Dynamic Database Research Articles

WITHDRAWN: Robust frequency affinity-based high utility itemset mining approach using multiple minimum utility

Abstract High-Utility-Itemset Mining (HUIM) is intended to discover exceedingly significant patterns by considering buy amounts and unit benefits of things. The greater part of the HUIM calculations is intended for static databases. In the present genuine applications, for example, market basket examination, business decision making and association data of web administrations, extensive volumes of databases are expanding slowly through embedding new information. The customary mining calculations are not reasonable for handling these dynamic databases and extricating helpful data. In this paper, a novel mining approach is introduced for incremental transactional databases to alter the discovered high utility itemsets. Instead of high utility values in each transaction, an affinity utility value is employed for discovering highly profitable itemsets with strong frequency affinity. Along with this, an upper bound knowledge-weighted utilization (KWU) is utilized to maintain the downward closure property. Wide spread experimental analyses showed that the suggested Itemset Mining approach is very effective and scalable for mining fascinating HUIP mining.

A third-party replication service for dynamic hidden databases

Much data on the web is available in hidden databases. Users browse their contents by sending search queries to form-based interfaces or APIs. Yet, hidden databases just return the top-k result entries and limit the number of queries per time interval. Such access restrictions constrict those tasks that require many/specific queries or need to access many/all data entries. For a temporary solution, an unrestricted local snapshot can be created by crawling the hidden database. Yet, keeping the snapshot permanently consistent is challenging due to the access restrictions of its origin. In this paper, we propose a replication approach providing permanent unrestricted access to the local copy of a hidden database with dynamic changes. To this end, we present an algorithm to effectively crawl hidden databases that outperforms the state of the art. Furthermore, we propose a new way to continuously control the consistency of the replicated database in an efficient manner. We also introduce the cloud-based architecture of a replication service for hidden databases. We show the effectiveness of the approach through a variety of reproducible experimental evaluations.

Research on Intelligent Algorithm of Identity Authentication Based on Facial Features

Identity recognition is a research hotspot in the information age. Nowadays, more and more occasions require identity recognition, especially in smart home. Identity recognition of the head of the household can avoid many troubles, such as home identification and network information authentication. Nowadays, in smart home identification, especially based on face recognition, system authentication is basically through feature matching. Although this method is convenient and quick to use, it lacks intelligence. Nowadays, for the make‐up, facelift, posture, and other differences, the accuracy of the system is greatly reduced. In this paper, the face recognition method is used for identity authentication. Firstly, the AdaBoost learning algorithm is used to construct the face detection and eye detection classifier to realize the detection and localization of the face and eyes. Secondly, the two‐dimensional discrete wavelet transform is used to extract facial features and construct a personal face dynamic feature database. Finally, an improved elastic template matching algorithm is used to establish an intelligent classification method for dynamic face elasticity models. The simulation shows that the proposed method can intelligently adapt to various environments without reducing the accuracy.

A Predictive Multi-Tenant Database Migration and Replication in the Cloud Environment

With the rapid adoption of multi-tenant databases, the cloud provider consolidates multiple tenants’ database on server machines, where the tenants share a common application and database instances. To ensure the quality of service (QoS) for the leased resources, both sides (i.e., the user and the provider) create a Service Level Agreement (SLA). Higher SLA violations result in high SLA contractual penalties and increase the possibility of losing the tenant. In addition, the unusual workload patterns of each tenant transactions require seamless adjustments due to the sudden burden changes and variability. As a result, to satisfy simultaneously availability and performance tenant requirements, it is necessary to perform reliable tenant migration and replication to distribute the workload to a flexible set of sites and avoid SLA violations. In this research, a cluster-based multi-tenant database management system (CB-MT DBMS) is proposed, which takes the migration and replication decisions in advance by monitoring and acting before the violation of the SLA occurs. In addition, a dynamic proactive multi-tenant database migration and replication MTDB-MR algorithm is proposed to reduce collisions and inconsistencies between migration and replication decisions for a group of violated tenants. Experimental results show that the proposed MTDB-MR algorithm is the ideal candidate for migration and replication of the violated multi-tenant databases, as it minimizes the total number of SLA violations, the number of multi-tenant clients SLA violations, client sites average response time and total execution time of each multi-tenant client site as compared to the previous algorithms.

IDSA: An Efficient Algorithm for Skyline Queries Computation on Dynamic and Incomplete Data With Changing States

Skyline queries have been widely used as an effective query tool in many contemporary database applications. The main concept of skyline queries relies on retrieving the non-dominated tuples in the database which are known skylines. In most database applications, the contents of the databases are dynamic due to the continuous changes made towards the database. Typically, the changes in the contents of the database occur through data manipulation operations (INSERT and/or UPDATE). Performing these operations on the database results in invalidating the most recent skylines before changes are made on the database. Furthermore, the presence of incomplete data in databases becomes frequent phenomena in recent database applications. Data incompleteness causes several challenges on skyline queries such as losing the transitivity property of the skyline technique and the test dominance process between tuples being cyclic . Reapplying skyline technique on the entire updated incomplete database to determine the new skylines is unwise due to the exhaustive pairwise comparisons. Thus, this paper proposes an approach, named Incomplete Dynamic Skyline Algorithm (IDSA) which attempts to determine the skylines on dynamic and incomplete databases. Two optimization techniques have been incorporated in IDSA, namely: pruning and selecting superior local skylines. The pruning process attempts to exploit the derived skylines before the INSERT/UPDATE operation made on the database to identify the new skylines. Moreover, selecting superior local skylines process assists in further eliminating the remaining non-skylines from further processing. These two optimization techniques lead to a large reduction in the number of domination tests due to avoiding re-computing of skylines over the entire updated database to derive the new skylines. Extensive experiments have been accomplished on both real and synthetic datasets, and the results demonstrate that IDSA outperforms the existing solutions in terms of the number of domination tests and the processing time of the skyline operation.

Method of building transformer acoustic diagnosis database based on multi physical field coupling modeling

In this paper, a method of building dynamic acoustic diagnosis database of dry-type transformer is proposed. The method is mainly combined with collecting the basic noise of transformer and simulating the acoustic signals of transformer under different working conditions to improve the fault database of transformer. Due to the incomplete working conditions on site, the simulated working conditions are relatively complete, and the marked acoustic signal file is generated, and then form the composition of the database. This paper focuses on the finite element simulation analysis of transformer vibration and noise radiation based on COMSOL, and the idea of establishing transformer biological diagnosis database. Improve the efficiency of transformer fault design and research, and provide reliable and convenient data services for researchers.

Immigrant earnings returns to post‐migration education: Evidence for Canada, 1999–2013

AbstractUsing the recently created Canadian Employer–Employee Dynamics Database (CEEDD), we analyze the earnings returns to investments in post secondary education by immigrants made after arrival in Canada. Fixed effects estimation results suggest that postsecondary education (PSE) in Canada increases annual earnings of men and women by 21.9% and 32.8%, respectively, for those enrolled full time and by 11.2% and 21.0% for those enrolled part time, respectively. Earnings effects of PSE are larger for immigrants admitted in the skilled worker category relative to family class immigrants and to refugees and are increasing in the level of education of the immigrants at landing. Immigrants from non‐traditional source countries generally have larger returns to PSE than immigrants from English‐language source countries, such as the US and the countries of Northwestern Europe.

МОДЕЛИРОВАНИЕ ВОЗМУЩЕНИЙ ПРИ ИССЛЕДОВАНИИ УПРАВЛЯЮЩИХ СИСТЕМ

The article is devoted to questions of synthesis of full-scale - model realizations of data series on the basis of natural data for modeling of controllable and uncontrollable influences at research of operating and projected control systems, and also in training systems of computer training. The possibility of formation of model effects on the basis of joint use of multivariate dynamic databases and natural data simulator is shown. Dynamic databases store information that characterizes the typical representative situations of systems in the form of special functions - generating functions. Multiple variability of dynamic databases is determined by the type of the selected generating function, the methods of obtaining parameters (coefficients) of this function, as well as the selected accuracy of approximation. The situation models recovered by generating functions are used as basic components (trends) in the formation of the resulting full-scale - model implementations and are input into the natural data simulator. The data simulator allows for each variant of initial natural data to form an implementation of the perturbation signal with given statistical properties on a given simulation interval limited by the initial natural implementation. This is achieved with the help of a two-circuit structure, where the first circuit is responsible for evaluation and cor-rection of initial properties of the natural signal, and the second - for iterative correction of deviations of properties of the final implementation from the specified ones. The resulting realizations reflect the properties of their full-scale components, which are difficult to describe by analytical models, and are supplemented by model values, allowing in increments to correct the properties to the specified ones. The given approach allows to form set of variants of course of processes on the basis of one situation with different set degree of uncertainty and conditions of functioning.

Immigration and firm productivity: evidence from the Canadian Employer-Employee Dynamics Database

Previous studies on the impact of immigration on productivity in developed countries remain inconclusive, and most analyses are abstracted from firms where production actually takes place. This study examines the empirical relationship between immigration and firm-level productivity in Canada. It uses the Canadian Employer-Employee Dynamics Database that tracks firms over time and matches firms with their employees. The study finds that there is a positive association between changes in the share of immigrants in a firm and changes in firm productivity. This positive effect of immigration on firm productivity is small, but it is stronger over a longer period. The effect tends to be larger for low-skilled immigrants as compared with highly-skilled workers, as firm productivity growth is more strongly associated with changes in the share of recent immigrants (relative to established immigrants), and immigrants who intended to work in non-high skilled occupations (relative to immigrants who intended to work in high-skilled occupations). Those differences are more pronounced in technology-intensive and knowledge-based industries. Immigration is found to have little estimated effects on capital intensity in a firm. Finally, this study finds that high skill and lower skill immigrants have similar effects on average worker earnings arising from the positive productivity effect of immigration, but only skilled immigrants are associated with higher firm profits.

Verifiable database supporting keyword searches with forward security

The primitive of verifiable database (VDB) enables the integrity of database when a resource-limited client outsources a huge dynamic database on an untrusted cloud server. Meanwhile, the client could perform various types of queries on the database such as index-based queries and keyword-based queries. In the real-world applications, it is more suitable to consider VDB schemes supporting full updating operations (i.e., replacement, insertion, and deletion). Inherently, forward/backward-secure searchable encryption schemes should be adopted to incorporate VDB schemes. In this paper, we first attempt to address the problem of how to perform forward-secure keyword searching on VDB schemes. Moreover, we present a concrete VDB construction that can support forward-secure keyword search and full updating operations. Security and efficiency analysis demonstrate that the proposed VDB schemes can achieve the desired security properties with high efficiency.

A Dynamic Searchable Symmetric Encryption Scheme for Multiuser with Forward and Backward Security

Dynamic Searchable Symmetric Encryption for Multiuser (M-DSSE) is an advanced form of symmetric encryption. It extends the traditional symmetric encryption to support the operations of adding and deleting the encrypted data and allow an authenticated group of data users to retrieve their respective desired encrypted data in the dynamic database. However, M-DSSE would suffer from the privacy concerns regarding forward and backward security. The former allows an attacker to identify the keywords contained in the added data by lunching file-injection attacks, while the latter allows to utilize the search results and the deleted data to learn the content. To our knowledge, these privacy concerns for M-DSSE have not been fully considered in the existing literatures. Taking account of this fact, we focus on the dynamic searchable symmetric encryption for multiuser meeting the needs of forward and backward security. In order to propose a concrete scheme, the primitives of Pseudorandom Functions (PRF) and the Homomorphic Message Authenticator (HMAC) are employed to construct the inverted index and update the search token. The proposed scheme is proven secure in the random model. And the performance analysis shows that the proposed scheme achieves the enhanced security guarantees at the reasonable price of efficiency.

Arabic real time entity resolution using inverted indexing

Arabic datasets that have two or more records for the same world entity (i.e. person, object, etc.) make institutions suffer from low quality and degraded performance due to duplication in their Arabic datasets without having any mechanism for detecting these duplicates. The operation that distinguishes records for the same real-world entity is called Entity Resolution (ER). It is considered as a tool for linking records across databases as well as for matching query records with existing databases in real-time. Indexing is a major step in the ER process that aims at reducing the search space. Several indexing techniques are available for use with the ER process in general for English Databases. However, such techniques are not validated if they work well with other languages, such as Arabic. The Dynamic Similarity Aware Inverted Index (DySimII) is one of the indexing techniques that are utilized with dynamic databases to match query records in real time and is demonstrated to work well with English language. In this paper, we propose a framework—Arabic Real Time Entity Resolution (ARTER)—that uses DySimII with Arabic databases to perform real time ER. We also examine using different string similarity functions required for comparing records in the matching process for the aim of evaluating which similarity function is more suitable for comparing Arabic strings. A real-world Arabic database is used to conduct our experimental evaluation where two stemmers and three similarity functions are used to see the effect on DySimII with Arabic dataset. The results represent that matching accuracy is improved using Asem stemmer when the number of corrupted attributes is increased, also testing the three similarity functions show that using winkler similarity function provides better matching accuracy while N-gram provides better results when used with Asem stemmer.

Optimization of Ship Language Information Processing Systems Based on Artificial Intelligence Technology

Feng, M.X., 2020. Optimization of ship language information processing systems based on artificial intelligence technology. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 179–182. Coconut Creek (Florida), ISSN 0749-0208.The movement and traffic management of ocean-going ships rely on language information processing systems. With the development of artificial intelligence (AI) and information technology, the intelligent control of ocean ships has realized the real-time, complete and accurate data processing. Based on the AI analysis technology, this paper aims to optimize the ship language information processing systems. The research results show that the language databases equipped in ocean-going ships include static databases, dynamic databases and historical databases; the ship language information platform can support the collaborative work of various different applications, helping to realize the data exchange and information integration between different language information systems; compared with the traditional language information processing system, the AI-based system consumes less time, and its query, retrieval and response to language data are faster. The research findings provide a theoretical basis for accelerating the application of the AI in ship navigation efficiency and safety.

Dislocation density-based plasticity model from massive discrete dislocation dynamics database

We present a dislocation density-based strain hardening model for single crystal copper through a systematic coarse-graining analysis of more than 200 discrete dislocation dynamics (DDD) simulations of plastic deformation under uniaxial tension. The proposed constitutive model has two components: a generalized Taylor relation connecting resolved shear stresses to dislocation densities on individual slip systems, and a generalized Kocks-Mecking model for dislocation multiplication. The DDD data strongly suggests a logarithmic dependence of flow stress on the plastic shear strain rate on each slip system, and, equivalently, an exponential dependence of the plastic shear strain rate on the resolved shear stress. Hence the proposed generalized Taylor relation subsumes the Orowan relation for plastic flow. The DDD data also calls for a correction to the Kocks-Mecking model of dislocation multiplication to account for the increase of dislocation density on slip systems with negligible plastic shear strain rate. This is accomplished by allowing the multiplication rate on each slip system to include contributions from the plastic strain rates of the two coplanar slip systems. The resulting constitutive model successfully captures the strain hardening rate dependence on the loading orientation as predicted by the DDD simulations, which is also consistent with existing experiments.

Contesting Science That Silences: Amplifying Equity, Agency, and Design Research in Literacy Teacher Preparation

ABSTRACTIn this article, we argue that the “science of reading” (SOR) construct is being used to shape the future of literacy teacher preparation and silence the voices and work of literacy teacher education researchers to the detriment of quality science, quality teaching, and quality teacher preparation. First, we briefly inspect the SOR movement in terms of its historical roots in experimental psychology. Next, we examine the claims being made by SOR advocates regarding the absence of attention to the SOR literature in teacher preparation programs, and the related claims for the negative consequences that occur when these so‐called underprepared teachers enter the workforce. Then, we present literature reviews, drawn from a large and dynamic database of research on literacy teacher preparation (over 600 empirical studies that were published between 1999 and 2018); the studies in the database have been excluded from the SOR. Finally, we conclude with a discussion of equity, agency, and design as a pathway forward in improving literacy teacher preparation.

BIM-DLCA: An integrated dynamic environmental impact assessment model for buildings

Life cycle assessment (LCA) is an important and widely used environmental impact quantification methodology. Recently, temporal variations have generated renewed interests in LCA, with dynamic LCA (DLCA) becoming a new research trend. Traditional data collection approaches are considerably time and effort intensive, with major challenges associated with the acquisition of high quality data that are available. This study integrates building information modeling (BIM) with DLCA to develop a new assessment model for buildings. It follows the basic LCA paradigm and includes five parts: goal and scope definition, BIM module, dynamic database, dynamic assessment, and interpretation. In the BIM-DLCA model, BIM was used to extract material information and the construction schedule. A number of analysis software and simulation tools were used to quantify the temporal elementary flows throughout the lifecycle of the building. Various forms of dynamic data were aggregated to create a database that supports dynamic assessment. A case study was used to testify the BIM-DLCA model and the case study's outcomes indicate that BIM has the potential to simplify data collection process and support DLCA. The proposed BIM-DLCA model is operable in practice. With the rapid development of digitalization and intelligence, the proposed BIM-DLCA model lays an important foundation for future assessment studies and promotes smart LCA.

Human and machine validation of 14 databases of dynamic facial expressions

With a shift in interest toward dynamic expressions, numerous corpora of dynamic facial stimuli have been developed over the past two decades. The present research aimed to test existing sets of dynamic facial expressions (published between 2000 and 2015) in a cross-corpus validation effort. For this, 14 dynamic databases were selected that featured facial expressions of the basic six emotions (anger, disgust, fear, happiness, sadness, surprise) in posed or spontaneous form. In Study 1, a subset of stimuli from each database (N = 162) were presented to human observers and machine analysis, yielding considerable variance in emotion recognition performance across the databases. Classification accuracy further varied with perceived intensity and naturalness of the displays, with posed expressions being judged more accurately and as intense, but less natural compared to spontaneous ones. Study 2 aimed for a full validation of the 14 databases by subjecting the entire stimulus set (N = 3812) to machine analysis. A FACS-based Action Unit (AU) analysis revealed that facial AU configurations were more prototypical in posed than spontaneous expressions. The prototypicality of an expression in turn predicted emotion classification accuracy, with higher performance observed for more prototypical facial behavior. Furthermore, technical features of each database (i.e., duration, face box size, head rotation, and motion) had a significant impact on recognition accuracy. Together, the findings suggest that existing databases vary in their ability to signal specific emotions, thereby facing a trade-off between realism and ecological validity on the one end, and expression uniformity and comparability on the other.

Multidimensional financial development, exporter behavior and export diversification

Financial development shapes export sector performance because exporters need external finance and face credit constraints. Previous empirical research has relied largely on single-country studies. The Exporter Dynamics Database (EDD), which features firm-level exports from over 60 countries, reveals differences in the microstructure of the export sector across countries. In this paper, we first provide new evidence that these differences are related to cross-country variation in financial development and structure. Second, we combine the EDD and multidimensional data on financial development with a global database on export diversification. This study is the first to examine how macrolevel export diversification is determined by the microcharacteristics of the export sector. This approach is novel in the empirical literature on export diversification. According to our cross-country analysis, access to domestic financial services positively contributes to export diversification by increasing the number of small exporters, as financial services ease the credit constraints these exporters face.

SARS-CoV-2 (COVID-19) structural and evolutionary dynamicome: Insights into functional evolution and human genomics

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has challenged the speed at which laboratories can discover the viral composition and study health outcomes. The small ∼30-kb ssRNA genome of coronaviruses makes them adept at cross-species spread while enabling a robust understanding of all of the proteins the viral genome encodes. We have employed protein modeling, molecular dynamics simulations, evolutionary mapping, and 3D printing to gain a full proteome- and dynamicome-level understanding of SARS-CoV-2. We established the Viral Integrated Structural Evolution Dynamic Database (VIStEDD at RRID:SCR_018793) to facilitate future discoveries and educational use. Here, we highlight the use of VIStEDD for nsp6, nucleocapsid (N), and spike (S) surface glycoprotein. For both nsp6 and N, we found highly conserved surface amino acids that likely drive protein–protein interactions. In characterizing viral S protein, we developed a quantitative dynamics cross-correlation matrix to gain insights into its interactions with the angiotensin I–converting enzyme 2 (ACE2)–solute carrier family 6 member 19 (SLC6A19) dimer. Using this quantitative matrix, we elucidated 47 potential functional missense variants from genomic databases within ACE2/SLC6A19/transmembrane serine protease 2 (TMPRSS2), warranting genomic enrichment analyses in SARS-CoV-2 patients. These variants had ultralow frequency but existed in males hemizygous for ACE2. Two ACE2 noncoding variants (rs4646118 and rs143185769) present in ∼9% of individuals of African descent may regulate ACE2 expression and may be associated with increased susceptibility of African Americans to SARS-CoV-2. We propose that this SARS-CoV-2 database may aid research into the ongoing pandemic.

Epidemics of HIV Infection among Heavy Drug Users of Depressants Only, Stimulants Only, and Both Depressants and Stimulants in Mainland China: A Series, Cross-Sectional Studies.

Background: Heavy drug users was a global consensus high-risk population of HIV infection. However, the specific impact of drug on HIV infection has not yet been established. Depressants and stimulants were most widely used drugs in mainland China, and mix use of the two drugs was also serious. We assessed the HIV infection rate and trends in heavy drug users by analyzing data from the National Dynamic Management and Control Database for Drug Users (NDMCDDU). Methods: All heavy drug users with HIV test results in NDMCDDU from 2008 to 2016 were grouped into depressants only group (DOG), stimulants only group (SOG), and both depressants and stimulants group (DSG). We used joinpoint regression to examine trends of HIV infection rates. Multivariable logistic regression was used to examine factors related to HIV infection. Results: A total of 466,033 heavy drug users with 9522 cases of HIV infection were included in this analysis. HIV infection rate was estimated at 2.97% (95% CI 2.91–3.04%) of 265,774 users in DOG, 0.45% (95% CI 0.42–0.49%) of 140,895 users in SOG, and 1.65% (95% CI 1.55–1.76%) of 59,364 users in DSG. In DOG, a U-shaped curve of HIV infection rate decreased from 3.85% in 2008 to 2.19% in 2010 (annual percent change (APC) −12.9, 95% CI −19.3–−6.0, p < 0.05), then increased to 4.64% in 2016 (APC 8.3, 95% CI 6.1–10.4, p < 0.05) was observed. However, SOG and DSG showed consistent increases from 0.15% in 2008 to 0.54% in 2016 (APC 8.2, 95% CI 4.8–11.8, p < 0.05) and from 0.78% in 2008 to 2.72% in 2016 (APC 13.5, 95% CI 10.7–16.4, p < 0.05), respectively. HIV infection rate of DOG in the southwest region presented a U-shaped trend. All groups showed significant increases in HIV infection in east and central regions. Conclusions: The U-shaped curve for HIV infection rate among DOG users and consistent increases among SOG and DSG users implies drug abuse is still a critical focus of HIV infection in China. It is urgently needed to reassess the effectiveness of current strategies on HIV prevention and control among drug users.