Detection of Semantic Duplicates in Temporal Domains Using Directed Acyclic Graphs

We demonstrate the feasibility of a generalized technique for semantic deduplication in temporal data domains using graph-based representations of data records. Structured data records with multiple timestamp attributes per record may be represented as a directed graph where the nodes represent the events and the edges represent event sequences. Edge weights are based on elapsed time between connecting nodes. In comparing two records, we may merge these directed graphs and determine a representative directed acyclic graph (DAG) inclusive of a subset of nodes and edges that maintain the transitive weights of the original graphs. This DAG may then be evaluated by weighting elapsed time equivalences between records at each node and measuring the fraction of nodes represented in the DAG versus the union of nodes between the records being compared. With this information, we establish a duplication score and use a specified threshold requirement to assert duplication. This method is referred to as Temporal Deduplication using Directed Acyclic Graphs (TD:DAG). TD:DAG significantly outperformed established ASNM and ASNM+LCS methods for datasets rep-resenting two disparate domains, COVID-19 government policy data and PlayStation Network (PSN) trophy data. TD:DAG produced highly effective and comparable F1 scores of 0.960 and 0.972 for the two datasets, respectively, versus 0.864/0.938 for ASNM+LCS and 0.817/0.708 for ASNM. © 2023 IEEE.

Towards Timeline Generation with Abstract Meaning Representation

Timeline summarization (TLS) is a challenging research task that requires researchers to distill extensive and intricate temporal data into a concise and easily comprehensible representation. This paper proposes a novel approach to timeline summarization using Meaning Representations (AMRs), a graphical representation of the text where the nodes are semantic concepts and the edges denote relationships between concepts. With AMR, sentences with different wordings, but similar semantics, have similar representations. To make use of this feature for timeline summarization, a two-step sentence selection method that leverages features extracted from both AMRs and the text is proposed. First, AMRs are generated for each sentence. Sentences are then filtered out by removing those with no named-entities and keeping the ones with the highest number of named-entities. In the next step, sentences to appear in the timeline are selected based on two scores: Inverse Document Frequency (IDF) of AMR nodes combined with the score obtained by applying a keyword extraction method to the text. Our experimental results on the TLS-Covid19 test collection demonstrate the potential of the proposed approach. © 2023 ACM.

TemporalDedup: Domain-Independent Deduplication of Redundant and Errant Temporal Data

Deduplication is a key component of the data preparation process, a bottleneck in the machine learning (ML) and data mining pipeline that is very time-consuming and often relies on domain expertise and manual involvement. Further, temporal data is increasingly prevalent and is not well suited to traditional similarity and distance-based deduplication techniques. We establish a fully automated, domain-independent deduplication model for temporal data domains, known as TemporalDedup, that infers the key attribute(s), applies a base set of deduplication techniques focused on value matches for key, non-key, and elapsed time, and further detects duplicates through inference of temporal ordering requirements using Longest Common Subsequence (LCS) for records of a shared type. Using LCS, we split each record's temporal sequence into constrained and unconstrained sequences. We flag suspicious (errant) records that are non-adherent to the inferred constrained order and we flag a record as a duplicate if its unconstrained order, of sufficient length, matches that of another record. TemporalDedup was compared against a similarity-based Adaptive Sorted Neighborhood Method (ASNM) in evaluating duplicates for two disparate datasets: (1) 22,794 records from Sony's PlayStation Network (PSN) trophy data, where duplication may be indicative of cheating, and (2) emergency declarations and government responses related to COVID-19 for all U.S. states and territories. TemporalDedup (F1-scores of 0.971 and 0.954) exhibited combined sensitivities above 0.9 for all duplicate classes whereas ASNM (0.705 and 0.732) exhibited combined sensitivities below 0.2 for all time and order duplicate classes. © 2023 World Scientific Publishing Company.

GeauxTrace: A Scalable Privacy-Protecting Contact Tracing App Design Using Blockchain

Contact tracing is the approach to identifying physical contact between human beings using a variety of data such as personal details and locations to discover the potential infection of diseases. Since the outbreak of the COVID-19 pandemic, contact tracing has been used extensively to quarantine the people at risk to stop the spread. Moreover, the data collected during contact tracing are typical spatiotemporal data, which can be used to study the disease and discover the spread pattern. However, both traditional labor-intensive and modern digital-based approaches have limitations in terms of cost and privacy concerns. In this paper, we proposed GeauxTrace, a Blockchain-based privacy-protecting contact tracing platform, which separates private data from proof of contact. Sensitive data collected by the front-end app via Bluetooth-based methods are stored locally, and only the proofs of contacts are uploaded onto the immutable private blockchain, which forms a global contact graph at the backend. Our approach not only enables multi-hop risky users to be notified but also reveals the infection patterns via the global graph, which could help study diseases and assist the policymaker. Our implementation shows the feasibility of the proposed platform in real-world scenarios and achieves the performance of 20-30 user requests per second. © 2022 IEEE.

Introduction to the Special Issue on Understanding the Spread of COVID-19, Part 1

Infectious diseases are transmitted between human hosts when in close contact over space and time. Recently, an unprecedented amount of spatial and spatiotemporal data have been made available that can be used to improve our understanding of the spread of COVID-19 and other infectious diseases. This understanding will be paramount to prepare for future pandemics through spatial algorithms and systems to collect, capture, curate, and analyze complex, multi-scale human movement data to solve problems such as infectious diseases prediction, contact tracing, and risk assessment. In exploring and deepening the conversation around this topic, the eight articles included in the first volume of this special issue employ diverse theoretical perspectives, methodologies, and frameworks, including but not limited to infectious diseases simulation, risk prediction, response policy design, mobility analysis, and case diagnosis. Rather than focusing on a narrow set of problems, these articles provide a glimpse into the diverse possibilities of leveraging spatial and spatiotemporal data for pandemic preparedness. © 2022 held by the owner/author(s).

Temporal Data Mining in AI-based Patient Navigation Service

The pandemic of COVID-19 reminds us of the basic important principles for prevention of infection: avoid the "Three Cs": closed spaces, crowded places and close contact settings. Outpatient clinics in Japan are typical examples of three Cs, where some kinds of decision support system are required to solve the above situation. This paper proposes data mining based patient navigation support system to prevent the Three Cs. Behind the systems, temporal data mining units plays an important role in providing temporal information to the patients, such as waiting time and human densities in the waiting rooms. It analyzes the data stored in hospital information systems, including patient information, logs of clinical orders. The analysis results show that several aspects of patients' waiting are visualized by temporal data mining. © 2022 IEEE.

Visualizing Temporal Data using Time-dependent Non-decreasing Monotone Functions

Ahstract-The occurrence of seasonal natural phenomena depends on the conditions leading to it and not directly on the progression of time, meaning its context varies across time and space. Examples of this include comparing plant growth, insect development or wildfire risk during the same time period at different locations or in different time periods at the same location. However, visualizing and comparing such phenomena usually implies plotting it across the time axis as it's perceived as temporal data. Since it's not directly dependent of time, identifying patters of recurrence using this technique is inefficient. Because of this, we proposed transforming (when needed) the dependent function to a non-decreasing monotone one, in order to preserve the monotonic property of time progression. Then we used the resulting function as a time axis replacement to achieve an equal ground of comparison between the different contexts in which the phenomenon occurs. We applied this technique to real data from seasonal natural phenomena, such as plant and insect growth, to compare its progression in different temporal and spatial contexts. Since the dependent function of the phenomenon was scientifically known, we were able to directly use the technique to infer its seasonality patterns. Furthermore, we applied the technique to real data from the coronavirus worldwide pandemic by hypothesizing its dependent function and analysing if it was able to reduce the existing temporal misalignment between different contexts, like years and countries. The results achieved were positive, although not as remarkable as when the dependent function was known. © 2022 IEEE.

Identifying Variability in U.S. COVID-19 Response Through Temporal Partial Ordering Detection

We gain insight to the COVID-19 pandemic response by the various U.S. states through analysis of open source emergency declaration, mitigation, and response policy data. We propose ASNM + POD, a Partial Ordering Detection extension to the Adaptive Sorted Neighborhood Method to identify redundancies and implied temporal ordering requirements to understand how various U.S. states respond to COVID-19. We further strengthen the well-established ASNM entity matching method and address key limitations of its Longest Common Subsequence extension (ASNM + LCS) through detection of all temporal order requirements. Partial order requirements are determined probabilistically through empirical review of all records' time-ordered event sequences. We demonstrate effectiveness against a COVID-19 U.S. state policy dataset comprised of daily time-series data pulled from February and October 2022, where attributes are partially and variably populated. ASNM + POD yielded an F1 of 0.995 and an MCC of 0.985, significantly outperforming both ASNM and ASNM + LCS with F1/MCC improvements of 22%/50% and 15%/37%, respectively. Finally, we highlight the limited consensus on policies enacted, the variability in timelines of policy activations/deactivations, and activity at and after the two-year mark. © 2022 IEEE.

Motorch: an on-device trajectory data management system during a pandemic

In this paper, we would like to demonstrate a personal trajectory management system called Motorch, which allows users to manage their trajectories and enables risk analytics based on a lightweight similarity measure called LCTS. At the back end, a web crawler collects the desensitized COVID-19 cases information from data sources (news, social media, etc.) and pushes them to Elasticsearch for storage after data cleaning. At the front end, Motorch implements a set of operations including data collection, data preprocessing, indexing, storage, and visualization in a mobile application. Motorch aims to help individuals manipulate their data and evaluate personal risk without uploading data to a server. © 2022 Owner/Author.

Fine-scale variation in the effect of national border on COVID-19 spread: A case study of the Saxon-Czech border region.

The global extent and temporally asynchronous pattern of COVID-19 spread have repeatedly highlighted the role of international borders in the fight against the pandemic. Additionally, the deluge of high resolution, spatially referenced epidemiological data generated by the pandemic provides new opportunities to study disease transmission at heretofore inaccessible scales. Existing studies of cross-border infection fluxes, for both COVID-19 and other diseases, have largely focused on characterizing overall border effects. Here, we couple fine-scale incidence data with localized regression models to quantify spatial variation in the inhibitory effect of an international border. We take as a case study the border region between the German state of Saxony and the neighboring regions in northwestern Czechia, where municipality-level COVID-19 incidence data are available on both sides of the border. Consistent with past studies, we find an overall inhibitory effect of the border, but with a clear asymmetry, where the inhibitory effect is stronger from Saxony to Czechia than vice versa. Furthermore, we identify marked spatial variation along the border in the degree to which disease spread was inhibited. In particular, the area around Löbau in Saxony appears to have been a hotspot for cross-border disease transmission. The ability to identify infection flux hotspots along international borders may help to tailor monitoring programs and response measures to more effectively limit disease spread.

PhyloView: A System to Visualize the Ecology of Infectious Diseases Using Phylogenetic Data

Since the onset of the COVID-19 pandemic, mil-lions of coronavirus sequences have been rapidly deposited in publicly available repositories. The sequences have been used primarily to monitor the evolution and transmission of the virus. In addition, the data can be combined with spatiotemporal information and mapped over space and time to understand transmission dynamics further. For example, the first COVID-19 cases in Australia were genetically related to the dominant strain in Wuhan, China, and spread via international travel. These data are currently available through the Global Initiative on Sharing Avian Influenza Data (GISAID) yet generally remains an untapped resource for data scientists to analyze such multi-dimensional data. Therefore, in this study, we demonstrate a system named Phyloview, a highly interactive visual environment that can be used to examine the spatiotemporal evolution of COVID-19 (from-to) over time using the case study of Louisiana, USA. PhyloView (powered by ArcGIsInsights) facilitates the visualization and exploration of the different dimensions of the phylogenetic data and can be layered with other types of spatiotemporal data for further investigation. Our system has the potential to be shared as a model to be used by health officials that can access relevant data through GISAID, visualize, and analyze it. Such data is essential for a better understanding, predicting, and responding to infectious diseases. © 2022 IEEE.

Leveraging Open Threat Exchange (OTX) to Understand Spatio-Temporal Trends of Cyber Threats: Covid-19 Case Study

Understanding the properties exhibited by Spatial-temporal evolution of cyber attacks improve cyber threat intelligence. In addition, better understanding on threats patterns is a key feature for cyber threats prevention, detection, and management and for enhancing defenses. In this work, we study different aspects of emerging threats in the wild shared by 160,000 global participants form all industries. First, we perform an exploratory data analysis of the collected cyber threats. We investigate the most targeted countries, most common malwares and the distribution of attacks frequency by localisation. Second, we extract attacks' spreading patterns at country level. We model these behaviors using transition graphs decorated with probabilities of switching from a country to another. Finally, we analyse the extent to which cyber threats have been affected by the COVID-19 outbreak and sanitary measures imposed by governments to prevent the virus from spreading.

STOPPAGE: Spatio-temporal data driven cloud-fog-edge computing framework for pandemic monitoring and management

Several global health incidents and evidences show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease), which has adversely affected all aspects of human lives. It is essential to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous data-sources to deliver insights for enhancing preparedness to combat the pandemic. Specifically, human mobility, travel history, and other transport statistics have significantly impact on the spread of any infectious disease. This article proposes a spatio-temporal knowledge mining framework, named STOPPAGE, to model the impact of human mobility and other contextual information over the large geographic areas in different temporal scales. The framework has two key modules: (i) spatio-temporal data and computing infrastructure using fog/edge based architecture;and (ii) spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. We created a pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hotspot zones. Further, we provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods. STOPPAGE outperforms the existing works and baseline methods in terms of accuracy by (Formula presented.) (18–21)% in predicting hotspots and reduces the power consumption of the smartphone significantly. The scalability study yields that the STOPPAGE framework is flexible enough to analyze a huge amount of spatio-temporal datasets and reduces the delay in predicting health status compared to the existing studies. © 2022 John Wiley & Sons Ltd.

Time Series Analysis of COVID-19 Cases in Humboldt County

The time series of COVID-19 daily cases in the U.S is analyzed by utilizing the county-level temporal data, from January 22, 2020 to October 18, 2021. Autocorrelation and partial autocorrelation show that time series of daily cases in Humboldt county has a 7-day seasonal pattern. Visualization and augmented Dickey-Fuller test show that time series of daily cases in Humboldt county is non-stationary. The seven-order difference reveals that the time series is stationary. There is a moderate positive correlation between daily cases and fully vaccination rate. Clustering analysis describes 33 counties have similar daily case pattern with Humboldt County by standard deviation of 0.003. This analysis can be used for future time-series forecasting and planning. © 2021 IEEE.

Privacy Preservation of COVID-19 Contact Tracing Data

Big data are everywhere. Examples of big data include contact tracing data of patients who contracted coronavirus disease 2019 (COVID-19). On the one hand, mining these contact tracing data can be for social good. For instance, it helps slow down the spread of COVID-19. It also helps people diagnosed with COVID-19 get referrals for services and resources they may need to isolate safely. On the other hand, it is also important to protect the privacy of these COVID-19 patients. Hence, we present in this paper a solution for privacy preservation of COVID-19 contact tracing data. Specifically, our solution preserves the privacy of individuals by publishing only their spatio-temporal representative locations. Evaluation results on real-life COVID-19 contact tracing data from South Korea demonstrate the effectiveness and practicality of our solution in preserving the privacy of COVID-19 contact tracing data.

Visualization Analysis of Spatiotemporal Data of COVID-19

Aiming at the problem of COVID-19 epidemic data visualization, this paper proposes a spatiotemporal visualization analysis method based on the technology of scraping crawler and visualization, and carries on the visualization analysis and research, intuitively shows the development and change of the epidemic situation in different countries and regions, and excavates its spatiotemporal variation rules. Firstly, we use scrapy crawler framework to collect COVID-19 epidemic data;then, the collected data were cleaned and processed to construct a spatiotemporal data set of COVID-19 epidemic;finally, pyecharts is used to analyze the dataset data visually. The results showed the changes and trends of epidemic situation in different countries and regions, and provided reference for epidemic prevention and control. © 2021 IEEE.

PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series

Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In the literature, the fusion of multi-source time-series has been achieved either by using ensemble learning models which ignore temporal patterns and correlation within features or by defining a fixed-size window to select specific parts of the data sets. On the other hand, many studies have shown major improvement to handle the irregularity of time-series, yet none of these studies has been applied to multi-source data. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. PIETS has the following characteristics: (1) irregularity encoders for multi-source samples that can leverage all available information and accelerate the convergence of the model;(2) parallelised neural networks to enable flexibility and avoid information over-whelming;and (3) attention mechanism that highlights different information and gives high importance to the most related data. Through extensive experiments on real-world data sets related to COVID-19, we show that the proposed architecture is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.

Smart Data Analytics on COVID-19 Data

Advances in computers, information and networks has brought a digital cyber world to our daily lives. They have generated numerous digital things (or cyber entities), which have resided in the cyber world. Meanwhile, countless real things in the conventional physical, social and mental worlds have possessed cyber mappings (or cyber components) to have a cyber existence in cyber world. Consequently, cyberization has been an emerging trend forming the new cyber world and reforming conventional worlds towards cyber-enabled hyper-worlds. As such, cybermatics helps build systematic knowledge about new phenomena, behaviors, properties and practices in the cyberspace, cyberization and cyber-enabled hyper-worlds. Cybermatics is characterized by catching up with the human intelligence (e.g. intelligent sensing, making decision and control, etc.), as well as learning from the nature-inspired attributes (e.g., dynamics, self-adaptability, energy saving). As a cybermatics technique, smart data analytics helps filter out the noise data and produce valuable data. In this paper, we focus on smart data analytics on health data related to coronavirus disease 2019 (COVID-19). It builds temporal and demographic hierarchies, which capture characteristics of COVID-19 patients, to discover valuable knowledge and information about temporal-demographic characteristics of these patients. Evaluation on real-life COVID-19 epidemiological data demonstrates the practicality of our solution in conducting smart data analytics on COVID-19 data. © 2021 IEEE.

Synthetic People Flow: Privacy-Preserving Mobility Modeling from Large-Scale Location Data in Urban Areas

Recently, there has been an increasing demand for traffic simulation and congestion prediction for urban planning, especially for infection simulation due to the Covid-19 epidemic. On the other hand, the widespread use of wearable devices has made it possible to collect a large amount of user location history with high accuracy, and it is expected that this data will be used for simulation. However, it is difficult to collect location histories for the entire population of a city, and detailed data that can reproduce trajectories is expensive. In addition, such personal location histories contain private information such as addresses and workplaces, which restricts the use of raw data. This paper proposes Agent2Vec, a mobility modeling model based on unsupervised learning. Using this method, we generate synthetic human flow data without personal information. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Spatio-temporal mixture process estimation to detect dynamical changes in population.

Population monitoring is a challenge in many areas such as public health and ecology. We propose a method to model and monitor population distributions over space and time, in order to build an alert system for spatio-temporal data changes. Assuming that mixture models can correctly model populations, we propose a new version of the Expectation-Maximization (EM) algorithm to better estimate the number of clusters and their parameters at the same time. This algorithm is compared to existing methods on several simulated datasets. We then combine the algorithm with a temporal statistical model, allowing for the detection of dynamical changes in population distributions, and call the result a spatio-temporal mixture process (STMP). We test STMPs on synthetic data, and consider several different behaviors of the distributions, to fit this process. Finally, we validate STMPs on a real data set of positive diagnosed patients to coronavirus disease 2019. We show that our pipeline correctly models evolving real data and detects epidemic changes.