Search

Search algorithms play an essential role in wide variety of applications in computer science. Different search algorithms can be applied to different data structures. Each data structure has its own advantages and limitations which in turn affect the search algorithm complexity and performance. In the context of Big Data such limitations can be more obvious and noticeable in reducing the performance offered by the data structure and search algorithms applied to it because of big data characteristics such as its huge volume. Also, string data sets can be more challenging. They can cause more overhead in preprocessing. Some string datasets such as personal names may have some skew that has negative effect on hash indexes by increasing the ratio of collisions produced. The work in this paper offers a technique for improving the famous Hash table Data Structure by reducing the collision ratio in hashing in the average case, thus reducing the total number of comparisons made by search algorithm. The advantage of reducing the collision ratio was achieved by building a hybrid approach making use of three common data structures, namely Hash Table, B-Tree and Linked List. The results obtained showed noticeable improvement for time complexity of data searching algorithm over the traditional data structures that were used for comparisons.

Text classification (TC) is an essential field in both text mining (TM) and natural language processing (NLP). Humans have a tendency to organize and categorize everything as they want to make things easier to understand. Therefore, text classification is an important step to achieve this goal. Arabic text classification (ATC) is a difficult process because the Arabic language has complications and limitations resulting from the nature of its morphology. In this paper, a proposed approach called the Master-Slaves technique (MST) was used to improve Arabic text classification. It consists of two main phases: in the first phase, a new Arabic corpus of 16757 text files was collected. These text files were classified into five categories manually. In the second phase, four different classifiers were implemented on the collected corpus. These classifiers are Naive Bayes (NB), K-Nearest Neighbour (KNN), Multinomial Logistic Regression (MLR) and Maximum Weight (MW). Naive Bayes classifier was implemented as Master and the others as Slaves. The results of these slave classifiers were used to change the probability of the Naive Bayes classifier (Master). The four classifiers used were implemented individually and the simple voting technique was implemented among them too on the collected corpus to check the effectiveness and efficiency of the proposed technique. All the tests were applied after the pre-processing of Arabic text documents (tokenization, stemming, and stop-word removal) and each document was represented as vector of weights. For the reliability of the results, 10-fold cross-validation was used in this paper. The results showed that the Master-slaves technique gives a good improvement in accuracy of text document classification with accepted algorithm complexity compared to other techniques.