'''Originally Posted By: Akash_Patel'''

Team Members: Govind Kalyankar, Akash patel, seungbeom ma<br><br>A) Document Partitioning :<br>In a document-partitioned index, each index server is responsible for a subset of the documents.<br><br>Query Processing in Document Partitioning:<br>There is a main server (the book calls a receptionist) that when presented a query forwards it to each partition server. These n servers then compute the top k query results on their partition and forward the answer back to the reception who merges the results, selecting the top m. <br><br>B) Term Partitioning:<br>Term partitioning addresses this problem by splitting the collection into sets of terms and assigning nodes to each of these sets.<br>Document partitioning works best when the index data on the individual nodes can be stored in main memory or on SSD.<br>Adding more machines doesn't affect the minimum of what each machine must do, so this is a bottleneck for the document partitioning approach.<br>Query Processing in term partitioning<br><br>Suppose a query q contains terms t1,...tq. Then the receptionist will forward the query to node v(t1) responsible for the term t1.<br>After creating a set of document score accumulators from t1's posting list, v(t1) forwards the query, along with the accumulator set to the node v(t2) which continues the process and so on.<br>Finally, v(tq), sends the final accumulator set to the receptionist where the top m results are selected.