The word mismatch problem is fundamental to Information retrieval. Query expansion process helps to overcome this problem. Based on the Arabic corpuses, the comparisons between two query expansion techniques (global and local query) have been conducted to determine the query effectiveness. First one represents the local context analysis which represents a local method, while a global method was the second technique that has been represented by the Association and similarity thesauruses. These techniques can be used in any special field or domain to improve the expansion process and to get more relevant documents for the user's query. This study introduces a comparison between these approaches and shows their effectiveness. Although, local context analysis has some advantages over the similarity thesaurus, Association thesaurus which is global is generally the most effective one.
Big data is facing many challenges in different aspects, which appear in characteristics such as: Velocity, Volume, Value and Veracity. Processing and analysis of big data are challenging issues to acquire quality information in order to support accurate medical drug practice. The quality of data taxonomy is indicated by three basic elements: are meaningful, predication and decision-making. These elements have been encouraged in previous work that focused on the same challenges of big data. Consequently, the proposed approach preserves the quality of medical drug data toward meaningful data lake by clustering. It consists of four components. Data collection and pre-processing represent the first component in the data lake. Profile data is treated with semi-structured data to clean it up. The second component is extracting data through enforcing rules on whole data to produce different groups and generate weight based on constraints within groups. In component three, data is organized and clustering. This component complies with schema profiling referring to component two in the data lake. Weight outputs of component three are inputs for component four, where K-Mean clustering is applied to obtain different clusters. Each cluster presents an alternative drug to achieve meaningful drug data that is consistent with component three in the data lake.This paper addressed two main challenges; the first challenge is extracting meaningful data from big data; whereas the second challenge is using big data technique with K-Mean clustering algorithm. An experimental approach was followed through using Food and Drug Administration (FDA) data and symptoms in R framework. ANOVA statistical test was carried out to calculate sum of square error, P- Value and F-Valuefor the evaluation of variances between clusters and variances within clusters. The results showed the efficiency of the proposed approach.
The various model that has been used to predict, datamining, and information retrieval are useful to use through the traditional database, due to big data the prediction should derive in a different role that conduct the hidden structure data based on a stability scale to allow discovering accrue unsupervised drug data. Especially, the drug data must be understandable to analysts. Following this approach, conduct the stability drug data through computation methods are quality measurements, preprocess data, k-mean cluster, and decision tree. This approach seeks to identify the data by two dimensions (vertically and horizontally), which extrapolations, compilation, and interpretation values of the dataset while considering individual attributes. A comparison with clusters defines the set for features using balance value by K-mean algorithm to determine the k clusters that consider the set of features based on two values 0 and 1, which given the discernible between dependent and independent class target, and pinpoint the relationship among them. Keywords: Big Data, Discretize, k-mean cluster Stability, Target drug
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.