Design and Implementation of a Graph-Based Solution for Tracking Manufacturing Products

Martinez‐Gil, Jorge; Stumpner, Reinhard; Lettner, Christian; Pichler, Mario; Fragner, Werner

doi:10.1007/978-3-030-30278-8_41

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…The advantage of using graphs is the ability to represent more complex data models and support dynamic schemas. In particular, graph databases have proven successful for companies that store their product data [16], financial data, and industrial data hierarchically.…”

Section: Research Resultsmentioning

confidence: 99%

“…Based on the search results with the keyword "graph database," the products discussed show different implementation trends. For example, the NoSQL type database is used in building applications for tourist navigation [12] indexing queries [13], developing applications [14], visualizing using GDB and LDA [15], and designing and implementing NoSQL for tracking applications [16]. The primary studies we analyzed vary widely, ranging from studies that discuss in general graph databases, such as descriptive studies [11], comparative studies [6][17] [18], literature reviews and surveys [19][20], to studies that discuss in particular, such as the article on partition graph [21], new framework or model [22], and graph for machine learning implementation [23] [24].…”

Section: Characterization Of Selected Studiesmentioning

confidence: 99%

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Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Rizaldy¹,

Fahriah

Hartono³

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Planning, developing, and updating software cannot be separated from the role of the database. From various types of databases, graph databases are considered to have various advantages over their predecessor, relational databases. Graph databases then become the latest trend in the software and data science industry, apart from the development of graph theory itself. The proliferation of research on GDB in the last decade raises questions about what topics are associated with GDB, what industries use GDB in its data processing, what the GDB models are, and what types of GDB have been used most frequently by users in the last few years. This article aims to answer these questions through a Literature Review, which is carried out by determining objectives, determining the limits of review coverage, determining inclusion and exclusion criteria for data retrieval, data extraction, and quality assessment. Based on a review of 60 studies, several research topics related to GDB are Semantic Web, Big Data, and Parallel computing. A total of 19 (30%) studies used Neo4j as their database. Apart from Social Networks, the industries that implement GDB the most are the Transportation sector, Scientific Article Networks, and general sectors such as Enterprise Data, Biological data, and History data. This Literature Review concludes that research on the topic of the Graph Database is still developing in the future. This is shown by the breadth of application and the variety of new derivatives of GDB products offered by researchers to address existing problems.

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Section: Research Resultsmentioning

confidence: 99%

Section: Characterization Of Selected Studiesmentioning

confidence: 99%

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Rizaldy¹,

Fahriah

Hartono³

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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show abstract

“…In our previous work [19], we presented a specific approach for the design and solution of a tailored solution for a manufacturing company located in Upper Austria. In the present work, our major contribution here is an extension of our previous work to build a general solution for appropriately tracking the manufacturing products that have different kinds of dependencies (evolution, distribution, and so on).…”

Section: Introductionmentioning

confidence: 99%

General Model for Tracking Manufacturing Products Using Graph Databases

Martinez‐Gil

Stumptner²,

Lettner

et al. 2020

Lecture Notes in Business Information Processing

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One of the major problems in the manufacturing industry consists of the fact that, when manufacturing a product, many parts from different lots are supplied and mixed to a certain degree during an indeterminate number of stages, what makes it very difficult to trace each of these parts from its origin to its presence in a final product. In order to overcome this limitation, we have worked towards the design of a general solution aiming to improve the traceability of the products from several manufacturers. This solution is based on the exploitation of graph databases which allows us to significantly reduce response times compared to traditional relational systems.

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Data Integration, Management, and Quality: From Basic Research to Industrial Application

Ehrlinger

Lettner

Fragner³

et al. 2022

Communications in Computer and Information Science

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Design and Implementation of a Graph-Based Solution for Tracking Manufacturing Products

Cited by 3 publications

References 3 publications

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

Systematic Literature Review: Current Products, Topic, and Implementation of Graph Database

General Model for Tracking Manufacturing Products Using Graph Databases

Data Integration, Management, and Quality: From Basic Research to Industrial Application

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