Temporal Data Model Research Articles

A model and query language for temporal graph databases

Graph databases are becoming increasingly popular for modeling different kinds of networks for data analysis. They are built over the property graph data model, where nodes and edges are annotated with property-value pairs. Most existing work in the field is based on graphs were the temporal dimension is not considered. However, time is present in most real-world problems. Many different kinds of changes may occur in a graph as the world it represents evolves across time. For instance, edges, nodes, and properties can be added and/or deleted, and property values can be updated. This paper addresses the problem of modeling, storing, and querying temporal property graphs, allowing keeping the history of a graph database. This paper introduces a temporal graph data model, where nodes and relationships contain attributes (properties) timestamped with a validity interval. Graphs in this model can be heterogeneous, that is, relationships may be of different kinds. Associated with the model, a high-level graph query language, denoted T-GQL, is presented, together with a collection of algorithms for computing different kinds of temporal paths in a graph, capturing different temporal path semantics. T-GQL can express queries like “Give me the friends of the friends of Mary, who lived in Brussels at the same time than her, and also give me the periods when this happened”. As a proof-of-concept, a Neo4j-based implementation of the above is also presented, and a client-side interface allows submitting queries in T-GQL to a Neo4j server. Finally, experiments were carried out over synthetic and real-world data sets, with a twofold goal: on the one hand, to show the plausibility of the approach; on the other hand, to analyze the factors that affect performance, like the length of the paths mentioned in the query, and the size of the graph.

Genetic programming-based regression for temporal data

Various machine learning techniques exist to perform regression on temporal data with concept drift occurring. However, there are numerous nonstationary environments where these techniques may fail to either track or detect the changes. This study develops a genetic programming-based predictive model for temporal data with a numerical target that tracks changes in a dataset due to concept drift. When an environmental change is evident, the proposed algorithm reacts to the change by clustering the data and then inducing nonlinear models that describe generated clusters. Nonlinear models become terminal nodes of genetic programming model trees. Experiments were carried out using seven nonstationary datasets and the obtained results suggest that the proposed model yields high adaptation rates and accuracy to several types of concept drifts. Future work will consider strengthening the adaptation to concept drift and the fast implementation of genetic programming on GPUs to provide fast learning for high-speed temporal data.

Prediction of Heart Disease Using Deep Convolutional Neural Networks

Heart diseases are currently a major cause of death in the world. This problem is severe in developing countries in Africa and Asia. A heart disease predicted at earlier stages not only helps the patients prevent it, but I can also help the medical practitioners learn the major causes of a heart attack and avoid it before its actual occurrence in patient. In this paper, we propose a method named CardioHelp which predicts the probability of the presence of cardiovascular disease in a patient by incorporating a deep learning algorithm called convolutional neural networks (CNN). The proposed method is concerned with temporal data modeling by utilizing CNN for HF prediction at its earliest stage. We prepared the heart disease dataset and compared the results with state-of-the-art methods and achieved good results. Experimental results show that the proposed method outperforms the existing methods in terms of performance evaluation metrics. The achieved accuracy of the proposed method is 97%.

Digital Effects and Inequality of People’s Quality of Life in the Globalization Era

Research background:Digital transformation are going on both in global economy and within separate states. This phenomenon became known as glocalization. In that respect the analysis of digitalization influence on people’s quality of life in different regions of large-space countries like Russia seems to be actual.Purpose of the article:Basing on calculation of digital component level of people’s quality of life to estimate temporal and spatial effects affecting it, to determine inequality in digital components of quality of life in different regions of Russia.Methods:Digital component of people’s quality of life was computed basing on the authorial Russian regional index including six components. To estimate spatial and temporal effects panel data models were used. Digital inequality was found with the use of R/P 10% ratio, variation ratio and cluster analysisfor 2015–2018.Findings & Value added:The Russian regional index of digital component of quality of life was defined for 85 regions and increased by

Social network analysis of open source software: A review and categorisation

Context: As companies have become large users of Open Source Software , it is important that they feel comfortable in their Open Source strategies. One of the critical differences between Open Source and Proprietary Software is the communication networks . Objective: This paper tries to set a base for understanding how open source teams are structured and how they change. This is vital to understanding Open Source Software Communities. Method: The paper looks into previous research on Social Network Analysis of Open Source Software, using a systematic literature review. Papers were gathered from Scopus, IEEEXplore and ACM Digital Library, and used or discarded based on predetermined inclusion and exclusion criteria. Research which focuses on the success factors of Open Source Software through Network Analysis is also examined. Results: A subjective categorisation is established for the papers: Structure, Lifecycle and Communication. It was found that the structure of a project has a large bearing on project success, with developers having previously worked together being indicative of project success. Other structure indicators of success are having a small but structured hierarchy, a diverse user and developer base, and project prominence. However, it was found that information on how these structures appear and evolve over time is lacking, and future research into temporal data models to determine project success information is suggested. Conclusions: A categorisation of existing research on Social Network Analysis is provided as a basis for further research. Further work into the lifecycle of OSS projects through Social Network Analysis of temporal project information is suggested.

Phenological Patterns and Seasonal Segregation of Coprophilous Beetles (Coleoptera: Scarabaeoidea and Hydrophilidae) on a Cattle Farm in SE-Michigan, United States Throughout the Year

The seasonal occurrence of species and timing of key events in their life (phenology) affects the ecology and evolution of every species, population, and species community (SC) on Earth. Shifting phenologies and mismatches between species may affect range limits and lead to extinctions and biodiversity loss, which are especially concerning in ecologically and economically important SCs like coprophilous beetles. This study provides phenological data for many species of coprophilous beetles from the Midwestern region of the US, includes the often-missing winter months, and compares the seasonal and successional differences between the “relocator” and “dweller” guilds. Beetles were sampled biweekly throughout the year from a 3.8 ha Farm in Adrian, SE-Michigan using 15 pitfall traps baited with cow manure. Samples were collected after 48 h and again after 72 h exposure time. Over the entire year, I caught 6069 specimens from 24 species of Scarabaeoidea and 13 species of Hydrophilidae. The ROSARIO null model for continuous temporal data suggested a significant seasonal overlap of the SC (p < 0.0001) and a cluster analysis based on Pianka’s niche overlap index identified five clusters at a minimum overlap of 55 % falling into three major phenological guilds. “Relocators” (6 Scarabaeinae, 2 Aphodiinae species) overlapped on average by 48.4 %, whereas “dwellers” (9 Aphodiinae, 10 Hydrophilidae species) overlapped by 36.0 % (Mann-Whitney test, p = 0.055). Small Hydrophilidae dwellers overlapped by 30.9 % whereas large hydrophilid dwellers overlapped by 96.9 %. Individual phenological patterns were compared to other sites in North America and Europe and predictions about geographical and phenological changes due to the man-made climate crisis and habitat loss and fragmentation are discussed. Differences between the seasonal overlaps of “relocators” and “dwellers” as well as small and large Onthophagus relocators and Hydrophilidae dwellers are suggesting profound ecological differences between the guilds and sub-guilds.

Vecchia Approximations of Gaussian-Process Predictions

Gaussian processes are popular and flexible models for spatial, temporal, and functional data, but they are computationally infeasible for large datasets. We discuss Gaussian-process approximations that use basis functions at multiple resolutions to achieve fast inference and that can (approximately) represent any spatial covariance structure. We consider two special cases of this multi-resolution-approximation framework, a taper version and a domain-partitioning (block) version. We describe theoretical properties and inference procedures, and study the computational complexity of the methods. Numerical comparisons and an application to satellite data are also provided.

A framework for end-to-end deep learning-based anomaly detection in transportation networks

We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks. The proposed EVT-LSTM model is derived from the popular LSTM (Long Short-Term Memory) network and adopts an objective function that is based on fundamental results from EVT (Extreme Value Theory). We compare the EVT-LSTM model with some established statistical, machine learning, and hybrid deep learning baselines. Experiments on seven diverse real-world data sets demonstrate the superior anomaly detection performance of our proposed model over the other models considered in the comparison study.

EXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research.

Until date, several machine learning approaches have been proposed for the dynamic modeling of temporal omics data. Although they have yielded impressive results in terms of model accuracy and predictive ability, most of these applications are based on "Black-box" algorithms and more interpretable models have been claimed by the research community. The recent eXplainable Artificial Intelligence (XAI) revolution offers a solution for this issue, were rule-based approaches are highly suitable for explanatory purposes. The further integration of the data mining process along with functional-annotation and pathway analyses is an additional way towards more explanatory and biologically soundness models. In this paper, we present a novel rule-based XAI strategy (including pre-processing, knowledge-extraction and functional validation) for finding biologically relevant sequential patterns from longitudinal human gene expression data (GED). To illustrate the performance of our pipeline, we work on in vivo temporal GED collected within the course of a long-term dietary intervention in 57 subjects with obesity (GSE77962). As validation populations, we employ three independent datasets following the same experimental design. As a result, we validate primarily extracted gene patterns and prove the goodness of our strategy for the mining of biologically relevant gene-gene temporal relations. Our whole pipeline has been gathered under open-source software and could be easily extended to other human temporal GED applications.

TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records.

Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of patients in those groups. Tensor factorization has been an effective tool for phenotyping. Most of the existing works assume either a static patient representation with aggregate data or only model temporal data. However, real EHR data contain both temporal (e.g., longitudinal clinical visits) and static information (e.g., patient demographics), which are difficult to model simultaneously. In this paper, we propose Temporal And Static TEnsor factorization (TASTE) that jointly models both static and temporal information to extract phenotypes. TASTE combines the PARAFAC2 model with non-negative matrix factorization to model a temporal and a static tensor. To fit the proposed model, we transform the original problem into simpler ones which are optimally solved in an alternating fashion. For each of the sub-problems, our proposed mathematical re-formulations lead to efficient sub-problem solvers. Comprehensive experiments on large EHR data from a heart failure (HF) study confirmed that TASTE is up to 14× faster than several baselines and the resulting phenotypes were confirmed to be clinically meaningful by a cardiologist. Using 60 phenotypes extracted by TASTE, a simple logistic regression can achieve the same level of area under the curve (AUC) for HF prediction compared to a deep learning model using recurrent neural networks (RNN) with 345 features.

ALSTM: An attention-based long short-term memory framework for knowledge base reasoning

Knowledge Graphs (KGs) have been applied to various application scenarios including Web searching, Q&A, recommendation system, natural language processing and so on. However, the vast majority of Knowledge Bases (KBs) are incomplete, necessitating a demand for KB completion (KBC). Methods of KBC used in the mainstream current knowledge base include the latent factor model, the random walk model and recent popular methods based on reinforcement learning, which performs well in their respective areas of expertise. Recurrent neural network (RNN) and its variants model temporal data by remembering information for long periods, however, whether they also have the ability to use the information they have already remembered to achieve complex reasoning in the knowledge graph. In this paper, we produce a novel framework (ALSTM) based on the Attention mechanism and Long Short-Term Memory (LSTM), which associates structure learning with parameter learning of first-order logical rules in an end-to-end differentiable neural networks model. In this framework, we designed a memory system and employed a multi-head dot product attention (MHDPA) to interact and update the memories embedded in the memory system for reasoning purposes. This is also consistent with the process of human cognition and reasoning, looking for enlightenment for the future in historical memory. In addition, we explored the use of inductive bias in deep learning to facilitate learning of entities, relations, and rules. Experiments establish the efficiency and effectiveness of our model and show that our method achieves better performance in tasks which include fact prediction and link prediction than baseline models on several benchmark datasets such as WN18RR, FB15K-237 and NELL-995.

A New Tidy Data Structure to Support Exploration and Modeling of Temporal Data

Mining temporal data for information is often inhibited by a multitude of formats: regular or irregular time intervals, point events that need aggregating, multiple observational units or repeated measurements on multiple individuals, and heterogeneous data types. This work presents a cohesive and conceptual framework for organizing and manipulating temporal data, which in turn flows into visualization, modeling, and forecasting routines. Tidy data principles are extended to temporal data by: (1) mapping the semantics of a dataset into its physical layout; (2) including an explicitly declared “index” variable representing time; (3) incorporating a “key” comprising single or multiple variables to uniquely identify units over time. This tidy data representation most naturally supports thinking of operations on the data as building blocks, forming part of a “data pipeline” in time-based contexts. A sound data pipeline facilitates a fluent workflow for analyzing temporal data. The infrastructure of tidy temporal data has been implemented in the R package, called tsibble. Supplementary materials for this article are available online.

Jamming Effect of Stand-Off Jammer to Main Lobe of LPI Radar

The classification of temporal medical data has received considerable attention, because it can reveal useful information hidden within a database. The objective of models for temporal medical data is to increase the classification accuracy. However, the problem with classifying temporal medical data for each patient is that we cannot deal with multiple values for each measurement, and classifiers cannot deal with multiple classes for each patient. This is because measurements in patient case series consist of a sequence of measurements taken at ordered points in time. This paper presents a novel inner distance combination transform method that converts temporal medical data into a new feature values that is then fed into a naive Bayes classifier. We compare the new transform method with mean, median, mode and the final treatment result. The experimental results show that the proposed method is more accurate than the other methods.

On the Fourier Residual Modification of Arima Models in Modeling Malaria Incidence Rates among Pregnant Women

This work provides a general overview and consideration of Box-Jenkins models for temporal data and its extension known as Fourier residual autoregressive moving average models. We examined the modeling and forecasting of malaria incidence rate during pregnancy at Bishop Shannahan Hospital, Nsukka using Autoregressive Integrated Moving Average (ARIMA) Models propounded by Box and Jenkins. We adoptted the Box-Jenkins methodology to build ARIMA model for malaria incidences during pregnancy for a period of 10 years spanning from January 2006 to December 2016. Among the candidate models considered, ARIMA (3,1,1) was identified to be the most robust based on some model performance measures. The model was further improved upon by incorporating Fourier residual modification on the fitted ARIMA model. The Fourier Residual Autoregressive Moving Average (FARIMA) model obtained yielded improved result. Besides, model evaluation criterion such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Mean Bias Error (MBE), Mean Absolute Scaled Error (MASE), were used to access the models. FARIMA Model out performed ARIMA Model. Several time series plots and tests like augmented dickey fuller test, correlogram, Ljung-Box test for serial correlation of the residuals, etc were carried out in this study to test for stationarity, identify the order of ARIMA model and serial correlation residual respectively.

Ethiopian Economic Features and Changing Persistence: A Time Series Analysis

Ethiopia was one of the countries least developed and it is among the countries in the bottom in the rank of GDP’s that UN lists. However, nowadays Ethiopia is one of the fastest growing economies in the world. In Ethiopia much effort has been made to build the national economy. Ethiopia has made significant strides towards becoming a middle income country by 2025. This paper provides an overview of Box-Jenkins model for temporal data. In this research we used time series analysis of some of Ethiopian economic features such as GDP, GDP growth rate and inflation rate. Box-Jenkins model was used to analyze 35-year data (1981-2015). GDP, GDP growth rate, and inflation rate were variables under the study to describe persistence change and to forecast future behaviors. We tried to find best model for description and predictive model for these series using different model selection tools. We compared different orders of Autoregressive Integrated Moving Average (ARIMA) using AIC, BIC and MSE to fit the observed data. The best from compared was ARIMA (2, 2, 2) for GDP, ARIMA (2, 1, 2) for GDP growth rate and ARIMA (1, 1, 1) for inflation rate. Since forecasting is important for many purposes, we forecast the series from best ARIMA models. Five year forecast showing that GDP is an increasing trend and the average forecast of GDP rates is showing an average of 10.028.

TempoX: A disciplined approach for data management in multi-temporal and multi-schema-version XML databases

Although multi-temporal XML databases supporting schema versioning are used in several domains, like e-commerce, e-health, and e-government, existing database management systems and XML tools do not provide any support for managing (inserting, updating, and deleting) temporal XML data or temporal XML schema versioning. Besides, whereas much research work has focused in the last decade on schema versioning in temporal XML databases, any attention has been devoted to manipulating data in such databases. To fill this theoretical and practical gap, we propose in this paper a generic approach, named TempoX (Temporal XML), for data manipulation in multi-temporal and multi-schema-version XML databases. Indeed, we (i) define a new multi-temporal XML data model supporting temporal schema versioning, named TempoXDM (Temporal XML Data Model), (ii) introduce the principles on which our approach is based, and (iii) provide the specifications of the basic data manipulation operations: “insert”, “replace”, “evolve”, and “delete”. Moreover, to show the feasibility of TempoX, we use it to propose a temporal XML update language, named TempoXUF (Temporal XQuery Update Facility), as an extension of the W3C XQuery Update Facility language to temporal and versioning aspects. Furthermore, to validate our language proposal, we develop a system prototype, named TempoXUF-Manager, that supports TempoXUF.

A lightweight and efficient temporal database management system in TDSQL

Driven by the recent adoption of temporal expressions into SQL:2011, extensions of temporal support in conventional database management systems (a.b.a. DBMSs) have re-emerged as a research hotspot. In this paper, we present a lightweight yet efficient built-in temporal implementation in Tencent's distributed database management system, namely TDSQL. The novelty of TDSQL's temporal implementation includes: (1) a new temporal data model with the extension of SQL:2011, (2) a built-in temporal implementation with various optimizations, which are also applicable to other DBMSs, and (3) a low-storage-consumption in which only data changes are maintained. For the repeatability purpose, we elaborate the integration of our proposed techniques into MySQL. We conduct extensive experiments on both real-life dataset and synthetic TPC benchmarks by comparing TD-SQL with other temporal databases. The results show that TDSQL is lightweight and efficient.

A class of multi-resolution approximations for large spatial datasets

Gaussian processes are popular and flexible models for spatial, temporal, and functional data, but they are computationally infeasible for large datasets. We discuss Gaussian-process approximations that use basis functions at multiple resolutions to achieve fast inference and that can (approximately) represent any spatial covariance structure. We consider two special cases of this multi-resolution-approximation framework, a taper version and a domain-partitioning (block) version. We describe theoretical properties and inference procedures, and study the computational complexity of the methods. Numerical comparisons and an application to satellite data are also provided.

Snapshot semantics for temporal multiset relations

Snapshot semantics is widely used for evaluating queries over temporal data: temporal relations are seen as sequences of snapshot relations, and queries are evaluated at each snapshot. In this work, we demonstrate that current approaches for snapshot semantics over interval-timestamped multiset relations are subject to two bugs regarding snapshot aggregation and bag difference. We introduce a novel temporal data model based on K -relations that overcomes these bugs and prove it to correctly encode snapshot semantics. Furthermore, we present an efficient implementation of our model as a database middleware and demonstrate experimentally that our approach is competitive with native implementations.

Precision Marketing Scheme based on Integrating Spatio-temporal Data Clustering and Neural Network

Precision marketing can effectively predict the user’s willingness to consume, with nearly one trillion of market space. The current precision marketing strategy still has remarkable problems such as the low correlation between data model and space; the marketing initial data redundancy and so on. Aiming at the low correlation between the model and space and the initial data redundancy in the marketing, this paper proposes a precise marketing scheme combining the spatiotemporal data clustering and the neural network. Through the establishment of multi - dimensional space - time large data model, effectively enhance the geographic data model, spatio - temporal data model and multi - dimensional data superposition. Design the large-scale data normalization clustering scheme, construct the normalized classification Euclidean distance and the adaptive precision marketing objective function, and realize the K-Means data clustering of the initial data redundancy. Through the neural network based spatial and temporal data precision marketing decision, design data input layer; deal with hidden layer and intelligent output layer; provide an effective program for accurate marketing.