Although by the end of 2020, most of companies will be running 1000 node Hadoop in the system, the Hadoop implementation is still accompanied by many challenges like security, fault tolerance, flexibility. Hadoop is a software paradigm that handles big data, and it has a distributed file systems so-called Hadoop Distributed File System (HDFS). HDFS has the ability to handle fault tolerance using data replication technique. It works by repeating the data in multiple DataNodes which means the reliability and availability are achieved. Although data replications technique works well, but still waste much more time because it uses single pipelined paradigm. The proposed approach improves the performance of HDFS by using multiple pipelines in transferring data blocks instead of single pipeline. In addition, each DataNode will update its reliability value after each round and send this updated data to the NameNode. The NameNode will sort the DataNodes according to the reliability value. When the client submits request to upload data block, the NameNode will reply by a list of high reliability DataNodes that will achieve high performance. The proposed approach is fully implemented and the experimental results show that it improves the performance of HDFS write operations.

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Annotation is considered one of the main applications that semantic web applies. The idea beyond annotation focused on adding metadata to existing information which facilitates machines dealing with data that have meanings and can be readable. Semantic annotation is one of the techniques used for the enrichment of web content semantically, which facilitates writing comments and evaluate previously annotated resources that can lead to better search results. Our framework aims to enrich ontology via embedding data directly to ontology in order to have completed and accurate data.

Abstract-Although planning techniques achieved a significant progress during recent years, solving many planning problem still difficult even for modern planners. In this paper, we will adopt landmark concept to hybrid planning setting -a method that combines reasoning about procedural knowledge and causalities. Landmarks are a well-known concept in the realm of classical planning. Recently, they have been adapted to hierarchical approaches. Such land marks can be extracted in a pre-processing step from a declarat ive hierarchical p lanning domain and problem description. It was shown how this technique allows for a considerable reduction of the search space by eliminating futile plan develop ment options before the actual planning. Therefore, we will present a new approach to integrate landmark pre-processing technique in the context of hierarchical planning with landmark technique in the classical planning. This integration allo ws to incorporate the ability of using extracted land mark tasks fro m hierarchical do main knowledge in the form of HTN and using landmark literals fro m classical planning. To this end, we will construct a transformation technique to transform the hybrid planning domain into a classical domain model. The methodologies in this paper have been implemented successfully, and we will present some experimental results that give evidence for the considerable performance increase gained through planning system.

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