IntroductionFinding needed information on the Web is not easy to achieve with a high degree of accuracy. Information retrieval systems have been designed to help users locate and retrieve their requests on the Web. Information retrieval systems are composed of some algorithms that try to make the search and retrieval of the requested information as accurate and fast as possible.Among all of these, "the text aspect has been the only data type that lends itself to a full functional The major obstacle to math search in current text search systems is that those systems do not differentiate between a user query that contains a mathematical expression and any other query that contains text term. Therefore, they process mathematical expressions as other texts, regardless of its nature of being well-structured and having properties that make it different from other forms of text.
Due to the widespread use of the internet, there are large amounts of information and documents available in several languages. The Arabic language is one of the available important languages in terms of its usage and structure. Search engines like Google and Yahoo support searching in Arabic, yet fail to get good results when slang terms are used in the query. There are difficulties associated with the Arabic language. The main goal of this research is to refine Arabic text-based searching by using Arabic slang terms in queries. This research proposed a framework to enable users to use their slang language in order to retrieve the relevant documents that have been posted in both forms – slang and classical. The framework is designed and implemented based on a context-free grammar that is used to map the user’s slang queries to the equivalent classical ones. On a classical dataset, results showed a 3% improvement on the average values of precision, recall, and F-measure achieved using classical-based queries rather than slang-based ones. Using slang-based queries gives 13% improvement on the average values of the used measures on a slang dataset and 7% improvement on the average values of the used measures on a hybrid dataset.
Current data mining techniques used to create failure predictors for online services require massive amounts of data to build, train, and test the predictors. These operations are tedious, time consuming, and are not done in real-time. Also, the accuracy of the resulting predictor is highly compromised by changes that affect the environment and working conditions of the predictor. We propose a new approach to creating a dynamic failure predictor for online services in real-time and keeping its accuracy high during the services run-time changes. We use synthetic transactions during the run-time lifecycle to generate current data about the service. This data is used in its ephemeral state to build, train, test, and maintain an up-to-date failure predictor. We implemented the proposed approach in a largescale online ad service that processes billions of requests each month in six data centers distributed in three continents. We show that the proposed predictor is able to maintain failure prediction accuracy as high as 86% during online service changes, whereas the accuracy of the state-of-the-art predictors may drop to less than 10%.
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