3D scientific data mining in ion trajectories

In physics, structure of glass and ion trajectories are essentially based on statistical analysis of data acquired through experimental measurement and computer simulation. Invariably, the details of the structure-transport relationships in the data have been mistreated in favour of ensemble average. In this study, we demonstrate a visual approach of such relationship using surface-based visualisation schemes. In particular, we demonstrate a scientific datasets of simulated 3D time-varying model and examine the temporal correlation among ion trajectories. We propose a scheme that uses a three dimensional visual representation with colour scale for depicting the timeline events in ion trajectories and this scheme could be divided into two major part such as global and local time scale. With a collection of visual examples from this study, we demonstrate that this scheme may offer an effective tool for visually mining 3D timeline events of the ion trajectories. This work will potentially form a basis of a novel analysis tool for measuring the effectiveness of visual representation to assist physicist in identifying possible temporal association among complex and chaotic atom movements in ion trajectories

Data analytics for modeling and visualizing attack behaviors: A case study on SSH brute force attacks

In this research, we explore a data analytics based approach for modeling and visualizing attack behaviors. To this end, we employ Self-Organizing Map and Association Rule Mining algorithms to analyze and interpret the behaviors of SSH brute force attacks and SSH normal traffic as a case study. The experimental results based on four different data sets show that the patterns extracted and interpreted from the SSH brute force attack data sets are similar to each other but significantly different from those extracted from the SSH normal traffic data sets. The analysis of the attack traffic provides insight into behavior modeling for brute force SSH attacks. Furthermore, this sheds light into how data analytics could help in modeling and visualizing attack behaviors in general in terms of data acquisition and feature extraction

STWalk: Learning Trajectory Representations in Temporal Graphs

Analyzing the temporal behavior of nodes in time-varying graphs is useful for many applications such as targeted advertising, community evolution and outlier detection. In this paper, we present a novel approach, STWalk, for learning trajectory representations of nodes in temporal graphs. The proposed framework makes use of structural properties of graphs at current and previous time-steps to learn effective node trajectory representations. STWalk performs random walks on a graph at a given time step (called space-walk) as well as on graphs from past time-steps (called time-walk) to capture the spatio-temporal behavior of nodes. We propose two variants of STWalk to learn trajectory representations. In one algorithm, we perform space-walk and time-walk as part of a single step. In the other variant, we perform space-walk and time-walk separately and combine the learned representations to get the final trajectory embedding. Extensive experiments on three real-world temporal graph datasets validate the effectiveness of the learned representations when compared to three baseline methods. We also show the goodness of the learned trajectory embeddings for change point detection, as well as demonstrate that arithmetic operations on these trajectory representations yield interesting and interpretable results.Comment: 10 pages, 5 figures, 2 table

Decision-making process framework at the planning phase of housing development project

Every housing development project needs to go through several procedures which consist of a decision-making process. By practising the decision-making process since the planning phase, the relevant decision-maker is assisted in analysing and organising all issues arise such as the problem in identification and selection of a suitable contractor for housing development. However, the decisions are made without knowing precisely what will happen in the future. The research’s primary purpose is to develop a process model for decision-making at Malaysia’s housing development planning phase. This study also examines the decision-making process practised among Malaysian private housing developers at the planning phase and classifies four main aspects of decision-making: methods, tools, criteria and information. The study then discovers whether the four main aspects (methods, tools, criteria and information) are strongly related to the decision making process. This study comprises the development of a theoretical framework by integrating the models that have been developed by numerous authors and researchers on the subject of decision making. Besides, 67 private housing developers have been chosen as respondents for a questionnaire survey in this study. The descriptive statistical analysis and the correlated analysis are conducted employing the Statistical Package for Social Sciences (SPSS). The results of this study show different findings for every four main aspects studied. However, it still answers the research objectives, and the relationship between the four main aspects of the decision-making process is accepted. This study is useful because it serves as a guide for private housing developers and governments in decision making at the planning phase of housing development. Moreover, this study provides a new process framework for decision making at the planning phase of housing development in Malaysia and assists housing developers and governments to make better predictions before proceeding to the construction phase

Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013