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This thesis explores the trends, status of big data in airlines and its implications in aviation investment. The paper highlights areas of interest based on these submissions, which include technologies, used cases, applications, products, suppliers, research and development. Within these areas of interest, the researcher categorises point objectives for the exploration.

Objectives of the Thesis

The thesis seeks to:

1. Identify the type and amount of data generated and transmitted in aviation.
2. Establish how airline data are transmitted and analysed under communication networks.
3. Identify the value chain system in aviation.
4. Explore research and development projects in aviation.
5. Explore data maintenance procedures in aviation.
6. Explore big data trends, applications, technologies and used cases.
7. Present recommendations for big data in airlines.

Analysing the Objectives of the Study

Objective 1: Technologies, Use Cases, and Applications

The thesis investigates the impact of big data on aircraft. The study objectives have been categorised into specific areas of knowledge. First, the researcher seeks to examine data transmission, its technologies, used cases, and applications. The paper examines data types, processes, data load, data transmission, data architecture and locations under this section. Data transmission have a continuous process in aviation.

The need for sufficient documentation facilitates aircraft efficiency, safety, and maintenance. Data transmission during flight operations can be classified under specific domains. The network architectures relay and classify accumulated data into its operational domain. Data types are classified under an operational domain, priority level, size, security sensitivity, data flow, and phase. These categorisations determine the collective efficiency of flight operations. Data transmissions are coded for maintenance information, flight description, cabin security, or distress information under the operational domain. This information is then coded based on the priority level. Data with high priority clearance are secured and transmitted continuously.

Data mining has been adopted to analyse bytes of information into useful contents for business intelligence. The aviation sectors integration and globalisation rely on data evaluation (Aktürk et al., 2014; Arikan et al., 2017). Aircrafts armed with flight recording gadgets and devices gather, record, store, and transmit over 400 variables of information. Data gathered and transmitted could be phased interval, elevation level, vertical acceleration, and passenger counts.

According to Marinus and Poppe (2015), airline investors utilise data mining for fuel rates optimisation, weather conditions, passenger investigation, freight improvement, revenue per excursion, gain per excursion, the price per seat or much more comprehensive one catering and managing expenses each chair. Within the following guide, the present uses of data mining in civil aviation industries have been assessed based on specific critical facets such as airports, airlines, freight, passenger, service efficiency, and security. According to Kumar & Zymbler (2019), data mining in aviation has not been deployed as it maintains its early phases of the studies to create knowledge. The knowledge of data mining provides the framework for flight data exchange in airlines.

Flight Data Exchange

Data recording has extensive use in aviation, and investors rely on technology to enhance its use cases. The flight data exchange (FDX) is a messaging tool that provides comparative summaries to indicate regions with a high priority level based on global benchmarking evaluations (Abdelghany et al., 2017; Dou, 2020; Ho-Huu et al., 2018). The program permits airlines to assess flight problems or business trends. Flight data can be operational, business, maintenance, or other related information (Dou, 2020).

Therefore, airlines benchmark their performance against different operators aggregate using similar aircraft forms, locations, database, and regions. Therefore, the impact of big data on aviation would advance business investments, global integration mitigates security challenges and enhance cost efficiency. The flight information is gathered from participants and processed from a pre-defined terminal and database. These outcomes are merged into a central database, enabling market drivers, aviation expert, airline regulators, and investors examine the statistics and benchmark against business performance (Dou, 2020). The need for reliable and accurate data management accounts for the use of high-efficiency data recorders.

Consequently, data recording manufacturers highlight the objectives of meeting the requirements of internal stakeholders. These adjustments objective is to supply data, which is invaluable in mitigating security dangers and supporting the aviation sector. The platform makes it possible for airline operators to filter information based on the set benchmark, category, and classification forms. Research by FAA reveals that an aircraft generates over 20 terabytes per flight (Badea et al., 2018).

Air traffic controllers cannot utilise most of the information generated during flights because such data coded and unstructured. As a result, big data analytics must be applied to decode and forecast the error encrypting information obtained from flight sensors. Data analyses are evaluated, and errors are identified and corrected where it exists. Airlines gather huge volumes of information such as during flight operations, and such data are relayed to relevant databases for processing and evaluation. Big data analytics make it easier to understand and decipher recorded information for management use.

Hrastovec and Solina (2016) stated that there is a growing demand to move from traditional analysis of predictive frameworks that provide precise and reliable information. Therefore, airline businesses must adopt an integrated perspective of passengers data covering distinct points and sources from databases. For example, passenger profile, trade information, shared behaviour, criticisms, and psychographic information are distinct analysis generated from encrypted tones of flight data. These used cases are made simple via advanced technologies of big data applications. Thus, big data makes it simpler to decode unstructured data collections characterised by substantial volume, higher speed, and selection accuracy.

Tones of data are generated from a single flight of an airline. The data volume and its importance have been a growing concern for aviation experts, investors, regulatory agencies, and security experts. The rise in terror attacks has increased the need for quality security architecture to prevent events that trigger attacks and plane highjacks (Lee, 2017; Marinus & Poppe, 2015; Mitroff & Sharpe, 2017; Ni et al., 2019; S. Wang et al., 2018). Consequently, the need for improved necessitated the research on the impact of big data in aviation. Given the need for quality data transmission, aircraft applications provide channels to collate, gather, and generate flight records.

Aircraft applications include maintenance tools, decision apps, performance assessment, fault diagnostics, trend prediction, and reliability analysis. Several sensor units attached to the aircraft enable these applications to generate unstructured data for flight operations. A typical commercial aircraft has smart sensors, strain sensors, vibration sensors, RFID sensors, piezoelectric sensors, and temperature and humidity sensors (Marinus & Poppe, 2015). These sensors relay unstructured data via complementary assess points and gateways to portable devices, cockpit displays and control system. This interface acts as remote servers that transmit communication links to air traffic centre, satellite imaging system, and ground station.

Consequently, data collection systems within the aircraft are categorised into a data acquisition engine and rules. The parallel calculation engine could be SQL, OLAP, predictive analysis, test analysis, search content, graphical presentation, and rule systems. Under storage, the file system that manages large flight data includes HDFS, NoSQL, In-memory database, metadata management, and tenant space management. Data sources for flight recording include QAR, ARCARS reports, reliability data, maintenance manual, design documents, business data, and other related information.

Big Data Technologies and Used Cases

1. Data analyses allow the cabin crew and pilot to reinforce their decision-making procedure with accurate information and business intelligence. For example, decoded data generated during flight form the basis for real-time decisions and flight patterns. The computational procedures to optimise flight routes enhance cost minimisation on fuel, reduce air transportation, prevent bad weather flights, and access safer destinations.
2. Airlines create enormous quantities of data during online communication, online cost comparison and ticket buying, online check and seat preference, personalisation of supplies, and beverage selection. Most airline businesses use sophisticated algorithms to accumulate and analyse the huge amounts of information made by flight detectors and data sensors attached to each aircraft. These sensors and recording device detectors track flight movements, forecast maintenance, identify errors decrease gas consumption, and re-evaluate turn-around-time at airports.
3. Data evaluation as a form of business intelligence has been a formative decision in aviation. The competitive edge of most airlines relies on data analytics. The capacity to analyse quantum data into specific points creates the difference in price regime, promotion packages, customer requirements, travel needs, and quality. An investor gains knowledge by applying informed decisions on flight operations and management. As a result, the management enhances the passengers travel experience using data analytics. By implication, investors such as Emirates airlines utilise business intelligence gathered across many touch-points to execute behavioural promotions, product differentiation, customised services and solutions.
4. As part of the used cases for flight data applications and evaluation data, recorders ad programs help airlines identify security trends and present information for safety performance indexes. The aggregate review enables operators to verify flight information beyond their restricted databases. Investors or airline management could use benchmarking tools to evaluate the safety performance for international flight routes and security patterns. These applications permit pilots to compare and escalate the decision-making process for unbiased and valid functionality. Data assembly allows collaborative frameworks and partnership among airlines as it offers comprehensive diagnoses when required.
5. Predetermined maintenance accepts the collapse pattern of most aircraft is predictable. The PM design presumes that aircraft declines inevitably via a defined order. The deduction varies with other predisposing factors such as aircraft ageing, ecological effects, procedure drift, and intricate communications between components and systems. Surveys on failing behaviour argued that maintenance challenges are caused by arbitrary procedures. Based on these facts, corrective maintenance is prone to inadequate repairs, while PM triggers over-maintenance. Thus, big data bridges the gap between CM and PM fault-detection approaches because the technology forecasts future failings based on the aircrafts health status during routine checks. The distinction between traditional repair approaches and big data induced checks is that signals are triggered by data evaluation and analysis. The principle of predictive maintenance utilises innovations to support a shift from fail and fix procedures to a dynamic design that predicts, prevent and fix engine challenges. Based on this understanding, the layers of the existing maintenance framework are summarised. Aircraft maintenance describes a mix of technological, management and supervisory schedules of equipment to retain, recover, or maintain the operations status. Therefore, mix approaches sustain the shift from rehabilitative maintenance methods to predictive pattern. Data accumulation during flights provides resources for most predictive maintenance routine. The uses of sensors and other observatory equipment enhance data retrieval processes. For example, a Boeing 787 provides data on equipment status, gas levels, altitude ratings or pressure gauge during flight operations. Big data technologies utilise smart intelligence designs to detect fault lines and hidden signals or anomalies of potential errors to maintain and prevent significant failures and engine collapse. Due to the catastrophic nature of mid-air disturbances, airlines are continuously designed to screen, evaluate, predict, and rate equipment performance. Most manufacturers are adopting a continuous shift in predictive approaches using advanced learning and technology.
6. Industries such as financial investment or retail financial, gain from big information analytics in the location of threat administration because the analysis is a crucial element for investments as BD aid in choosing financial investments by analysing the chance of gains versus the likelihood of losses. Additionally, the network sources of BD can be analysed for appraisal of direct threat exposures. Accordingly, BD can benefit companies by allowing the metrology of threats. Performance analytics can be utilised to incorporate the risk profiles handled in isolation across different divisions and threat accounts. The strategy aids risk reduction since a detailed view of risk factors is given to decision-makers.

Additionally, BD tools can manage the rapid growth in network information and lower data challenges by increasing the capability to scale and store investment data. In addition to improving cyber observation and data-intensive remedies, organisations can incorporate streams of data and computerised analyses to protect equipment and workstations against attacks. Fraud detection department deploys BD to resolve sharp practices and detect unauthorised transactions. Analytics are utilised in automated detection. However, companies are harnessing the potentials of data mining capability and artificial intelligence to enhance their systems.

Stream computing describes the handling of enormous data from numerous sources with low latency in real-time. New sources of information producing situations, which include universality of area services, mobile tools, and sensor prevalence, a new paradigm necessitated. It can be related to the high-velocity circulation of information from real-time resources such as IoT, sensors, transaction data and mobile data.

Cargo vehicles could be offered best routes based on real-time web traffic info raising gas efficiency, delivery time, and reduced delivery costs. Investors could harness business profit using the appropriate smart device, which is powered by big data technology. Infrastructure could additionally be equipped with smart sensing units. A recent innovation is smart designs that showed the possibility of developing concrete casting with sensors that give security purposes information. The smart device allows investors to conduct operation within the no-fly wing as the concrete slabs relay movement and body heat to detect unauthorised movement.

Consequently, the smart device generates maintenance data required to sustain the strength integrity of the building. Automation is a significant impact on aviation workforce. Up until now, media attention on this subject has concentrated on concerns that robots and artificial intelligence could displace human intelligence. Nonetheless, a Next-Generation labour forces production, requiring humans and smart devices, will be a crucial trend.

Developments in robotics, AI and IoT would create safer skies, prevent flight delays and enhance service delivery. An automated workstation supported by an attendant powered by online intelligence could offer cafeteria pointers based on their social media control. The capability of individuals and smart devices to cooperate properly would support the goal of the NextGeneration workforce. Capital funding and synergy with technologies would enhance employees ability to provide a spot-on assessment of data retrieved from flight cabins, weather details, and other information.

Data Applications in Airlines

According to Hong and Park, (2019), the capacity to decode encrypted messages during flight operations results in faster implementation in messaging validation. The author stated that efficient programs and data recorders aid quick resolutions of flight challenges. Therefore, big data analytics would enhance faster implementation of fight issues, route changes and a better travel experience. Ben Ahmed et al. (2017) argued that effective and effective data deployment strategies aid smooth communication and better compliance among the flight crew, cabin control towers, and ground control management.

The author stated that bad weather conditions require prompt resolution and guided communications. As a result, big data analytics could reduce the waiting time for faster conflict resolution to prevent traffic jams or poor visual and audio communication. The authors argued that the cabin crew understands flight requirements for standard compliance. Thus, effective standard-compliant systems may require fewer alterations that may affect compliance skills.

A review by De Bruecker et al. (2018) revealed that efficient data transmission systems could adjust operations cost. As a result, the predictive maintenance schedule can advert huge airline losses due to engine malfunction. The schedules for prompt aircraft maintenance, reduce operational cost and improve quality service delivery by preventing flight delays and cancellations. Therefore, big data in aircraft could mitigate maintenance challenges, reduce the cost of operations, and aid informed decision-making process. Mitroff and Sharpe (2017) revealed that airlines rely on reliable information for daily flight operations. The author revealed that aircraft data depend on harmonised systems that work with predictable server components.

For example, API, designs restrict unconfirmed machine information. Thus, aviation experts and control tower managers must comply with data interrogation standards to avoid human errors. Big data removes human complacency due to ineffective data interrogation by decoding and verify the level of reliability after processing flight-encrypted information. These advanced technologies enhance flight operations and communication reliability.

As stated earlier, huge volumes of data are transmitted during flight operations. The encrypted and unstructured data are processed based on the airlines data transmission channel via an open-ended connection. For example, data transmission encryption includes the aircrafts location, engine information, flight gauge, and aircraft health status. After logging and transmission, information from data centres is routed offline. The flight information is evaluated and examined by the airlines data centre and conclusive results sent to the cabin crew (Nikolopoulos & Petropoulos, 2018; Park & Pan, 2018; S. Wang et al., 2018).

The database centre transmits data retrieval information and recommendations to the airport control towers for periodic aircraft maintenance. To offer the aircraft precise and accurate maintenance, the aircrafts flight information and other usable variables form the basis and source of information for flight configurations. According to Dubey et al. (2018), big data analytics support SAP predictive repairs. Aircraft maintenance is controlled to guarantee safe flight performance.

The International Civil Aviation Organization has organised national regulations according to global standards (Sun et al., 2018). Aviation Data Exchange programs launching opened the door to information sharing using a reliable third party to acquire accumulated statistical evaluation of the business performance via benchmarking applications to enhance safety standards (Arikan et al., 2017). ASIAS was established to enhance aviation security and operational efficiency. The FAA empowered the ASIAS system to track flight risks, identify movements, suggest mitigation activities, and confirm the effect of its execution (Badea et al., 2018). To guarantee the security of patented information, procedural data are de-identified to avoid infringement. These data exchange systems provide enabling channels for safer and efficient data transmission.

Data4Safety is another exchange system that contains IT infrastructure, which will allow the information sharing process, such as information assemblage, storage, processing, and security. In the execution stage, the exchange system enhances security via mining strategies. The impact of distinct data serving systems has been pivotal in enhancing safer flight operations (Bertsimas & Gupta, 2016; Bogicevic et al., 2017; Marinus & Poppe, 2015).

The GADM is an aviation management program that provides reliable geographical data for airline operators. According to Anderson (2019), over 92 per cent of IATA members have consented to share information from over 500 organisations. The program permits data providers to code and decodes reports on security metrics and trends, such as analyses on events, operational intelligence and ground maintenance document. These programs foster a model change in security evaluation, shifting from a responsive strategy based on investigating incidents to a pre-emptive strategy based on data evaluation.

The aviation PHM programs assemble distinct data from aviation companies, airports, aircraft, spare parts warehouse, fix mill, maintenance centres, overhaul foundation, and other relevant sources. The data are recorded and transmitted as a ground data reference to terminal databases for pre-emptive and predictive evaluation (Arikan et al., 2017; Atkinson et al., 2016; Benlic, 2018; Bertsimas & Gupta, 2016). The program relies on the cloud-computing host, as it utilises data mining to acquire knowledge and needs of various aircraft. Based on its architecture, the program offers support for aviation, government agencies, and investors.

The PHM centre provides integrated solutions using large data exploration to comprehend the efficient utilisation of reliable information and enhance aviation investments performance efficiency. The program utilises predictive programs to improve repair services. Sternberg et al. 2017 reviewed several flight prediction procedures and identified distinct designs to evaluate their strengths, flaws, and performance. The authors found that machine-learning strategies showed substantial growth in the past ten years. The findings showed that the system transmits data to several regression models using the recurrent neural network (RNN).

A study by Khanmohammadi et al. (2014) demonstrates that data collection during flights is the bedrock for a sustainable aviation market. The author argued that big data technology would enhance data learning and mitigate system challenges caused by human error. Thus, big data would enhance data evaluation and accurate readings in aircraft management. The need for big data architecture has been overdue, and its impact on flight operations is strategic and effective.

An airliner avionics program is an incorporated design with multiple backup units. The backup units are called the central control computers (CCC). The system is designed to retrieve tons of data from the aircraft via sensor gadgets. The sensor recorders provide data for flight navigation, communication relay, command calculations, and video signals. The system is designed to mitigate data failures of input uncertainty.

As a result, the backup systems resume play when the master device collapses. This integrated structure impacts and controls the FDR structure. As the information is processed at central control computers, the developer makes an exchange evaluation of the CCC recording form. According to Hong and Park (2019), big data deployment in multi-source operational systems promotes the air transportation system. An aircraft is a complex systems design to move above gravity and strong weather presence. An aircraft is designed to operate manually and automatically, and it is an assembly of many different components and subsystems that complements the function of several related parts.

A commercial aircrafts main components include the fuselage, wings, empennage, power plant, and the landing gear. These components are designed to collate data for its operations and functionality. Given the transmission of data tones, an aircraft generates information and relays composite data to the ground control tower for informed decision-making. A Boeing 787 Dreamliner aircraft has several functional parts designed to transmit specific data.

A Boeing 787 Dreamliner has its avionics system, flight control systems, landing gear system, furnishing system, environment control unit, anti-ice unit, engines, auxiliary power compartment, wheels brakes, engines starting systems, electric systems, hydraulic system, and pneumatic system. The complementary part of an airline subsystem includes the navigation unit, instrumentation and recording, vacuum system, safety compartment, fire protection unit, communication and electrical system (Blasch et al., 2015; Cimmino et al., 2020; Dubey et al., 2018).

As stated earlier, these systems rely on data transmission from one data bus to another. The pilot and crew utilise these data transmission to understand the engines health status, navigation systems, and the environment. The mechanical components include fuel and hydraulics, safety systems, flight control unit, armament and escape unit, anti-icing unit, parachute systems, landing gear and environmental control unit.

Aircraft Systems and Subsystems

The ECU enhances the travel experience of the passengers, animal, and other cargoes. The ECU regulates the airlines ozone content, control emergency oxygen within the aircraft, humidity, temperature, and pressure. Given the ECUs nature and function, it could be designed as a vapour cycle systems or air cycle unit. A life support system is an aircraft component that regulates oxygen, pressure gauge and other toxic constituents. Based on these assumptions, aircraft stores liquid oxygen, gaseous oxygen chemical and on-board oxygen systems. Information transfer guides the unit regulation from one component to another. The pilot relies on a multiform network that relays information from the airlines body sensors. These sensors collect, stores, and records different unstructured data for interpretation.

The anti-ice unit regulates the movement and position of an aircraft. The system provides resistance against ice formation on the airlines probes, radar unit, and other components. Data communication writing the aircraft sends command information to each part of the ice protection unit (Cimmino et al., 2020). As a result, the thermal system relates to each components temperature level against the regulated benchmark. When the systems receive a distress signal, it performances routine maintenance by triggering backup activations to remove or defrost a certain part. These systems prevent ice formation on fuel lids, cabin decks, and landing gears.

The Aircraft Hydraulic System (AHS) includes the airlines landing brakes, gear and flight control panels. AHS is a major component that guarantees flight safety. As a result, sensitive data are retrieved from the aircrafts body sensors and relayed to other parts that trigger heat exchanges, pump activations, and oil treatment. For example, an aircrafts operating pressure varies between 3500  8500 psi. The AHS controls the psi level based on data projection from each related sensor points. The aircraft fuel system (FS) regulates the fuel movements pump pressure within the engine component.

The FS relays valuable information about fuel consumption, engine combustion, and related components. The fuel tank is fitted with body sensors to collect information regarding fuel transmission, cockpit display, and engine temperature and fuel gauge. The continuous monitoring proves that aircraft data in the huge volume are transmitted between systems and ground control towers. Based on each aircrafts design, body sensors are fitted in fuel tanks, pumps, and fuel valves. As explored in the thesis, airline data systems and subsystems would be inefficient where systems attacks and data theft are not controlled and prevented. Therefore, it is a growing concern that big data could fuel data confidentiality challenges and security issues.

Data Confidentiality and Security Breach

Big data management could create security challenges in aviation and other sectors. A survey by Dubey et al. (2018) observes that air transportation is a sensitive sector that requires confidential data approaches. The authors note that security issues go beyond data theft to modern business vulnerabilities. The authors argue that fake data generation is a concern for aviation experts. Consequently, access to authorised flight data records makes hinder the advancement of big data in aviation. The cost of conducting security audits to prevent data theft is expensive, and investors may resort to traditional data processing channels as an alternative.

The use of distributed frameworks creates avenues for data theft. As a result, most organisations are unwilling to share confidential information with other communities. Watson (2019) introduced safety problems with large information and recommended ways of averting safety risks. The safety issue inherent in large data incorporates source collection points and cloud management. The design for data transmission makes it challenging to close vulnerable source points. As a result, data breaches influence numerous businesses and human safety. The paper recommends data tracking via end-to-end encryption to prevent unauthorised data access during flight operations.

The paper recommends that data investors verify to avoid unqualified personnel. Data encryption slows large volume transfers from source points. As a result, data transfers are sometimes not encrypted during transmission (Ben Ahmed et al., 2017; Benlic, 2018). Although such information is unstructured and coded, its access by unauthorised individuals could be risky in aviation. Consequently, large volumes of data require time for investigation.

Therefore, such a scenario creates intrusion detection difficulties since the machine occupied times are extended. Although security-tracking methods enhance information protection, breach detection remains difficult even for remote-controlled systems (Adacher et al., 2017; Dubey et al., 2019; Hrastovec & Solina, 2016; Kumar & Zymbler, 2019; Pinto Leite & Voskuijl, 2020; Santos et al., 2017). Thus, aviation experts and investors must encourage continuous tracking to acquire accurate situational awareness. The strategy would prevent false alerts and streamline intrusion detection.

Data monitoring, Control and Regulation

The first step with flight operation is a tracking feature that identifies the system and observes state by gathering, fusing, and evaluating information from various sensing units. The extent and accuracy of the surveillance syste

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