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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Therefore, the issue of scalability of PPR has attracted a lot of attention. The processor is connected to a communication infrastructure e.
BinRank: Scaling Dynamic Authority Based Search Using Materialized Sub Graphs
The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. The embodiment was chosen and described in order subgrzphs best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Apache Tomcat Web Server 5. A variety of algorithms are in use for keyword searches in databases and on the Internet. System and methodology for cost-based subquery optimization using a left-deep tree join enumeration algorithm.
BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs(2010)
In the off-line mode, ObjectRank precomputes top-k results for a query workload in advance. For example, on the full Wikipedia dataset, BinRank can answer any query in less than one second, by precomputing about a thousand sub-graphs, which takes only about 12 hours on a single CPU. For 2we execute ObjectRank for each bin using the terms in the bins as random walk starting points and keep only those nodes that receive non-negligible scores. The computer system also includes a main memorypreferably random access memory RAMand may also include a secondary memory 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.
BinRank: Scaling Dynamic Authority Based Search Using Materialized Sub Graphs – AngelList
As discussed above, Dcaling uses the convergence threshold that is inversely proportional to the size of the baseset, i. ObjectRank is executed for each such term individually, and the resulting top-K lists are stored.
Empirical results support this. 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 This means, that just a small portion of G is materialiaed to a specific keyword.
Any combination of one or more computer usable or computer readable medium s may be utilized.
Query execution in accordance with the invention easily scales to large clusters by distributing the sub-graphs between the nodes of the cluster. Source Codes Authority-bazed SourceCodes.
BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs – Semantic Scholar
Examples of such means may include a program cartridge and cartridge interface such as that found in video game devicesa removable memory chip such as an Sarch, or PROM and associated socket, and other removable storage units and interfaces which allow software and data to be transferred from the removable storage unit to the computer system.
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. In general, deserialization speed can be greatly improved by increasing the transfer rate of the disk subsystem.
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.
In block 62 materailized, important nodes are identified for each partition based on the random walk.
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. In fact, the inventors have discovered that terms with strong semantic connections can generate good RSGs for each other. However, to get to that situation, the bin computation process will have to check intersections for every pair of terms.
ObjectRank performs top-K relevance search over a database modeled as a labeled directed graph. It can be demonstrated that it is feasible to use the entire dataset dictionary as the workload, in order to be able to answer any query.
The above-discussed Personalized Page Rank and ObjectRank algorithms both suffer from scalability issues. A method according to claim 2 wherein said grouping of terms in said dataset is a partitioning.
The ObjectRank system 10 stores a graph as a row-compressed adjacency matrix. First, for many of the keywords in the materializef, the number of objects with non-negligible ObjectRank values is much less than V. Personalized Page Rank performs an expensive fixpoint iterative computation over the entire Web graph.