Transaction Matrix Research Articles

Identification and Visualization of Clusters Using Network Theory Methods: The Case of the Greek Production System

The interest in clusters in the economy and regional space, which has persisted for nearly three decades, has reignited the understanding of the economy as a system of interdependencies between industries. Although the concept of clusters can be traced back to contributions dating from the early 20th century, they have become a central focus of regional development policies in recent decades, as they have been linked to enhancements of innovation, the knowledge economy, and ultimately, territorial competitiveness. Arguably, the most effective and comprehensive way to present the systemic nature of the economy is through input–output tables. The main feature of these tables, on which this work is based, is that they describe the relationships and flows between industries (or products) during the production process. These fundamental relationships among the industries in the production system are depicted in the inter-industry (and intra-industry) transaction matrix of an economy’s input–output tables. To analyze these relationships, we use network theory, in the context of which the transaction matrix can be seen as the adjacency matrix of a directed, weighted graph (or network) with loops. In this study, clusters are identified for the case of Greece, using two different approaches based on the modularity of the network, utilizing the 2010 input–output tables for this country. As a result, five clusters of industries that structure the country’s production system across 62 industries are identified, which are also presented through graphical visualizations.

Prospects for the Application of Data Analysis Methods in the Evaluation of English Teaching in Colleges and Universities

Abstract This paper studies the specific application of association rule analysis in teaching evaluation in colleges and universities and analyzes the existing cases of the Apriori algorithm applied to college management under association rules. Aiming at the performance bottleneck of the Apriori algorithm in dealing with complex databases, the AMC algorithm based on matrix compression is proposed, and the data storage form is optimized through transaction matrix mapping. The improved AMC algorithm is applied to correlation analysis of English teaching evaluation data in colleges and universities, focusing on the correlation relationship between teaching characteristics, teaching-related factors, and evaluation grades based on confidence level. A confidence level of 69% will be achieved when the teaching effect is good, and the evaluation grade reaches basic satisfaction. A confidence level of 71% can be achieved when the teaching management is excellent and the evaluation grade is satisfactory.

Analysis of Algebra Structure Implementation Input-Output Applications in the Economic Field

Mathematicsi isi ai sciencei thati is usefuli ini iall fields, and is also useful in many other sciences. Mathematical models are useful, for example, in ieconomics. One of them uses matrix theory and Gauss-Jordan ielimination in iinput-output ianalysis ito studyi the inter-sectoral iinterconnectedness iof the economy. This study describes the input-output ianalysis. This isi thei implementationi ofi matrixi materiali wherei each sector usesi as inputi the output produced by the output of other sectors, on the other hand the output produced is also the input of other sectors. This research is a qualitative research (library research) with a literary approach. Data collection techniques were carried out by reviewing various books, journals, articles, information or sources deemed appropriate and relevant to the research. Content analysis is used as a data analysis technique whose purpose is to maintain research accuracy and prevent errors in data sources. This is followed by a literature review and a literature review. The results of this study are the transaction matrix, value added ratio of economic activity, technological input-output coefficient matrix, interdependence coefficient matrix and finali demandi usingi the iGauss-Jordani eliminationi method.

Big Data Mining Analysis of Power Grid Based on Apriori Optimization

To ensure the safe and stable operation of the power grid system and strengthen the refined management level of the State Grid Corporation of China, a grid operation big data association analysis model based on Apriori association algorithm is proposed. This paper discusses the logic framework of the power big data platform, and describes the specific implementation from the functional architecture, technical architecture and other aspects. Aiming at the problem of massive heterogeneous power data and low sharing degree, the transaction matrix is pruned and compressed by setting the minimum support degree and establishing the frequent set matrix, and the implementation process of the improved algorithm on the MapReduce programming model of Hadoop platform is provided. Based on the research of Spark programming model, the improved algorithm is implemented in parallel style. The experimental results show that the efficiency of the algorithm after parallel processing has been greatly improved, which can also effectively improve the calculation amount and efficiency of power big data.

МЕТОДОЛОГИЧЕСКИЕ ПОДХОДЫ К ФОРМИРОВАНИЮ ИНСТИТУЦИОНАЛЬНОГО КАРКАСА В ЦИФРОВОЙ СРЕДЕ/METHODOLOGICAL APPROACHES TO FORMATION OF AN INSTITUTIONAL FRAMEWORK IN THE DIGITAL ENVIRONMENT/ԹՎԱՅԻՆ ՄԻՋԱՎԱՅՐՈՒՄ ԻՆՍՏԻՏՈՒՑԻՈՆԱԼ ՇՐՋԱՆԱԿԻ ՁԵՎԱՎՈՐՄԱՆ ՄԵԹՈԴԱԲԱՆԱԿԱՆ ՄՈՏԵՑՈՒՄՆԵՐԸ

The relevance of the study is related to global trends in the development of information technologies, their rapid penetration into all spheres of human activity and, in this context, the state policy for transition to a digital economy. Based on the theoretical approaches of the institutional neoclassical theory, which is based on the analysis of transactions, this article develops a methodology for formation of an institutional framework in the context of digital transformation. The process of applying the proposed methodology is modeled and some examples of its application in the healthcare sector are given. The presented methodology uses the typology of transactions based on social relationships proposed by J. Commons, taking into account the specifics of the digital environment, which requires special attention to issues of national digital sustainability and reliability of transaction mechanisms based on the use of information technologies. The article describes a step-by-step method for identifying and analyzing transactions based on a transaction matrix, and also provides an example of analyzing and identifying the institutions required for a particular industry, depending on the type of transactions.

Uncertainty Analysis of Business Interruption Losses in the Philippines Due to the COVID-19 Pandemic

In this study, we utilize an input–output (I–O) model to perform an ex-post analysis of the COVID-19 pandemic workforce disruptions in the Philippines. Unlike most disasters that debilitate physical infrastructure systems, the impact of disease pandemics like COVID-19 is mostly concentrated on the workforce. Workforce availability was adversely affected by lockdowns as well as by actual illness. The approach in this paper is to use Philippine I–O data for multiple years and generate Dirichlet probability distributions for the Leontief requirements matrix (i.e., the normalized sectoral transactions matrix) to address uncertainties in the parameters. Then, we estimated the workforce dependency ratio based on a literature survey and then computed the resilience index in each economic sector. For example, sectors that depend heavily on the physical presence of their workforce (e.g., construction, agriculture, manufacturing) incur more opportunity losses compared to sectors where workforce can telework (e.g., online retail, education, business process outsourcing). Our study estimated the 50th percentile economic losses in the range of PhP 3.3 trillion (with telework) to PhP 4.8 trillion (without telework), which is consistent with independently published reports. The study provides insights into the direct and indirect economic impacts of workforce disruptions in emerging economies and will contribute to the general domain of disaster risk management.

A Running Pattern Recognition Algorithm for Publish/Subscribe Distributed Systems

Publish/subscribe distributed systems are often used in critical applications. It is necessary to monitor their running patterns in real time to detect abnormal status. Therefore, identifying the normal running pattern is the precondition of monitoring publish/subscribe distributed systems. Based on Apriori algorithm, this paper presents a weighted frequent itemset mining algorithm for running pattern recognition of publish/subscribe distributed systems. By introducing the transaction matrix, the algorithm only needs to scan the transaction database once. By weighting the items from two aspects of influence and frequency, the support of the items with few occurrences but much importance can be improved, so that the running pattern containing small frequency events can be mined out. Experimental results show that the algorithm can effectively mine the running patterns, and has better performance than Apriori algorithm and FP-growth algorithm.

Analysis of the Global Banking Network by Random Matrix Theory

Since the financial crisis of 2008, the network analysis of financial systems has attracted a lot of attention. In this paper, we analyze the global banking network via the method of Random Matrix Theory. By applying that method on a cross border lending network, it is shown that while the connectivity between different parts of the network has risen and the profile of transactions has diversified, the role of hubs remains important in the weighted perspective. The largest eigenvalue of the transaction matrix as the leading mode of the system shows sharp growth since 2002. As well, it is observed that its growth has diminished since 2008. This indicates that the crisis of 2008 has left a long-lasting footprint on the financial system. Analyzing the mean value of the participation ratio reveals the fact that the role of countries in forming small modes, has increased since 2002. In our final analysis, we provide snapshots of the hubs in the network over time. We observe that the share of countries in total transactions is not equal to their share in shaping the eigenvector of the largest eigenvalue. In 2018 for example, while the United Kingdom leads the share of transactions, it is the United States that has the largest value in the leading eigenvector. The proposed technique in the paper can be useful for analyzing different types of interaction networks between countries.

Article Review of The Critical and Empirical Research in Islamic Economics

Abstract This article depicts the nature scope and methodology of reconstruction of Islamic economics as well as it speaks about possibilities on theoretical and empirical research in this field. The article also intends to revel needy of reconstruction of Islamic economics, and its role in early stage and contemporary period and how it would be in future. Islamic economics through the shade of quran and sunnah clearly mandate about allocation of resources, goods and services as well distribution of output, input and wealth in an economy. It is also giving the solution for the problems such as financial crises, poverty, inflation, economic inequalities and unemployment. In addition, methodologically, description of the ground realities and prescription of solutions for economic issues are concern of Islamic economics, whereas, field of fiqh plays massive role in developing of Islamic economics. Theoretically, this article discusses about Islamic economics theory and its advantages, this includes Islamic economic system, Shari’ah based transaction matrix, institutional matters, etc. on the empirical side, there are some points affiliated with data and empirical works are discussed.

A Stock-Flow Consistent Quarterly Model of the Italian Economy

Macro-economists and political officers need rigorous, albeit realistic, quantitative models to forecast the future paths and dynamics of some variables of interest while being able to evaluate the effects of alternative scenarios. At the heart of all these models lies a standard macroeconomic module that, depending on the degree of sophistication and the research questions to be answered, represents how the economy works. However, the complete absence of a realistic monetary framework, along with the abstraction of banks and more generally of real–financial interactions — not only in dynamic stochastic general equilibrium (DSGE) models but also in central banks’ structural econometric models — made it impossible to detect the rising financial fragility that led to the Great Recession. In this paper, we show how to address the missing links between the real and financial sectors within a post-Keynesian framework, presenting a quarterly stock-flow consistent (SFC) structural model of the Italian economy. We set up the accounting structure of the sectoral transactions, describing our “transaction matrix” and “balance sheet matrix,” starting from the appropriate sectoral data sources. We then “close” all sectoral financial accounts, describe portfolio choices, and define the buffer stocks for each class of assets and sector in the model. We describe our estimation strategy, present the main stochastic equations, and, finally, discuss the main channels of transmissions in our model.

What Can Be Learned from Billions of Invoices? The Construction and Application of China’s Multiregional Input-Output Table Based on Big Data from the Value-Added Tax

ABSTRACT The big data on the value-added tax (VAT), which links upstream production and downstream consumption, can serve as a key basis for a structured macroeconomic analysis. This paper discusses the construction and aggregation method of the transaction matrix between enterprises based on this data and develops a multiregional input-output (MRIO) table. The intraregional structure of this table is consistent with the IO table in China’s national statistics and includes more detail on the domestic value chain. We use big data on the VAT in creating an analytical tool to track profit shifting and tax avoidance. Therefore, this new MRIO table can coordinate consumption-based measures with the “destination principle” in international VAT statistics.

An improved apriori algorithm based on support weight matrix for data mining in transaction database

Data mining is a process to discover hidden information or knowledge automatically from huge database. In order to reduce the number of scanning databases and reflect the importance of different items and transaction so as to extract more valuable information, an improved Apriori algorithm is proposed in this paper, which is to build the 0–1 transaction matrix by scanning transaction database for getting the weighted support and confidence. The items and transactions is weighted to reflect the importance in the transaction database. The experiment results, both qualitative and quantitative, have shown that our improved algorithm shortens the running time and reduces the memory requirement and the number of I/O operations. Meanwhile, the support for rare items tends to increase, while the support for other items decreases slightly, thus the hidden and valuable items can be effectively extracted.

Going Global to Local: Connecting Top-Down Accounting and Local Impacts, A Methodological Review of Spatially Explicit Input-Output Approaches.

Environmentally Extended Input–Output Databases (EEIOs) provide an effective tool for assessing environmental impacts around the world. These databases have yielded many scientific and policy relevant insights, especially through the national accounting of impacts embodied in trade. However, most approaches average out the spatial variation in different factors, usually at the level of the nation, but sometimes at the subnational level. It is a natural next step to connect trade with local environmental impacts and local consumption. Due to investments in earth observation many new data sets are now available, offering a huge potential for coupling environmental data sets with economic models such as Multi-Region Input–Output (MRIO) models. A key tool for linking these scales are Spatially Explicit Input–Output (SIO) models, which provide both demand and supply perspectives by linking producers and consumers. Here we define an SIO model as a model having a resolution greater than the underlying input–output transaction matrix. Given the increasing interest in this approach, we present a timely review of the methods used, insights gained, and limitations of various approaches for integrating spatial data in input–output modeling. We highlight the evolution of these approaches, and review the methodological approaches used in SIO models so far. We investigate the temporal and spatial resolution of such approaches and analyze the general advantages and limitations of the modeling framework. Finally, we make suggestions for the future development of SIO models.

A big data driven sustainable manufacturing framework for condition-based maintenance prediction

Smart manufacturing refers to a future-state of manufacturing and it can lead to remarkable changes in all aspects of operations through minimizing energy and material usage while simultaneously maximizing sustainability enabling a futuristic more digitalized scenario of manufacturing. This research develops a big data analytics framework that optimizes the maintenance schedule through condition-based maintenance (CBM) optimization and also improves the prediction accuracy to quantify the remaining life prediction uncertainty. Through effective utilization of condition monitoring and prediction information, CBM would enhance equipment reliability leading to reduction in maintenance cost. The proposed framework uses a CBM optimization method that utilizes a new linguistic interval-valued fuzzy reasoning method for predicting the information. The proposed big data analytics framework in our study for estimating the uncertainty based on backward feature elimination and fuzzy unordered rule induction algorithm prediction errors, is an innovative contribution to the remaining life prediction field. Our paper elaborates on the basic underlying structure of CBM system that is defined by transaction matrix and the threshold value of failure probability. We developed this framework for analysing the CBM policy cost more accurately and to find the probabilistic threshold values of covariate that corresponds to the lowest price of predictive maintenance cost. The experimental results are performed on a big dataset which is generated from a sophisticated simulator of a gas turbine propulsion plant. A comparative analysis confirms that the method used in the proposed framework outpaces the classical methods in terms of classification accuracy and other statistical performance evaluation metrics.

Volume of the steady-state space of financial flows in a monetary stock-flow-consistent model

We show that a steady-state stock-flow consistent macro-economic model can be represented as a Constraint Satisfaction Problem (CSP). The set of solutions is a polytope, which volume depends on the constraints applied and reveals the potential fragility of the economic circuit, with no need to study the dynamics. Several methods to compute the volume are compared, inspired by operations research methods and the analysis of metabolic networks, both exact and approximate. We also introduce a random transaction matrix, and study the particular case of linear flows with respect to money stocks.

Islamic Economics and Prospects for Theoretical and Empirical Research

This paper examines the nature, scope and methodology of Islamic economics, and draws attention to some possibilities for theoretical and empirical research in this field. Islamic economics is defined as the systematic study of the problem of allocation of resources, production of goods and services and distribution of output, income and wealth in an economy in the light of the Qur’ān and the Sunnah. It has relevance for not only Muslims but also others interested in durable solutions to problems of financial crises, inflation, unemployment, poverty and economic inequalities. The discipline is concerned with the description (of ground realities) and prescription (of solutions for economic issues) in the light of the leads in the Qur’ān and the Sunnah. The Sharīʿah basis of Islamic economics, with a supportive role for fiqh, holds the key in building Islamic economics. The approach recommended for this purpose is reconstruction of Islamic economics rather than Islamization of economics. On the theoretical side, this paper lays emphasis on basic research for strengthening the theory of Islamic economics to fill the knowledge gap on, among other things, Islamic economic system, Islamic economy, the Sharīʿah-based transactions matrix, nature and role of government in modern times and review of economic policy agenda. On the empirical side, it highlights some matters related to data and empirical work.

Multiplier Accounting of Indian Mining Industry: The Application

In the previous paper (Hussain and Karmakar in Inst Eng India Ser, 2014. doi: 10.1007/s40033-014-0058-0 ), the concepts of input–output transaction matrix and multiplier were explained in detail. Input–output multipliers are indicators used for predicting the total impact on an economy due to changes in its industrial demand and output which is calculated using transaction matrix. The aim of this paper is to present an application of the concepts with respect to the mining industry, showing progress in different sectors of mining with time and explaining different outcomes from the results obtained. The analysis shows that a few mineral industries saw a significant growth in their multiplier values over the years.

Limitations of Official Statistical System of India and Data Gaps:A Note

The note on official statistical system of India focuses on gaps and limitations of data gathered by different agencies of Government of India in general and CSO in particular. It concentrates on data limitations and gaps relating to input output modelling and employment of human resources in Indian economy. Major gaps in data base of input output modelling relate to (i) total want of such data as are required for the compilation of capital coefficients matrix of Indian economy; (ii) Want of data relating to inter and intra-state trade along with price differentials among the economies of the states; (iii) Lack of I-O matrix with uniform classification without which inter-temporal comparative study is not possible. Change in technology is also difficult to infer without such tables; (iv) Both inter-state and intra-state growth differentials warrant that each input output table has an inter-regional transaction matrix as its supplement; (v) No data are available relating to transactions that take place between rural and urban establishments and people within a district and among the districts of a state; (vi) Sector-wise data relating to employment with education-occupation profile and corresponding wage/salary rates are not available. Data relating to nonformal and unorganized sector are also not available. This makes it extremely difficult to examine changing factor endowment of Indian economy during the course of development. It needs sector-wise data of employment and sectorwise wage rates are also needed.

Algorithms of Mining Maximum Frequent Itemsets Based on Compression Matrix

Association rule mining is one of the most important and well researched techniques of data mining, the key procedure of the association rule mining is to find frequent itemsets , the frequent itemsets are easily obtained by maximum frequent itemsets. so finding maximum frequent itemsets is one of the most important strategies of association data mining. Algorithms of mining maximum frequent itemsets based on compression matrix are introduced in this paper. It mainly obtains all maximum frequent itemsets by simply removing a set of rows and columns of transaction matrix, which is easily programmed recursive algorithm. The new algorithm optimizes the known association rule mining algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity, and highly promotes the efficiency of association rule mining.

Mining Frequent Itemsets Algorithm Based on Compression Matrix

Association rule mining is one of the most important and well researched techniques of data mining. The key procedure of the association rule mining is to find frequent itemsets. In this paper, a new mining frequent itemsets algorithm based on matrix is introduced. Frequent itemsets are obtained by compressing the transaction matrix efficiently by a new strategy. The new algorithm optimizes the known mining frequent itemsets algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity. It is more feasible especially when the degrees of the frequent itemsets are high.