In this document, the algorithm behind Google PageRanking and their techniques have been put up. The basic algorithm used by Google, for PageRanking and other applications are Markov model or Markov Chain model and Hidden Markov model. These algorithms are used to search and rank websites in the Google search engine. PageRank is a way of measuring the importance of website pages. Markov chain model and Hidden Markov model is a mathematical system model. It describes transitions from one state to another in a state space. The Markov model is based on the probability the user will select the page and based on the number of incoming and outgoing links, ranks for the pages are determined. HMM also finds its application within Mapper/Reducer. These algorithms are a link analysis algorithm.

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Recycling of engineering design is increasing exponentially in order to reduce the conceptual designing phase time due to rapid increase in technology. This leads to increase in frequent retrieval of the 3D models through web searching mechanism. So, it becomes a necessary challenge to design a 3D search engine which can search 3D models accurately as well as efficiently. The 3D searching is a technique developed to help the web users for extracting 3D models like complex engineering parts, various purpose graphical models, etc. The search engine takes input in the form of queries like text, 2D sketches, 3D sketches, 3Dmodel, 2Dmodel etc. When input queries is matched with the existing model in the database or similar model that are present in the database, the best multiple result is provided to user. This paper describes the basics of query by sketch which takes input by sketching manually with the aid of user interface. This input is normalized and further processed by Voxelization, Skeltonization and create Skeletal graph which is then mapped to database to extract best possible match. However, the main focus of this paper is to discuss the process of Voxelization method in very simple steps, as it plays a very important aspect in 3D searching.

Geographical Information System is known for its analytical capability. The spatial analysis function of GIS is the major factors that distinguish the GIS from other information system. This analysis provides with the functions such as spatial interpolation, buffering, and overlay operations [2]. Among all these functions, the focus on the study here is on overlay operation for polygon on polygon, using MapReduce and Random Forest. The capability to overlay multiple data layers in a vertical fashion is the most required and common technique in geographic data processing. In this study, we focus on the comparison between polygon overlay operation with and without spatial index in MapReduce and also proposed method for polygon overlay using Random Forest and its comparison with MapReduce algorithm.