Data Definition Research Articles

Data as assemblage

PurposeA definition of data called data as assemblage is presented. The definition accommodates different forms and meanings of data; emphasizes data subjects and data workers; and reflects the sociotechnical aspects of data throughout its lifecycle of creation and use. A scalable assemblage model describing the anatomy and behavior of data, datasets and data infrastructures is also introduced.Design/methodology/approachData as assemblage is compared to common meanings of data. The assemblage model's elements and relationships also are defined, mapped to the anatomy of a US Census dataset and used to describe the structure of research data repositories.FindingsReplacing common data definitions with data as assemblage enriches information science and research data management (RDM) frameworks. Also, the assemblage model is shown to describe datasets and data infrastructures despite their differences in scale, composition and outward appearance.Originality/valueData as assemblage contributes a definition of data as mutable, portable, sociotechnical arrangements of material and symbolic components that serve as evidence. The definition is useful in information science and research data management contexts. The assemblage model contributes a scale-independent way to describe the structure and behavior of data, datasets and data infrastructures and supports analyses and comparisons involving them.

Presentation of Financial Assets in the Monetary Statistics

The purpose of the study. Currently, the issues of determining, presenting and analyzing indicators of economic assets, in particular, financial ones, are relevant, since financial assets, along with nonfinancial ones, constitute the most important characteristic of national wealth as a level of development of the country and are the basis for constructing national accounts, indicators of monetary statistics and other sections of macroeconomic statistics. In modern conditions, the role of these constructions in making effective management decisions at the macro level, individual sectors, economic entities, on the development of the national economy as a whole is increasing. The aim of the work is to systematize theoretical and practical developments on the definition and presentation of data on financial assets in modern monetary statistics of Russia, to identify areas for improving methods for assessing financial assets, a system of indicators characterizing their presence, structure and dynamics.Materials and methods. In the paper, the authors considered the essence, functions, classifications of financial assets and liabilities, applied structural and dynamic data analysis, as well as methods of theoretical research in the form of generalization, comparison and special analytical procedures based on official statistics from Rosstat, the Bank of Russia, the Ministry of Finance and international statistical organizations.Results. The work formulates the main directions of the study of financial assets, based on modern international statistical standards, taking into account domestic practice. Topical issues of the theory and practice of statistical collection and presentation of data on financial assets in monetary statistics, the application of definitions and classifications of international standards to Russian official statistics are considered. The features of the reflection of data on financial assets at the macro level, in the system of indicators of monetary statistics, have been formulated. The authors carried out a quantitative assessment of the structure and dynamics of indicators of monetary statistics in Russia, which characterize the state and movement of financial assets and liabilities.Conclusion. A statistical study of transactions with financial assets and in connection with the formation of monetary aggregates makes it possible to identify not only the main trends in the development of these processes, but also makes it possible to characterize the contribution of a particular subsector to the total value of financial intermediation, to analyze the relationship between the financial corporations’ sector and other sectors, and also a comprehensive change in the volume and composition of stocks and flows of financial assets as a result of exchanges between residents and non-residents.The authors comprehensively reviewed the methodological framework for constructing money aggregates and reviews of the financial corporations’ sector, incl. in the context of financial instruments. Statistical information on financial assets in monetary statistics is presented in terms of the formation of the money supply, other aggregates, the base, as well as the interaction between the institutional subsectors of the financial sector. A comprehensive description of the presence and movement of financial assets in the composition of monetary statistics can be obtained from the monetary surveys, containing data on the flows and stocks of financial assets and liabilities of financial corporations (residents) in relation to all other sectors (residents), as well as non-residents.All considered data are comparable [17,18] since basically, one classification of financial instruments is applied, a different degree of detail can be applied in practice for different constructions, but the base is the same. Questions of the theory and practice of presenting indicators of financial assets in the considered sections of macrostatistics and their analysis are a relevant and promising direction for improving national statistical accounting. Work in this direction continues, and the process of actively introducing international standards into domestic statistics has been going on for quite a long time. In addition, telecommuting, collecting information via the Internet, the emergence of a large number of new financial instruments and transactions, including modified ones, in the business turnover of institutional units complicate their identification and classification.The authors of the presented study have previously considered in their articles the problems of macroeconomic calculations, which allowed them in their work to consider in more detail the topical issues related to the processes of reflection of financial assets in monetary statistics.

Big data with cloud computing: Discussions and challenges

With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, Amazon Web Services, International Business Machine cloud, Hortonworks, and MapR. A comparative analysis of various cloud-based big data frameworks is also performed. Various research challenges are defined in terms of distributed database storage, data security, heterogeneity, and data visualization.

Augmented Reality in Indonesia's Primary School: Systematic Mapping Study

Augmented reality is an increase trending technology in education. Research in the field of AR for Education is increasing every year. A large number of research reviews have focused on the use of AR in education worldwide and at all levels of education, but there have not been many reviews of the use of AR at the elementary school level in Indonesia. This study aims to determine the trends of using AR in elementary school in Indonesia. This research used a Systematic Mapping Study approach, which consists of the stages of definition research question, conduct research, screening papers, keywording using abstract, and data extraction and mapping process. Subjects of elementary education, grade, type of AR application, and research method and facets, have been used in classifying AR. Inclusion and exclusion criteria were used in determining the relevant papers. This study examining 42 articles containing “augmented reality”, “elementary school” and “Indonesia” in their titles, abstracts, and keywords. Articles were published between 2017 and 2021. The results have shown that science learning is the most subject to be the object of AR research, followed by mathematics, language, and culture. Most of the AR implementations are implemented in class IV, V and VI. The most widely used type of AR application is marker-based AR. The most widely used research method is R&D which focuses on developing AR products.

Research Software vs. Research Data I: Towards a Research Data definition in the Open Science context.

Background: Research Software is a concept that has been only recently clarified. In this paper we address the need for a similar enlightenment concerning the Research Data concept. Methods: Our contribution begins by reviewing the Research Software definition, which includes the analysis of software as a legal concept, followed by the study of its production in the research environment and within the Open Science framework. Then we explore the challenges of a data definition and some of the Research Data definitions proposed in the literature. Results: We propose a Research Data concept featuring three characteristics: the data should be produced (collected, processed, analyzed, shared & disseminated) to answer a scientific question, by a scientific team, and has yield a result published or disseminated in some article or scientific contribution of any kind. Conclusions: The analysis of this definition and the context in which it is proposed provides some answers to the Borgman's conundrum challenges, that is, which Research Data might be shared, by whom, with whom, under what conditions, why, and to what effects. They are completed with answers to the questions: how? and where?

Able Construction: A Spreadsheet Activity for Teaching Bayes’ Theorem

Using classroom activities to motivate the teaching and learning of Bayes’ theorem is not new. However, many of the textbook exercises and published simulations gloss over how the requisite probabilities are determined. In our case study, Able Construction is a fictional company hoping to exploit historical bidding data to inform its own bidding strategy on a municipal construction project. Unlike most other classroom activities, we challenge students to calculate the necessary probabilities directly from a given dataset. In our experience with implementing this case in introductory business analytics courses at the undergraduate- and graduate-level, we find that this spreadsheet activity gives students the opportunity to exercise their own judgement regarding data manipulation and definition of states of nature. This autonomy in analysis develops in students a deeper appreciation for practical skills required for possible analytics careers after graduation, and leads to engaging discussions of the applicability of Bayes’ theorem in practice.Supplementary InformationThe online version contains supplementary material available at 10.1007/s43069-021-00119-3.

Balancing openness with Indigenous data sovereignty: An opportunity to leave no one behind in the journey to sequence all of life

The field of genomics has benefited greatly from its “openness” approach to data sharing. However, with the increasing volume of sequence information being created and stored and the growing number of international genomics efforts, the equity of openness is under question. The United Nations Convention of Biodiversity aims to develop and adopt a standard policy on access and benefit-sharing for sequence information across signatory parties. This standardization will have profound implications on genomics research, requiring a new definition of open data sharing. The redefinition of openness is not unwarranted, as its limitations have unintentionally introduced barriers of engagement to some, including Indigenous Peoples. This commentary provides an insight into the key challenges of openness faced by the researchers who aspire to protect and conserve global biodiversity, including Indigenous flora and fauna, and presents immediate, practical solutions that, if implemented, will equip the genomics community with both the diversity and inclusivity required to respectfully protect global biodiversity.

OffFog: An Approach to Support the Definition of Offloading Policies on Fog Computing

IoT devices deployed in Smart Cities usually have significant resource limitations. For this reason, offload tasks or data to other layers such as fog or cloud is regularly adopted to smooth out this issue. Although data offloading is a well-known aspect of fog computing, the specification of offloading policies is still an open issue due to the lack of clear guidelines. Therefore, we propose OffFog—an approach to guide the definition of data offloading policies in the context of fog computing. In order to evaluate OffFog, we extended the well-known simulator iFogSim and conducted an experimental study based on an urban surveillance system. The results demonstrated the benefits of implementing data offloading based on OffFog recommended policies. Furthermore, we identified the best configuration involving design decisions such as data compression, data criticality, and storage thresholds. The best configuration produced at least 76% improvement in network latency and 5% in the average execution time compared to the iFogSim default strategy. We believe these results represent a significant step towards establishing a systematic decision framework for data offloading policies in the context of fog computing.

How Technological Advances in the Big Data Era Make it Impossible to Define the ‘Personal’ in GDPR’s ‘Personal Data’

In the EU processing of personal data is subject to strict regulations that are laid out in the GDPR. The GDPR definition of personal data is: any information relating to an identified or identifiable natural person. Hence, if the definition applies, that data falls under the realm of the GDPR. If data is anonymised however, and as such does not constitute ‘personal data’, GDPR regulations are not applicable. This makes the definition of personal data very important. Or better yet, determining when, according to the GDPR, data should be seen as personal data. This approach of the GDPR, where data is either in scope of the regulation or not, can be considered a binary or ‘black and white’ approach to the applicability of the Regulation. This article describes the advances in technology of the big data era and how these advances make it impossible to adequately make the distinction between data and personal data. It focusses on the definition of personal data and discusses the shortcomings of the binary approach. Specific attention is paid to recital 26 GDPR, which provides some guidance to determine whether a natural person is identifiable. The difficulty of the binary approach in the definition of personal data is presented against the background of the technological developments of the last decades and in the years to come (the big data era), as these development challenge the adequate demarcation of personal data even further and demand for a better approach. The article concludes with reflections on what will be a sustainable approach for the demarcation of the scope of data protection and as such a maintainable and preferable basis for privacy protection in the future. A risk based approach is suggested in which different degrees of re-identifiability are recognized, as opposed as a too simplistic binary approach. Keywords: Anonymisation | Big Data | Data Elimination | Definition | GDPR | Personal Data

Smart City Rail Automatic Fare Collection System Under the Background of Big Data

With the rapid development of Internet technology, the process of urban construction is accelerating and the level of urbanization is further improved. In the smart city rail automatic fare collection system, there are a large number of data information and data that need to be processed manually. The traditional manual method not only consumes human, material and financial resources, but also has low efficiency. Therefore, this paper proposes a smart city rail automatic fare collection system based on big data design. Firstly, this paper expounds the concept of smart city rail transit and studies the function of automatic fare collection system. Then it studies the definition and characteristics of big data, designs the method of system development, and tests the performance of the system. The test results show that the system runs smoothly, accounts for a relatively small amount of memory, has a fast response speed and low delay. Most passengers are satisfied with the system.

An Novel Optimization Model for Ideological and Political Course in Colleges under the Environment of BD Mining Technology.

The big data (BD) era and pertinent computer science, pedagogy, and educational psychology knowledge are the foundations of this essay, based on the idea of the holistic development of human beings, the IPE (Ideological and Political Education) carrier theory, the new idea of educational development, and the strategy of strengthening the nation through education; the challenges IPECU (Ideological and political education in Colleges and Universities) faces and its path toward optimization are discussed relatively in detail in this paper, which conforms to the context of mega data thinking. The influence of IPECU's subject, object, and educational effect is discussed in this paper, along with tips for how to improve and innovate IPECU in the context of big data. In addition, it is noted that in the age of mega data, we must not only bravely take on challenges, but also actively seize opportunities if we are to realize the innovative and optimized development of IPECU. Moreover, the definitions of mega data and big data prediction function are provided in this paper. IPE can be used with the big data prediction function, which is further explored in this article. In order to realize the innovation of IPE mode in universities, this paper aims to encourage the integration and development of IPE and big data in academic settings.

Irbis: статический анализатор помеченных данных для поиска уязвимостей в программах на C/C++

Static taint analysis can be used to find various security weaknesses and vulnerabilities in programs by discovering dataflow paths from taint sources to taint sinks. In most cases the data is called ”tainted” if it was obtained from an untrusted source without proper sanitization. In this paper we present a static taint analyzer Irbis. It implements analysis based on IFDS (Interprocedural Finite Distributive Subset) dataflow problem, as well as various extensions aimed at improving accuracy and completeness of the analysis. It supports different definitions of tainted data, which enables it to find such weaknesses as out of buffer access, use of freed memory, hardcoded passwords, data leaks and discover dataflow paths between user-defined sources and sinks. All sources, sinks and propagators definitions are stored in JSON format and can be adjusted to meet the users’ needs. We compare analysis results on Juliet Test Suite for C/C++ with several other analyzers, such as Infer, Clang Static Analyzer and Svace. Irbis manages to demonstrate 100% coverage on taint-related subset of tests for implemented CWEs, while suppressing all the false positives using heuristics. We also show performance and false positive rate on real projects, with examples of real vulnerabilities, which can be detected by Irbis.

Industrial Automation Information Analogy for Smart Grid Security

Industrial automation or assembly automation is a strictly monitored environment, in which changes occur at a good speed. There are many types of entities in the focusing environment, and the data generated by these devices is huge. In addition, because the robustness is achieved by sensing redundant data, the data becomes larger. The data generating device, whether it is a sensing device or a physical device, streams the data to a higher-level deception device for calculation, so that it can be driven and configured according to the updated conditions. With the emergence of the Industry 4.0 concept that includes a variety of automation technologies, various data is generated through numerous devices. Therefore, the data generated for industrial automation requires unique Information Architecture (IA). IA should be able to satisfy hard real-time constraints to spontaneously change the environment and the instantaneous configuration of all participants. To understand its applicability, we used an example smart grid analogy. The smart grid system needs an IA to fulfill the communication requirements to report the hard real-time changes in the power immediately following the system. In addition, in a smart grid system, it needs to report changes on either side of the system, i.e., consumers and suppliers configure and reconfigure the system according to the changes. In this article, we propose an analogy of a physical phenomenon. A point charge is used as a data generating device, the streamline of electric flux is used as a data flow, and the charge distribution on a closed surface is used as a configuration. Finally, the intensity changes are used in the physical process, e.g., the smart grid. This analogy is explained by metaphors, and the structural mapping framework is used for its theoretical proof. The proposed analogy provides a theoretical basis for the development of such information architectures that can represent data flows, definition changes (deterministic and non-deterministic), events, and instantaneous configuration definitions of entities in the system. The proposed analogy provides a mechanism to perform calculations during communication, using a simple concept on the closed surface to integrate two-layer cyber-physical systems (computation, communication, and physical process). The proposed analogy is a good candidate for implementation in smart grid security.

MatCalib: a Matlab software package for Bayesian modeling of radiocarbon ages subject to temporal order constraints

<abstract> <p>Radiocarbon ages must be calibrated due to the remarkable fluctuations of the atmospheric radiocarbon level. The traditional method (e.g., Calib) does not make use of any constraint such as the temporal/stratigraphical ordering of the ages, thereby resulting in one or several large age ranges. Bayesian age modeling is advantageous over the traditional method in several aspects. First, it can provide precise age estimates by applying some constraints known <italic>a priori</italic>. Second, it may provide a timing of an archaeological feature or a geological event that is unable to be dated directly. Although several Bayesian age modeling frameworks have been developed, inexperienced users may need not only a more user-friendly environment for data entry and definition of their project-specific problem, but also a powerful post-processing tool for analyzing and visualizing the results. Here a hierarchical Bayesian model with a minimum level of structural complexity is presented. It provides users with a flexible and powerful framework to incorporate radiocarbon ages into a sequence along a one-dimensional continuum so that it best reveals their temporal order, thereby yielding a more precise timing. The accompanying Matlab software package not only complements the existing MatCal package designed to calibrate radiocarbon ages individually, but also serves as an alternative to the online tools of Bayesian radiocarbon age modeling such as OxCal and BCal.</p> </abstract>

Retracted] Design of Moral Education Management System for Higher Vocational Students Based on Multisource Sensing Data Fusion

Based on the specific circumstances of colleges and universities, this study presents the moral education management system and leverages multisource sensor data and other technologies to develop an information system in line with the moral education management of modern college students. Using multisource sensor data fusion and learning data definition, the fused education data is expressed in a standardized way, resulting in a common and standard data interchange format. This foundation is used to build a shareable and reusable data management system that enables data sharing and interchange across multiple heterogeneous data sources, aids the intelligent education system in obtaining more comprehensive and complete recorded data, and improves data sharing timeliness. As a result, learning behavior analysis results are more objective, immediate, and accurate, allowing the intelligent education system’s response to be more prompt and intelligent. This study, which is based on multisource sensor data fusion, also discusses platform selection, system architecture, database design, and methods and suggestions for overcoming critical challenges and can be used as a reference for other university information management systems with vast amounts of data.

НАСКОЛЬКО ОТКРЫТЫ ОТКРЫТЫЕ ДАННЫЕ? ИССЛЕДОВАНИЕ ДВУХ ИРЛАНДСКИХ ВЕБ-САЙТОВ С ОТКРЫТЫМИ ПРАВИТЕЛЬСТВЕННЫМИ ДАННЫМИ

In discussions of Open Government Data (hereafter referred to as open data or OGD) in the academic literature, the question of what is meant by the word "open"? has to date received only limited attention. The use of Open Government Data (OGD) has spread globally as governments make more of their data available via electronically accessible formats for individuals to use and share. OGD is seen as something that has the potential to play an important role in achieving transparency, and accountability, enabling new forms of civic participation, and stimulates economic growth and development. However, there is a need to examine OGD datasets to determine whether they are truly open. The purpose of this paper is to investigate OGD openness based on a number of existing or proposed open data definitions and principles. The principles proposed by the work of the Open Government Working Group will be analyzed. The methodology that has been used to collect the data includes OGD website content analysis, participant observation and semi-structured interviews using purposeful and snowball sampling techniques. The results in this paper are just part of the overall study. This research has not been funded. The research limitation is it has been done only in the Irish context. It is an original study with primary data. The conclusion of this research mainly demonstrates that OGD in the Irish context is progressing but requires further work to be open based on the principles proposed by the Open Government Working Group works.

Natural Stored and Inherited Relations

A stored and inherited relation (SIR) is a 1NF stored relation (SR) enlarged with inherited attributes (IAs). We show that one may consider a usual scheme of an SR R with foreign keys as implicitly defining a natural SIR R. IAs do not introduce normalization anomalies, while a usual select-project-join query to SR R with foreign keys becomes select-project only towards natural SIR R. Such queries become thus logical navigation free (LNF), without any additional data definition work, unlike at present. We show how the usual SR schemes provide for natural SIRs. We show also that making a popular DBS SIR-enabled should be simple. Pre-existing relations with foreign keys could be trivially converted to SIRs, preserving existing applications, while providing for LNF queries for new ones. We postulate every major DBS becoming SIR-enabled “better sooner than later”.

New challenges of energy consulting as a digital transformation tool

The current sphere of energy and housing and communal services in Russia is characterized by the need for technological development and import substitution of knowledge-intensive production technologies and information products, a large number of problems and challenges for which the necessary knowledge and experience are still virtually absent. The object of the research is the introduced concept of “energy consulting”, and the subject is new tasks and specifics of this term through the prism of digital transformation. The article formulates the role and new tasks of energy consulting in the conditions of energy transition, technological modernization and digitalization. In the course of the work, an analysis of existing literature resources, international analytical agencies reports was carried out, the author’s knowledge summarizing the experience of his work as a professional consultant in the implementation of energy projects at the level of interdepartmental state interaction was used. The objectives of the study are to clarify the interpretation, to define the functions and tasks of the concept of “energy consulting” through the prism of the classical definition of consulting, taking into account the industry specifics of the energy and housing sector in changing economic and political conditions. The key methodology of the study is the semantic analysis of archival data definitions of consulting as a concept, as well as the synthesis and analysis of information in the field of consulting services in the energy and housing sector. The practical significance of the results of the work lies in the formation of a new range of tasks of energy consulting as a concept, the definition of new directions for the development of consulting in the energy and housing and communal sector in the new economic, political and infrastructural realities of the Russian Federation.

정주지 부문 온실가스 인벤토리 산정을 위한 공간 범위별 이산화탄소 흡수량 비교 분석 : 서울시를 대상으로

Land Use, Land-Use Change and Forestry includes the only carbon sink among greenhouse gas (GHG) inventory, and the significance of accurate statistics has increased. However, the exact amounts of GHG absorption and emissions cannot be calculated due to the absence of definition and activity data in the settlement sector. Therefore, this study established the concept and activity data of settlement area in South Korea by reviewing the definition suggested by IPCC and Annex 1 countries and calculated GHG absorption. Considering the possibility of connecting current MRV guidelines, exisiting national statistics, country and urban planning data, and unifying management authority, settlement can be defined in three ways, as a Land category, Urban area, and Urban & Plan management. The study built activity data based on concept 1 and 2, which rarely overlap with the other land categories, and the study area is Seoul, with the largest proportion of settlements in the country. Of the area of Korea, 10% was used as activity data based on systematic sampling, and GHG absorption was estimated applying the factors identified by the IPCC. The result showed GHG absorption by ‘land category’ and ‘urban area’ of 72,990.7 tCO₂·yrSUP-1/SUP and 201,287.73 tCO₂·yrSUP-1/SUP, respectively. This means that CO₂ absorption can vary depending on the factors that influence the area of vegetation within the settlement. The study identified the share of GHG statistics in settlement sector based on outcomes and contributed to the basis for calculation at the national level.

A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.