homomorphic encryption (HE) technology to effectively cut off the connection between users and data during data upload. In addition, the EADC scheme introduces a sophisticated data grouping protocol to improve the overall efficiency of the system. Analysis shows that the scheme can achieve efficient data collection without compromising user privacy.

Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks

Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoin datasets and one NFT dataset, using sequences of three transactions and up to three users. We show that the commonly used technique of simply counting temporal motifs over all users and all time can give misleading conclusions. Here we also study the motifs contributed by each user and discover that the motif distribution is heavy-tailed and that the key players have diverse motif signatures. We study the motifs that occur in different time periods and find events and anomalous activity that cannot be seen just by a count on the whole dataset. Studying motif completion time reveals dynamics driven by human behaviour as well as algorithmic behaviour.

Modelling plausible scenarios for the Omicron SARS-CoV-2 variant from early-stage surveillance

We used a spatially explicit agent-based model of SARS-CoV-2 transmission combined with spatially fine-grained COVID-19 observation data from Public Health Scotland to investigate the initial rise of the Omicron (BA.1) variant of concern. We evaluated plausible scenarios for transmission rate advantage and vaccine immune escape relative to the Delta variant based on the data that would have been available at that time. We also explored possible outcomes of different levels of imposed non-pharmaceutical intervention. The initial results of these scenarios were used to inform the Scottish Government in the early outbreak stages of the Omicron variant. Using the model with parameters fit over the Delta variant epidemic, some initial assumptions about Omicron transmission rate advantage and vaccine escape, and a simple growth rate fitting procedure, we were able to capture the initial outbreak dynamics for Omicron. We found that the modelled dynamics hold up to retrospective scrutiny. The modelled imposition of extra non-pharmaceutical interventions planned by the Scottish Government at the time would likely have little effect in light of the transmission rate advantage held by the Omicron variant and the fact that the planned interventions would have occurred too late in the outbreak's trajectory. Finally, we found that any assumptions made about the projected distribution of vaccines in the model population had little bearing on the outcome, in terms of outbreak size and timing. Instead, it was the landscape of prior immunity that was most important.

A detection of street trees and green space: Understanding contribution of urban trees to climate change mitigation

A detection of street trees and green space: Understanding contribution of urban trees to climate change mitigation

Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference.

Sensors are the key to environmental monitoring, which impart benefits to smart cities in many aspects, such as providing real-time air quality information to assist human decision-making. However, it is impractical to deploy massive sensors due to the expensive costs, resulting in sparse data collection. Therefore, how to get fine-grained data measurement has long been a pressing issue. In this article, we aim to infer values at nonsensor locations based on observations from available sensors (termed spatiotemporal inference), where capturing spatiotemporal relationships among the data plays a critical role. Our investigations reveal two significant insights that have not been explored by previous works. First, data exhibit distinct patterns at both long- and short-term temporal scales, which should be analyzed separately. Second, short-term patterns contain more delicate relations, including those across spatial and temporal dimensions simultaneously, while long-term patterns involve high-level temporal trends. Based on these observations, we propose to decouple the modeling of short- and long-term patterns. Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns. Furthermore, we propose a graph recurrent network with a time skip strategy to alleviate the gradient vanishing problem and model the long-term dependencies. Experimental results on four public real-world datasets demonstrate that our method effectively captures both long- and short-term relations, achieving state-of-the-art performance against existing methods.