Data reconciliation and fusion methods: a survey

Bakhtouchi, Abdelghani

doi:10.1016/j.aci.2019.07.001

Cited by 6 publications

(20 citation statements)

References 52 publications

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“…However, this process requires the MSMEs to integrate their internal information systems with the digital business supply chain. (Abboora et al, 2016;Ertz et al, 2016;Bakhtouchi, 2019;Charlesworth, 2018:307;Shakr & Zomaya, 2019:314). This process enables online transactions with a digital receipt, reconciliation, and financial reporting preparations to be processed automatically by the system.…”

Section: Accounting and Reconciliationmentioning

confidence: 99%

Digital Receipts of Online Transactions in the Reconciliation Process and the Preparation of Financial Reports

Kosadi¹,

Ginting²,

Merliana³

2021

J. Indones.Econ.Bus.

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Introduction/Main Objectives: This research seeks to analyze the use of digital receipts and multi-platform e-commerces’ data integration and the influence they have on the process of reconciliation and the preparation of financial reports by micro, small, and medium-sized enterprises (MSMEs). Background Problems: The use of multi-platform online transactions requires the validation and conversion of the data, which can be an issue during the reconciliation process and the preparation of financial reports. The issue of the data’s integration is due to differences in the interfaces used for the multi-platform transactions and the MSMEs internal abilities. Novelty: The integration and treatment of online transactions via multi-platforms should an internal records or database for the MSMEs, the reconciliation process and preparing financial reports. Research Methods: This research uses a quantitative method with partial least squares structural equation modeling (PLS-SEM) analysis and descriptive analysis to reveal the reconciliation process and the conditions under which financial reports are prepared. Finding/Results: The problems of integrating data from various platforms into internal reports causes duplication of the internal reporting and a long reporting process and the chance of errors. The digital receipt is treated as proof of a manual transaction. Records’ duplication is a technical issue that causes delays in the processing time and reconciliation. None of the MSMEs have a machine-to-machine based (automized) reconciliation process. Conclusion: Transaction receipts from all the platforms affect the transactions’ recording, the reconciliation process, and the preparation of financial reports. Digital receipts provide high levels of confidence because of their completeness, accuracy, easiness, efficiency, simplicity, and suitability for use by MSMEs. The adoption of online sales and payments by MSMEs is highly effective, yet this has not been followed-up with the data’s integration into the reconciliation and accounting process for preparing financial reports.

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Section: Accounting and Reconciliationmentioning

confidence: 99%

Digital Receipts of Online Transactions in the Reconciliation Process and the Preparation of Financial Reports

Kosadi¹,

Ginting²,

Merliana³

2021

J. Indones.Econ.Bus.

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“…A FinTech system usually consists of a cluster of databasebacked applications manipulating large amounts of data, making the data records exhibit specific properties. To improve the reliability, the developers often specify data constraints to describe target properties and set up a data reconciliation (DR) platform [14] to examine them in the runtime. Once a data constraint is violated, developers can receive detailed runtime information to guide further system diagnosis.…”

Section: A Equivalent Data Constraints In Fintech Systemsmentioning

confidence: 99%

Verifying Data Constraint Equivalence in FinTech Systems

Wang¹,

Fan²,

Yao³

et al. 2023

Preprint

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Data constraints are widely used in FinTech systems for monitoring data consistency and diagnosing anomalous data manipulations. However, many equivalent data constraints are created redundantly during the development cycle, slowing down the FinTech systems and causing unnecessary alerts. We present EQDAC, an efficient decision procedure to determine the data constraint equivalence. We first propose the symbolic representation for semantic encoding and then introduce two light-weighted analyses to refute and prove the equivalence, respectively, which are proved to achieve in polynomial time. We evaluate EQDAC upon 30,801 data constraints in a FinTech system. It is shown that EQDAC detects 11,538 equivalent data constraints in three hours. It also supports efficient equivalence searching with an average time cost of 1.22 seconds, enabling the system to check new data constraints upon submission.

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“…In academic research on instance analysis, the preprocessing that mines associations between data include categorized data. For example, for the data conflict problem in data fusion, in [14], the conflict can be divided into two categories: uncertain conflict and contradictory conflict, and then the duplicate data of the same representation are fused, thus solving problems such as the possible conflict between different values for the same attribute. Reference [15] proposes that solutions such as name and description matching in schema matching can be used for element-level instance analysis.…”

Section: Related Workmentioning

confidence: 99%

Entity Relationship Modeling for IoT Data Fusion Driven by Dynamic Detecting Probe

Tao

Guo

et al. 2022

Security and Communication Networks

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To solve the problem of integrating and fusing scattered and heterogeneous data in the process of data space construction, we propose a novel entity association relationship modeling approach driven by dynamic detecting probes. By deploying acquisition units between the business logic layer and data access layer of different applications and dynamically collecting key information such as global data structure, related data, and access logs, the entity association model for enterprise data space is constructed from three levels: schema, instance, and log. At the schema association level, a multidimensional similarity discrimination algorithm combined with semantic analysis is used to achieve the rapid fusion of similar entities; at the instance association level, a combination of feature vector-based similarity analysis and deep learning is used to complete the association matching of different entities for structured data such as numeric and character data and unstructured data such as long text data; at the log association level, the association between different entities and attributes is established by analyzing the equivalence relationships in the data access logs. In addition, to address the uncertainty problem in the association construction process, a fuzzy logic-based inference model is applied to obtain the final entity association construction scheme.

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Data reconciliation and fusion methods: a survey

Cited by 6 publications

References 52 publications

Digital Receipts of Online Transactions in the Reconciliation Process and the Preparation of Financial Reports

Digital Receipts of Online Transactions in the Reconciliation Process and the Preparation of Financial Reports

Verifying Data Constraint Equivalence in FinTech Systems

Entity Relationship Modeling for IoT Data Fusion Driven by Dynamic Detecting Probe

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