الفهرس 
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Abstract
Due to the era of big data with the large amount of data, storing, indexing and querying multi-dimensional big data efficiently become an emerging topic. In recent years, daily production of data is rapidly increases that produces huge volumes of data that crucial need to index efficiently, which is one of common problems, especially in case of multi-dimensional big data. R-tree and its variants have proven to be efficient for indexing multi-dimensional big data. Unfortunately, R-tree suffers from the curse of dimensionality problem. Many researchers continue to use the R-tree in their studies as it is the most famous tree-like structure for indexing multi-dimensional data. However, the main issue of using R-tree is when increasing the dimensions of multi-dimensional data the performance of R-tree decreases rapidly. This thesis proposes solutions of the above-mentioned issues by introducing a new indexing structure called ParISSS. The name of ParISSS is taken from Parallel Indexing System Structure based on Spark, which is an efficient system for indexing multi-dimensional big data. ParISSS introduces six types of computing nodes, the reception-node is used to insert and index data, the normal-node manipulates store indexed data, the resolution-node handles distribute the reception-index to a normal-node, the representative-node receives queries from the user, and the reply-node and check-node are used to send the results to the user. In addition, BR*-tree structure is proposed as a storing structure in ParISSS, to improve the storing and searching processes. Set of algorithms are also introduced to distribute and store indexed data in BR*-tree structure efficiently. Finally, an accuracy evaluation equation is introduced to evaluate the system accuracy. Finally, set of comprehensive experiments are done to evaluate ParISSS and comparing its results with several existing indexing systems. The results obtained show the superiority of ParISSS over others.