An Efficient Schema Transformation Technique for Data Migration from Relational to Column-Oriented Databases

Data transformation is the core process in migrating database from relational database to NoSQL database such as column-oriented database. However,there is no standard guideline for data transformation from relational database toNoSQL database. A number of schema transformation techniques have been proposed to improve data transformation process and resulted better query processingtime when compared to the relational database query processing time. However,these approaches produced redundant tables in the resulted schema that in turnconsume large unnecessary storage size and produce high query processing timedue to the generated schema with redundant column families in the transformedcolumn-oriented database. In this paper, an efficient data transformation techniquefrom relational database to column-oriented database is proposed. The proposedschema transformation technique is based on the combination of denormalizationapproach, data access pattern and multiple-nested schema. In order to validate theproposed work, the proposed technique is implemented by transforming data fromMySQL database to MongoDB database. A benchmark transformation techniqueis also performed in which the query processing time and the storage size arecompared. Based on the experimental results, the proposed transformation technique showed significant improvement in terms query processing time and storagespace usage due to the reduced number of column families in the column-orienteddatabase.

A Middleware for Polyglot Persistence and Data Portability of Big Data PaaS Cloud Applications

Vendor lock-in can occur at any layer of the cloud stack-Infrastructure,Platform,and Software-as-a-service.This paper covers the vendor lock-in issue at Platform as a Service(PaaS)level where applications can be created,deployed,and managed without worrying about the underlying infrastructure.These applications and their persisted data on one PaaS provider are not easy to port to another provider.To overcome this issue,we propose a middleware to abstract and make the database services as cloud-agnostic.The middleware supports several SQL and NoSQL data stores that can be hosted and ported among disparate PaaS providers.It facilitates the developers with data portability and data migration among relational and NoSQL-based cloud databases.NoSQL databases are fundamental to endure Big Data applications as they support the handling of an enormous volume of highly variable data while assuring fault tolerance,availability,and scalability.The implementation of the middleware depicts that using it alleviates the efforts of rewriting the application code while changing the backend database system.A working protocol of a migration tool has been developed using this middleware to facilitate the migration of the database(move existing data from a database on one cloud to a new database even on a different cloud).Although the middleware adds some overhead compared to the native code for the cloud services being used,the experimental evaluation on Twitter(a Big Data application)data set,proves this overhead is negligible.

A Distribution Management System for Relational Databases in Cloud Environments

For a transaction processing system to operate effectively and efficiently in cloud environments, it is important to distribute huge amount of data while guaranteeing the ACID （atomic, consistent, isolated, and durable） properties. Moreover, database partition and migration tools can help transplanting conventional relational database systems to the cloud environment rather than rebuilding a new system. This paper proposes a database distribution management （DBDM） system, which partitions or replicates the data according to the transaction behaviors of the application system. The principle strategy of DBDM is to keep together the data used in a single transaction, and thus, avoiding massive transmission of records in join operations. The proposed system has been implemented successfully. The preliminary experiments show that the DBDM performs the database partition and migration effectively. Also, the DBDM system is modularly designed to adapt to different database management system （DBMS） or different partition algorithms.

法学教育现代化：定位、支点与趋势--以法学本科教育为中心

法学教育现代化不仅关乎法学学科教育的现代化,而且决定了培养法治国家建设人才的质量。高等院校法学教育现代化定位应以法律人的现代化为逻辑起点,是通识教育基础上法学专业教育的差异化探索、大众教育基础上法学精英教育的错位化发展与知识传承基础上法学创新教育的异质化展开。打造高素质的专兼职教师队伍、聚焦迁移性能力的广谱法学教学模式、确立基于人的全面发展与法学学科属性的有机课程体系等是法学教育现代化内涵式发展的关键支点。在应对法学教育与信息技术、大数据深度融合的趋势中,法学教育内容拓展、法学教育方式调适和法学人才跨界要求等是法学教育现代化需重点关注的内容。

Prediction of epidemics dynamics on networks with partial differential equations:A case study for COVID-19 in China

Since December 2019,the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade,national policies and the natural environment.To closely monitor the emergence of new COVID-19 clusters and ensure high prediction accuracy,we develop a new prediction framework for studying the spread of epidemic on networks based on partial differential equations(PDEs),which captures epidemic diffusion along the edges of a network driven by population flow data.In this paper,we focus on the effect of the population movement on the spread of COVID-19 in several cities from different geographic regions in China for describing the transmission characteristics of COVID-19.Experiment results show that the PDE model obtains relatively good prediction results compared with several typical mathematical models.Furthermore,we study the effectiveness of intervention measures,such as traffic lockdowns and social distancing,which provides a new approach for quantifying the effectiveness of the government policies toward controlling COVID-19 via the adaptive parameters of the model.To our knowledge,this work is the first attempt to apply the PDE model on networks with Baidu Migration Data for COVID-19 prediction.

Population migration across the Qinghai-Tibet Plateau:Spatiotemporal patterns and driving factors

Developing a comprehensive understanding of inter-city interactions is crucial for regional planning.We therefore examined spatiotemporal patterns of population migration across the Qinghai-Tibet Plateau(QTP)using migration big data from Tencent for the period between 2015 and 2019.We initially used decomposition and breakpoint detection methods to examine time-series migration data and to identify the two seasons with the strongest and weakest population migration levels,between June 18th and August 18th and between October 8th and February 15th,respectively.Population migration within the former period was 2.03 times that seen in the latter.We then used a variety of network analysis methods to examine population flow directions as well as the importance of each individual city in migration.The two capital cities on the QTP,Lhasa and Xining,form centers for population migration and are also transfer hubs through which migrants from other cities off the plateau enter and leave this region.Data show that these two cities contribute more than 35%of total population migration.The majority of migrants tend to move within the province,particularly during the weakest migration season.We also utilized interactive relationship force and radiation models to examine the interaction strength and the radiating energy of each individual city.Results show that Lhasa and Xining exhibit the strongest interactions with other cities and have the largest radiating energies.Indeed,the radiating energy of the QTP cities correlates with their gross domestic product(GDP)(Pearson correlation coefficient:0.754 in the weakest migration season,WMS versus 0.737 in the strongest migration season,SMS),while changes in radiating energy correlate with the tourism-related revenue(Pearson correlation coefficient:0.685).These outcomes suggest that level of economic development and level of tourism are the two most important factors driving the QTP population migration.The results of this analysis provide critical clarification guidance regarding huge QTP development differences.

中国流动人口概念和数据的有效性与国际可比性

The two concepts of“liudong renkou(floating population or FP)”and“renkou liudong(mobility of the floating population or MOFP)”,along with relevant data based on these two concepts,have long been used extensively in China’s research and policy making,playing a central role in Chinese studies of migration.Unlike the concepts of“migrant”and“migration”in the international literature,which are focused on people’s spatial mobility,“liudong renkou”and“renkou liudong”are identified and measured by the separation of one’s place of residence from one’s place of household registration(hukou),an approach inconsistent with relevant international practices.By analyzing various census data and data from the China Migrant Dynamic Survey(CMDS),this article examines the validity and reliability of these two concepts and the data based on them in the international context,revealing that they have become increasingly invalid and unreliable for the purpose of measuring migration events since China’s reform and opening up in the late 1970s.The results further demonstrate that these two concepts and the data based on them have become increasingly detached from real migration events and processes.They may become invalid by overestimating the volume of the mobile population,ineffective due to systematic omission of certain mobile populations(such as urban-urban migrants),or misleading as to the changing direction of migration flows.In addition,data on the floating population cannot be used to calculate migration rates and are not comparable in the international context.The concepts of“liudong renkou”and“renkou liudong”and data based on these two concepts may still need to be used in China for a long period of time due to the continuing existence of the hukou system and its roles in the provision of public services,social welfare and social security.However,we argue that concepts,measurements,and methods of data collection in research on migration in China should be gradually shifted to and focused on migrations as spatial events;further,transition data,based on an individual’s residence five years ago and one year ago,should be gradually adopted as the main data source and included in the short form of future censuses;additionally,migration event data based on population registration and administrative records should be used more fully,so that China’s migration research can be conducted on the solid basis of valid and reliable data sources.

Least-Squares Seismic Inversion with Stochastic Conjugate Gradient Method

With the development of computational power, there has been an increased focus on data-fitting related seismic inversion techniques for high fidelity seismic velocity model and image, such as full-waveform inversion and least squares migration. However, though more advanced than conventional methods, these data fitting methods can be very expensive in terms of computational cost. Recently, various techniques to optimize these data-fitting seismic inversion problems have been implemented to cater for the industrial need for much improved efficiency. In this study, we propose a general stochastic conjugate gradient method for these data-fitting related inverse problems. We first prescribe the basic theory of our method and then give synthetic examples. Our numerical experiments illustrate the potential of this method for large-size seismic inversion application.