Sketch of Big Data Real-Time Analytics Model

Big Data has drawn huge attention from researchers in information sciences, decision makers in governments and enterprises. However, there is a lot of potential and highly useful value hidden in the huge volume of data. Data is the new oil, but unlike oil data can be refined further to create even more value. Therefore, a new scientific paradigm is born as data-intensive scientific discovery, also known as Big Data. The growth volume of real-time data requires new techniques and technologies to discover insight value. In this paper we introduce the Big Data real-time analytics model as a new technique. We discuss and compare several Big Data technologies for real-time processing along with various challenges and issues in adapting Big Data. Real-time Big Data analysis based on cloud computing approach is our future research direction

Mobile Application Security Platforms Survey

Nowadays Smartphone and other mobile devices have become incredibly important in every aspect of our life. Because they have practically offered same capabilities as desktop workstations as well as come to be powerful in terms of CPU (Central processing Unit), Storage and installing numerous applications. Therefore, Security is considered as an important factor in wireless communication technologies, particularly in a wireless ad-hoc network and mobile operating systems. Moreover, based on increasing the range of mobile application within variety of platforms, security is regarded as on the most valuable and considerable debate in terms of issues, trustees, reliabilities and accuracy. This paper aims to introduce a consolidated report of thriving security on mobile application platforms and providing knowledge of vital threats to the users and enterprises. Furthermore, in this paper, various techniques as well as methods for security measurements, analysis and prioritization within the peak of mobile platforms will be presented. Additionally, increases understanding and awareness of security on mobile application platforms to avoid detection, forensics and countermeasures used by the operating systems. Finally, this study also discusses security extensions for popular mobile platforms and analysis for a survey within a recent research in the area of mobile platform security

Mobile application testing matrix and challenges

The adoption of smart phones and the usages of mobile applications are increasing rapidly. Consequently, within limited time-range, mobile Internet usages have managed to take over the desktop usages particularly since the first smart phone-touched application released by iPhone in 2007. This paper is proposed to provide solution and answer the most demandable questions related to mobile application automated and manual testing limitations. Moreover, Mobile application testing requires agility and physically testing. Agile testing is to detect bugs through automated tools, whereas the compatibility testing is more to ensure that the apps operates on mobile OS (Operation Systems) as well as on the different real devices. Moreover, we have managed to answer automated or manual questions through two mobile application case studies MES (Mobile Exam System) and MLM (Mobile Lab Mate) by creating test scripts for both case studies and our experiment results have been discussed and evaluated on whether to adopt test on real devices or on emulators? In addition to this, we have introduced new mobile application testing matrix for the testers and some enterprises to obtain knowledge fro

Distributed Contextual Anomaly Detection from Big Event Streams

The age of big digital data is emerged and the size of generating data is rapidly increasing in a millisecond through the Internet of Things (IoT) and Internet of Everything (IoE) objects. Specifically, most of today’s available data are generated in a form of streams through different applications including sensor networks, bioinformatics, smart airport, smart highway traffic, smart home applications, e-commerce online shopping, and social media streams. In this context, processing and mining such high volume of data stream becomes one of the research priority concern and challenging tasks. On the one hand, processing high volumes of streaming data with low-latency response is a critical concern in most of the real-time application before the important information can be missed or disregarded. On the other hand, detecting events from data stream is becoming a new research challenging task since the existing traditional anomaly detection method is mainly focusing on; a) limited size of data, b) centralised detection with limited computing resource, and c) specific anomaly detection types of either point or collective rather than the Contextual behaviour of the data. Thus, detecting Contextual events from high sequence volume of data stream is one of the research concerns to be addressed in this thesis. As the size of IoT data stream is scaled up to a high volume, it is impractical to propose existing processing data structure and anomaly detection method. This is due to the space, time and the complexity of the existing data processing model and learning algorithms. In this thesis, a novel distributed anomaly detection method and algorithm is proposed to detect Contextual behaviours from the sequence of bounded streams. Capturing event streams and partitioning them over several windows to control the high rate of event streams mainly base on, the proposed solution firstly. Secondly, by proposing a parallel and distributed algorithm to detect Contextual anomalous event. The experimental results are evaluated based on the algorithm’s performances, processing low-latency response, and detecting Contextual anomalous behaviour accuracy rate from the event streams. Finally, to address scalability concerned of the Contextual events, appropriate computational metrics are proposed to measure and evaluate the processing latency of distributed method. The achieved result is evidenced distributed detection is effective in terms of learning from high volumes of streams in real-time