HI5033 Database System Report Sample
Assignment Description
This assessment requires individual completion. Students are expected to complete a critique and conduct a literature review to discuss a contemporary issue relating to Database Systems and identify appropriate approaches to address this issue. The topic is “Mobile Database Management Systems: Ensuring Data Security and Efficiency”.
Each student is required to search the literature and find a minimum of ten (10) academic research papers (references) related to this topic. Subsequently, the student must critically analyse the selected references and provide an in-depth discussion on how they reflect the topic.
Deliverable Description
You need to submit the final version of your assignment in Week 12. The structure of the final report consists of 4 sections as follows:
• Introduction
State the purpose and objectives of the report.
• Discussion
Discuss the references, and critically analyse them and discuss how they reflect the topic.
• Conclusion
Summarize your findings by emphasizing the key points of the report.
• Reference
Provide the list of references following the Adapted Harvard Referencing style.
Your literature review should present the current state of knowledge in the specific area of the topic, and it should have a narrative that flows smoothly from one paragraph to the next. Additionally, the final submission should consist of no fewer than 2,500 words.
Solution
Introduction
The recent explosion of mobile devices at an exponential rate has completely changed the face of data management; effective and robust Mobile Database Management Systems are required to make it safe, secure, and responsive. This report presents an overview of the current status of MDBMS regarding technology, its challenges and future directions for enhancement of data security, efficiency improvement, and embracing the new trends.
This report evaluates the critical aspects of MDBMS first by giving an overview of the role it has in the mobile computing environment. Contemporary issues of data security threats and efficiency challenges are addressed in the report. Design mechanisms for enhancing data security are discussed, such as encryption techniques, authentication methods, and safe protocols of data transmission and efficiency improvements comprising data caching, compressing data, and adoption of distributed and cloud-based solutions.
Looking Ahead Emerging trends that are going to define the future of MDBMS are its integration with AI and ML so that data is managed intelligently through intelligent decisions and with most minor human intervention; the imposition of blockchain so that it makes MDBMS more secure and transparent; and the implementation of edge computing so that it can process real-time data more efficiently at the edge of the network.
Discussion
They are specially designed database systems for mobile devices like Smartphones laptops, and tablets. As they put it, "simplify, speed up and make a more autonomous mobile database environment. MDBMS is an advanced database system intended for mobile devices. In addition, they have synchronization mechanisms to keep the data in mobile devices in sync with central servers. As if that is not enough, MDBMS has rich sets of security measures implemented to keep their sensitive data safe, and it's achieved through encryption, authentication, and access control techniques. They also provide facilities for performance optimization through efficient processing of queries and transactions along with managing battery consumption for university assignment help.
Contemporary Issues in MDBMS
Study Conducted by Zhang (2022) state that mobile database management systems have become more integral parts of mobile applications, there are several contemporary issues that they are confronted with. Security and efficiency of data are the most important ones with the proliferation of mobile devices, which have become an indispensable part of life, both in personal and professional domains, ensuring a high degree of security and efficiency of MDBMS operation has become imperative. The answer to these issues has to be based upon a proper understanding of some of the unique challenges the mobile environment brings.
Data Security Challenges
The security of sensitive data is one of the prime concerns in MDBMS. Mobile devices are particularly vulnerable to many different types of security threats that range from unauthorized access to a range of cyber-attacks, phishing, malware, and hacking, among others, apart from the physical theft of devices. The inherent mobility of these devices increases all these threats, more often using them in unsecured environments. This, in turn, exposes them to malicious networks and applications (Zhang, 2020, p4(2)).
.png)
Figure 1: Security Threats
Source: (Gontovnikas, 2021)
Severe breaches may result in the loss of personal information, financial data, and confidential corporate information in MDBMS. These may result from a lack of encryption protocols for the software and inadequate handling of authentication and authorization processes. In this case, weak password policies that allow easy guess or lack of multi-factorial authentication will give attackers easy time to gain unauthorized access to the database. Besides, most mobile devices connect to public Wi-Fi; this is easily exploited by the attackers to intercept data in transmission (Zhang, 2020, p4(4)). The security threats above call for comprehensive security measures in MDBMS. The data in transit and data at rest must be encrypted such that any unauthorized person cannot access it. Robust authentication methods like biometric security should be used. This can be enhanced by multi-factor authentication. The software used would be updated and patched regularly regarding some of the recognized security vulnerabilities that are exploitable. Also, MDM solutions can remotely manage and secure the device to ensure the application of corporate policies, even on personal devices.
Efficiency Challenges
Another critical issue of efficiency in MDBMS is that of performance and scalability. Since mobile devices, in general, have limited resources such as CPU power, memory, and even batter life to run applications, performance of database operations can be highly influenced. Synchronizing data between the mobile device and central servers is the first and foremost efficiency challenge. Multiple times, mobile devices get intermittent connectivity, that makes this synchronizing consistent and timely very tough (Tyagi et al., 2020, p3(3)).
That demands efficient query optimization and indexing in the database to minimize resource utilization and response time. Also, there is a need for efficient load-balancing techniques which help in workload distribution over the system without any component becoming a Bottleneck for a system. Various strategies can be employed by MDBMS to address efficiency challenges such as these. Adaptive synchronization can be used to optimize data transfer based on the network condition and device status.
Approaches to Enhancing Data Security in MDBMS
The issue of data security is very significant in MDBMS because of the high sensitivity of handled data and the risks inherent in mobile devices. Security can thereby be enhanced through various defence layers like encryption techniques, authentication, and authorization, as well as secure transmission protocols. This means that every approach helps protect data against unauthorized access and different types of breaches (Lo'ai & Saldamli, 2021, p5(2)).
Encryption Techniques
Encryption is a fundamental mechanism for protecting data within MDBMS. It changes data into a coded format where only the one possessing the decryption key can understand it. Multiple methods of encryption are available for securing data on mobile devices.
Symmetric encryption uses the same key for both, which is commonly used as it's very efficient and fast. The problem takes place in crucial management since that key needs to be shared and stored securely. AES is the most widely adopted symmetric block cipher, which is robust and works well.
Asymmetric encryption, however, makes use of a pair. It involves a public key for encryption and a private one for decryption. This method provides better security because the private key is not distributed at all. RSA stands for Rivest-Shamir-Adleman. It is applied as an asymmetric encryption algorithm to secure sensitive data in MDBMS (Jamaludin & Romindo, 2020, p3(4)).
Authentication and Authorization Methods
Authentication and authorization are the second elements of secure MDBMS. Authentication is determining whether a user or equipment attempting to use the MDBMS is who he /she claims to be, while Authorization is the operation of deciding what an already authenticated can and cannot do in MDBMS.
Multi-factor authentication provides this extra layer of security by requiring more than one form of verification. This may be something that a user knows-such as his password in those forms, upheld by something that the user has, like a smartphone, hardware token, or even something that the user just is-biometric verification such as a fingerprint or facial recognition. Difficulties in access are made very high even if a single factor tertiary was invaded (Manurung, 2020, p3(4)).
Authorization mechanisms make sure that a user with specific responsibilities and roles can work on the database at the proper level. RBAC is commonly used for managing permissions within MDBMS. In RBAC, roles are assigned to the users, and each role will have a set of permissions. That would limit a user who logs in with a particular role to only the data and actions pertinent to his role, reducing the chance of accidental or deliberate wrongdoing with the application data.
Secure Transmission of Data
Secure transmission is necessary to keep data safe while the data is in transit between mobile devices and servers. Encryption in transit simply ensures that data isn't grabbed or changed by interceptors. SSL/TLS widely known as transport layer security, is a protocol commonly used, end-to-end encryption of data through networks. Data sent through this protocol remains confidential and changed. It does so by making the channel of communication between the client, which is the mobile device, and the server to be very secret. Man-in-the-middle attack is therefore prevented due to the implementation of TLS in MDBMS, where an attacker intercepts the transmission and changes it. Virtual private networks can be used to enhance data transmission security as well.
Approaches to Improving Efficiency in MDBMS
According to Hort et al (2021), the efficiency in Mobile Database Management Systems is crucial as it is directly proportional to providing optimum performance, optimum allocation, and smoothness in using applications. Due to the inherent limitations of mobile devices in terms of CPU power, memory, and the life of the battery, several techniques of efficiency can be implemented in data management for mobile devices. According to Hort, some of the essential methods of efficiency in data management of the mobile database management system are- Data Caching and Indexing, Data Compression, Distributed Solutions, and Cloud-based solutions.
Data Caching and Indexing
Data caching and indexing are the primary methods of enhancement of the performance of MDBMS. Caching uses temporary storage for frequently accessed data so that there is minimum access to the same data again and again from the central database. It not only speeds up the process of data retrieval but also minimizes the load on the database server. At the same time, it also conserves battery life in mobile devices by reducing network usage (Hort et al., 2021, p5(2)).
Effective caching of data requires identifying data associated with the most frequent use and making that data easily accessible by the user. LRU and MRU are the two most common dynamic cache techniques referred to as caching algorithms, ensuring that the content of the cache is replaced with new data, evicting less frequently used data from the cache for newer data.
On the other hand, indexing is the method of quickening the rate at which the data shall be retrieved by giving the ability to quickly search and access database records by using data structures called indexes. Indexes could be created for frequently queried columns, thereby bringing down the query response time by a considerable margin. All the same, it is essential to weigh the benefits of the use of indexing with associated overhead. Maintaining an index increases storage requirements and update times.
Data compression
Another efficient method for MDBMS optimization is data compression. It can reduce the space and bandwidth of stored and transmitted data; preservation of the already limited mobile device resources is essential.
There are various compression algorithms available, each with different trade-offs in terms of compression ratio and processing time. Lossless compression methods, such as ZIP and LZW -Lempel-Ziv-Welch, do not lose data. Therefore, they are used for compressing critical data where the data must be reproduced in an exact form. Whereas lossy compression methods such as JPEG for images and MP3 for sound can be used where critical data is not a concern even though the file size is reduced drastically (Hughes, 2023, p4(2)).
Distributed and Cloud-based Solutions
Various distributed and cloud-based solutions can be highly beneficial to enhance efficiency and scalability in MDBMS. With large volumes of data and high transaction loads generated by both the increasing number of users proliferating and the fast expansion of mobile devices, a robust cloud-based database infrastructure and services are required to undertake heavy loads involving large volumes of data processing and storage, to relieve the processing and storage of large amounts of data by mobile devices (Achache et al., 2020, p25(4)).
In a distributed MDBMS configuration, there is a distribution of data among many servers or nodes. In such a configuration, one has parallel processing. One also achieves improved load balancing. It offers horizontal scaling, where adding more users and transactions won't affect performance. Cloud services like Amazon RDS, Google Cloud Fire store, and Azure Cosmos DB allow for managed databases with high availability, automatic backups, and elastic scaling. Also, the edges can be used to process data closer to the point of generation, that is, the mobile devices themselves.
Future Directions and Emerging Trends of MDBMS
With continued evolution, several emerging trends and future directions define the prospects for mobile device data management. Among these are AI and ML integration, blockchain adoptions, and advancement in edge computing. These developments hold the key to further enhancing the capabilities, security, and efficiency of MDBMS in the coming years.
Artificial intelligence and machine learning in MDBMS
Artificial Intelligence and Machine Learning change MDBMS altogether. The two bring intelligence to data management and other related decisions. AI can run algorithms on vast volumes of data obtained through mobile devices and bring about trends, patterns, and other insights responsible for changing the user experience and efficiency of operations (Gill et al., 2022, p3(2)). For instance, Predictive analytics based on Artificial Intelligence may forecast the users' behaviour. It can change/tune the working of the MDBMS towards the optimization of resource utilization and betterment/enrichment of performances.
ML algorithms can also be helpful in MDBMS for data pre-processing, anomaly detection, and pattern recognition. As the ML models learn continuously from data patterns and user interactions, they will dynamically change the configuration of databases and optimize query performance to improve the system's efficiency.
Blockchain Technology
This blockchain technology can be used to improve the security aspects, transparency, and integrity of the data within MDBMS. In recent times, this technology has been used to make the technique of ledger techniques decentralized and distributed. In this process, the transactions are recorded at different nodes with characteristics where it is secure and immutable. The blockchain in MDBMS can be used for effectually creating transparent data transactions along with auditable transaction data with which the records of data could not be altered retroactively.
Mechanisms of authentication and authorization based on blockchain could make MDBMS more trustworthy and freer from the control of intermediaries. Second, smart contracts are self-executing contracts that have predefined rules and conditions. These can work automatically to manage data policies within MDBMS; these would thereby ensure compliance but at reduced administrative costs (Samann et al., 2021, p6(4)).
Edge Computing
Edge computing is gaining more and more relevance in MDBMS; mobile devices are generating and processing vast volumes of data at the network's edge. It is the process of processing data nearer to where it is being generated, thus reducing latency and enhancing real-time data processing capabilities. Secondly, in MDBMS, edge computing can offload computation-intensive tasks from centralized servers to edge devices like smartphones and IoT devices. This can greatly improve response times for such critical applications as real-time analytics and augmented reality by minimizing latency associated with data transmission to distant servers.
Conclusion
It is a result of the exponential growth of the use of mobile devices that necessitates the need for robust solutions for data security, efficiency, and future readiness. MDBMS is therefore vital in ensuring safe and efficient data management in dynamism in the mobile computing environment. These have been mainly based on advanced encryption techniques, multi-factor authentication, and efficient data handling strategies such as caching and compression. In the future, integrating AI and ML will enhance decision-making and system performance optimization. Blockchain technology will bring unprecedented transparency and security to data transactions. However, edge computing will play an essential role in ensuring that this network is processed in real-time to enable MDBMS. The security and efficiency of MDBMS can be enhanced by exploiting such promising trends while the path to innovation in applications concerning mobile data management is opened.
References
Achache, A., Baaziz, A., & Sari, T. (2020). The Impact of Data Mining and SaaS-Cloud Computing: A Review. International Journal of Data Science and Applications, 3(1), 23-35. https://dergipark.org.tr/en/download/article-file/3727792
Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., ... & Uhlig, S. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19. https://arxiv.org/pdf/2203.04159
Gontovnikas, M. (2021). The 9 Most Common Security Threats to Mobile Devices in 2021. [online] Auth0 - Blog. Available at: https://auth0.com/blog/the-9-most-common-security-threats-to-mobile-devices-in-2021/ [Accessed 15 Jun. 2024].
Hort, M., Kechagia, M., Sarro, F., & Harman, M. (2021). A survey of performance optimization for mobile applications. IEEE Transactions on Software Engineering, 48(8). https://discovery.ucl.ac.uk/id/eprint/10126540/1/09397392.pdf
Hughes, J. (2023). Comparison of lossy and lossless compression algorithms for time series data in the Internet of Vehicles. https://www.diva-portal.org/smash/get/diva2:1774911/FULLTEXT01.pdf
Jamaludin, J., & Romindo, R. (2020). Implementation of Combination Vigenere Cipher and RSA in Hybrid Cryptosystem for Text Security. IJISTECH (International Journal of Information System and Technology), 4(1). https://ijistech.org/ijistech/index.php/ijistech/article/viewFile/85/85
Lo'ai, A. T., & Saldamli, G. (2021). Reconsidering big data security and privacy in cloud and mobile cloud systems. Journal of King Saud University-Computer and Information Sciences, 33(7). https://www.sciencedirect.com/science/article/pii/S1319157819303337
Manurung, D. T. (2020). Designing of user authentication based on multi-factor authentication on wireless networks. Jour of Adv Research in Dynamical & Control Systems, 12(1), 149. DOI: 10.5373/JARDCS/V12I1/20201030
Samann, F. E., Abdulazeez, A. M., & Askar, S. (2021). Fog Computing Based on Machine Learning: A Review. International Journal of Interactive Mobile Technologies, 15(12). https://www.academia.edu/download/105547858/9415.pdf
Tyagi, A. K., Nair, M. M., Niladhuri, S., & Abraham, A. (2020). Security, privacy research issues in various computing platforms: A survey and the road ahead. Journal of Information Assurance & Security, 15(1). https://ak-tyagi.com/static/pdf/6.pdf
Zhang, Y. (2022). Development and application of artificial intelligence multimedia technology based on big data. Mobile Information Systems, 2022(1). https://onlinelibrary.wiley.com/doi/pdf/10.1155/2022/2073091
Fill the form to continue reading
Would you like to schedule a callback?
Send us a message and we will get back to you
Highlights
Earn While You Learn With Us
Confidentiality Agreement
Money Back Guarantee
Live Expert Sessions
550+ Ph.D Experts
21 Step Quality Check
100% Quality
24*7 Live Help
On Time Delivery
Plagiarism-Free
81 Isla Avenue Glenroy, Mel, VIC, 3046 AU
