Modeling and Analysis of Data Dependencies in Business Process for Data-Intensive Services

With the growing popularity of data-intensive services on the Internet, the traditional process-centric model for business process meets challenges due to the lack of abilities to describe data semantics and dependencies, resulting in the inflexibility of the design and implement for the processes. This paper proposes a novel data-aware business process model which is able to describe both explicit control flow and implicit data flow. Data model with dependencies which are formulated by Linear-time Temporal Logic(LTL) is presented, and their satisfiability is validated by an automaton-based model checking algorithm. Data dependencies are fully considered in modeling phase, which helps to improve the efficiency and reliability of programming during developing phase. Finally, a prototype system based on j BPM for data-aware workflow is designed using such model, and has been deployed to Beijing Kingfore heating management system to validate the flexibility, efficacy and convenience of our approach for massive coding and large-scale system management in reality.

A Real-time Data Model Based on Temporal Data

Real-time database systems contain not only transaction timing constraints, but also data timing constraints. This paper discusses the temporal characteristics of data in real-time databases and offers a definition of absolute and relative temporal consistency. In real-time database systems, it is often the case that the policies of transaction schedules only consider the deadline of real-time transactions, making it insufficient to ensure temporal correctness of transactions. A policy is given by considering both the deadlines of transactions and the “data deadline” to schedule real-time transactions. A real-time relational data model and a real-time relational algebra based on the characteristics of temporal data are also proposed. In this model, the temporal data has not only corresponding values, but also validity intervals corresponding to the data values. At the same time, this model is able to keep historical data values. When validity interval of a relation is [NOW, NOW], real-time relational algebra will transform to traditional relational algebra.

Research of storage technique for C-Temporal Relation Data Model

As there is datum redundancy in tradition database and temporal database in existence and the quantities of temporal database are increasing fleetly. We put forward compress storage tactics for temporal datum which combine compress technology in existence in order to settle datum redundancy in the course of temporal datum storage and temporal datum of slow acting domain and momentary acting domain are accessed by using each from independence clock method and mutual clock method .We also bring forward strategy of gridding storage to resolve the problems of temporal datum rising rapidly.

Detection and Mapping of Violent Crime Hotspots in Southwestern Nigeria

The increasing rate of insecurity in Nigeria, especially the southwest requires a paradigm shift from popular approach to crime hotspots detection. This study employed geospatial technologies to integrate spatio-temporal crime, social media and field observation data from the communities in all the six states in the southwest to develop crime hotspots that can serve as preliminary information to assist in allocating resources for crime control and prevention. Historical crime data from January 1972 to April, 2021 were compiled and updated with rigorous field survey in September, 2021. The field data were encoded, input to the SPSS 17 and analyzed using descriptive statistics and multivariate analysis. A total 936 crime locations data were geolocated and exported to ArcGIS 10.5 for spatial mapping using point map operation and further imported to e-Spatial web-based and QGIS for the generation of hotspot map using heatmap tool. The results revealed that armed robbery, assassination and cultism were more pronounced in Lagos and Ogun States. Similarly, high incidences of farmers/herdsmen conflicts are observed in Oyo and Osun States. Increasing incidences of kidnapping are common in all the south-western states but very prominent in Ondo, Lagos and Oyo States. Most of the violent crime incidents took place along the highways, with forests being their hideouts. Violent crimes are dominantly caused by high rate of unemployment while farmer/herdsmen conflicts were majorly triggered by the scarcity of grazing fields and destruction of arable crops. The conflicts have resulted in the increasing cases of rape and disruption of social group, intake of hard drugs, cult-related activities, low income and revenue generation, and displacement of farmers and infrastructural damages. The study advocates regular retraining and equipping of security agents, establishment of cattle ranch, and installation of sophisticated IP Camera at the crime hotspots to assist in real-time crime monitoring and management.

Assessing Landsat-8 and Sentinel-2 spectral-temporal features for mapping tree species of northern plantation forests in Heilongjiang Province,China

Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,from Landsat-8(L8)and Sentinel-2(S2),have been proven useful in mapping general forest types,yet we do not know quantitatively how their spectral features(e.g.,red-edge)and temporal frequency of data acquisitions(e.g.,16-day vs.5-day)contribute to plantation forest mapping to the species level.Moreover,it is unclear to what extent the fusion of L8 and S2 will result in improvements in tree species mapping of northern plantation forests in China.Methods:We designed three sets of classification experiments(i.e.,single-date,multi-date,and spectral-temporal)to evaluate the performances of L8 and S2 data for mapping keystone timber tree species in northern China.We first used seven pairs of L8 and S2 images to evaluate the performances of L8 and S2 key spectral features for separating these tree species across key growing stages.Then we extracted the spectral-temporal features from all available images of different temporal frequency of data acquisition(i.e.,L8 time series,S2 time series,and fusion of L8 and S2)to assess the contribution of image temporal frequency on the accuracy of tree species mapping in the study area.Results:1)S2 outperformed L8 images in all classification experiments,with or without the red edge bands(0.4%–3.4%and 0.2%–4.4%higher for overall accuracy and macro-F1,respectively);2)NDTI(the ratio of SWIR1 minus SWIR2 to SWIR1 plus SWIR2)and Tasseled Cap coefficients were most important features in all the classifications,and for time-series experiments,the spectral-temporal features of red band-related vegetation indices were most useful;3)increasing the temporal frequency of data acquisition can improve overall accuracy of tree species mapping for up to 3.2%(from 90.1%using single-date imagery to 93.3%using S2 time-series),yet similar overall accuracies were achieved using S2 time-series(93.3%)and the fusion of S2 and L8(93.2%).Conclusions:This study quantifies the contributions of L8 and S2 spectral and temporal features in mapping keystone tree species of northern plantation forests in China and suggests that for mapping tree species in China's northern plantation forests,the effects of increasing the temporal frequency of data acquisition could saturate quickly after using only two images from key phenological stages.

Effect of Spatial and Temporal Scales on Habitat Suitability Modeling:A Case Study of Ommastrephes bartramii in the Northwest Pacific Ocean

Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.

Monthly gravity field recovery from GRACE orbits and K-band measurements using variational equations approach

The Gravity Recovery and Climate Experiment（GRACE） mission can significantly improve our knowledge of the temporal variability of the Earth＇s gravity field.We obtained monthly gravity field solutions based on variational equations approach from GPS-derived positions of GRACE satellites and K-band range-rate measurements.The impact of different fixed data weighting ratios in temporal gravity field recovery while combining the two types of data was investigated for the purpose of deriving the best combined solution.The monthly gravity field solution obtained through above procedures was named as the Institute of Geodesy and Geophysics（IGG） temporal gravity field models.IGG temporal gravity field models were compared with GRACE Release05（RL05） products in following aspects：（i） the trend of the mass anomaly in China and its nearby regions within 2005-2010; （ii） the root mean squares of the global mass anomaly during 2005-2010; （iii） time-series changes in the mean water storage in the region of the Amazon Basin and the Sahara Desert between 2005 and 2010.The results showed that IGG solutions were almost consistent with GRACE RL05 products in above aspects（i）-（iii）.Changes in the annual amplitude of mean water storage in the Amazon Basin were 14.7 ± 1.2 cm for IGG,17.1 ± 1.3 cm for the Centre for Space Research（CSR）,16.4 ± 0.9 cm for the GeoForschungsZentrum（GFZ） and 16.9 ± 1.2 cm for the Jet Propulsion Laboratory（JPL） in terms of equivalent water height（EWH）,respectively.The root mean squares of the mean mass anomaly in Sahara were 1.2 cm,0.9 cm,0.9 cm and 1.2 cm for temporal gravity field models of IGG,CSR,GFZ and JPL,respectively.Comparison suggested that IGG temporal gravity field solutions were at the same accuracy level with the latest temporal gravity field solutions published by CSR,GFZ and JPL.

τSQWRL:A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

Temporal ontologies allow to represent not only concepts,their properties,and their relationships,but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view.They are widely used to formally represent temporal data semantics in several applications belonging to different fields(e.g.,Semantic Web,expert systems,knowledge bases,big data,and artificial intelligence).They facilitate temporal knowledge representation and discovery,with the support of temporal data querying and reasoning.However,there is no standard or consensual temporal ontology query language.In a previous work,we have proposed an approach namedτJOWL(temporal OWL 2 from temporal JSON,where OWL 2 stands for"OWL 2 Web Ontology Language"and JSON stands for"JavaScript Object Notation").τJOWL allows(1)to automatically build a temporal OWL 2 ontology of data,following the Closed World Assumption(CWA),from temporal JSON-based big data,and(2)to manage its incremental maintenance accommodating their evolution,in a temporal and multi-schema-version environment.In this paper,we propose a temporal ontology query language forτJOWL,namedτSQWRL(temporal SQWRL),designed as a temporal extension of the ontology query language—Semantic Query-enhanced Web Rule Language(SQWRL).The new language has been inspired by the features of the consensual temporal query language TSQL2(Temporal SQL2),well known in the temporal(relational)database community.The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any(complex)temporal query on time-varying ontologies generated from time-varying big data.Some examples,in the Internet of Healthcare Things(IoHT)domain,are provided to motivate and illustrate our proposal.

Fuzzy Association Degree with Delayed Time in Temporal Data Model

This paper presents an expression of the semantic proximity. Based on the temporal data model, a method of the temporal approximation is given. Using these concepts, this paper provides an evaluated method of fuzzy and dynamic association degree with delayed time and a superposition method of association degrees. Particularly, by means of the fuzzy and dynamic association degree, the connection between the weather data of two regions can be discovered.

The Modelling of Temporal Data in the Relational Database Environment

This research takes the view that the modelling of temporal data is a fundamental step towards the solution of capturing semantics of time. The problemsinhereat in the mod6iling of time are not unique to datahase processing. Therepresentation of temporal knowledge and temporal reasoning arises in a widerange of other disciplines. ln this paper an account is given of a techniquefor modelling the semantics of temporal data and its associated normalizationmcthod. It discusses the techniques of processing temporal data by employinga Time Sequence (TS) data model. It shows a number of different strategieswhich are used to classify different data properties of temporal data, and it goeson.to develop the model of temporal data and addresses issues of temporal dataapplication design by introducing the concept of temporal data normalisation.

Linking temporal records

Many data sets contain temporal records which span a long period of time; each record is associated with a time stamp and describes some aspects of a real-world en- tity at a particular time （e.g., author information in DBLP）. In such cases, we often wish to identify records that describe the same entity over time and so be able to perform interest- ing longitudinal data analysis. However, existing record link- age techniques ignore temporal information and fall short for temporal data. This article studies linking temporal records. First, we ap- ply time decay to capture the effect of elapsed time on entity value evolution. Second, instead of comparing each pair of records locally, we propose clustering methods that consider the time order of the records and make global decisions. Ex- perimental results show that our algorithms significantly out- perform traditional linkage methods on various temporal data sets.

Temporal scatterplots

Visualizing high-dimensional data on a 2D canvas is generally challenging.It becomes significantly more difficult when multiple time-steps are to be presented,as the visual clutter quickly increases.Moreover,the challenge to perceive the significant temporal evolution is even greater.In this paper,we present a method to plot temporal high-dimensional data in a static scatterplot;it uses the established PCA technique to project data from multiple time-steps.The key idea is to extend each individual displacement prior to applying PCA,so as to skew the projection process,and to set a projection plane that balances the directions of temporal change and spatial variance.We present numerous examples and various visual cues to highlight the data trajectories,and demonstrate the effectiveness of the method for visualizing temporal data.