Othman Mohd Yusop

Analyzing Traffic Accident and Casualty Trend Using Data Visualization

Motor vehicle is the backbone of the modern transportation system worldwide. However, the excessive number of motor vehicles tend to cause traffic accidents leading to numerous casualties. Analyzing existing works on this area, this study has identified prime reasons behind traffic accidents and casualties. They include driving over the speed limit, Age of drivers and pedestrians, environmental condition, location and road types. It has also reviewed and identified several data visualization methods and visualization techniques that have been proposed by many researchers. The objective of this research endeavour is to identify the factors behind traffic accidents, determine the techniques that are used to visualize data, develop a dashboard using data visualization tools to visualize traffic accident trend and to evaluate the functionality of the dashboard which is developed on United Kingdom’s (UK) traffic accident dataset from 2014 to 2016. Upon performing data cleaning, pre-processing and filtering, the raw data has been converted into cleaned, filtered and processed data to create a coherent and properly linked data model. Then, using the Power BI visualization tool, various interactive visualizations have been produced that illustrated several significant trends in accident and casualties. The visualization trend revealed that between 2014 to 2016, majority of the accidents in the UK occurred in the urban area, in the single carriageway, on the dry road surface, under the daylight with fine weather, and when the speed limit was below 30 mph. This research may assist UK’s traffic management authority to identify the underlying factors behind the traffic accident and to detect the traffic accident and casualty trend in order to take necessary steps to reduce casualties in traffic accidents.

Data Quality Issues in Big Data: A Review

Data with good quality has precedence when analyzing and using big data to deduce value from such tremendous volume of data in today’s business environments. Decisions and insights derived from poor data has a negative and unpredictable consequences to organizations. At present, due to the lack of comprehensive and intensive research in the field of data quality, especially large data, there is an urgent need to address this issue by researchers to reach the optimal way to estimate and evaluate the quality of large data. Thus, enabling institutions to make rational decisions based on evaluation outputs. In this paper, the current research on the quality of large data was reviewed and summarized by exploring the basic characteristics of large data. The main challenges facing the quality of information were also discussed in the context of large data. Some of the initiatives suggested by the researchers to evaluate the quality of the data have been highlighted. Finally, we believe that the results of these reviews will enhance the conceptual measurements of the large data quality and produce a concrete groundwork for the future by creating an integrated data quality assessment and evaluation models using the suitable algorithms.

The challenges of Extract, Transform and Loading (ETL) system implementation for near real-time environment

Organization with considerable investment into data warehousing, the influx of various data types and forms requires certain ways of prepping data and staging platform that support fast, efficient and volatile data to reach its targeted audiences or users of different business needs. Extract, Transform and Load (ETL) system proved to be a choice standard for managing and sustaining the movement and transactional process of the valued big data assets. However, traditional ETL system can no longer accommodate and effectively handle streaming or near real-time data and stimulating environment which demands high availability, low latency and horizontal scalability features for functionality. This paper identifies the challenges of implementing ETL system for streaming or near real-time data which needs to evolve and streamline itself with the different requirements. Current efforts and solution approaches to address the challenges are presented. The classification of ETL system challenges are prepared based on near real-time environment features and ETL stages to encourage different perspectives for future research.

Energy harvesting in wireless sensor networks: A survey

With the emergence of Internet-of-Things (IoT) technologies, the application of Wireless Sensor Network (WSN) has become pervasive in our physical environment. These technologies rely on the WSN to capture environmental data and transmit it through the network to the specified location or storage. Given the massive data that captured by the WSN and the data transmission that happen in the process, there is a challenge for any developers and researchers in WSN in terms of a continuous operation of the WSN device. This is because WSNs are battery-powered and often deployed in a remote location that is hard to access for a battery replacement. The purpose of this paper is to survey the energy harvesting for improving the lifetime of the wireless sensor networks powered by the environmental energy. Energy harvesting takes advantages of the stray energy from the environment as the sustainable source of power.

HMAC Authentication Mechanisms in a Grid Computing Environment Using Gridsim Toolkit

Recently the authentication of grid computing environment has been to secure the security of local and remote entities in the surrounding. The existing authentication mechanisms mostly use public key infrastructure (PKI), but the potential PKI vulnerability and implementation constraints are still cannot be a void. In order to satisfy the security needs of grid computing environment, this paper proposes an authentication mechanism using HMAC (a hash, MAC) to secure the user identification that bears the characteristic of public key replacement attack resistance, which run the experiment in the GridSim toolkit simulator.

Software Maintenance Testing Approaches to Support Test Case Changes – A Review

Software Maintenance Testing is essential during software testing phase. All defects found during testing must undergo a re-test process in order to eliminate the flaws. By doing so, test cases are absolutely needed to evolve and change accordingly. In this paper, several maintenance testing approaches namely regression test suite approach, heuristic based approach, keyword based approach, GUI based approach and model based approach are evaluated based on software evolution taxonomy framework. Some of the discussed approaches support changes of test cases. Out of the review study, a couple of results are postulated and highlighted including the limitation of the existing approaches.