Discovery of temporal association patterns, temporal association rules from temporal databases is extensively studied by academic research community and applied in various industrial applications. Temporal association pattern discovery is extended to similarity based temporal association pattern discovery from time-stamped transaction datasets by researchers Yoo and Sashi Sekhar. They introduced methods for pruning through distance bounds, and have also introduced SEQUENTIAL and SPAMINE algorithms for pattern mining that are based on snapshot data scan and lattice data scan strategies respectively. Our previous (...) research introduced algorithms G-SPAMINE, MASTER, Z-SPAMINE for time profiled association pattern discovery. These algorithms applied distance measures SRIHASS, ASTRA, and KRISHNA SUDARSANA for similarity computations. SEQUENTIAL, SPAMINE, G-SPAMINE, MASTER, Z-SPAMINE approaches are all based on snapshot and lattice database scan strategies and prunes temporal itemsets by making use of lower bound, upper bound support time sequences and upper-lower distance bound, lower bound distance values. The major limitation of all these algorithms is their inevitability to eliminate dataset scanning process for knowing true supports of itemsets and essential need to have dataset available in memory. To eliminate the requirement of retaining dataset in main memory, algorithms VRKSHA and GANDIVA are two pioneering research contributions that introduced tree structure for time profiled temporal association mining. VRKSHA is based on snapshot tree scan technique while GANDIVA is a lattice tree scan based approach. VRKSHA and GANDIVA both apply Euclidean distance function, but they do not estimate support and distance bounds. This research introduces the pioneering work ULTIMATE that uses a novel tree structure. The tree is generated using similarity measure ASTRA. ULTIMATE uses support bound and distance bound computations for pruning temporal patterns. Experiment results showed that ULTIMATE outperforms SEQUENTIAL, SPAMINE, G-SPAMINE, MASTER, VRKSHA, GANDIVA algorithms. (shrink)

In this full review paper, the recent emerging trends in Computing Structures, Software Science, and System Applications have been reviewed and explored to address the recent topics and contributions in the era of the Software and Computing fields. This includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art, framework, implemented approaches and techniques research projects in the areas of Software Technology & Automation, Networking, Systems, Computing Sciences and Software Engineering, Big Data and E-learning. Based on this systematic (...) review, we have put some recommendations and suggestions for researchers, practitioners and scholars to improve their research quality in this area. (shrink)

The impact of Online Social Networks on human lives is foreseen to be very large with unprecedented amount of data and users. OSN users share their ideas, photos, daily life events, feelings and news. Since OSNs’ security and privacy challenges are more potential than ever before, it is necessary to enhance the protection and filtering approaches of OSNs contents. This paper explores OSNs’ threats and challenges, and categorize them into: account-based, URL-based and content-based threats. In addition, we analyze the existing (...) protection methods and highlight their limitations and weaknesses. Based on that, we propose a comprehensive, user-level, proactive and real-time OSNs’ protection system, called Hybrid Real-time Social Networks Protector. HRSP has three components; a user-level security protocol and two classification models. The protocol defines a structure for OSN’s cryptographic services, including encryption, access control and users’ authentication. The classification models employ machine learning, black lists, white lists and users’ feedback, in order to classify URLs into: Benign, Risk and Inappropriate classes, and contents into: Benign, Hate speech and Inappropriate classes. We constructed two data sets of 150,000 URLs and 22,000 tweets to build and test the two classification models. Results show an overall accuracy of 93.2% for the URL model and 84.4% for the content model, while the protocol implementation produces compatible size and time overhead. The components of HRSP are integrated and have compatible design with OSN platforms. (shrink)

In recent decades, there has been a proliferation among the scientific community of works that focus on Alan Turing’s contributions to the design and development of the modern computer. However, there are significant discrepancies among these studies, to such a point that some of them cast serious doubts on Alan Turing’s work with respect to today’s computer, and there are others that staunchly defend his leading role, as well as other studies that set out more well-balanced opinions. Faced with this (...) situation, the aim of this paper is to analyse the evidence existing today in order to be able to draw a conclusion about whether or not Turing anticipated the trivialisation of the modern computer memory and, likewise, if his universal a-machine is the precursor of the general-purpose computer so omnipresent today. As a result of our research, the authors conclude that Turing did indeed play a leading role in the appearance of the modern computer, although he was not the only one or the first in the field of Computing Science, albeit he was the most influential, both in scope and in depth. (shrink)

Similarity profiled association mining from time stamped transaction databases is an important topic of research relatively less addressed in the field of temporal data mining. Mining temporal patterns from these time series databases requires choosing and applying similarity measure for similarity computations and subsequently pruning temporal patterns. This research proposes a novel z-space based interest measure named as Krishna Sudarsana for time-stamped transaction databases by extending interest measure Srihass proposed in previous research. Krishna Sudarsana is designed by using the product (...) based fuzzy Gaussian membership function and performs similarity computations in z-space to determine the similarity degree between any two temporal patterns. The interest measure is designed by considering z-values between z = 0 and z = 3.09. Applying the Krishna Sudarsana requires moving the threshold value given by user to a different transformation space which is a defined as a function of standard deviation. In addition to proposing interest measure, new expressions for standard deviation and equivalent z-space threshold are derived for similarity computations. For experimental evaluation, we considered Naïve, Sequential and Spamine algorithms that applies Euclidean distance function and compared performance of these three approaches to Z-Spamine algorithm that uses Krishna Sudarsana by choosing various test cases. Experiment results proved the performance of the proposed approach is better to Sequential approach that uses snapshot database scan strategy and Spamine approach that uses lattice based database scan strategy. (shrink)