Business intelligence software as a service (SAAS)

Ouf, Shimaa; Nasr, Mona; Radwan, Amr, M.; Mosaad, Mohamed M.; Kamal, Khaled Y.; Mostafa, Fatma; Said, Rehab; Mohamed, Sherook

doi:10.1109/iccsn.2011.6014350

Cited by 6 publications

(7 citation statements)

References 1 publication

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“…The advantages of this new computational paradigm with respect to alternative technologies are clear, especially regarding BI . First, cloud application providers strive to give the same or better service and performance as if the software programs were locally installed on end‐user computers, so the users do not need to spend money buying complete hardware equipment for the software to be used.…”

Section: Introductionmentioning

confidence: 99%

Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks

Fernández

Río

López

et al. 2014

WIREs Data Min & Knowl

192

107

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The term 'Big Data' has spread rapidly in the framework of Data Mining and Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable elapsed time. When the scalability term is considered, usually traditional parallel-type solutions are contemplated, such as the Message Passing Interface or high performance and distributed Database Management Systems. Nowadays there is a new paradigm that has gained popularity over the latter due to the number of benefits it offers. This model is Cloud Computing, and among its main features we has to stress its elasticity in the use of computing resources and space, less management effort, and flexible costs. In this article, we provide an overview on the topic of Big Data, and how the current problem can be addressed from the perspective of Cloud Computing and its programming frameworks. In particular, we focus on those systems for large-scale analytics based on the MapReduce scheme of data are recorded everyday resulting in a large volume of information; this incoming information arrives at a high rate and its processing involves real-time requirements implying a high velocity; we may find a wide variety of structured, semi-structured, and unstructured data; and data have to be cleaned before the integration into the system in order to maintain veracity.1 This 4V property is one of the most widespread definitions of what is known as the Big Data problem, 2,3 which has become a hot topic of interest within academia and corporations. The current explosion of data that is being generated is due to three main reasons 4 : (1) hundreds of applications such as mobile sensors, social media services, and other related devices are collecting information continuously; (2) storage capacity has improved so much that collecting data is cheaper than ever, making preferable to buy more storage space rather than deciding what to delete; (3) Machine Learning and information retrieval approaches have reached a significant improvement in the last years, thus enabling the acquisition of a higher degree of knowledge from data. 5,6 Corporations are aware of these developments. Gaining critical business insights by querying and analyzing such massive amounts of data is becoming a necessity. This issue is known as Business Intelligence (BI), 7,8 which refers to decision support systems that combine data gathering, data storage, and knowledge management with analysis to provide input to the decision process. 9 Regarding the former issues, a new concept appears as a more general field, integrating data warehousing, Data Mining (DM), and data visualization for Business Analytics. This topic is known as Data Science. 10,11The data management and analytics carried out in conventional database systems (and other rela...

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Section: Introductionmentioning

confidence: 99%

Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks

Fernández

Río

López

et al. 2014

WIREs Data Min & Knowl

192

107

View full text Add to dashboard Cite

show abstract

“…The attacker may cross this control if he/she has genuine tenant credentials. However, the attacker will be countered by the second control in VMs 7,8,9. In this way, the attacker will have to breach all control layers successfully before reach the VMs hosting the BI databases. In order to breach the databases for stealing data of other tenants, the attacker will have to use exploit tools (like, Metasploit, NetMapper or Pinger).…”

Section: The Multi-layer Security Modelmentioning

confidence: 99%

“…BI with OLAP on Cloud can be configured by converting traditional data warehouse tables into XML data files, interlinking the data files using hierarchical DTD structures, defining all data files with the help of suitable structured metadata, and hosting the data files on distributed servers configured to generate a massive parallel processing environment when multi-tenant query loads are applied [6] [7]. The computational power of Cloud can be assigned selectively to the OLAP queries through Cloud elasticity (like, Amazon Elastic Compute 2) and the service-oriented platforms (like, Amazon Web Services and Google App Engine) can be used for organizing the XML data files, their DTD structures, and the metadata repositories [8]. The reporting and visualization services may be delivered through dashboard services in BI applications delivered through service-oriented SaaS [8].…”

Section: Introductionmentioning

confidence: 99%

A Multi-layer Hierarchical Inter-cloud Connectivity Model for Sequential Packet Inspection of Tenant Sessions Accessing BI as a Service

Al-Aqrabi

Liu

Hill

et al. 2014

2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security

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Business Intelligence (BI) has gained a new lease of life through Cloud computing as its demand for unlimited hardware and platform resources expandability is fulfilled by the Cloud elasticity features. BI can be seamlessly deployed on the Cloud given that its multilayered model coincides with the Cloud multilayer models. It is considered by many Cloud service providers as one of the prominent applications services on public, outsourced private and outsourced community Clouds. However, in the shared domains of Cloud computing, BI is exposed to security and privacy threats by virtue of exploits, eavesdropping, distributed attacks, malware attacks, and such other known challenges on Cloud computing. Given the multi-layered model of BI and Cloud computing, its protection on Cloud computing needs to be ensured through multilayered controls. This paper proposed a multi-layered hierarchical inter-cloud connectivity model for sequential packet inspection of tenant sessions accessing BI as a service on cloud computing through an algorithm for ensuring multi-level session inspections, and ensuring maximum security controls at all the seven layers, and prevent an attack from occurring. The simulations present the effects of distributed attacks on the BI systems by attackers posing as genuine Cloud tenants. The results reflect how the attackers are blocked by the multilayered security and privacy controls deployed for protecting the BI servers and databases.

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“…Whilst cloud computing is an example of how technologies and operating models can come together to offer new capabilities for businesses [7,2], there are still cases of organisations being threatened by new risks which are attributed explicitly to the adoption of cloud services [8,9,14].…”

Section: Introductionmentioning

confidence: 99%

“…Prior work [4,5,7,6] has explored the particular case of multi-party authentication in cloud based architectures, for the provision of enterprise Business Intelligence systems [8,6,9]. We have developed and extended this work to support the development of specific use cases where the availability of 5G network infrastructure can enable new business possibilities through enhanced performance.…”

Section: Introductionmentioning

confidence: 99%

A Multi-layer Security Model for 5G-Enabled Industrial Internet of Things

Al-Aqrabi

Johnson

Hill

et al. 2019

Communications in Computer and Information Science

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This article considers the need for secure communications between Industrial Internet of Things (IIoT) devices that operate in 5Genabled environments. 5G networks enable greater data throughput and lower latency, which presents new opportunities for the secure authentication of business transactions between IoT hardware. We propose an approach to developing a flexible and secure model for IIoT components in 5G environments. Using the NIST seven layer model of cloud computing, in conjunction with Physically Unclonable Function (PUF) hardware provided via FPGAs, we demonstrate algorithms that replicate common authorisation challenges in 5G enabled IoT scenarios.

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Business intelligence software as a service (SAAS)

Cited by 6 publications

References 1 publication

Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks

Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks

A Multi-layer Hierarchical Inter-cloud Connectivity Model for Sequential Packet Inspection of Tenant Sessions Accessing BI as a Service

A Multi-layer Security Model for 5G-Enabled Industrial Internet of Things

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