Conventional Database Research Articles

QB4MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web

The accessibility of devices that track the positions of moving objects has attracted many researchers in Mobility Online Analytical Processing (Mobility OLAP). Mobility OLAP makes use of trajectory data warehousing techniques, which typically include a path of moving objects at a particular point in time. The Semantic Web (SW) users have published a large number of moving object datasets that include spatial and non-spatial data. These data are available as open data and require advanced analysis to aid in decision making. However, current SW technologies support advanced analysis only for multidimensional data warehouses and Online Analytical Processing (OLAP) over static spatial and non-spatial SW data. The existing technology does not support the modeling of moving object facts, the creation of basic mobility analytical queries, or the definition of fundamental operators and functions for moving object types. This article introduces the QB4MobOLAP vocabulary, which enables the analysis of mobility data stored in RDF cubes. This article defines Mobility OLAP operators and SPARQL user-defined functions. As a result, QB4MobOLAP vocabulary and the Mobility OLAP operators are evaluated by applying them to a practical use case of transportation analysis involving 8826 triples consisting of approximately 7000 fact triples. Each triple contains nearly 1000 temporal data points (equivalent to 7 million records in conventional databases). The execution of six pertinent spatiotemporal analytics query samples results in a practical, simple model with expressive performance for the enabling of executive decisions on transportation analysis.

Novel approaches on bulk‐loading of large scale spatial datasets

SummaryIndex structures that are bulk‐loaded adaptively based on the query workload have been the subject of many database research, primarily in conventional databases holding one‐dimensional data. Similar problems are also valid for spatial databases, and novel approaches are needed in these systems. In this study, we first consider the adaptive indexing approaches in a broad sense and then specifically investigate some possible forms of adaptivity on bulk‐loading of large spatial datasets while retaining some core mechanics of well‐known spatial data structures. In our first design, we examined how the internal configuration of the R‐tree spatial index could be better for various workload properties, such as the workload's average aspect ratio. Our experiments observed that Adaptive Sort‐Tile‐Recursive (STR)‐tree performs better than the original STR‐tree. Our second design, the main contribution of this paper, focused on incremental indexing, another form of adaptivity. We concentrate on the data‐to‐insight latency and loaded spatial data faster within this multi‐stage process. We used the Partitioned B‐tree loaded with Morton codes of spatial objects to achieve this improvement by compromising spatial proximity. Finally, the response time of the first query in our design has been better than using the ordinary STR‐tree index by a factor of 34%.

Hash Tables for a Digital Lexicon

This paper deals with the construction of digital lexicons within the scope of Natural Language Processing. Data Structures called Hash Tables have demonstrated to generate good results for Natural Language Interface for Databases and have data dispersion, response speed and programming simplicity as main features. The storage of the desired information is done by associating a key through the hashing functions that is responsible for distributing the information in this table. The objective of this paper is to present the tool called Visual TaHs that uses a sparse table to a real lexicon (Lexicon of Herbs), improving performance results of several implemented hash functions. Such structure has achieved satisfactory results in terms of speed and storage when compared to conventional databases and can work in various media, such as desktop, Web and mobile.

Using Conventional Bibliographic Databases for Social Science Research: Web of Science and Scopus are not the Only Options

Although large citation databases such as Web of Science and Scopus are widely used in bibliometric research, they have several disadvantages, including limited availability, poor coverage of books and conference proceedings, and inadequate mechanisms for distinguishing among authors. We discuss these issues, then examine the comparative advantages and disadvantages of other bibliographic databases, with emphasis on (a) discipline-centered article databases such as EconLit, MEDLINE, PsycINFO, and SocINDEX, and (b) book databases such as Amazon.com, Books in Print, Google Books, and OCLC WorldCat. Finally, we document the methods used to compile a freely available data set that includes five-year publication counts from SocINDEX and Amazon along with a range of individual and institutional characteristics for 2,132 faculty in 426 U.S. departments of sociology. Although our methods are time-consuming, they can be readily adopted in other subject areas by investigators without access to Web of Science or Scopus (i.e., by faculty at institutions other than the top research universities). Data sets that combine bibliographic, individual, and institutional information may be especially useful for bibliometric studies grounded in disciplines such as labor economics and the sociology of professions. Policy highlightsWhile nearly all research universities provide access to Web of Science or Scopus, these databases are available at only a small minority of undergraduate colleges. Systematic restrictions on access may result in systematic biases in the literature of scholarly communication and assessment.The limitations of the largest citation databases influence the kinds of research that can be most readily pursued. In particular, research problems that use exclusively bibliometric data may be preferred over those that draw on a wider range of information sources.Because books, conference papers, and other research outputs remain important in many fields of study, journal databases cover just one component of scholarly accomplishment. Likewise, data on publications and citation impact cannot fully account for the influence of scholarly work on teaching, practice, and public knowledge.The automation of data compilation processes removes opportunities for investigators to gain first-hand, in-depth understanding of the patterns and relationships among variables. In contrast, manual processes may stimulate the kind of associative thinking that can lead to new insights and perspectives.

AN EFFICIENT ND-POINT DATA STRUCTURE FOR QUERYING FLOOD RISK

Abstract. Governments use flood maps for city planning and disaster management to protect people and assets. Flood risk mapping projects carried out for these purposes generate a huge amount of modelling results. Previously, data submitted are highly condensed products such as typical flood inundation maps and tables for loss analysis. Original modelling results recording critical flood evolution processes are overlooked due to cumbersome management and analysis. This certainly has drawbacks: the ‘static’ maps impart few details about the flood; also, the data fails to address new requirements. This significantly confines the use of flood maps. Recent development of point cloud databases provides an opportunity to manage the whole set of modelling results. The databases can efficiently support all kinds of flood risk queries at finer scales. Using a case study from China, this paper demonstrates how a novel nD-PointCloud structure, HistSFC, improves flood risk querying. The result indicates that compared with conventional database solutions, HistSFC holds superior performance and better scalability. Besides, the specific optimizations made on HistSFC can facilitate the process further. All these indicate a promising solution for the next generation of flood maps.

A Knowledge Graph Approach for the Detection of Digital Human Profiles in Big Data

Digital transformation is a long process that changes the managing human profiles in both offline and online approaches. This generates the amount of huge data stored in both relational databases and many others like social networks or graph databases. To exploit effectively big data, several measures and algorithms in Picture Fuzzy Graph (PFG) are applied to solve many complex problems in the real-world problems. The paper has presented a novel approach using a knowledge graph to find a human profile including the detection of humans in large data. In the proposed model, digital human profiles are collected from conventional databases combination with social networks in real-time, and a knowledge graph is created to represent complex-relational user attributes of human profile in large datasets. PFG is applied to quantify the degree centrality of nodes. Furthermore, techniques and algorithms on the graph are used to classify the nodes. The experiments in the knowledge graph implemented to illustrate the proposed model. The main contribution in this paper is to identify the right persons among complex-relational groups, locations in real-time based on large datasets on the social networks, relational databases and graph databases.

A systematic approach to estimate an inter-unit common-cause failure probability

Inter-unit common-cause failure (CCF) analysis is one of the technical issues for a probabilistic risk assessment for multiple nuclear reactors in a site. It is because inter-unit CCF events are rare and the symmetric assumption in intraunit CCF analysis is no longer valid. To overcome these difficulties, we propose a systematic approach to estimate the inter-unit CCF probability using conventional CCF databases and engineering judgments. The proposed approach explicitly considers two sources of asymmetry in inter-unit CCF: the commonalities of common-cause coupling factors and surveillance tests. The proposed approach starts from a symmetric CCF probability of components under a nonstaggered test scheme and extends it to take the asymmetry into account. The proposed approach is applied to the CCF analysis of the hypothetical case of three nonidentical emergency diesel generators and is compared with the conventional CCF analysis. The proposed approach can reduce the conservativeness in the inter-unit CCF probabilities estimated by the conventional CCF analysis and improve the quality of multiunit probabilistic risk assessment (PRA), where inter-unit CCF events significantly contribute to the site risk. The proposed approach demonstrates its applicability to an inter-unit CCF analysis where the asymmetries play an important role in inter-unit CCF probability.

Research on the Multidimensional Data Cube Method of the Quantum Science Experiment Satellite

Scientific experiment satellite should be target-oriented, which requires several mission teams to develop a phase experiment plan and take operations according to the actual situation. These experiment plans depend on the data generated by mission operation and are integrated from the strategy set derived from some data analysis of each subsystem of the scientific satellite mission. Along with the operation of QUESS on orbit, a large amount of operational data will be generated. To solve this problem, current mainstream approach mainly relies on log statistics and data statistics of conventional database systems. However, these methods consume more energy and time, and require more professional skills of analysts, which cannot meet the requirements of multi-angle and multi-granularity research and judgment tasks. Moreover, the scalability of the methods is very poor. When the observation angle of the problem changes, it is often necessary to reorganize the statistical analysis of the data. In view of the above situations, a multi-dimensional data modeling and analysis method based on data cube is proposed, which can face different topics and support multi-level, multi-angle and multi-granularity statistical analysis of data, providing good support for decision makers.

Experimental investigation on dispersing graphene-oxide in biodiesel/diesel/ higher alcohol blends on diesel engine using response surface methodology

ABSTRACT Lower alcohols have long been the figureheads of diesel/biodiesel additives in characterizing renewable fuels. Next-generation alcohol like n-octanol occupied the reified position due to their better fuel properties. In this paper, combustion, performance and, emission of different graphene-oxide nanoparticles (nanoGO) added jatropha biodiesel, n-octanol and petrodiesel blends are investigated in a 4-stroke DI diesel engine. This article also aims to optimize the engine inputs accountable for better performance and emission characteristics of a diesel engine running with nanoGO dispersed biodiesel/diesel/higher alcohol blends. Full Factorial Design-based Response Surface Methodology (RSM) is utilized to model the experiments using Design-Expert software to optimize engine responses. Validation of the developed model is carried out using sophisticated error and performance metrics, namely, TheilU2, Kling-Gupta Efficiency (K-G Eff), and Nash-Sutcliffe coefficient of efficiency (N-S Eff) along with the conventional statistical database. The model optimized engine inputs of 3.898% n-Octanol, and 49.772 ppm nanoGO at 99.2% load with a desirability index of 0.997 as the optimum engine parameters. The experimental validation revealed that the model optimized blend at full load witnessed a reduction of 15.6% CO, 21.78% HC.u, and 3.26% NOx emission compared to petrodiesel. However, a slight increase in brake specific energy consumption (2.95%) is also recorded because of the lower heating value of the blend.

Shear Strength of HVFA-SCC Beams without Stirrups

Various concretes have been developed to meet the principles of sustainability. High volume fly ash-self compacting concrete (HVFA-SCC) is one example. The utilization of HVFA-SCC for structural applications, however, raises a concern among designers: that HVFA-SCC may not be as strong as conventional concrete when carrying shear forces. This concern is related to slow strength development and relatively smoother crack surface formation in HVFA-SCC, which, consequently, reduces the aggregate interlock mechanism contribution to the shear strength. In this respect, the design code for estimating the shear strength of HVFA-SCC may not be valid for the reason that the code was developed on the basis of the conventional concrete database. Previous research on the shear strength of HVFA-SCC was limited and no database can be extracted to justify the validity of the shear design code. This research was conducted to clarify the suitability of shear design code for HVFA-SCC. The research began with a limited laboratory investigation, followed by a numerical investigation to expand the range of results. Two types of HVFA-SCC beams with dimensions of 100 mm × 150 mm × 1700 mm were prepared, utilizing 50% and 60% fly ash. The shear behavior obtained from the laboratory investigations was then numerically modeled with the help of 3D ATENA Engineering software. The numerical model was used to explore the influence of reinforcement ratio, shear span to beam effective depth ratio, and beam size on the shear strength of the HVFA-SCC beam. The results were compared with the shear strength database of conventional and unconventional concrete beams to judge if the provisions in the design code can be applied to the shear design of an HVFA-SCC beam. The results confirm that the ACI shear design code is applicable for HVFA-SCC.

IoT-CCAC: a blockchain-based consortium capability access control approach for IoT.

Access control is a critical aspect for improving the privacy and security of IoT systems. A consortium is a public or private association or a group of two or more institutes, businesses, and companies that collaborate to achieve common goals or form a resource pool to enable the sharing economy aspect. However, most access control methods are based on centralized solutions, which may lead to problems like data leakage and single-point failure. Blockchain technology has its intrinsic feature of distribution, which can be used to tackle the centralized problem of traditional access control schemes. Nevertheless, blockchain itself comes with certain limitations like the lack of scalability and poor performance. To bridge the gap of these problems, here we present a decentralized capability-based access control architecture designed for IoT consortium networks named IoT-CCAC. A blockchain-based database is utilized in our solution for better performance since it exhibits favorable features of both blockchain and conventional databases. The performance of IoT-CCAC is evaluated to demonstrate the superiority of our proposed architecture. IoT-CCAC is a secure, salable, effective solution that meets the enterprise and business’s needs and adaptable for different IoT interoperability scenarios.

Mining crowdsourced data on bicycle safety critical events

Cycling has become a popular transportation mode for short term trips. Due to the high exposure bicycle trips, the number of collisions and near miss events has been increasing significantly. This study explores the pattern of the bicycle-related collision or near miss events by using a unique crowdsourced dataset collected from BikeMaps.org. The dataset not only contains near miss events, which are not included in the conventional state-maintained crash databases, and it also includes the psychological impact of the event on the cyclist. The taxicab correspondence analysis (TCA) results reveal patterns for bike-related collision or near miss events and associated impact on the cyclists involved. Several factors such as inclement weather, windy condition, poor lighting conditions, wet ground, loose sand, or dirt pavement are associated with the increasing probability of the collision or near-miss events. The study indicates that collision or near miss events have a greater impact on cyclists if the events occurred when cyclists already have taken extra caution while cycling. These cyclists tend to cycle less and be more careful after these events. Interestingly, the results find that frequent cyclists are not psychologically affected by collisions occurred during recreational trips. The finding of this study could help researchers further understand bike collisions/near miss events and provide better countermeasures to mitigate the frequency of bike collisions.

MACHINE LEARNING BASED STUDENT PERFORMANCE ANALYSIS SYSTEM

The academic output of the student is normally stored in various formats in the student administration system (files, documents, records, photographs and other formats). These data can be collected for valuable knowledge from the students. However, it is difficult to analyze the increasing amount of data of students through conventional statistical techniques and database management tools. For universities to gather valuable information, a tool is therefore required. This helpful knowledge can be used to predict the success of students. Leistungs analyze learning results is a framework that aims for success in the areas of student interest at various levels and dimensions. This paper proposes a complete structure as a rule-based recommendation method not only for analyzing and forecasting the success of students but also for presenting their reasons. The proposed system analyzes demographic details for pupils, studies and psychological features so that students, teachers and parents can collect all possible knowledge. To seek maximum accuracy in academic predictions across a range of powerful techniques of data mining. The system successfully recognizes the limitations of the student and makes adequate recommendations. The practical case study on 200 students indicates the excellent performance of the proposed system compared to the current framework.

Correlational Analysis of the Availability and Usage of Geographic Information Systems by Students and Lecturers an Higher Educational Institutions

There has been a rapid development of Information and communication Technologies (ICTs) globally, and this includes the Geographic Information Systems (GISs) which has evolved over the years and aimed at solving many geographic problems as fast and efficiently as possible. The importance of GIS to students and lecturers in higher educational institutions is it’s ability to easily analyse locations together with the conventional database systems. This paper assessed the correlation between the availability and use of GIS in Higher Educational Institutions (HEIs). The study is exploratory in nature and employed the use of questionnaires as instrument to collect data. Simple random sampling technique was used to select one hundred and ten (110) lecturers one thousand five hundred and seventy-nine (1579) students from a university. Findings revealed that GIS for analysis is still at its lowest ebb and hence calls for an action to motivate or encourage lecturers and students to use it. Though there are indications that GIS will be of great value in schools, students and lecturers they will be happy to use it for their teaching and learning. The study also revealed availability of GIS was not tied to its usage.

Development of an intelligent controller for robot-aided assessment and treatment guidance in physical medicine and rehabilitation

In this study, an intelligent controller was developed for a rehabilitation robot called DIAGNOBOT, which can be used for assessment and treatment in the rehabilitation of wrist and forearm. The controller has a decision support system structure strengthened with conventional statistical methods and databases. The controller uses the patient's biomechanical parameters to make an assessment and proposes a treatment in line with this. In accordance with the recommended treatment, it produces the control parameters, torque, and position information for the control of the rehabilitation robot. The system's ability of assessment and treatment was tested with voluntary patients. Through these test studies, the treatments recommended by the developed intelligent controller have been proven to have a very high rate of accuracy.

Tracking the diffusion of industrial symbiosis scholarship using bibliometrics: Comparing across Web of Science, Scopus, and Google Scholar

AbstractPrevious bibliometric analyses of industrial symbiosis (IS) research have focused on a limited body of literature owing to the scope of keyword searches or limitations of library databases. This study seeks to apply bibliometrics to explore broader, epistemological questions, particularly about the structure and geospatial development of IS as a sub‐field of industrial ecology. We also evaluate the benefits of using Google Scholar, in addition to the conventional databases Web of Science (WoS) and Scopus, for better understanding academic domains. By using WoS and Scopus, 805 articles on IS that met our criteria were identified, published in 212 journals from 1995 through 2018. On average, the cumulative number of relevant articles grew at an exponential rate of 18% per year—more than double the estimated growth of global scientific output. We observed the largest increases in articles that: (1) model the material and energy flows in IS clusters; (2) propose strategies and ideas for implementing symbiosis; and (3) evaluate the performance of IS networks. By the end of 2018, 54 countries were featured in IS articles retrieved from WoS and Scopus, with China as the single most studied country. The analysis of Google Scholar suggested that it can capture more IS articles than the conventional databases owing to its unique characteristic of searching the entire text of documents rather than solely their metadata as with WoS and Scopus. Google Scholar revealed IS discourse from additional countries and disciplines previously omitted, enabling a more acute view of its patterns of diffusion.

Conventional database management systems

Before the advent of Database Management Systems, computer programmers or developers spent a lot of time writing codes to create and manage databases also known as data files before venturing into the actual programming work, thereby making it possible for only trained computer professionals to operate and manipulate databases or data files. It left the programmers who are targeting high performance application to deal with first data integrity and security challenges, inefficient process to access and manipulate stored data, data recovery challenges in the case of crashes and many other challenges. In this paper, we present a review of the traditional database management system (DBMS).First; we gave a detailed explanation of the meaning, types and uses of a database management system. We highlighted the features of Conventional Database Management Systems and the challenges it faces today especially in the handling of high-profile data for many enterprise systems, hardware requirements, human resources, network challenges and costs are also discussed in this research. Finally, we propose a solution to the challenges facing the conventional database system. From our findings, we discovered that the Big Data technology is a potential solution to the limitations of the conventional database management systems. This research will be beneficial to scholars and researchers who are interested in Database management systems.

Machine Learning Models and Big Data Tools for Evaluating Kidney Acceptance

The rise of on-demand healthcare and the unprecedented growth of electronic health records has given rise to big data opportunities and data analysis using machine learning. The massive and disparate data management using conventional databases is incredibly challenging and expensive to manage. It often requires specialized analytical tools for developing advanced data-driven capabilities and performing data analytics. This paper explores the capability of an open-source framework ‘Apache Spark’ capable of processing large amounts of data on clusters of nodes to analyze Big data and integrate technologies to provide decision support systems in healthcare settings. Next, we propose machine learning models on top of Apache Spark to expedite the decision-making in allocating organs such as kidney selection for the right candidate, thus increasing donor utilization by locating a recipient within the allotted time. The proposed models help in identifying waitlisted candidates willing to accept kidneys that may otherwise be discarded.

MULTI-JOIN-ORDERING QUERY OPTIMIZATION ALGORITHM FOR HIVE WAREHOUSE WITH MAPREDUCE

According to the Digital Report of July, 2021, Billions of users around the world uses Mobile Phones, Internet, social media every second. This huge range of heterogeneous digital data is called Big Data, and is measured in terms of terabytes or petabytes. It is difficult to the conventional relational databases to handle these heterogeneous data for data analytics, but is still in use significantly in the growth of Big Data. To handle SQL-based structured queries, Hadoop is one of the prominent and well-suited solution that allows Big Data to be stored and processed. Hive support SQL queries on Hadoop. Hive warehouse, is the oldest SQL-engine on the top of the Hadoop framework and to store the processed data, it uses HDFS (Hadoop Distributed File System). On the Hadoop, MapReduce is an execution engine that executes SQL-based queries. In the Query Optimization, join ordering always plays a significant role because when the order of tables in joining operation is changed, execution time of the query is reduced to a greater extent. The main problem of the Hive is that it does not enhance the order of the join for an SQL-query and also does not give assurance for an optimal execution plan. Its time complexity is measured in exponential (Shan, Y., & Chen, Y., 2015).The main focus of this paper is to discover the finest join ordering solution for a Hive query optimization problem through appropriate search algorithms and to improve SQL-based Hive queries performance with MapReduce–based system.

SARS-Cov-2 Interactome with Human Ghost Proteome: A Neglected World Encompassing a Wealth of Biological Data.

Conventionally, eukaryotic mRNAs were thought to be monocistronic, leading to the translation of a single protein. However, large-scale proteomics have led to a massive identification of proteins translated from mRNAs of alternative ORF (AltORFs), in addition to the predicted proteins issued from the reference ORF or from ncRNAs. These alternative proteins (AltProts) are not represented in the conventional protein databases and this “ghost proteome” was not considered until recently. Some of these proteins are functional and there is growing evidence that they are involved in central functions in physiological and physiopathological context. Based on our experience with AltProts, we were interested in finding out their interaction with the viral protein coming from the SARS-CoV-2 virus, responsible for the 2020 COVID-19 outbreak. Thus, we have scrutinized the recently published data by Krogan and coworkers (2020) on the SARS-CoV-2 interactome with host cells by affinity purification in co-immunoprecipitation (co-IP) in the perspective of drug repurposing. The initial work revealed the interaction between 332 human cellular reference proteins (RefProts) with the 27 viral proteins. Re-interrogation of this data using 23 viral targets and including AltProts, followed by enrichment of the interaction networks, leads to identify 218 RefProts (in common to initial study), plus 56 AltProts involved in 93 interactions. This demonstrates the necessity to take into account the ghost proteome for discovering new therapeutic targets, and establish new therapeutic strategies. Missing the ghost proteome in the drug metabolism and pharmacokinetic (DMPK) drug development pipeline will certainly be a major limitation to the establishment of efficient therapies.