Relational Database Technology Research Articles

Buildings of Texas

This note describes an ongoing project at the University of Texas Libraries (UTL) that is transforming the way we think about place, people, and events in managing archival collections. This project was developed around a dataset donated by architectural historians, Gerald Moorhead and Mario Sánchez to the Alexander Architectural Archives. This dataset was collected by a team of researchers studying architecturally significant buildings for the two-volume publication, Buildings of Texas. Our team at UT Libraries has used it as a test-bed for geolocating built works in Texas and mapping our Architectural collections. This dataset presented a clear opportunity to develop map-based digital exhibitions and finding aids for archival material, but also posed several challenges due to naming ambiguities, vague building location descriptions, and repeated references to people and architectural firms that were difficult to disambiguate and interconnect. In an attempt to overcome these challenges and develop a set of methods for dealing with similar and related collections, our cross-disciplinary team is evaluating ways to develop a flat spreadsheet into a collection of inter-related datasets. Our goal was a system that could be managed more flexibly, be easily represented through spatial and non-spatial visualizations, and—crucially—contain references to concepts and typologies defined in widely-used ontologies. We have also sought ways to contribute our data as a local authority to both the Getty vocabularies and Wikidata, as a means to broaden representation and allow for multivocality and multiplicity. We have found unique advantages to a team-approach to the cleaning and data normalization process that transformed a single spreadsheet into a graph database. Our experimentation in this process has facilitated our own visualization of the connections between places, people, and events and, in turn, has informed the way we will present this material in our online exhibitions. In addition to the graph database, we explored traditional relational database technologies, which turned out to be much easier to use for mapping the data and managing it with GIS software. Throughout the project, we have been mindful of breaking down traditional modes of archival description, of contributing to but also looking beyond perceived authorities, and about the value of the work we do for architectural artifacts and landscapes, but also any cultural heritage community of practice seeking to meaningfully describe artifacts, events, people, and places over time.

A Centralized MongoDB-Based Repository Design for IIoT Data: The ecoKI Project*

One of the challenges of cyber-physical systems is the data acquisition in real- time through different communication protocols within the scope of the Industrial Internet of Things (IIoT). This IIoT data is mainly specified as big in amount, continuous in nature, unstructured, and ambiguous format. It is also enigmatic in terms of structure for an overview of the manufacturing plant and which data belongs to which sensor or machine. Presently, relational database technologies are inadequate to cater to such kinds of IIoT data due to their strict data structure and relations. For this reason, we presented the ontology-based data repository model implementation for the process industry using MongoDB, which is a cross- platform document-oriented, most sought-after ample data storage database system nowadays. Firstly, we have designed a JSON-based data-model schema for the IIoT data repository, which gathers the data with the help of one of the most commonly used communication protocols within the scope of IIoT, OPC UA. Secondly, our schema stores the meta-data and data using embedded referencing through the same data-model. Hence, the scalability, flexibility, and variety of the data storage system have increased. Lastly, with the execution of insertion and search queries, we show how fast to get the desired data element hierarchy or track any particular data, which becomes possible due to the proposed ontology. This effectiveness of the ontology-based data-model also minimize the overhead of the machine learning engineer job.

Big data and cloud computing: roles and relationships, techniques and tools

In the past years, the increase in data has been accompanied by rapid growth in various fields. It is difficult to analyze large volumes of data using traditional and relational database technology. Therefore, new databases have emerged, and for this reason, big data has become one of the new topics in IT and business today. Also, the cloud environment is increasingly used to store and process big data. Cloud processing refers to processing anything, including Big Data Analytics, on the "cloud". A "cloud" is a collection of high-powered servers from providers that can often view and query large data sets much faster than a regular computer. These two topics differ from each other in various aspects, including definition, collection references, usage method, form and format, and application. In this research, the dimensions and basic concepts, characteristics, tools and techniques, classification, and communication of data are examined. Big has been dealt with cloud computing, and in addition, storage systems, opportunities and challenges, and big data design principles in the cloud environment have been analyzed.

Distance learning quality assessment of universities based on interval monotonic decision tree algorithm

The standards and procedures of teaching quality evaluation systems improved with the continuous advancement of Video Internet of Things (VIoT) in the distance learning reform across colleges and universities. The old teaching quality rating approach is no longer adequate. We explore the idea of developing and designing the quality evaluation system for distance learning in this paper using an efficient interval monotonic decision tree algorithm. We use relational database technology to centralize the management of various knowledge elements in the VIoT system. It enables the VIoT system to perform operations like browse, delete, add, and modify knowledge that enhances knowledge transparency for designers and introduces intervals. We designed the interval monotonic decision tree algorithm with an evaluation domain partitioned. The proposed method creates the decision evaluation matrix, summarizes it, and obtains the evaluation conclusion using the complete weighting technique. Aspects of the system logic are tested using object-oriented software engineering, object-oriented design and analysis of requirements, and black-box testing.

The Time Machine in Columnar NoSQL Databases: The Case of Apache HBase

Not Only SQL (NoSQL) is a critical technology that is scalable and provides flexible schemas, thereby complementing existing relational database technologies. Although NoSQL is flourishing, present solutions lack the features required by enterprises for critical missions. In this paper, we explore solutions to the data recovery issue in NoSQL. Data recovery for any database table entails restoring the table to a prior state or replaying (insert/update) operations over the table given a time period in the past. Recovery of NoSQL database tables enables applications such as failure recovery, analysis for historical data, debugging, and auditing. Particularly, our study focuses on columnar NoSQL databases. We propose and evaluate two solutions to address the data recovery problem in columnar NoSQL and implement our solutions based on Apache HBase, a popular NoSQL database in the Hadoop ecosystem widely adopted across industries. Our implementations are extensively benchmarked with an industrial NoSQL benchmark under real environments.

Freshwater fish diversity database of Central India: Implementation and utility

The modern relational database technologies provide reasonable approaches to address the biodiversity problems facilitating storage, efficient searches and retrieval of information about species of interest. This contribution focuses on development of a digital database to view fish and fisheries information using integrated tools. To address storing and reporting of information on the fish diversity of the central states of India, MS Access relational database management system for backend and Visual Basic technologies for frontend under Windows operating platform were used. Development of the database was essential to store the data compiled from various published sources; to provide information-oriented services as well as to keep the data up-to-date with the recent information. Presently, the database covers 208 species of fishes belonging to 13 orders, 35 families and 82 genera from Madhya Pradesh; 155 species belonging to 11 orders, 30 families and 81 genera from Rajasthan and 144 species belonging to 11 orders, 30 families and 85 genera from Chhattisgarh. The application features of the database includes search, query and action command button tools that provide ability for retrieving and viewing the information of interest from the database. Keywords: Central India, Chhattisgarh, Database, Freshwater fish diversity, Madhya Pradesh, Rajasthan

МЕДИЧНА ІНФОРМАЦІЙНА СИСТЕМА МОНІТОРИНГУ СТАНУ ЗДОРОВ'Я НАСЕЛЕННЯ ІЗ ЗАХИСТОМ ПЕРСОНАЛЬНИХ ДАНИХ

Наведено результати розробки нової медичної інформаційної системи на основі баз даних для моніторингу ряду показників стану здоров'я населення. Інформаційна система розроблена нами для моніторингу в часі ряду біохімічних та інших чисельних показників, що характеризують стан організму обстежуваної особи в умовах тих територій українського Полісся, що зазнали екологічного забруднення внаслідок Чорнобильської катастрофи. Система є мережевою, Інтернет-базованою, містить ряд взаємопов'язаних електронних баз упорядкованих даних із відповідним виводом цієї інформації на монітори, в тому числі даних біохімічної лабораторії щодо наявності речовин-забруцню-вачів довкілля в організмі. Передбачена можливість попереднього автоматизованого аналізу даних, а також захист персональних даних пацієнтів та інших обстежених осіб за допомогою оригінальної розробки електронного апаратного ключа. Наведено інформацію про структуру розробленої системи, її функції та функції деяких її найважливіших блоків, зразки розробленого авторами програмного забезпечення та засобів захисту інформації.

RETRACTED ARTICLE: Multimedia Imaging Model of Information System Based on Self-Organizing Capsule Neural Network and Game Theory

With the advent of multimedia technology, the application fields of computers are further broadened, such as computers widely used in video conferencing, video telephony, shopping mall shopping guides, tour guides, computer-aided integrated manufacturing systems, geographic information systems, computer-aided teaching, etc., with new The emergence of application fields and the increasing demands of people for complex data management are increasing, and the requirements for database management systems are increasing. In general, numbers and characters are formatted data, and existing database technologies, especially relational database technology, can be used for good access management. However, text, sound, and images are unformatted data, so traditional relational models cannot be described and processed. To this end, people began to research multimedia database technology in order to manage multimedia data. In this paper, we proposed a multimedia model of management system based on self-organizing capsule neural network and game theory. Experimental results show that the proposed method has higher efficiency and robustness.

Cassandra-based data repository design for food supply chain traceability

PurposeThe purpose of this paper is to design a supply chain database schema for Cassandra to store real-time data generated by Radio Frequency IDentification technology in a traceability system.Design/methodology/approachThe real-time data generated in such traceability systems are of high frequency and volume, making it difficult to handle by traditional relational database technologies. To overcome this difficulty, a NoSQL database repository based on Casandra is proposed. The efficacy of the proposed schema is compared with two such databases, document-based MongoDB and column family-based Cassandra, which are suitable for storing traceability data.FindingsThe proposed Cassandra-based data repository outperforms the traditional Structured Query Language-based and MongoDB system from the literature in terms of concurrent reading, and works at par with respect to writing and updating of tracing queries.Originality/valueThe proposed schema is able to store the real-time data generated in a supply chain with low latency. To test the performance of the Cassandra-based data repository, a test-bed is designed in the lab and supply chain operations of Indian Public Distribution System are simulated to generate data.

Обновление многотабличных представлений на основе коммутативных преобразований базы данных

In modern relational database technologies, views implement the external layer of the ANSI-SPARC architecture, which encapsulates details of the database conceptual structure from end-users. However, when using views, we need to solve the problem of correct view updating: DBMS must execute insertion, deletion, and updating tuples of the view while providing correct modifications of corresponding target relation(s) of this view. To solve this problem, the SQL standard introduces a strict restriction: only one tuple in the target relation can correspond to the modified tuple in the view. In addition, triggers are not a satisfactory solution of this problem because of necessity of such a trigger for each view of the database, and unpredictable sequence in execution of triggers that belong to the same view, etc. The paper presents an approach to solve the problem of correct view updating based on the commutative database transformations. This does not limit the tuple uniqueness in the target relation that corresponds to the updated tuple in the view. We describe the DBMS Coprocessor, which is deployed on the client computer and provides commutative transformations in the database relations stored on the server side. The coprocessor generates a transaction's script that implements commutative transformations and runs the transaction on the server. We present implementation of the Coprocessor for the PostgreSQL open-source DBMS. Experimental evaluation confirms the effectiveness of the proposed approach in OLAP and OLTP applications.

UPFBase—A freshwater fish diversity database of Uttar Pradesh, India

Worldwide, global databases on fishes lack fish diversity information at regional scale of a country, which has fascinated many fishery workers to know the regional scale fish diversity. Databases are essential part of the biodiversity science and have been used widely in the biological research. The present study discusses development, services and utility of the database application (UPFBase) providing information on the freshwater fish biodiversity of Uttar Pradesh. To develop UPFBase, data on the fish biodiversity of this region was compiled from different sources that include primary data generated from different projects and secondary data from published literatures. The collected data were screened and then digitized. Microsoft ACCESS relational database and Visual Basic language technologies were used for designing and implementing the standalone database application with the data management capability. Presently, UPFBase provides taxonomy, synonyms, local name, common name, morphology, biology, distribution, habitat, economic importance, conservation status and other fishery information on 129 fishes belonging to 11 orders and 27 families. UPFBase is user friendly and provides ease in working through search, query and action command button tools. It can easily be deployed on the mobile storages devices like CD-ROM, Pen drive, PCMCIA etc and can be installed on any Windows based Intel x86 machines. This version of UPFBase was built for countries, where computational hardware and software resources are in scare and it is expected that it might play imperious role in knowing and managing the indigenous fish diversity for decision making and posterity.

Design and Research of Crime Analysis and Early Warning System

After the 21st century, social crimes were characterized by remoteness, high intelligence, and high technology. This paper combines the actual needs of prevention, carries out the overall planning and design of the crime analysis and early warning system, further learns the relational database technology and advanced network technology, proposes a fairly effective implementation plan, and promotes the informationization of crime early warning. The level has a very important practical guiding significance, which is conducive to reducing crime and thus maintaining public order.

SiteWit Corporation: SQL or NoSQL? That is the Question!

Ricardo Lasa, CEO and co-founder of SiteWit Corporation, and his team faced a critical technology challenge in scaling the core database systems to meet rapidly escalating data volumes. Should he stick with well-known relational database technologies? Or should he re-implement core components in newer, highly distributed NoSQL databases in search of competitive advantages?

SiteWit Corporation: SQL or NoSQL? That is the Question!

Ricardo Lasa, CEO and co-founder of SiteWit Corporation, was always chastising his technical team that the “biggest risk facing the company is the engine.” SiteWit provided cross-platform services aimed at helping small (or even medium-sized) business customers effectively advertise on search engines like Google (AdWords) and Bing (adCenter), as well as other online social networking or display advertising venues. Essentially, SiteWit was a web analytics company that tracks all the detailed organic and paid advertising traffic on client websites. SiteWit used this very detailed data to deliver software-as-a-service (SaaS) products that handled a variety of tasks from automated keyword bidding to campaign optimization. These products relied on a foundation of website analytic data warehousing and automated data mining, so data quality was of paramount concern. Lasa and his team faced a critical technology challenge in scaling the core database systems to meet rapidly escalating data volumes. Should he stick with well-known relational database technologies? His core team was well versed in the Microsoft technology stack and had worked together for more than a decade on software-as-a-service (SaaS) applications. Or should he re-implement core components in newer, highly distributed NoSQL databases in search of competitive advantages? So, the decision could be summarized as follows: SQL or NoSQL? That was the question which had four possible solutions: 1. Do nothing. SiteWit Corporation was a lean startup with limited resources. Did we really need to add new technologies and more uncertainty at this stage? 2. Proceed cautiously with NoSQL technology through limited experiments. It might be reasonable to pick some component that could be implemented using NoSQL technology to gain experience and validate the technology. 3. Develop a new product, alone or through a partnership, that makes use of NoSQL technologies. A couple of potential SiteWit partners were experimenting with or even already resting firmly on NoSQL technologies, so one strategy might be to learn through collaboration. 4. Take a leap of faith. Again, SiteWit was an early stage company facing plenty of risk factors. Adding a few more for an important competitive advantage may be a reasonable tradeoff.

Hadoop based Weblog Analysis: A Review

The growth of websites and the Internet has opened up new research, social, entertainment, education and business opportunities. With the fast growth of the Internet, the digital data generated by the websites is becoming so massive that the traditional text software and relational database technology faces a bottleneck while processing such massive data and the results generated by these technologies are not satisfactory. Cloud computing offers a good solution for this problem. Cloud computing is not only capable of storing such massive data but also capable of processing and analyzing such voluminous data faster, by making use of distributed storage and distributed computing technology. A weblog is a group of connected web pages that consists of a log or daily record of information, particular fields or views which is altered, every now and then, by owner of site, other websites or by website users. An enterprise weblog analysis system based on Hadoop architecture with Hadoop Distributed File System (HDFS), Hadoop MapReduce Software Framework and Pig Latin Language aids the business decision-making process of the system administrators and helps them to collect and identify the potential value which is hidden within such huge data generated by the websites. Such a weblog analysis includes the analysis of an Internet site’s entry log as well as provides information about the amount of visitors, days of week and rush hours, views, hits, very often accessed pages, application server traffic trends, performance reports at varying intervals and statistical reports which indicate the performance of program.

Visualized Spatiotemporal Data Management System for Lifecycle Health Monitoring of Large-Scale Structures

AbstractLifecycle structural health monitoring (SHM) systems provide an abundance of information that is greatly beneficial for securing structural safety over the whole service life. In application to large-scale structures, the management of accumulated massive data from a sophisticated long-term SHM system poses a challenge. A robust data management system (DMS), which not only facilitates spatiotemporal data management but also enables display in an attractive way, is highly desirable. This article presents the development of an effective visualized DMS specific for managing immense and heterogeneous SHM data by integrating nested relational database, three-dimensional (3D) model, and virtual reality (VR) technology and demonstrates its application to an instrumented supertall structure. A custom nested data model is designed to store redundant inherent temporal data and hierarchical inherent spatial data. Strategies for speeding up querying massive data are set up in the database. Making use of OpenS...

MongoDB-Based Repository Design for IoT-Generated RFID/Sensor Big Data

Internet of Things (IoT)-generated data are characterized by its continuous generation, large amount, and unstructured format. The existing relational database technologies are inadequate to handle such IoT-generated data due to the limited processing speed and the significant storage-expansion cost. Thus, big data processing technologies, which are normally based on distributed file systems, distributed database management, and parallel processing technologies, have arisen as a core technology to implement IoT-generated data repositories. In this paper, we propose a sensor-integrated radio frequency identification (RFID) data repository-implementation model using MongoDB, the most popular big data-savvy document-oriented database system now. First, we devise a data repository schema that can effectively integrate and store the heterogeneous IoT data sources, such as RFID, sensor, and GPS, by extending the event data types in electronic product code information services standard, a de facto standard for the information exchange services for RFID-based traceability. Second, we propose an effective shard key to maximize query speed and uniform data distribution over data servers. Last, through a series of experiments measuring query speed and the level of data distribution, we show that the proposed design strategy, which is based on horizontal data partitioning and a compound shard key, is effective and efficient for the IoT-generated RFID/sensor big data.

Statistical Analysis of Web Server Logs UsingApache Hive in Hadoop Framework

Web log file is log file automatically created and maintained by a web server.Analyzing web server access logs files will offer valuable insight into website usage. Because of the tremendous usage of web, the web log files are growing at faster rate and the size is becoming huge. Processing this explosive growth of log files using relational database technology has been facing a bottle neck. To analyze such large datasets we need parallel processing system and reliable data storage mechanism. Hadoop rides the big data where massive quantity of information is processed using cluster of commodity hardware. In this paper based on the architecture of Hadoop Distributed File System and HadoopMapReduce framework and HiveQL query language, we present the methodology used in preprocessing of huge volume of web log files and finding the statics of website and learning the user behavior.

Training Database Technology in DBMS MS Access

The article describes the methodological issues of learning relational database technology and management systems relational databases. DBMS Microsoft Access is the primer for learning of DBMS. This methodology allows to generate some general cultural competence, such as the possession of the main methods, ways and means of production, storage and processing of information, computer skills as a means of managing information. Also must formed professional competence such as the ability to collect, analyze and process the data necessary for solving the professional tasks, the ability to use solutions for analytical and research tasks modern technology and information technology.

Comparisons Between MongoDB and MS-SQL Databases on the TWC Website

Owing to the huge amount of data in websites to be analysed, web innovative services are required to support them with high scalability and availability. The main reason of using NoSQL databases is for considering the huge amount of data and expressing large-scale distributed computations using Map-Reduce techniques. To enhance the service quality of customers and solve the problems of the huge amount of data existing in the websites such as Facebook, Google, and Twitter, the relational database technology was gradually replaced with the NoSQL database to improve the performance and expansion elasticity in recent years. In this paper, we compare both NoSQL MongoDB and MS-SQL databases, and discuss the effectiveness of the inquiry. In addition, relational database cluster systems often require larger server efficiency and capacity to be competent, but it incurs cost problems. On the other hand, using NoSQL database can easily expand the capacity without any extra costs. Through the experiments, it shows that NoSQL MongoDB is about ten times efficient for reading and writing than MS-SQL database. This verifies that the NoSQL database technology is quite a feasible option to be used in the future.