scholarly journals Business Process Reengineering of a Large-Scale Public Forest Enterprise Through Harvester Data Integration

2021 ◽  
Vol 43 (1) ◽  
Author(s):  
Eric R. Labelle ◽  
Julia Kemmerer
Keyword(s):  
Supply Chain ◽  
Data Integration ◽  
Business Process ◽  
Data Exchange ◽  
Large Scale ◽  
Personal Contact ◽  
Production Data ◽  
Practical Implementation ◽  
Negative Consequences ◽  
Data Types

Despite the extensive use of cut-to-length mechanized systems, harvester data remains largely underutilized by most stakeholders in Germany. Therefore, the goal of this study was to determine how business processes should be restructured to allow for a continuous use of forest machine data, with the main focus on harvester production data, along the German wood supply chain. We also wanted to identify possible benefits and challenges of the restructuring through a qualitative analysis of the newly designed business process. The Bavarian State Forest Enterprise was chosen for a case study approach. Based on expert interviews, the current and to-be processes were modeled. Results obtained from the qualitative data indicated that an integration of harvester data is achievable in Germany. Harvester data from forest operations can be provided to all subsequent activities along the supply chain. Core changes were the addition of a digital work order, the data exchange between harvester and forwarder, the pile order and the exchange of production data. Benefits for every stakeholder were determined. Through the reengineered process, harvesting and timber information are available and known at an earlier stage of the process, throughput information stations could be eliminated and working comfort could be improved. Ecological benefits could also be achieved through an anticipated reduction of CO2 emissions and protection of sensitive nature areas. Negative consequences of harvester data integration could appear in the social sphere and were in line with the reduction of personal contact. Challenges for the implementation in reality, besides the legal situation, could be the availability of on-board computers in forwarders, cost for new IT applications, willingness of stakeholders to cooperate and availability of internet access. Further research should be focused on the combination of harvester data with other data types and the practical implementation of the TB process.

scholarly journals Integration of Harvester Production Data in German Wood Supply Chains: Legal, Social and Economic Requirements

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 460
Author(s):  
Florian Hartsch ◽  
Julia Kemmerer ◽  
Eric R. Labelle ◽  
Dirk Jaeger ◽  
Thilo Wagner
Keyword(s):  
Supply Chain ◽  
Data Integration ◽  
Data Exchange ◽  
Qualitative Content Analysis ◽  
Personal Data ◽  
Economic Conditions ◽  
Production Data ◽  
Similar Data ◽  
Wood Supply ◽  
Data Ownership

Digitalization and its associated technology are shaping the world economy and society. Data collection, data exchange, and connection throughout the wood supply chain have become increasingly important. There exist many technologies for the implementation of Industry 4.0 applications in forestry. For example, the integration of harvester production data throughout the wood supply chain seems to have strong optimization potential but it is faced with several challenges due to the high number of stakeholders involved. Therefore, the objective of this article is to analyze the legal, social, and economic conditions surrounding the integration of harvester production data integration in Germany. For analysis of the legal and economic conditions, a narrative literature analysis was performed with special consideration of the relevant German and European legal references. For determination of the social conditions, a qualitative content analysis of 27 expert interviews was performed. Results showed that legal ownership of harvester production data cannot be clearly defined in Germany, but there exist several protection rights against misuse, which can define an ownership-similar data sovereignty. Furthermore, harvester data use can be restricted in the case where personal data are traceable, based on European data protection law. From a social perspective, the stakeholders interviewed in the study had different opinions on data ownership. Stakeholders require specific criteria on the data (interfaces) and other factors for the acceptance of new structures to allow successful harvester data integration. From an economic perspective, harvester production data are tradeable through varying transaction forms but, generally, there is no accepted and valid formula in existence for calculating the value or price of harvester data. Therefore, the authors advise discussing these issues with key stakeholders to negotiate and agree on data ownership and use in order to find a suitable solution to realize optimization potentials in the German wood supply chain.

scholarly journals FoodSQRBlock: Digitizing food production & supply chain with blockchain & QR code in the cloud

2021 ◽  
Author(s):  
Somdip Dey ◽  
Suman Saha ◽  
Amit Singh ◽  
Klaus D. Mcdonald-Maier
Keyword(s):  
Supply Chain ◽  
Food Safety ◽  
Production System ◽  
Food Production ◽  
Large Scale ◽  
Production Data ◽  
Blockchain Technology ◽  
Large Scale Integration ◽  
Scale Integration ◽  
Food Production System

<div><div><div><p>Food safety is an important issue in today’s world. Traditional agri-food production system doesn’t offer easy traceability of the produce at any point of the supply chain, and hence, during a food-borne outbreak, it is very difficult to sift through food production data to track produce and origin of the outbreak. In recent years, blockchain based food production system has resolved this challenge, however, none of the proposed methodologies makes the food production data easily accessible, traceable and verifiable by consumers or producers using mobile/edge devices. In this paper, we propose FoodSQRBlock (Food Safety Quick Response Block), a blockchain technology based framework, which digitizes the food production information, and makes it easily accessible, traceable and verifiable by the consumers and producers by using QR codes. We also propose a large scale integration of FoodSQRBlock in the cloud to show the feasibility and scalability of the framework, and experimental evaluation to prove that.</p></div></div></div>

scholarly journals FoodSQRBlock: Digitizing food production & supply chain with blockchain & QR code in the cloud

2020 ◽  
Author(s):  
Somdip Dey ◽  
Suman Saha ◽  
Amit Singh ◽  
Klaus D. Mcdonald-Maier
Keyword(s):  
Supply Chain ◽  
Food Safety ◽  
Production System ◽  
Food Production ◽  
Large Scale ◽  
Production Data ◽  
Blockchain Technology ◽  
Large Scale Integration ◽  
Scale Integration ◽  
Food Production System

<div><div><div><p>Food safety is an important issue in today’s world. Traditional agri-food production system doesn’t offer easy traceability of the produce at any point of the supply chain, and hence, during a food-borne outbreak, it is very difficult to sift through food production data to track produce and origin of the outbreak. In recent years, blockchain based food production system has resolved this challenge, however, none of the proposed methodologies makes the food production data easily accessible, traceable and verifiable by consumers or producers using mobile/edge devices. In this paper, we propose FoodSQRBlock (Food Safety Quick Response Block), a blockchain technology based framework, which digitizes the food production information, and makes it easily accessible, traceable and verifiable by the consumers and producers by using QR codes. We also propose a large scale integration of FoodSQRBlock in the cloud to show the feasibility and scalability of the framework, and experimental evaluation to prove that.</p></div></div></div>

scholarly journals Scalable analysis of multi-modal biomedical data

GigaScience ◽  
2021 ◽  
Vol 10 (9) ◽  
Author(s):  
Jaclyn Smith ◽  
Yao Shi ◽  
Michael Benedikt ◽  
Milos Nikolic
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Treatment Options ◽  
Complex Data ◽  
Biomedical Data ◽  
Data Types ◽  
Large Scale Data Processing ◽  
Scalable Analysis ◽  
Targeted Medicine ◽  
The Impact

Abstract Background Targeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look of the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable data integration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex datatypes. Solution To address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types. Performance We outline research and clinical applications for the platform, including data integration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on “flattening” complex data structures, and runs efficiently when alternative approaches are unable to perform at all.

scholarly journals FoodSQRBlock: Digitizing Food Production and the Supply Chain with Blockchain and QR Code in the Cloud

2021 ◽  
Vol 13 (6) ◽  
pp. 3486
Author(s):  
Somdip Dey ◽  
Suman Saha ◽  
Amit Kumar Singh ◽  
Klaus McDonald-Maier
Keyword(s):  
Supply Chain ◽  
Food Safety ◽  
Production System ◽  
Food Production ◽  
Large Scale ◽  
Production Data ◽  
Blockchain Technology ◽  
Large Scale Integration ◽  
Scale Integration ◽  
Food Production System

Food safety is an important issue in today’s world. The traditional agri-food production system does not offer easy traceability of the produce at any point of the supply chain, and hence, during a food-borne outbreak, it is very difficult to sift through food production data to track produce and the origin of the outbreak. In recent years, the blockchain based food production system has resolved this challenge; however, none of the proposed methodologies makes the food production data easily accessible, traceable and verifiable by consumers or producers using mobile/edge devices. In this paper, we propose FoodSQRBlock (Food Safety Quick Response Block), a blockchain technology based framework that digitises the food production information and makes it easily accessible, traceable and verifiable by the consumers and producers by using QR codes. We also propose a large-scale integration of FoodSQRBlock in the cloud to show the feasibility and scalability of the framework, as well as give an experimental evaluation to prove this.

scholarly journals FoodSQRBlock: Digitizing food production & supply chain with blockchain & QR code in the cloud

2020 ◽  
Author(s):  
Somdip Dey ◽  
Suman Saha ◽  
Amit Singh ◽  
Klaus D. Mcdonald-Maier
Keyword(s):  
Supply Chain ◽  
Food Safety ◽  
Production System ◽  
Food Production ◽  
Large Scale ◽  
Production Data ◽  
Blockchain Technology ◽  
Large Scale Integration ◽  
Scale Integration ◽  
Food Production System

<div><div><div><p>Food safety is an important issue in today’s world. Traditional agri-food production system doesn’t offer easy traceability of the produce at any point of the supply chain, and hence, during a food-borne outbreak, it is very difficult to sift through food production data to track produce and origin of the outbreak. In recent years, blockchain based food production system has resolved this challenge, however, none of the proposed methodologies makes the food production data easily accessible, traceable and verifiable by consumers or producers using mobile/edge devices. In this paper, we propose FoodSQRBlock (Food Safety Quick Response Block), a blockchain technology based framework, which digitizes the food production information, and makes it easily accessible, traceable and verifiable by the consumers and producers by using QR codes. We also propose a large scale integration of FoodSQRBlock in the cloud to show the feasibility and scalability of the framework, and experimental evaluation to prove that.</p></div></div></div>

scholarly journals Data integration of structured and unstructured sources for assigning clinical codes to patient stays

2015 ◽  
Vol 23 (e1) ◽  
pp. e11-e19 ◽  
Author(s):  
Elyne Scheurwegs ◽  
Kim Luyckx ◽  
Léon Luyten ◽  
Walter Daelemans ◽  
Tim Van den Bulcke
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Early Data ◽  
Data Sources ◽  
Data Types ◽  
Late Data ◽  
Electronic Health Record Data ◽  
Medical Specialties ◽  
Electronic Health

Abstract Objective Enormous amounts of healthcare data are becoming increasingly accessible through the large-scale adoption of electronic health records. In this work, structured and unstructured (textual) data are combined to assign clinical diagnostic and procedural codes (specifically ICD-9-CM) to patient stays. We investigate whether integrating these heterogeneous data types improves prediction strength compared to using the data types in isolation. Methods Two separate data integration approaches were evaluated. Early data integration combines features of several sources within a single model, and late data integration learns a separate model per data source and combines these predictions with a meta-learner. This is evaluated on data sources and clinical codes from a broad set of medical specialties. Results When compared with the best individual prediction source, late data integration leads to improvements in predictive power (eg, overall F-measure increased from 30.6% to 38.3% for International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnostic codes), while early data integration is less consistent. The predictive strength strongly differs between medical specialties, both for ICD-9-CM diagnostic and procedural codes. Discussion Structured data provides complementary information to unstructured data (and vice versa) for predicting ICD-9-CM codes. This can be captured most effectively by the proposed late data integration approach. Conclusions We demonstrated that models using multiple electronic health record data sources systematically outperform models using data sources in isolation in the task of predicting ICD-9-CM codes over a broad range of medical specialties.

scholarly journals Scalable Analysis of Multi-Modal Biomedical Data

2020 ◽  
Author(s):  
Jaclyn M Smith ◽  
Yao Shi ◽  
Michael Benedikt ◽  
Milos Nikolic
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Treatment Options ◽  
Complex Data ◽  
Biomedical Data ◽  
Data Types ◽  
Large Scale Data Processing ◽  
Scalable Analysis ◽  
Targeted Medicine ◽  
The Impact

Targeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look of the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable data integration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex datatypes. To address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types. We outline research and clinical applications for the platform, including data integration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on flattening complex data structures, and runs efficiently when alternative approaches are unable to perform at all.

The Necessity and Ways of Legitimation of Large-scale Private Property in Russia: Raising a Problem

2007 ◽  
pp. 4-26
Author(s):  
G. Yavlinsky
Keyword(s):  
Large Scale ◽  
Private Property ◽  
Negative Consequences ◽  
Specific Nature ◽  
Considerable Part ◽  
Russian People ◽  
National Importance ◽  
Historical Tradition ◽  
Special Rules ◽  
Economic Assets

Results of privatization campaign in 1990’s continue to meet strong opposition from a very considerable part of Russian people and authorities actually refuse to consider the rights of private owners legitimate and not subject to violation. One of the reasons for this, besides historical tradition, is a specific nature of Russian privatization of 1990’s. The article brings to discussion a set of measures aimed at overcoming its negative consequences. While insisting on the need to honor all previous government obligations and commitments, the paper proposes a one-time special tax (windfall tax) to be levied on those who benefited most from privatization deals that were not just and fair, and special rules to be set for the use and sale of economic assets of national importance. The author also considers possible ways to legitimize private property, as well as chances to achieve а broad public consensus on this issue in Russia.

From Supply Chain Management to Blockchain-Based Management

2019 ◽  
pp. 59-63
Author(s):  
G. V. Zubakov ◽  
O D. Protsenko ◽  
I. O. Protsenko
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Digital Economy ◽  
Practical Implementation ◽  
Chain Management ◽  
Mutual Trade ◽  
Blockchain Technology ◽  
Distributed Ledger ◽  
Supply Chain Management System ◽  
The Government

The presented study addresses the current problems in the implementation of the distributed ledger (blockchain) technology in supply chain management mechanisms in the context of the digital economy. Aim. The study aims to analyze the application of the blockchain technology in modern economic processes from the perspective of logistics.Tasks. The authors consider the possibility of using the blockchain technology in the supply chain management system and explore ways to use the findings of the Eurasian Economic Commission (EEC) in the fieldof digital economy to organize information standardization processes within the supply chains of foreign and mutual trade.Methods. This study uses general scientific methods of cognition to examine approaches to the implementation of the blockchain technology in transport and logistics processes and to find opportunities for the implementation of smart contracts to ensure the traceability of the entire chain of commodity and information fl ws.Results. Implementation of the distributed ledger (blockchain) technology in the logistics processes of foreign and mutual trade increases the transparency of information fl ws and the speed of decisionmaking. This technology would allow the parties to negotiate directly, minimizing potential risks and the time required to approve a supply deal.Conclusions. The authors consider the possibility of using a systematic approach to the digitalization of transport and logistics processes and the subsequent standardization of information interaction at the B2B, B2G, and G2G levels, segmented by separate fields of transport and foreign trade and individual economic sectors. As a conclusion, the study assesses the prospects of the practical implementation of blockchain mechanisms in the creation of industrial platforms — digital platforms that provide integrated services for businesses and the government using a single window system.

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