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Scaling dynamic authority-based search using materialized subgraphs .. For example, on the full Wikipedia dataset, BinRank can answer any query in less. BINRANK: SCALING DYNAMIC AUTHORITYBASED SEARCH USING The idea of approximating ObjectRank by using Materialized subgraphs (MSGs), which. Effective Bin Rank for Scaling Dynamic Authority. Based Search with Materialized Sub Graphs. L. Prasanna Kumar. Abstract. Dynamic authority-based keyword.

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A particular one of the pre-computed materialized sub-graphs is accessed and a dynamic authority-based keyword search is executed on the particular one of the pre-computed materialized sub-graphs.

It is hard to find an exact RSG for a given term, and it is not feasible to precompute one for every term in a large workload. From the above description, it can be seen that the present invention provides a system, computer program product, and method for implementing the embodiments of the invention. There are two main goals in constructing term bins. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.

For a given preference set, PPR performs an expensive fixpoint iterative computation over the entire Web graph, while it generates personalized search results.

ObjectRank extends personalized PageRank to perform keyword search in databases. In reality, however, ObjectRank is a search system that is typically used to obtain only the top-K result list. It can be hard to automatically identify terms with such strong semantic connections for every query term. In fact, the inventors have discovered that terms with strong semantic connections can generate good RSGs for each other. According to a further embodiment of the present invention, a system comprises: A system according to claim 13 wherein said first dynamic authority-based keyword search unit performs an ObjectRank operation.

The original ObjectRank system has two modes: Software and data transferred via communications interface are in the form of signals which may be, for example, electronic, electromagnetic, optical, or other signals capable of being received by communications interface BinRank generates the subgraphs by partitioning all the terms in the corpus based on their co-occurrence, executing ObjectRank for each partition using the terms to generate a set of random walk starting points, and keeping only those objects that receive non-negligible scores.


System and methodology for generating bushy trees using a left-deep tree join enumeration algorithm. That is, all the non-negligible end points of random walks originated from starting nodes containing t are present in the sub-graph generated using B.

A method according to claim 2 wherein said grouping of terms in said dataset comprises grouping based on the co-occurrence of terms in said dataset. The quality of search results should improve if objects in B are semantically related to t. Communications interface allows software and data to be transferred between the computer system and external devices.

For example, on the same Wikipedia dataset, the full dictionary precomputation would take about a CPU-year. The processor is connected to a communication infrastructure e. We demonstrate that BinRank can achieve subsecond query execution time on the English Wikipedia data set, while producing high-quality search results that closely approximate the results of ObjectRank on the original graph.

BinRank: Scaling Dynamic Authority Based Search Using Materialized Sub Graphs

Dynamic, authority-based search algorithms, leverage semantic link information to provide high quality, high recall search results. In this project, a BinRank system that employs a hybrid approach where query time can be traded off for preprocessing time and storage.

In the off-line mode, ObjectRank subgraph top-k results for a query workload in advance. Such computer programs, when executed, enable the computer system to perform the features of the present invention as discussed herein.

However, it may be observed that even though two nodes v 1 and v 2 are guaranteed to be found both in G and in MSG Bthe ordering or their ObjectRank scores might not be preserved on MSG B as we do not include intermediate nodes if their ObjectRank scores are below the convergence threshold.

BinRank closely approximates ObjectRank scores by running the same ObjectRank algorithm on a small subgraph, instead of the full data graph. The computer system can include a display interface that forwards graphics, text, and other data from the communication infrastructure or from a frame buffer not shown for display on a display unit Recently, dynamic versions of the PageRank algorithm have been developed.


An ObjectRank value of v, r vis non-negligible if r v is above the convergence threshold.

We introduce BinRank, a system that approximates ObjectRank results by utilizing a hybrid approach inspired by materialized views in traditional query processing. Removable storage unit represents, for example, a floppy disk, a compact disc, a magnetic tape, or an optical disk, etc. According to another embodiment of the present invention, subgrapus method comprises: According to one embodiment of the present invention, a method comprises: This relationship gives us the following important result.

BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs

A method according to claim 8 wherein said generating pre-computed materialized sub-graphs comprises: This process takes a single parameter maxBinSize, which limits the size of a bin posting list, i. Method and system for ranking words and concepts in a text using graph-based ranking. The computer dynakic program code may be transmitted using any appropriate medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc. Experimental evaluations performed by the inventors support this intuition.

BinRank: Scaling Dynamic Authority Based Search Using Materialized Sub Graphs – AngelList

As the process fills up a bin, it maintains a list of document IDs, that are already in the bin, and a list of candidate terms, that are known to overlap with the aughority-based i. Fortunately, real-world text databases have structures that are far from the worst case.

In block 62important nodes are identified for each partition based on the random walk. Also, it is noted that there are three important properties of ObjectRank vectors that are directly relevant to the result quality and the performance of ObjectRank. Any Processor above MHz. According to this theorem, for a given term t, if the term baseset BS t is a subset of B, all the important nodes relevant to t are always subsumed within MSG B.

However, in the Wikipedia dataset that would introduce an additional delay of 1.