Abstract

     With the emergence of battery-based electric vehicles, transportation systems gradually leave using fossil fuel-based combustion engines. Due to their reasonable performance, Lithium-ion batteries have become one of the major batteries used for electric vehicles. Although these batteries are being used in most companies, their high production cost, rare raw material, and short life cycle have raised important incentives for their recovery process. However, locating a recovery center for end-of-life Lithium-ion batteries is a multi-aspect decision making problem influenced by many criteria. For this purpose, a novel integrated decision-making model is developed based on Measuring Attractiveness by a Categorical Based Evaluation TecHnique (MACBETH) for calculating the criteria weights and Weight Aggregated Sum Product ASsessment (WASPAS) methods under the fuzzy environment with Dombi norms for evaluating the alternatives to address recovery center location selection problem considering technical as well as environmental, economic, and social aspects. To show the reliability and applicability of the developed method, a real-world case study in Istanbul is investigated. The developed method is used to evaluate six potential locations for the possible establishment of a recovery center. Results showed that Tuzla district is the most suitable location for opening a recovery center for end-of-life Lithium-ion batteries. Tuzla is in a very good position in terms of proximity to suppliers, transportation and location. To illustrate the robustness of the obtained results, extensive sensitivity analysis tests are performed.

Introduction

     Electric vehicles (EVs) are an appealing solution for the decarburization of the transportation sector (Romero-Ocaño et al., 2022, Zhang et al., 2020). It is estimated that more than 125 million EVs will be on the road worldwide by 2030 (Hua et al., 2020). Lithium-ion batteries (LiBs) have exponential growth and a key portion of industry investments (Chen et al., 2019, Cui et al., 2022). An automobile Lithium-ion battery (ALiB) is a major component of an EV (Pelletier et al., 2017, Ramoni and Zhang, 2013). ALiBs provide the required energy storage for EVs due to the superiority of high energy density, high output voltage, low self-discharge rate, and long cycling life (Tang et al., 2019, Wang et al., 2022). They are composed of a cathode, an anode, an electrolyte, and a separator (Olivetti, Ceder, Gaustad, & Fu, 2017). The useful lifetime of ALiBs is 120,000–240,000 km (Onat, Kucukvar, Tatari, & Zheng, 2016).

     Approximately 11 million ALiBs are expected to be sold worldwide by 2020 (Li et al., 2018; Alamerew & Brissaud, 2020). Due to the degradation in capacity and quality, the service life of these complex multiple material products, which belong to class 9 of dangerous goods, is 5–10 years (Li et al., 2018; Chen et al., 2019, Tang et al., 2019, Alamerew and Brissaud, 2020, Li et al., 2020). ALiB is replaced when the capacity has reached 70–80 % of its initial capacity (Alamerew and Brissaud, 2020, Hua et al., 2020, Ramoni and Zhang, 2013).

Conclusions

     The emergence of EVs has been a noticeable point for transportation systems to transform from fossil fuel-based vehicles to cleaner vehicles which require lower energy costs and also produce lower negative environmental, economic, and social impacts. The utilization of ALiBs is of great importance for EVs, but more and more valuable resources are being depleted without appropriate recovery. Therefore, countries should consider establishing recovery centers for ALiBs as soon as possible. However, locating a recovery center is a complex and multi-aspect decision-making problem. For this purpose, we developed a novel decision-making approach based on the MACBETH-D-WASPAS model under the fuzzy environment. The proposed integrated fuzzy decision-making approach empowers experts in the field of LiB management to enhance their decision-making capabilities and select the most suitable location for an EoL ALiB recovery center. Besides, the real-life case study of Istanbul is provided to show the feasibility and applicability of the developed methodology for solving the recovery center location selection problem. Results showed that Sariyer and Büyükçekmece are top first and second locations that a recovery center for an EoL ALiB recovery center. On the other hand, results pointed out that Ümraniye is the least preferred location for establishment of an EoL ALiB recovery center.

Abstract

     Advanced models of Artificial Intelligence enable systems of IoT to work with great flexibility to the needs of users. In this article we present our developed IoT system for driving support by the use of type-2 fuzzy logic control module. We have developed the IoT system to collect the data about driving conditions and evaluate them adjusting to the needs of user. Applied module of fuzzy logic of the second type was used in analysis of accelerometers signals to flexibly adjust to uncertainty of evaluation of driving expectations of each driver. Our developed system was tested in different cars by driving on various roads and results show excellent efficiency.

Introduction

     Development of real-time Internet of Things (IoT) applications improves technological advances in intelligent transportation and car diagnostic systems. We can read about recent ideas which make Cyber–Physical Systems (CPS) not only supportive to humans but also predictive in potential malfunctions of appliances that we all use in our daily routine. In smart car IoT systems are developed by using sensors and devoted smart technologies to compose innovative control processing.

     IoT systems for cars can work with many different features used in control and diagnostics to autonomous driving aspects. In Cao et al. (2020) was presented a model for smart front detection by using Singular Spectrum Decomposition (SSD) model. Model explained in Luque-Vega, Michel-Torres, Lopez-Neri, Carlos-Mancilla, and González-Jiménez (2020) was using SPIN-V sensors to improve procedure of safe parking. We can also implement models which will prevent collisions and help to maintain elements of car engine and suspension in good conditions. Idea presented in Krishnan (2018) was based on readings from ultrasonic sensor which measured distances to objects and helped in managing of safe drive. Very important for smart cars is also design of software. In practice we can use operating system in smartphones and just implement apps to connect to car sensors, i.e. Minnetti et al. (2020) proposed a smartphone based flush measurement to improve car body assembly procedure. System presented in Saeliw, Hualkasin, Puttinaovarat, and Khaimook (2019) was developed as mobile app to support parking procedure by using rfid infrastructure. In Xu et al. (2020) was presented an interesting discussion on networking, communication and applicable wireless technologies which serve as data transfers for smart cars. The model presented in Olivares-Rojas, Reyes-Archundia, Gutiérrez-Gnecchi, González-Murueta, and Cerda-Jacobo (2020) used 5G internet infrastructure to improve smart metering of multi-tire objects.

Conclusions

     In this article we present a type-2 fuzzy system for smart driving support. Proposed system is working in IoT infrastructure of accelerometer sensors placed in a car. Data collected in the car is stored in data base from which can be shared with other users of the system, so that results of each driving can be compared to other drivers and also all users can benefit from shared knowledge of road conditions in different localizations. Our proposed model is using knowledge base developed for expert system evaluating road conditions. Proposed model composition enabled better adjustment to driving style which may differ for different people. Thus, all the features of driving can be better evaluated with tolerance to the style of driving of different people.

     Results of our experiments showed that developed system is very efficient. We have done experiments by using different cars in various locations. We have also asked various drivers to collect data for us. Proposed type-2 fuzzy logic system enabled flexible adjustment to various driving style and various expectations of drivers, however showing correct result of road condition evaluation in each case.

1- Introduction

2- Background overview and related work

3- Proposed systems

4- Proposed system evaluation

5- Conclusions

References

Abstract

Vehicular Ad hoc Networks (VANETs) have gained a great attention due to the rapid development of mobile Internet and Internet of Things (IoT) applications. On the other hand, the competition in the automotive industry has turned into an unprecedented race to who will be the first to provide the fully autonomous cars. However, the fully autonomous driving is still a bit far from deployment, and for now, they are providing automation only at a certain level and, at the same time, are offering connected services through their mobility service platforms. With Fog and Edge computing integrated in VANETs, these mobility platforms will be standardized to provide services for every car on the road, which will help VANETs to accomplish one of its main goals, the road safety. In this paper, we propose an intelligent Fuzzy-based Driver Monitoring System (FDMS) for safe driving. We present and compare two fuzzy-based systems: FDMS1 and FDMS2. To make a decision, FDMS1 considers Vehicle’s Environment Temperature (VET), Noise Level (NL) and Heart Rate (HR). While, for FDMS2, we consider Respiratory Rate (RR) as a new parameter to decide Driver’s Situational Awareness (DSA). We evaluate the ability of the driver to safely operate the vehicle by monitoring his condition and subsequently, based on the system output, a smart box informs the driver and provides assistance. We show through simulations and experiments the effect of the considered parameters on the determination of the driver’s situation and demonstrate the actions that can be performed accordingly.

Introduction

Every year the lives of approximately 1.35 million people are cut short as a result of a road traffic accident. Between 20 and 50 million more people suffer non-fatal injuries, with many incurring a disability as a result of their injury [1]. For years, in order to improve the road safety, many governments have undertaken initiatives by launching Intelligent Transport Systems (ITSs). ITS is a new road and traffic system which combines state-of-the-art information, communication, and control technologies to properly create an information network based on people, vehicles and roads. As a key part of ITS, the Vehicular Ad hoc Networks (VANETs) not only aim to help reducing the traffic accidents but also to enhance the traffic efficiency and the travel comfort of passengers and drivers. In VANETs, vehicles have networking capabilities and they send/receive valuable information such as safety warnings and traffic information to/from adjacent vehicles and roadside units (RSUs) via vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication.

1 Introduction

2 Classic Modal Logic

3 Fuzzy Modal Logic

4 Simulation and bisimulation

5 Bisimilarity

6 Modal Invariance

7 Conclusion

References

This paper intends to contribute with a new fuzzy modal logic to model and reason about transition systems involving uncertainty in behaviours. Our formalism supports fuzziness at transitions and on the proposition symbols assignment levels. Against of other approaches in the literature, our bisimulation and bisimilarity notions generalise the analogous standard notions of classic modal logic and of process algebras. Moreover, the outcome of our logic is also fuzzy, with the semantic interpretation of connectives supported by the G¨odel algebra.

Introduction

For 50 years, fuzzy sets and fuzzy logic have been an area of active research (cf. [13]). Fuzzy automata [17], fuzzy Markov processes [1], fuzzy petri nets [25,16], fuzzy reactive frames [24] and fuzzy discrete event systems [22] are some of the formalisms that have been considering to model computational systems that deal with uncertainty and fuzzy sets. In this work, we will focus on fuzzy transition systems or fuzzy labelled transition systems, which are a generalisation of transition systems or labelled transition systems (widely used in computer science) with [0, 1]- weights on the transitions [26,27,15]. It is well known that bisimulations and simulations are a worth way of comparing two transition systems. They have been considered in several frameworks, such as fuzzy automata [7,8] fuzzy Markov process [9], fuzzy discrete systems [22], weighted labelled transition system [26,27,15]. All of them have special motivations and, consequently, different formulations. For example, in [26,6,5] bisimulations are defined as equivalence relations. There are other approaches that focus on horizontal and vertical bisimulations [15] and some other which define bisimulations as fuzzy relations [7].

1- INTRODUCTION

2- THE BASIC CONCEPTS OF IMPROVED ANT COLONY OPTIMIZATION

3- THE BASIC CONCEPTS AND FRAMEWORK OF INTERVAL TYPE-2 TSK TYPE FUZZY LOGIC SYSTEM

4- THE DESIGN OF A2-C1 TYPE INTERVAL TSK FUZZY LOGIC SYSTEM BASED ON IACO

5- APPLICATION EXAMPLES

6- CONCLUSION

REFERENCES

Abstract

An Improved Ant Colony Optimization (IACO) is proposed to design A2-C1 type fuzzy logic system (FLS) in the paper. The design includes parameters adjustment and rules selection, and the performance of the intelligent fuzzy system, which can be improved by choosing the most optimal parameters and reducing the redundant rules. In order to verify the feasibility of the proposed algorithm, the intelligence fuzzy logic systems based on the algorithms are applied to predict the Mackey-Glass chaos time series. The simulations show that both the IACO and ACO have better tracking performances. The results compared with classical algorithm BP ( back-propagation design) shows the tracking performance of IACO is more precise, the result compared with ACO shows that either the training result or the testing result, the tracking performance of IACO is better, and IACO has a faster convergence rate than ACO, the results compared with the Intelligent type-1 fuzzy logic systems show that both the A2-C1 type FLS based on IACO and ACO have better tracking performance than type-1 fuzzy logic system.

INTRODUCTION

Nowadays, fuzzy logic system is applied into a variety of fields. Professor L. A. Zadeh proposed the conception of fuzzy set first time [1], from that time on, fuzzy logic system attracts a large number of scholars to research. Although, fuzzy logic systems have some advantages, but there are several shortcomings, the main shortcomings are that it is too difficult to obtain the optimal parameters and rule explosion. Recent years, it has been a research hot spot that adopting intelligent algorithms to design of fuzzy logic system, respectively. Lian et al. optimized the radial basis function neural network based on QPSO, and the result was better than BP algorithm and least square method [2]. Zhai et al. combined the QPSO with the non single point interval type-2 fuzzy logic system and used to design of image noise filter, the result was better than traditional non fuzzy methods [3]. Yazdi combined GA with fuzzy logic system, and it was applied to the optimization of off center braced frame system, compared with the structure optimized only by GA, the effect was more remarkable [4]. Juang et al. combined ant colony optimization with fuzzy cluster to design fuzzy logic system, and used for nonlinear plant tracking control, compared with PSO and GA, the results were more better [5].

I- INTRODUCTION

II- MODELING OF THE WIND POWER SYSTEM

III- FUZZY CONTROL OF WECS

IV- SIMULATION RESULTS

V- CONCLUSION

REFERENCES

Abstract

This paper concentrates on fuzzy logic control (FLC) of variable speed wind turbines with doubly fed induction generator(DFIG). The wind energy conversion system (WECS) is equipped with a wind turbine, a mechanical transmission system, a DFIG, a Rotor Side converter (RSC), a Grid Side converter (GSC), and a common DC-link capacitor. The main objective is to regulate the active power by extracting the maximum of power and to have a null stator reactive power. In this study, four FLC are implemented separately in RSC, GSC, DC bus voltage, and the maximum power point tracking (MPPT). The performances of the proposed controllers have been tested and compared with a PI controller. The obtained results show a satisfactory performance in terms of stability, precision, and robustness under variable wind speed conditions.

INTRODUCTION

In the last few years there has been a growing interest in renewal energy resources and their penetration into the power supply grid. Indeed, a considerable attention has been paid to wind energy generation. Currently, the wind system using a DFIG is the more used for production of the electric energy[1][2]. WECS includes a wind turbine, a mechanical transmission system, a DFIG, and a partial scale power electronic converter. Power electronic converter includes two main parts connected by a DC-link capacitor; RSC which feeds rotor circuit and GSC that rectifies grid voltage. Our work consists of modelling of the chain of conversion of the wind energy where the DFIG operates at variable speed. Thereafter, we consider the regulating of powers in order to ensure an optimum operation. Different structures and algorithms strategy can be used for control of power converters. In this article, a Mamdani fuzzy logic controller [3] is applied to control separately the RSC ,GSC, and the MPPT. FLC doesn’t require the knowledge of the exact model, it has the advantage to be robust and simple to design [3][4]. We implement this command strategy in order to meet the following specifications: Good static precision in order to obtain optimal energy production and a unit power factor; Good rejection of disturbances that can be frequent on such a system; Robustness to any parametric variations of the system.

1. Introduction

2. Preliminaries

3. U* V-Type Double-Domain Fuzzy Order Variable Precision Rough Set

4. Uncertainty Measure of U* V-Type Double-Domain Fuzzy Order Variable Precision Rough Set

5. An illustrate Example

6. Conclusion

7. Acknowledgement

8. References

Aiming at the problem that the U × V-type double-domain rough set can’t deal with fuzzy data in the past, this paper firstly established the variable precision rough set model on the basis of U × V-type double-domain rough set,and made an in-depth study on the upper approximation set and the lower approximation set of this model.Then the variable precision rough set model of U × V-type double-domain fuzzy order information system is established by introducing the U × V-type double-domain fuzzy dominance relation, and its related properties are discussed.The uncertainty measures are studied by defining the U × V-type double-domain fuzzy order precision rough set roughness and rough entropy. The conclusions are that as the precision threshold increases, its roughness and roughness entropy decrease gradually. These conclusions are verified by an example, which provides a theoretical basis for further revealing the uncertainty measure law of U × V-type double-domain fuzzy order variable precision rough set.

Introduction

With the rapid development of the real society, the complex data acquired in many fields has made the Pawlak rough set insufficiency in many aspects. For example, the object in question is always on the same domain set, but the problem to be solved in reality is cannot be described by a domain.Another example is that the knowledge or concept that people come into contact with in practical problems is vague and uncertain, and knowledge or concept cann’t be described with precise knowledge, such as medical diagnosis problems, one disease corresponds to multiple symptoms, and one symptom is manifested in multiple diseases. This problem obviously cannot be described by a domain domain, and a precise definition cannot be used to express the severity of diseases and conditions.Therefore, many scholars have established a rough set model of two universes by extending the single domain to the double-domain [6-9], and some scholars have extended the exact set to the fuzzy set and established the fuzzy rough set model [10- 11].

1- Introduction

2- Methods

3- Results

4- Discussion

References

Abstract

The human brain has been uniquely equipped with the remarkable ability to acquire more than one language, as in bilingual individuals. Previous neuroimaging studies have indicated that learning a second language (L2) induced neuroplasticity at the macrostructural level. In this study, using the quantitative MRI (qMRI) combined with functional MRI (fMRI) techniques, we quantified the microstructural properties and tested whether second language learning modulates the microstructure in the bilingual brain. We found significant microstructural variations related to age of acquisition of second language in the left inferior frontal region and the left fusiform gyrus that are crucial for resolving lexical competition of bilinguals’ two languages. Early second language acquisition contributes to enhance cortical development at the microstructural level.

Introduction

One of the key characteristics of the bilingual brain is that when processing the target language, bilinguals need to successfully monitor and resolve lexical interference from the non-target language that competes for representation and selection (Crinion et al., 2006; Green, Crinion, & Price, 2006; Hernandez, Li, & MacWhinney, 2005; Kovelman, Baker, & Petitto, 2008; Price, Green, & von Studnitz, 1999; Tan et al., 2011; Thierry & Wu, 2007; Xu, Baldauf, Chang, Desimone, & Tan, 2017). This has been argued to lead to cognitive advantages on executive tasks due to bilingualism (Bialystok, Craik, & Luk, 2008; Bialystok, Craik, Klein, & Viswanathan, 2004; Birke Hansen et al., 2016; Colzato et al., 2008; Costa, Hernández, & Sebastián-Gallés, 2008; Gold, Kim, Johnson, Kryscio, & Smith, 2013; Perani et al., 2017; Prior & MacWhinney, 2010). Past neuroimaging studies have demonstrated that learning a second language (L2) induced neuroplasticity at the macrostructural level, as indexed by gray matter density (Grogan, Green, Ali, Crinion, & Price, 2009; Mechelli et al., 2004), white matter integrity (Elmer, Hänggi, Meyer, & Jäncke, 2011; Hamalainen, Sairanen, Leminen, & Lehtonen, 2017; Kuhl et al., 2016; Pliatsikas, Moschopoulou, & Saddy, 2015) and cortical thickness and volume (Klein, Mok, Chen, & Watkins, 2014; Li, Legault, & Litcofsky, 2014). Moreover, significant functional and structural imaging data points to the neural correlates of both L2 age of acquisition (AoA) and L2 proficiency. Early evidence suggests that childhood bilingualism may lead to distinct neural representations for L1 vs. L2, as compared with adulthood bilingualism (Kim, Relkin, Lee, & Hirsch, 1997). Later studies found out that proficiency, instead of AoA, may be the more important factor for determining the patterns of activation in L1 vs. L2 (Chee, Hon, Lee, & Soon, 2001). It is unclear, however, whether effects due to AoA and proficiency can be separated or isolated, as age and proficiency are often confounded or conflated (Kim et al., 1997; Hernandez, 2013). The neuroimaging measures used in previous studies, however, are qualitative because they are derived from uncalibrated T1-weighted images, which are sensitive to multiple features of tissue organization and microstructure (Mezer et al., 2013). To quantitatively evaluate microstructural properties in vivo, we employed the qMRI technique to compute the brain macromolecular tissue volume (MTV) and quantitative T1, which linearly contributes to iron and myelin concentrations (Stüber et al., 2014).

1- Introduction

2- Theoretical framework and research background

3- Fuzzy Logic-Time Driven Activity-based costing model (FL-TDABC)

4- A numerical example, results, and discussion

5- Conclusion and recommendations

References

Abstract

Hospital traditional cost accounting systems have inherent limitations that restrict their usefulness for measuring the exact cost of healthcare services. In this regard, new approaches such as Time Driven-Activity based Costing (TDABC) provide appropriate information on the activities needed to provide a quality service. However, TDABC is not flawless. This system is designed for conditions of relatively accurate information that can accurately estimate the cost of services provided to patients. In this study, the fuzzy logic in the TDABC model is used to resolve the inherent ambiguity and uncertainty and determine the best possible values for cost, capacity, and time parameters to provide accurate information on the costs of the healthcare services. This approach has not yet been tested and used in determining the costs of services of a healthcare setting. Therefore, the aim of this study is to present a new Fuzzy Logic-TDABC (FL-TDABC) model for estimating healthcare service costs based on uncertainty conditions in hospitals. The proposed model is implemented in a sample of the hospital laboratory section and the results are compared with the TDABC system. The TDABC model, by allocating the activity costs including fixed costs and not considering the uncertainty regarding the cost, capacity, and time required for each patient, often estimates the unused capacity and costs with a higher margin of error. The results show that the maximum difference in the prescribed costs was 4.75%, 3.72%, and 2.85% in blood bank, microbiology, and hematology tests, respectively, mostly due to uncertainty in the costs of consumables, equipment and manpower (on average 4.54%, 3.8%, and 3.59%, respectively). Also, The TDABC system, in comparison with the proposed system, estimates the unused capacity of the resource with more error. Cost of unused capacity derived using FL-TDABC were 80% of costs derived using TDABC. In conditions where the information is ambiguous, using the new system in hospitals can lead to a more accurate estimate of the cost compared to the TDABC system. Moreover, it helps hospital managers to make appropriate decisions about the use of capacity, capital budgeting, cost control, and etc.

Introduction

Hospitals and healthcare centers as major operating units of the healthcare sector play an important role in promoting public health. One of the important factors in the continued service of this sector is how to manage resources and control costs. Balancing costs with generated revenues is one of the main challenges of many hospitals in the country. In most hospitals in the country, costing and determining the cost of services are directed via traditional costing systems (TCSs) based on reimbursement, which does not provide information on the exact amount of healthcare costs. Due to the prime focus of these systems on reimbursement, less attention has been paid to understanding the exact amount of true healthcare costs and implementing strategies to reduce them. A costing approach that can provide true cost of patient care services would enable hospitals to manage resources, improve the efficiency of processes, adapt medical skills to processes, increase patient satisfaction, and ultimately reduce the costs. The lack of such knowledge will lead to the lack of understanding the link between costs and process improvements. Moreover, it could be an obstacle to reduce costs in hospitals and healthcare centers, leading to higher healthcare costs. Therefore, designing and implementing a system that provides suitable decision-making grounds for managers to better manage resources in order to reduce the cost of healthcare is of high necessity.

Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This requires re-architecting their manufacturing legacy information systems to get integrated with contemporary data analytics platforms. A systematic re-architecting approach is required incorporating careful and thorough evaluation of goals for data analytics adoption. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases. Objective: We propose an approach to reason about goals, obstacles, and to select suitable big data solution architecture that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A systematic goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with data analytics platforms at the requirement level and (ii) A systematic analysis of the architectural decisions under uncertainty incorporating stakeholders’ preferences. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected manufacturing collaboration system to a new big data architecture.

Introduction

Product lifecycle management is a data intensive process comprising market analysis, product design, development, manufacturing, distribution, post-sale, and recycling (Stark, 2015). The process involves a variety of voluminous data, e.g. customers’ comments on social media, product functions, product configuration, and failure incidences reported by installed sensors to monitor parameters of environment and products. Manufacturing organisations view such data as a valuable business asset to achieve good performance and to reduce cost in the product lifecycle. They also regularly seek to increase their productivity using new advanced information technologies that place further demand on their data processing storage requirements such as Internet of Thing (IoT) and radio-frequency identification (RFID) tags in their daily production. For example, Toyota automotive company equip cars with smart sensors and continuously collecting data about its locks, location, ignitions, and tyres which can be later used by the manufacturer assembly. Continuous product innovations lead to further product data generation coupled with a great diversity of types, sources, meaning, and format. Given its increasing volume and variety, manufacturing data is increasingly difficult to process using common manufacturing data platforms be they computer aided design (CAD), supply chain management (SCM) manufacturing execution system (MES), or enterprise resource planning (ERP). Indeed, the high volume, velocity, variety, veracity, and value adding data requirement all point to the need to complement manufacturing systems with big data platforms (McAfee, Brynjolfsson, & Davenport, 2012). New platforms such as Apache Hadoop, Google’s Dremel, or S4 are promising ways forward to address the abovementioned processing complexity (Lycett, 2013). They provide a support for capturing, processing, and visualising large volume of data sets that organisational systems may have collected over the years.