Real-time data integration of an internet-of-things-based smart warehouse: a case study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ammar Mohamed Aamer ◽  
Chelinka Rafiesta Sahara
Keyword(s):  
Internet Of Things ◽  
Data Integration ◽  
Real Time ◽  
Inventory Management ◽  
Radio Frequency Identification ◽  
Low Cost ◽  
Future Research ◽  
Time Data ◽  
Content Type ◽  
Real Time Data

Purpose Creating a real-time data integration when developing an internet-of-things (IoT)-based warehouse is still faced with challenges. It involves a diverse knowledge of novel technology and skills. This study aims to identify the critical components of the real-time data integration processes in IoT-based warehousing. Then, design and apply a data integration framework, adopting the IoT concept to enable real-time data transfer and sharing. Design/methodology/approach The study used a pilot experiment to verify the data integration system configuration. Radio-frequency identification (RFID) technology was selected to support the integration process in this study, as it is one of the most recognized products of IoT. Findings The experimentations’ results proved that data integration plays a significant role in structuring a combination of assorted data on the IoT-based warehouse from various locations in a real-time manner. This study concluded that real-time data integration processes in IoT-based warehousing could be generated into three significant components: configuration, databasing and transmission. Research limitations/implications While the framework in this research was carried out in one of the developing counties, this study’s findings could be used as a foundation for future research in a smart warehouse, IoT and related topics. The study provides guidelines for practitioners to design a low-cost IoT-based smart warehouse system to obtain more accurate and timely data to support the quick decision-making process. Originality/value The research at hand provides the groundwork for researchers to explore the proposed theoretical framework and develop it further to increase inventory management efficiency of warehouse operations. Besides, this study offers an economical alternate for an organization to implement the integration software reasonably.

Cassandra-based data repository design for food supply chain traceability

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandeep Kumar Singh ◽  
Mamata Jenamani
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Query Language ◽  
Data Repository ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Traceability System ◽  
Real Time Data

Purpose The purpose of this paper is to design a supply chain database schema for Cassandra to store real-time data generated by Radio Frequency IDentification technology in a traceability system. Design/methodology/approach The real-time data generated in such traceability systems are of high frequency and volume, making it difficult to handle by traditional relational database technologies. To overcome this difficulty, a NoSQL database repository based on Casandra is proposed. The efficacy of the proposed schema is compared with two such databases, document-based MongoDB and column family-based Cassandra, which are suitable for storing traceability data. Findings The proposed Cassandra-based data repository outperforms the traditional Structured Query Language-based and MongoDB system from the literature in terms of concurrent reading, and works at par with respect to writing and updating of tracing queries. Originality/value The proposed schema is able to store the real-time data generated in a supply chain with low latency. To test the performance of the Cassandra-based data repository, a test-bed is designed in the lab and supply chain operations of Indian Public Distribution System are simulated to generate data.

scholarly journals The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 147-153
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Antonio Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Decision Making ◽  
Data Acquisition ◽  
Real Time ◽  
Failure Detection ◽  
Cyber Physical Systems ◽  
Future Research ◽  
Time Data ◽  
Physical Systems ◽  
The Real ◽  
Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

scholarly journals Usability in 4D AR: Visualising Multi-temporal Real-time Geo-data in Augmented Reality Environments

2015 ◽  
Vol 9 (4) ◽  
pp. 23 ◽  
Author(s):  
Bernd Resch ◽  
Andreas Wichmann ◽  
Nicolas Göll
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Temporal Variations ◽  
Time Base ◽  
Future Research ◽  
Time Data ◽  
Orientation Data ◽  
Information Density ◽  
Real Time Data ◽  
Multi Temporal

Even though advantages of 3D visualisation of multi-temporal geo-data versus 2D approaches have been widely proven, the particular pertaining challenge of real-time visualisation of geo-data in mobile Digital Earth applications has not been thoroughly tackled so far. In the emerging field of Augmented Reality (AR), research needs comprise finding the optimal information density, the interplay between orientation data in the background and other information layers, using the appropriate graphical variables for display, or selecting real-time base data with adequate quality and suitable spatial accuracy. In this paper we present a concept for integrating real-time data into 4D (three spatial dimensions plus time) AR environments, i.e., data with “high” spatial and temporal variations. We focus on three research challenges: 1.) high-performance integration of real-time data into AR; 2.) usability design in terms of displaying spatio-temporal developments and the interaction with the application; and 3.) design considerations regarding reality vs. virtuality, visualisation complexity and information density. We validated our approach in a prototypical application and extracted several limitations and future research areas including natural feature recognition, the cross-connection of (oftentimes monolithic) AR interface developments and well-established cartographic principles, or fostering the understanding of the temporal context in dynamic 4D Augmented Reality environments.

scholarly journals Design of an IoT based Real Time Environment Monitoring System using Legacy Sensors

2018 ◽  
Vol 210 ◽  
pp. 03008
Author(s):  
Aparajita Das ◽  
Manash Pratim Sarma ◽  
Kandarpa Kumar Sarma ◽  
Nikos Mastorakis
Keyword(s):  
Air Pollution ◽  
Light Intensity ◽  
Real Time ◽  
Environmental Conditions ◽  
Low Cost ◽  
Environment Monitoring ◽  
Time Data ◽  
Graphical Information ◽  
Real Time Data ◽  
Sun Light

This paper describes the design of an operative prototype based on Internet of Things (IoT) concepts for real time monitoring of various environmental conditions using certain commonly available and low cost sensors. The various environmental conditions such as temperature, humidity, air pollution, sun light intensity and rain are continuously monitored, processed and controlled by an Arduino Uno microcontroller board with the help of several sensors. Captured data are broadcasted through internet with an ESP8266 Wi-Fi module. The projected system delivers sensors data to an API called ThingSpeak over an HTTP protocol and allows storing of data. The proposed system works well and it shows reliability. The prototype has been used to monitor and analyse real time data using graphical information of the environment.

Big Data Management in the Context of Real-Time Data Warehousing

2013 ◽  
pp. 150-176
Author(s):  
M. Asif Naeem ◽  
Gillian Dobbie ◽  
Gerald Weber
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Real Time ◽  
Real Life ◽  
Skewed Distribution ◽  
Stream Data ◽  
Time Data ◽  
Master Data ◽  
Real Time Data ◽  
Resource Aware

In order to make timely and effective decisions, businesses need the latest information from big data warehouse repositories. To keep these repositories up to date, real-time data integration is required. An important phase in real-time data integration is data transformation where a stream of updates, which is huge in volume and infinite, is joined with large disk-based master data. Stream processing is an important concept in Big Data, since large volumes of data are often best processed immediately. A well-known algorithm called Mesh Join (MESHJOIN) was proposed to process stream data with disk-based master data, which uses limited memory. MESHJOIN is a candidate for a resource-aware system setup. The problem that the authors consider in this chapter is that MESHJOIN is not very selective. In particular, the performance of the algorithm is always inversely proportional to the size of the master data table. As a consequence, the resource consumption is in some scenarios suboptimal. They present an algorithm called Cache Join (CACHEJOIN), which performs asymptotically at least as well as MESHJOIN but performs better in realistic scenarios, particularly if parts of the master data are used with different frequencies. In order to quantify the performance differences, the authors compare both algorithms with a synthetic dataset of a known skewed distribution as well as TPC-H and real-life datasets.

An Intelligent and Secure Framework for Wireless Information Technology in Healthcare for User and Data Classification in Hospitals

2019 ◽  
pp. 248-276
Author(s):  
Masoud Mohammadian ◽  
Dimitrios Hatzinakos ◽  
Petros Spachos ◽  
Ric Jentzsh
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Data Classification ◽  
Patient Data ◽  
Hospital Environment ◽  
Time Data ◽  
Privacy And Security ◽  
Real Time Data ◽  
Frequency Identification

Real time data acquisition and evaluation are required to save lives. Such data with utilization and application of the latest technologies in hospitals around the world can improve patient treatments and well beings. The delivery of patient's medical data needs to be secure. Secure and accurate real time data acquisition and analysis of patient data and the ability to update such data will assist in reducing cost while improving patient's care. A wireless framework based on radio frequency identification (RFID) can integrate wireless networks for fast data acquisition and transmission, while maintaining the privacy issue. This chapter discusses the development of a framework that can be considered for secure patient data collection and communications in a hospital environment. A new method for data classification and access authorization has also been developed, which will assist in preserving privacy and security of data. Several Case studies are offered to show the effectiveness of this framework.

Trends and Technologies in Big Data Processing

2020 ◽  
pp. 17-42
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

An Intelligent and Secure Framework for Wireless Information Technology in Healthcare for User and Data Classification in Hospitals

2016 ◽  
pp. 452-479
Author(s):  
Masoud Mohammadian ◽  
Dimitrios Hatzinakos ◽  
Petros Spachos ◽  
Ric Jentzsh
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Radio Frequency Identification ◽  
Data Classification ◽  
Patient Data ◽  
Hospital Environment ◽  
Time Data ◽  
Privacy And Security ◽  
Real Time Data ◽  
Frequency Identification

Real time data acquisition and evaluation are required to save lives. Such data with utilization and application of the latest technologies in hospitals around the world can improve patient treatments and well beings. The delivery of patient's medical data needs to be secure. Secure and accurate real time data acquisition and analysis of patient data and the ability to update such data will assist in reducing cost while improving patient's care. A wireless framework based on radio frequency identification (RFID) can integrate wireless networks for fast data acquisition and transmission, while maintaining the privacy issue. This chapter discusses the development of a framework that can be considered for secure patient data collection and communications in a hospital environment. A new method for data classification and access authorization has also been developed, which will assist in preserving privacy and security of data. Several Case studies are offered to show the effectiveness of this framework.

Customer behavior analysis using real-time data processing

2019 ◽  
Vol 31 (1) ◽  
pp. 265-290 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Fazal Ijaz ◽  
Muhammad Syafrudin ◽  
M. Alex Syaekhoni ◽  
Norma Latif Fitriyani ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Association Rule ◽  
Customer Behavior ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Big Data Technology

PurposeThe purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.Design/methodology/approachIn order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.FindingsFirst, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.Research limitations/implicationsThis research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.Practical implicationsThe proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.Social implicationsThe proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.Originality/valueThe key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

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