Parallel Execution Techniques Research Articles

Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis

In today's digitalized era, the world wide web services are a vital aspect of each individual's daily life and are accessible to the users via uniform resource locators (URLs). Cybercriminals constantly adapt to new security technologies and use URLs to exploit vulnerabilities for illicit benefits such as stealing users' personal and sensitive data, which can lead to financial loss, discredit, ransomware, or the spread of malicious infections and catastrophic cyber-attacks such as phishing attacks. Phishing attacks are being recognized as the leading source of data breaches and the most prevalent deceitful scam of cyber-attacks. Artificial intelligence (AI)-based techniques such as machine learning (ML) and deep learning (DL) have proven to be infallible in detecting phishing attacks. Nevertheless, sequential ML can be time intensive and not highly efficient in real-time detection. It can also be incapable of handling vast amounts of data. However, utilizing parallel computing techniques in ML can help build precise, robust, and effective models for detecting phishing attacks with less computation time. Therefore, in this proposed study, we utilized various multiprocessing and multithreading techniques in Python to train ML and DL models. The dataset used comprised 54 K records for training and 12 K for testing. Five experiments were carried out, the first one based on sequential execution followed by the next four based on parallel execution techniques (threading using Python parallel backend, threading using Python parallel backend and number of jobs, threading manually, and multiprocessing using Python parallel backend). Four models, namely, random forest (RF), naïve bayes (NB), convolutional neural network (CNN), and long short-term memory (LSTM) were deployed to carry out the experiments. Overall, the experiments yielded excellent results and speedup. Lastly, to consolidate, a comprehensive comparative analysis was performed.

Enhanced subquery optimizations in Oracle

This paper describes enhanced subquery optimizations in Oracle relational database system. It discusses several techniques -- subquery coalescing, subquery removal using window functions, and view elimination for group-by queries. These techniques recognize and remove redundancies in query structures and convert queries into potentially more optimal forms. The paper also discusses novel parallel execution techniques, which have general applicability and are used to improve the scalability of queries that have undergone some of these transformations. It describes a new variant of antijoin for optimizing subqueries involved in the universal quantifier with columns that may have nulls. It then presents performance results of these optimizations, which show significant execution time improvements.

PreSPI: design and implementation of protein-protein interaction prediction service system.

With the recognition of the importance of computational approach for protein-protein interaction prediction, many techniques have been developed to computationally predict protein-protein interactions. However, few techniques are actually implemented and announced in service form for general users to readily access and use the techniques. In this paper, we design and implement a protein interaction prediction service system based on the domain combination based protein-protein interaction prediction technique, which is known to show superior accuracy to other conventional computational protein-protein interaction prediction methods. In the prediction accuracy test of the method, high sensitivity (77%) and specificity (95%) are achieved for test protein pairs containing common domains with learning sets of proteins in a Yeast. The stability of the method is also manifested through the testing over DIP CORE, HMS-PCI, and TAP data. The functions of the system are divided into core, subsidiary, and general service function categories. The core function category includes the functions that can be provided only by using the domain combination based protein-protein interaction prediction method. Interaction prediction for a single protein pair and visualization of interaction probability distributions are the functions in this category. The subsidiary function category includes the functions that can be derived from the core functions. Domain combination pair search with high appearance probability and construction of protein interaction network are the functions in this category. Lastly, the general service function category includes the functions that can be implemented by collecting and organizing the protein and domain data in the Internet. Performance, openness and flexibility are the major design goals and they are achieved by adopting parallel execution techniques, Web Services standards, and layered architecture respectively. In this paper, several representative user interfaces of the system are also introduced with comprehensive usage guides.

Parallelism and its price

We describe the use of parallel execution techniques and measure the price of parallel execution in NonStop SQL/MP, a commercial parallel database system from Tandem Computers. Non-Stop SQL uses intra-operator parallelism to parallelize joins, groupings and scans. Parallel execution consists of starting up several processes and communicating data between them. Our measurements show (a) Startup costs are negligible when processes are reused rather than created afresh (b) Communication costs are significant — they may exceed the costs of operators such as scan, grouping or join. We also show two counter-examples to the common intuition that parallel execution reduces response time at the expense of increased work — parallel execution may reduce work or may increase response time depending on communication costs.

An empirical study of Fortran programs for parallelizing compilers

Some results are reported from an empirical study of program characteristics, that are important in parallelizing compiler writers, especially in the area of data dependence analysis and program transformations. The state of the art in data dependence analysis and some parallel execution techniques are examined. The major findings are included. Many subscripts contain symbolic terms with unknown values. A few methods of determining their values at compile time are evaluated. Array references with coupled subscripts appear quite frequently; these subscripts must be handled simultaneously in a dependence test, rather than being handled separately as in current test algorithms. Nonzero coefficients of loop indexes in most subscripts are found to be simple: they are either 1 or -1. This allows an exact real-valued test to be as accurate as an exact integer-valued test for one-dimensional or two-dimensional arrays. Dependencies with uncertain distance are found to be rather common, and one of the main reasons is the frequent appearance of symbolic terms with unknown values. >