Minufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78536120190101Service Flow Management with deadline and budget Constraints using Genetic Algorithm in Heterogeneous Computing183512110.21608/ijci.2019.35121ENAhmedAbdelHamedDepartment of Computer Science, Faculty of Computers and Information, Menoufia UniversityMedhat A.TawfikDepartment of Computer Science, Menoufia University, EgyptArabiKeshkFaculty of Computer and Information Menoufia UniversityJournal Article20180819The service flow management is one from the most challenges especially in heterogeneous environments which has several and various processors for computing. Service flow is used to explain services configuration process when service’s formation according to the precedence relations of configuration should be considered. Its management should take into account multi-objective constraints. The total execution time should not be completed after the specified time that leading to consider the deadline constraint into account. Also the cost minimization that is a critical issue shouldn’t be ignored. Obtaining the optimal management in a sensible time is so hard because there are many candidate with different processing power and price, constraints from the user and the precedence of heterogeneous services. In this paper, the service flow management problem is solved by a genetic algorithm that considers deadline and cost constraints. It focuses on the improvement of execution time to meet the deadline constraint and minimizes the execution cost according to the budget in heterogeneous computing. The results from the applied experiments proves that the proposed algorithm can be able to minimize total cost, and consolidate the execution time with the deadline constraint. It reach to a near-optimal solution in reasonable time. It outperforms the compared algorithms in the metric of Schedule Length Ratio (SLR), cost, risk ratio, speed up and completion time measurements.<br /><br />https://ijci.journals.ekb.eg/article_35121_f4bffd48abea6c533301eff223a92b18.pdfMinufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78536120190101A Novel Scalable and Effective Partitioning Approach for Big Data Reduction9193512210.21608/ijci.2019.35122ENM. G.MalhatComputer Science dept., Faculty of computers and Information,
Menoufia University, Egypt0000-0002-0136-4805M.ElmenshawyComputer Science dept., Faculty of Computers and Information, Menofia University, EgyptHamdyMousaFaculty of Computer and Information Menoufia University0000-0001-9503-9124A. B.ElsisiComputer Science dept., Faculty of computers and Information, Menofia University, EgyptJournal Article20180801The continuous increment of data size makes the traditional instance selection methods ineffective to reduce big training datasets in a single machine. Recent approaches to solving this technical problem partition the training dataset into subsets prior to apply the instance selection methods into each subset separately. However, the performance of the applied instance selection methods to subsets is negatively affected, especially when the number of partitioned subsets is increased. In this work, we propose a novel scalable and effective automated partitioning approach, called overlapped distance-based class-balance partitioning. This approach distributes the training dataset instances to the partitioned subsets based on a given distance metric and ensures the equal representation of data classes into partitioned subsets. Moreover, the instances might be assigned to two subsets once they satisfy the dynamic threshold. We implement and test empirically the scalability and effectiveness of the proposed approach using condensed nearest neighbor method over eight standard datasets. The proposed approach is compared empirically and analytically with stratification partitioning approach and a non-overlapped version from our approach with respect to 1) the reduction rate, classification accuracy, and effectiveness metrics, and 2) the scalability aspect, where the number of subsets is increased. The comparison results demonstrate that our approach is more scalable and effective than other partitioning approaches with respect to these standard datasets.<br /><br />https://ijci.journals.ekb.eg/article_35122_1b45234f35b4bc58530d71e622733fb3.pdfMinufiya University; Faculty of Computers and InformationIJCI. International Journal of Computers and Information1687-78536120190101A Review of Open Information Extraction Techniques20283509910.21608/ijci.2019.35099ENSallyAliDept. of Computer Science, Faculty of Computers and Information
Menoufia University, EgyptHamdyMousaFaculty of Computer and Information Menoufia University0000-0001-9503-9124M.HussienDept. of Computer Science, Faculty of Computers and Information, Menoufia University, EgyptJournal Article20180916Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level.
Nowadays, massive amount of data flows all the time. Approximately between 20 or 30 percent of these data is text. This data is always organized in semi-structured text, which cannot be used directly. To make use of such huge amounts of textual data, there is a need to detect, extract, and structure the information conveyed through this data in a fast and scalable manner. This can be performed using Information Extraction Techniques. However, the task of information extraction is one of the main challenges in Natural Language Processing and there are limitations for its implementation on a large scale of data. Open Information Extraction (OIE) is an open-domain and relation-independent paradigm to perform information extraction in an unsupervised manner. This technique can lead to high-speed and scalable performance. The review of previous research proposals reveals that there are OIE experiments among different languages, such as English, Portuguese, Spanish, Vietnamese, Chinese, and Germany. This paper reviews the OIE techniques, compare their performance in some languages, and then integrates these results with the languages complexity levels to reveal the relationship between the suitable model and the language complexity level.Keywords—Open Information Extraction; Natural Language Processinghttps://ijci.journals.ekb.eg/article_35099_2751a97dec8ca23f3e6ca98f27cee4b6.pdf