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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Personalized Page Rank is a modification of PageRank that performs search personalized on a preference set that contains web pages that a user likes.

In the off-line mode, ObjectRank precomputes top-k results for a query workload in advance. A computer program product according to claim 18 wherein said authority-based keyword search is an ObjectRank algorithm.

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

System and methodology for cost-based subquery optimization using a left-deep tree join enumeration algorithm. According to a further embodiment of the present invention, a system comprises: These signals are provided to communications interface via a communications path i.

For example, on the same Wikipedia dataset, the full dictionary precomputation would take about a CPU-year. A computer program product according to claim 18 wherein generating further comprises: A method according to claim 8 wherein said dynamic random walk is a dynamic Personalized PageRank algorithm.

BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs – Semantic Scholar

A system according to claim 14 wherein said second dynamic authority-based keyword search unit receives a set of ObjectRank calibrating parameters. However, notice that edges of different edge types may transfer different amounts of authority. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

Once the MSG is constructed and stored in MSG storage 26it is serialized to a binary file on disk in the same row-compressed adjacency matrix format to facilitate fast deserialization.


BinRank: Scaling Dynamic Authority-Based Search Using Materialized Subgraphs

Thus, scores below threshold are effectively indistinguishable from zero, and objects that have such scores are not at all relevant to the query term.

During a query processing stage, a query processor 14 binrahk the ObjectRank process on the sub-graphs instead of the full graph and produces high quality approximations of top-K lists, at a small fraction of the cost. The intuition for applying the threshold is that differences between the scores that are within the threshold of each other are noise after ObjectRank execution. As previously discussed, a set of MSGs is constructed for terms of a dictionary or a authority-baseed by partitioning the terms into a set of term bins based on their sacling.

The method includes generating a set of pre-computed materialized sub-graphs from a dataset and receiving a search query having one or more search query terms. In case that the entire data graph does not fit in main memory, the system 10 can apply parallel PageRank computation techniques, such authority-bwsed hypergraph partitioning schemes. The query pre-processor 12 of the BinRank system 10 starts with a set of workload terms W for which MSGs will be eynamic.

Dynamic authority-based keyword search algorithms, such as ObjectRank mayerialized personalized PageRank, leverage semantic link information to provide high quality, high scalling search in databases, and the Web. 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.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. 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 We observed in the Wikipedia dataset that a single MSG can be used for to terms, on average.


The sub-graphs are precomputed offline.

ObjectRank extends Personalized PageRank to perform keyword search in databases. No claim element herein is to be construed under the provisions of 35 U. Papers about XML tend to cite papers that talk about schemas and vice versa.

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 bin i.

Removable storage unit represents, for example, a floppy disk, a compact disc, a magnetic tape, or an optical disk, etc.

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 EPROM, 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. For a given keyword query q, a query dispatcher 32 retrieves from the Lucene index 16 the posting list bs q used as the baseset for the ObjectRank execution and the bin identifier b q.

A method according to claim 1 wherein said generating further comprises for each term, storing in a field of a text index corresponding term group identifiers. Recently, dynamic versions of the PageRank algorithm have been developed.