XNorthwind : grammar-driven synthesis of large datasets for DB applications

Abstract: Relational databases often come with sample databases. One known example is the Northwind database, often used as data repository for software testing and development purposes. The Northwind database includes hypothetical records of customers, companies, products, employee and so on. The number of records in the Northwind is however considered inadequate for large applications, where a developer or user may need a lot more, possibly, millions of records. In this paper, we have used a Context-free Grammar in describing the rules for the synthesis of exponentially many hypothetical datasets that are similar to the Northwind database. We referred to the resulting database as XNorthwind (Extended Northwind). The new grammar was implemented, resulting in thousands of unique data values across the eight different Northwind Data Tables. These datasets will find applications in training and development environments. A survey of 112 participants’ perceptions showed that 94.6% agreed that the XNorthwind can be useful

Design and Development of Techniques to Ensure Integrity in Fog Computing Based Databases

The advancement of information technology in coming years will bring significant changes to the way sensitive data is processed. But the volume of generated data is rapidly growing worldwide. Technologies such as cloud computing, fog computing, and the Internet of things (IoT) will offer business service providers and consumers opportunities to obtain effective and efficient services as well as enhance their experiences and services; increased availability and higher-quality services via real-time data processing augment the potential for technology to add value to everyday experiences. This improves human life quality and easiness. As promising as these technological innovations, they are prone to security issues such as data integrity and data consistency. However, as with any computer system, these services are not without risks. There is the possibility that systems might be infiltrated by malicious transactions and, as a result, data could be corrupted, which is a cause for concern. Once an attacker damages a set of data items, the damage can spread through the database. When valid transactions read corrupted data, they can update other data items based on the value read. Given the sensitive nature of important data and the critical need to provide real-time access for decision-making, it is vital that any damage done by a malicious transaction and spread by valid transactions must be corrected immediately and accurately. In this research, we develop three different novel models for employing fog computing technology in critical systems such as healthcare, intelligent government system and critical infrastructure systems. In the first model, we present two sub-models for using fog computing in healthcare: an architecture using fog modules with heterogeneous data, and another using fog modules with homogeneous data. We propose a unique approach for each module to assess the damage caused by malicious transactions, so that original data may be recovered and affected transactions may be identified for future investigations. In the second model, we introduced a unique model that uses fog computing in smart cities to manage utility service companies and consumer data. Then we propose a novel technique to assess damage to data caused by an attack. Thus, original data can be recovered, and a database can be returned to its consistent state as no attacking has occurred. The last model focus of designing a novel technique for an intelligent government system that uses fog computing technology to control and manage data. Unique algorithms sustaining the integrity of system data in the event of cyberattack are proposed in this segment of research. These algorithms are designed to maintain the security of systems attacked by malicious transactions or subjected to fog node data modifications. A transaction-dependency graph is implemented in this model to observe and monitor the activities of every transaction. Once an intrusion detection system detects malicious activities, the system will promptly detect all affected transactions. Then we conducted a simulation study to prove the applicability and efficacy of the proposed models. The evaluation rendered this models practicable and effective

A parallelized database damage assessment approach after cyberattack for healthcare systems

In the current Internet of things era, all companies shifted from paper-based data to the electronic format. Although this shift increased the efficiency of data processing, it has security drawbacks. Healthcare databases are a precious target for attackers because they facilitate identity theft and cybercrime. This paper presents an approach for database damage assessment for healthcare systems. Inspired by the current behavior of COVID-19 infections, our approach views the damage assessment problem the same way. The malicious transactions will be viewed as if they are COVID-19 viruses, taken from infection onward. The challenge of this research is to discover the infected transactions in a minimal time. The proposed parallel algorithm is based on the transaction dependency paradigm, with a time complexity O((M+NQ+Nˆ3)/L) (M = total number of transactions under scrutiny, N = number of malicious and affected transactions in the testing list, Q = time for dependency check, and L = number of threads used). The memory complexity of the algorithm is O(N+KL) (N = number of malicious and affected transactions, K = number of transactions in one area handled by one thread, and L = number of threads). Since the damage assessment time is directly proportional to the denial-of-service time, the proposed algorithm provides a minimized execution time. Our algorithm is a novel approach that outperforms other existing algorithms in this domain in terms of both time and memory, working up to four times faster in terms of time and with 120,000 fewer bytes in terms of memory

A Real Time Web Based Electronic Triage, Resource Allocation and Hospital Dispatch System for Emergency Response

Disasters are characterized by large numbers of victims and required resources, overwhelming the available resources. Disaster response involves various entities like Incident Commanders, dispatch centers, emergency operations centers, area command and hospitals. An effective emergency response system should facilitate coordination between these various entities. Victim triage, emergency resource allocation and victim dispatch to hospitals form an important part of an emergency response system. In this present research effort, an emergency response system with the aforementioned components is developed. Triage is the process of prioritizing mass casualty victims based on severity of injuries. The system presented in this thesis is a low-cost victim triage system with RFID tags that aggregate all victim information within a database. It will allow first responders\u27 movements to be tracked using GPS. A web-based real time resource allocation tool that can assist the Incident Commanders in resource allocation and transportation for multiple simultaneous incidents has been developed. This tool ensures that high priority resources at emergency sites are received in least possible time. This web-based tool also computes the patient dispatch schedule from each disaster site to each hospital. Patients are allocated to nearest hospitals with available medical facilities. This tool can also assist resource managers in emergency resource planning by computing the time taken to receive required resources from the nearest depots using Google Maps. These web-based tools complements emergency response systems by providing decision-making capabilities

Improving average ranking precision in user searches for biomedical research datasets

Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorisation method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries. Our system provides competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP among the participants, being +22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. Our similarity measure algorithm seems to be robust, in particular compared to Divergence From Randomness framework, having smaller performance variations under different training conditions. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. In particular, the use of data driven query expansion methods could be an alternative to the complexity of biomedical terminologies

Implementation of RFID based Village Security System

This project focuses on a development of Radio Frequency Identification (RFID) implemented at dormitory in Universiti Teknologi Petronas (UTP). By using the RFID tag instill in students’ metric card, assigned with unique bar code, students are able to enter their house and rooms.The door of the house is auto locked and need to be swapped only once .While the door to enter students’ respective room need to be swapped twice to be locked. All these entry and exit will be recorded in a system to ensure the safety of the students and their procession. The system can only be logged in by the admin with password as these data are confidential. Once the admin is able to enter , two major task can be done. Firstly assigning the students with the unique bar code and deleting them once the duration is over and secondly able to track the students entry and exit of the house and room

Single DNA Molecule Analysis – New Tools for Medical Diagnosis

The DNA molecule, the blueprint of life, contains an enormous amount of information. The information is coded by the combination of four bases; adenine, cytosine, guanine, and thymine, that, together with the sugar-phosphate backbones, make up the DNA double helix. There are variants in the human DNA sequence that are related to the onset and progression of disease. Under different conditions the DNA can also be damaged, which if not repaired correctly can result in a shortened life span, rapid ageing and development or progression of a variety of diseases, including cancer. Human disease can also be induced by external factors in our surroundings, such as pathogens. One of the cornerstones in modern medicine has been the use of antibiotics to prevent and treat these pathogenic infections, but the global spread of antibiotic resistance is today one of the largest threats to mankind according to the World Health Organization. One consequence of a large global increase in antibiotic resistance would be that routine surgery or chemotherapy treatment might be considered too perilous, because there are no drugs available to prevent or treat the bacterial infections that are closely connected with these procedures.Novel techniques are needed to characterize different features of DNA in medicine and diagnostics. Single molecule analysis is one method to unveil different kinds of information from individual biomolecules, such as DNA. This thesis uses fluorescence microscopy to shine light upon such information in single DNA molecules from both humans and bacteria, and with that unveil important biological and medical characteristics of that DNA. It describes one method for identifying and quantifying DNA damage induced by a chemotherapy agent, helping to understanding the processes of DNA damage and repair related to diseases and medical treatments. Another method developed is for rapid identification of bacterial infections, with the classification of bacterial sub-species groups and identification of antibiotic resistance genes on plasmids. The methods have the potential to rapidly provide comprehensive diagnostics information, to optimize either early antibiotic treatment or chemotherapy treatment, and thereby enable future precision medicine management

15th Annual Undergraduate Research Day at the Capitol

Abstract booklet from the 15th Annual Undergraduate Research Day at the Capitol

Implementations of Artificial Intelligence in Various Domains of IT Governance: A Systematic Literature Review

Background: Artificial intelligence (AI) has become increasingly prevalent in various industries, including IT governance. By integrating AI into the governance environment, organizations can benefit from the consolidation of frameworks and best practices. However, the adoption of AI across different stages of the governance process is unevenly distributed. Objective: The primary objective of this study is to perform a systematic literature review on applying artificial intelligence (AI) in IT governance processes, explicitly focusing on the Deming cycle. This study overlooks the specific details of the AI methods used in the various stages of IT governance processes. Methods: The search approach acquires relevant papers from Elsevier, Emerald, Google Scholar, Springer, and IEEE Xplore. The obtained results were then filtered using predefined inclusion and exclusion criteria to ensure the selection of relevant studies. Results: The search yielded 359 papers. Following our inclusion and exclusion criteria, we pinpointed 42 primary studies that discuss how AI is implemented in every domain of IT Governance related to the Deming cycle. Conclusion: We found that AI implementation is more dominant in the plan, do, and check stages of the Deming cycle, with a particular emphasis on domains such as risk management, strategy alignment, and performance measurement since most AI applications are not able to perform well in different contexts as well as the other usage driven by its unique capabilities. Keywords: Artificial Intelligence, Deming cycle, Governance, IT Governance domain, Systematic literature revie