Recovery and Concurrency Challenging in Big Data and NoSQL Database Systems

Abed, Amira Hassan

doi:10.35444/ijana.2020.11041

Cited by 5 publications

(7 citation statements)

References 6 publications

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“…They are the best fit for organizations collecting large amounts of data, and offer increased stability through commodity hardware and focusing on analytical processing of large quantity datasets. NoSQL databases do not require consistency to fulfill higher partitioning and availability, which are grouped in property known as the BASE [51]. BASE ensures NoSQL database reliability despite the loss of consistency.…”

Section: Nosql Databasesmentioning

confidence: 99%

Distributed Intrusion Detection Systems in Big Data: A Survey

Hameed

AlHabshy

ElDahshan

2021

Al-Azhar Bulletin of Science

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We live in a time where data stream by the second, which makes intrusion detection a more difficult and tiresome task, and in turn intrusion detection systems require an efficient and improved detection mechanism to detect the intrusive activities. Moreover, handling the size, complexity, and availability of big data requires techniques that can create beneficial knowledge from huge streams of the information, which imposes the challenges on the process of both designing and management of both Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) in terms of performance, sustainability, security, reliability, privacy, energy consumption, fault tolerance, scalability, and flexibility. IDSs and IPSs utilize various methodologies to guarantee security, accessibility and reliability of enterprise computer networks. This paper presents a comprehensive study of the Distributed Intrusion Detection Systems in Big Data, and presents intrusion detection and prevention techniques that utilize machine learning, big data analytics techniques in distributed systems of the intrusion detection.

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Section: Nosql Databasesmentioning

confidence: 99%

Distributed Intrusion Detection Systems in Big Data: A Survey

Hameed

AlHabshy

ElDahshan

2021

Al-Azhar Bulletin of Science

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“…These are the simplest NoSQL databases. Key-value stores focus on high availability, performance, and scalability, by storing data in a distributed storage system (Abed, 2020).…”

Section: Key-value Nosql Storesmentioning

confidence: 99%

“…The graph uses the Index-Free adjacency technique, in which each node contains a pointer pointing to the adjacent node. For every node in the Graph database, the system can access its direct neighbor, without the need to consider all edges (Abed, 2020). This is known as the index-free adjacency property; and it is the key feature of the graph databases (Meier & Kaufmann, 2019).…”

Section: Nosql Graph Storesmentioning

confidence: 99%

Data in the time of COVID-19: a general methodology to select and secure a NoSQL DBMS for medical data

ElDahshan

AlHabshy

Abutaleb

2020

PeerJ Computer Science

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Background As the COVID-19 crisis endures and the virus continues to spread globally, the need for collecting epidemiological data and patient information also grows exponentially. The race against the clock to find a cure and a vaccine to the disease means researchers require storage of increasingly large and diverse types of information; for doctors following patients, recording symptoms and reactions to treatments, the need for storage flexibility is only surpassed by the necessity of storage security. The volume, variety, and variability of COVID-19 patient data requires storage in NoSQL database management systems (DBMSs). But with a multitude of existing NoSQL DBMSs, there is no straightforward way for institutions to select the most appropriate. And more importantly, they suffer from security flaws that would render them inappropriate for the storage of confidential patient data. Motivation This paper develops an innovative solution to remedy the aforementioned shortcomings. COVID-19 patients, as well as medical professionals, could be subjected to privacy-related risks, from abuse of their data to community bullying regarding their medical condition. Thus, in addition to being appropriately stored and analyzed, their data must imperatively be highly protected against misuse. Methods This paper begins by explaining the five most popular categories of NoSQL databases. It also introduces the most popular NoSQL DBMS types related to each one of them. Moreover, this paper presents a comparative study of the different types of NoSQL DBMS, according to their strengths and weaknesses. This paper then introduces an algorithm that would assist hospitals, and medical and scientific authorities to choose the most appropriate type for storing patients’ information. This paper subsequently presents a set of functions, based on web services, offering a set of endpoints that include authentication, authorization, auditing, and encryption of information. These functions are powerful and effective, making them appropriate to store all the sensitive data related to patients. Results and Contributions This paper presents an algorithm to select the most convenient NoSQL DBMS for COVID-19 patients, medical staff, and organizations data. In addition, the paper proposes innovative security solutions that eliminate the barriers to utilizing NoSQL DBMSs to store patients’ data. The proposed solutions resolve several security problems including authentication, authorization, auditing, and encryption. After implementing these security solutions, the use of NoSQL DBMSs will become a much more appropriate, safer, and affordable solution to storing and analyzing patients’ data, which would contribute greatly to the medical and research effort against COVID-19. This solution can be implemented for all types of NoSQL DBMSs; implementing it would result in highly securing patients’ data, and protecting them from any downsides related to data leakage.

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“…These databases offer increased stability over commodity hardware and focus on the analytical processing of large volume datasets. NoSQL databases don't require consistency to fulfill higher partitioning and availability, which grouped in a property known as BASE [9,13,15]. BASE is a property that ensures the NoSQL database reliability despite the loss of consistency.…”

Section: -Characteristics Of Nosql Databasesmentioning

confidence: 99%

“…This database stores the data in graphs that use the Index-Free adjacency technique in which each node holds a pointer pointing to the adjacent node. Graph databases are schema-free and come up with efficient storage of semi-structured and unstructured data [15,29]. In Graph databases, the queries are expressed as traversals which make them much faster than the relational databases [2].…”

Section: Nosql Graph-storesmentioning

confidence: 99%

A COMPARATIVE STUDY AMONG THE MAIN CATEGORIES OF NoSQL DATABASES

2020

Al-Azhar Bulletin of Science

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Relational databases are usually used for data storage and retrieval. They are suitable for limited data volume. But when it comes to Bigdata, we need to use more flexible databases that satisfy the need to handle semi-structured and unstructured data. These databases are called NoSQL (Not only SQL) databases. This type of database was developed to interact with data of large volumes. NoSQL databases provide many features such as scalability, availability, replication models, file sharing, and schema-free. This paper's main purpose is to present a comparative study of the five main categories of NoSQL databases; key-value stores, document stores, column family stores, graph stores databases, and object store NoSQL systems. Also, it discusses the famous database management systems for each one of these five categories. The comparison criteria used are performance, scalability, flexibility, complexity, and functionality. Moreover, this paper presents an overview of big data concepts. It briefly discusses the SQL databases versus NoSQL databases in terms of their high-level characteristics. Furthermore, this paper emphasizes the advantages and disadvantages of NoSQL databases. It illustrates the query languages in both SQL and NoSQL databases and represents the most common uses for each category to help users choose the most convenient DBMS for their organization.

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Recovery and Concurrency Challenging in Big Data and NoSQL Database Systems

Cited by 5 publications

References 6 publications

Distributed Intrusion Detection Systems in Big Data: A Survey

Distributed Intrusion Detection Systems in Big Data: A Survey

Data in the time of COVID-19: a general methodology to select and secure a NoSQL DBMS for medical data

A COMPARATIVE STUDY AMONG THE MAIN CATEGORIES OF NoSQL DATABASES

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