Big Log Data Research Articles

Influencing factors and countermeasures on intelligent transformation and upgrading of logistics firms: A case study in China.

This study explores the influencing factors on intelligent transformation and upgrading of China's logistics firms under smart logistics, and designs the corresponding framework to guide the practice of firms. By analyzing the characteristics of smart logistics and the transformation and upgrading needs of traditional logistics, from the micro perspective of logistics firms, this paper constructs influencing factor index system of smart transformation and development from four dimensions: logistics technology innovation, logistics big data sharing, logistics management upgrading and logistics decision-making transformation. Logistics firms are divided into firms with medium scale and above and small and medium-sized firms according to their scale. Then EWIF-AHP model is proposed to measure the weight of index system and score the decision-making, so as to evaluate the impact of various influencing factors on transformation and development of logistics firms. The results show that, for logistics firms above medium scale, logistics technology innovation and logistics big data sharing have the most significant impact on transformation and development, followed by logistics management upgrading and logistics decision-making transformation. For small and medium-sized logistics firms, the biggest factor is the upgrading of logistics management, followed by the upgrading of logistics technology, which is almost as important as the influencing factors of the upgrading of logistics management, and followed by the sharing of logistics big data and the transformation of logistics decision-making. Therefore, corresponding countermeasures and suggestions for intelligent transformation of logistics firms have been put forward.

RFID-based logistics big data asset evaluation and data mining research

Abstract With the rapid rise of e-commerce platforms, in view of the sharp increase in the amount of data in the logistics system, the timely update and processing of relevant logistics information data have assumed a particular relevance. In this paper, we fully draw on the excellent performance of radio frequency identification (RFID) technology and data mining technology, begin by using RFID technology to authenticate logistics commodities, move on to extracting relevant feature information and finally carry out a detailed comparison between k-nearest neighbour algorithm, support vector machine (SVM) algorithm, logistic regression (LR) algorithm and improved LR algorithm. The algorithm provides a solution method for asset information collection channel and data mining classification algorithm. It meets the needs of different customers, and provides a variety of working modes, which helps to improve the time and operation efficiency of data processing algorithms. The results show that the SVM algorithm only achieves 93.8% accuracy when iterating 50 times for the classification results of the classification samples containing the objective functions of x 1 and x 2. The improved LR algorithm stochastic gradient descent algorithm has a classification accuracy of 94.6% after 50 iterations. The RFID-based logistics big data asset evaluation and data mining research identification algorithm has obvious advantages, and the accuracy rate reaches 97.3%.

A Blockchain-Based Privacy Protection Application for Logistics Big Data

Logistics business is generally managed by logistics orders in plain text, and there is a risk of disclosure of customer privacy information in every business link. In order to solve the problem of privacy protection in logistics big data system, a new kind of logistics user privacy data protection scheme is proposed. First of all, an access rights management mechanism is designed by combining block chain and anonymous authentication to realize the control and management of users' access rights to private data. Then, the privacy and confidentiality protection between different services is realized by dividing and storing the data of different services. Finally, the participants of the intra-chain private data are specified by embedding fields in the logistics information. The blockchain node receiving the transaction is used as the transit node to synchronize the intra-chain privacy data, so as to improve the intra-chain privacy protection within the business. Experimental results show that the proposed method can satisfy the privacy requirements and ensure considerable performance.

Study on the application of big data techniques for the third-party logistics using novel support vector machine algorithm

PurposeThis paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.Design/methodology/approachIn this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.FindingsWith the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.Research limitations/implicationsThe threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.Practical implicationsIt can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.Originality/valueThe classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

Research on Visual Logistics Big Data Information Service System

Big data visualization is a new technology, from the core functions of data collection, storage, calculation, scheduling, advanced search, and other core functions, and based on cloud computing to scientifically manage data to ensure data security and stability. How to use big data visualization in logistics management is a problem that needs to be solved now. The paper introduces the main functional modules of the urban logistics big data platform and its system design and implementation. The platform is an information service platform with “big data” as the core and “data analysis” as the center, using cloud computing, significant data tooling, and other information technologies are used to establish an urban logistics big data platform to realize comprehensive functions such as credit evaluation of logistics and warehousing enterprises, display of warehousing space distribution and analysis of warehousing resource usage, and improve the public service capabilities of logistics institutes.

Design of a Support System for Complicated Logistics Location Integrating Big Data

Logistics location is an important component of logistics planning that affects traffic pressure and vehicle emissions. To date, there has not been an adequate study of the integration of big data into the location for a complicated logistics system. This study developed a decision support system that can address location problems for complicated logistics systems, e.g., a multilevel urban underground logistics system (ULS), using logistics big data. First, information needed in the logistics location, such as the traffic performance index (TPI) and the origin/destination (OD) matrix, was collected and calculated using a big data platform, and this information was digitized and represented based on a geographic information system (GIS) tool. Second, a two‐stage location model for a ULS was designed to balance the construction costs and traffic congestion. The first stage is establishing a set‐covering model to identify optimum locations for secondary hubs based on the ant colony optimization algorithm, and the second stage is clustering of the secondary hubs to determine locations for primary hubs using the iterative self‐organizing data analysis technique algorithm (ISODATA). Finally, the Xianlin district of Nanjing, China, was chosen as a case study to validate the effectiveness of the proposed system. The system can be used to facilitate logistics network planning and to promote the application of big data in logistics.

Research on Application of Cold Chain Logistics Management Based on Big Data Technology

Compared with developed countries, the cold chain logistics of agricultural products in china is wasteful, which not only consumes a lot of labor and natural resources, but also reduces the domestic and international competitiveness of agricultural products. The paper analyzes outstanding issues such as cold chain "broken chain", "information islands", and cold chain "food safety". Through the integration of advanced information technologies such as big data and mobile internet, a cold chain logistics engineering management technology platform is established to establish a national The data analysis center and the national cold chain logistics big data real-time monitoring and early warning platform, open up the logistics information chain, realize the full traceability of logistics information, promote the effective docking of logistics information and public service information, and encourage the sharing of information on logistics platforms between regions and industries. interconnection.

Supply Chain Management and Logistics Big Data Challenges in Bulgaria

Abstract The article focuses on the challenges of applying big data approaches to logistics and supply chain management in Bulgaria, while also presenting the challenges, opportunities and problems from a broader perspective. This includes a review of big data in logistics and its subsystems in relation to integration and transformation processes, as well as the role of big data and the relevant technologies in supply chain management. The research framework for the study of Bulgarian companies focuses on their experiences, both positive and negative, with big data. Data was collected using an electronic questionnaire. The respondents were from small, medium-sized and large companies alike. The results demonstrate and explain the companies’ experiences with big data and the level of application thereof. The findings showed that the main areas for the use of big data are order processing, communication processes, reporting systems, inventory management and vehicle database management.

Optimization Application of Fresh Agricultural Products Efficient Logistics Distribution System from the Perspective of Big Data

As a sunrise industry in the development of modern agriculture, the development of fresh agricultural products industry has a huge space. Because of its various categories, and planting all over the country, the logistics distribution problems. The idea of “Internet +” modern agriculture combines big data with fresh agricultural logistics. By analyzing the current situation and mode of logistics distribution of fresh agricultural products, we find out the weak links of fresh agricultural products logistics distribution, and analyze the best way of fresh agricultural products logistics distribution system by using big data technology, which can effectively reduce the wastage of fresh agricultural products in logistics and transportation. Provide optimization platform for logistics distribution between regions. It is expected to provide important theoretical and practical guidance for the development of modern logistics big data of fresh agricultural products.

Log Data Modeling and Acquisition in Supporting SaaS Software Performance Issue Diagnosis

Data logging is helpful for the operation and maintenance manager of SaaS-based solutions to diagnose performance issues. However, long-running SaaS software may generate huge amounts of log data which is difficult to analyze, and it lacks a systematic approach to collect the running log and lacks a unified data structure to normalize the performance-related data. All these threaten the timeliness of SaaS performance issue diagnosis. In this paper, we propose an architecture for log collection and analysis to support the assessment of performance and diagnosis of performance issues of SaaS-based application in cloud computing. The architecture has the three-tier structure and includes a pivot data model to integrate heterogeneous log. The two high-level metrics in the model of Average Response Time (ART) and Request Timeout Rate (RTR) are calculated by statistical measurement and the lower-level metrics are monitored in real-time. Operation and maintenance managers can evaluate the performance of SaaS software based on the high-level metrics, then timely locate the issues from the low-level metrics and take appropriate measures. Thereupon, this study presents the general-purpose technique for the architecture to support real-time big log data collection, access, computation, storage. The proposal has been implemented and validated in a case study.

Research on Intelligent Decision Platform and Key Technologies Based on Logistics Big Data

With the development of computer technology, Internet of Things, smart mobile terminal devices and social networking sites, the era of big data has already arrived. The management and use of massive data is a key issue that many companies are facing today. This article first explained the basic concepts of big data, analyzed and summarized the data sources of big data, and conducted research and analysis on the intelligent decision-making platform and key technologies based on logistics big data.

An empirical study on the intra-urban goods movement patterns using logistics big data

ABSTRACTMovement patterns of intra-urban goods/things and the ways they differ from human mobility and traffic flow patterns have seldom been explored due to data access and methodological limitations, especially from systemic and long timescale perspectives. However, urban logistics big data are increasingly available, enabling unprecedented spatial and temporal resolutions to this issue. This research proposes an analytical framework for exploring intra-urban goods movement patterns by integrating spatial analysis, network analysis and spatial interaction analysis. Using daily urban logistics big data (over 10 million orders) provided by the largest online logistics company in Hong Kong (GoGoVan) from 2014 to 2016, we analyzed two spatial characteristics (displacement and direction) of urban goods movement. Results showed that the distribution of goods displaceFower law or exponential distribution of human mobility trends. The origin–destination flows of goods were used to build a spatially embedded network, revealing that Hong Kong became increasingly connected through intra-urban freight movement. Finally, spatial interaction characteristics were revealed using a fitting gravity model. Distance lacked substantial influence on the spatial interaction of goods movement. These findings have policy implications to intra-urban logistics and urban transport planning.

Activating Strategies of Logistics Big Data through Case Studies

본 논문에서는 4차산업혁명을 이끄는 요소기술로서 각광받고 있는 빅데이터 기술을 물류분야에 접목시켜서 활성화 할 수 있는 방안을 찾고자 한다. 이를 위하여 우선 빅데이터의 기술적 정의를 살펴보고 국내외 기술동향을 분석하였다. 이후 우리나라의 공공분야에서 수집관리하고 있는 물류관련 빅데이터를 조사 분석하였다. 이와 함께 해외의 공공분야 물류 빅데이터 구축상황을 조사분석하고 국내사례와 비교하여 시사점을 도출하였다. 그리고 기업이 주도하고 있는 민간분야의 국내외 물류 빅데이터 활용실태를 조사하였다. 이를 바탕으로 시사점을 도출하고 물류 빅데이터 활성화에 대한 법제도적 애로요인을 분석하여 개인정보보호법, 빅 데이터 공개방식, 빅데이터 원천관리 등 개선방안을 제시하였다.In this paper, we try to find a way to combine Big Data technologies that are leading The Fourth Industrial Revolution into logistics. To that end, we first investigated technical definitions of big data and analyzed the technical trends at South Korea and advanced countries. After that, this paper shows variety types of data collected and managed by South Korea’s governmental institutions, which have a great possibility to be aggregated as Big Data. Along with that, we investigated overseas success cases of Big Data applications in logistics fields and provided comparative analysis between domestic cases and foreign cases. This paper also examines current utilization status of Big Data technologies in the field of logistics led by private sectors. Based on findings, we suggested ways to make betterments for a practical application of Big Data technologies. These suggestions include elaborating Personal Information Protection Act, deep considerations on a varied methods of data release, and an advanced management tool for handling Big Data sources.

Cold-Chain Logistics Big Data Problems Based on Food Safety Research

Cold-chain logistics "information island" and "food security" problem, this article through the analysis of the connotation and the present situation of cold chain logistics big data, fusion beidou navigation, Internet of things, cloud computing, big data, advanced information technology such as mobile Internet, establish a food safety data management platform, through the chain of logistics information, realize the logistics information fully traceable, promote logistics information and public service information effective docking, encourage regional and industry logistics information sharing platform, realize the connectivity, fundamentally solve the problem of food safety.

Infrastructure with r package for anomaly detection in real time big log data

Analyzing and detecting anomalies in huge amount of data are a big challenge. On one hand we are faced with the problem of storing a large amount of data, on the other to process it and detect anomalies in reasonable or even real time. Real time analytics can be defined as the capacity to use all available enterprise data and sources in the moment they arrive or happen in the system. In this paper, we present an infrastructure that we have implemented in order to analyze data from big log files in real time. Also we present algorithms that are used for anomaly detection in big data. The algorithms are implemented in R language. The main components of the infrastructure are Redis, Logstash, Elasticsearch, elastic-R client and Kibana. We explore implementation of several filters in order to post-process the log information and produce various statistics that suit our needs in analyzing log files containing SQL queries from a big national system in education. The post-processing of the SQL queries is mainly focused on preparing the log information in adequate format and information extraction. The other interesting part of the paper is to compare the anomaly detection algorithms and to conclude which of them is better to us for our needs. Also we add the elastic-R client to the infrastructure we develop for big data analytic in order to detect anomalies. The purpose of the analysis is to monitor performance and detect anomalies in order to prevent possible problems in real time.

Logistics business analytics for achieving environmental sustainability

Although many logistics firms seek to attain environmental sustainability, they have little knowledge of how to generate appropriate eco-strategies that can achieve environmental sustainability. Moreover, few studies have investigated how to go green in the field of logistics management by exploring logistics big data. This study aims to tackle environmental sustainability issues in logistics systems through big data analytics. Specifically, this study proposes a research framework of green logistics business analytics involving descriptive, predictive and prescriptive analytics. Logistics companies can utilize the proposed business analytics framework to identify key eco-drivers and thereby implement effective environmental logistics management.

Visualization of RFID-enabled shopfloor logistics Big Data in Cloud Manufacturing

Cloud Manufacturing twining with Internet of Things (IoT) has been waked up to achieve final intelligent manufacturing. With the IoT technologies such as radio frequency identification (RFID) implemented in manufacturing sites, enormous data will be generated. Such data are so complex, abstract, and variable so that it is difficult to make full use of the data which carry great myriad of useful information and knowledge. This paper presents a visualization approach for the RFID-enabled shopfloor logistics Big Data from Cloud Manufacturing. An innovative RFID-Cuboid model is used for reconstructing the RFID raw data given the production logic and time series. Several contributions are highlighted. Firstly, a possible approach to integrate IoT and Cloud Manufacturing is introduced to upgrade and transform the traditional industry for an intelligent future. Secondly, an RFID-Cuboid model is proposed by using the production logic and time stamps to chain the RFID data so that the data could be interpreted. Thirdly, a real-life case is reported to show the feasibility and practicality of the proposed visualization approach to help different end-users to ease their daily operations. Lessons and insights from this case are meaningful for the implementation of IoT-enabled Cloud Manufacturing and Big Data analytics in industry field.

Big Data Based Logistics Data Mining Platform: Architecture and Implementation

With the development of intelligent logistics, enormous amount of logistics data are be-coming one of the sources of big data. Building the logistics information platform with big data mining and analysis capabilities to make full use of the huge logistics data is the inexorable trend for intelligent logistics. This paper studied the characteristics of the logistics big data, then, a big data based logistics data mining platform is designed and implemented by utilizing big data processing and storage techniques. The architecture and functions of the platform will be described in detail. This paper also studied the mining steps and requirements for logistics data mining, which is significant for practical applications.

Preliminary analysis for integration of spot-scanning proton beam therapy and real-time imaging and gating

PurposeSpot-scanning proton beam therapy (PBT) can create good dose distribution for static targets. However, there exists larger uncertainty for tumors that move due to respiration, bowel gas or other internal circumstances within the patients. We have developed a real-time tumor-tracking radiation therapy (RTRT) system that uses an X-ray linear accelerator gated to the motion of internal fiducial markers introduced in the late 1990s. Relying on more than 10 years of clinical experience and big log data, we established a real-time image gated proton beam therapy system dedicated to spot scanning. Materials and methodsUsing log data and clinical outcomes derived from the clinical usage of the RTRT system since 1999, we have established a library to be used for in-house simulation for tumor targeting and evaluation. Factors considered to be the dominant causes of the interplay effects related to the spot scanning dedicated proton therapy system are listed and discussed. Results/conclusionsTotal facility design, synchrotron operation cycle, and gating windows were listed as the important factors causing the interplay effects contributing to the irradiation time and motion-induced dose error. Fiducial markers that we have developed and used for the RTRT in X-ray therapy were suggested to have the capacity to improve dose distribution. Accumulated internal motion data in the RTRT system enable us to improve the operation and function of a Spot-scanning proton beam therapy (SSPT) system. A real-time-image gated SSPT system can increase accuracy for treating moving tumors. The system will start clinical service in early 2014.

클라우드 환경에서의 효율적인 빅 데이터 처리를 위한 로그 데이터 수집 아키텍처

Big data management is becoming increasingly important in both industry and academia of information science community. One of the important categories of big data generated from software systems is log data. Log data is generally used for better services in various service providers and can also be used as information for qualification. This paper presents a big data management architecture specialized for log data. Specifically, it provides the aggregation of log messages sent from multiple clients and provides intelligent functionalities such as analyzing log data. The proposed architecture supports an asynchronous process in client-server architectures to prevent the potential bottleneck of accessing data. Accordingly, it does not affect the client performance although using remote data store. We implement the proposed architecture and show that it works well for processing big log data. All components are implemented based on open source software and the developed prototypes are now publicly available.