Fast Nearest Neighbor Search with Keywords

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Project seminar onFast Nearest Neighbor Search
with Keywords

• By-
• Jadhav Pradip.
• Narkhede Rahul.
• Mandalik Sagar.
• Mamdyal Ankit.

Guided By- Prof Mrs. M . N . Kale. Department of
information technology
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Outline

• Introduction.
• Literature survey.
• Project statement.
• System requirement specification.
• Planning scheduling.
• Conclusion.
• referances

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IntroductionNeed

• Conventional spatial queries, such as range
  search and nearest neighbor retrieval, involve
  only conditions on objects.
• The importance of spatial databases is reflected
  by the convenience of modeling entities of
  reality in a geometric manner.
• For example, locations of restaurants, hotels,
  hospitals and so on are often represented as
  points in a map.

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Static Network, Variable QueriesFind Gas
Stations, Hotels, Markets etc
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Basic concept

• Today, many modern applications call for novel
  forms of queries that aim to find objects
  satisfying both a spatial predicate, and a
  predicate on their associated texts.
• Currently the best solution to such queries is
  based on the IR2-tree, which, as shown in this
  paper.

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Application

• We have seen plenty of applications calling for a
  search engine that is able to efficiently support
  novel forms of spatial queries that are
  integrated with keyword search.
• All things that user want to search like
  hospital, hotel, restaurant, and so on.

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Literature surveyRelated work

• there are methods like
• spatial index
• inverted index
• nearest neighbor search.
• the objective is to enable a querying interface
  that is similar to that of search engines, and
  can be easily used by naive users without
  knowledge

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Spatial index

• spatial index is used for creating indices
  because there is huge amount of data need to be
  stored for searching that data stored in the form
  of xml documents.
• If the data storage created in the form of
  indices then space required is less also time
  needed for searching the keyword is less

inverted index
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Inverted index

• The reversed index data structured in a central
  module of a usual search engine indexing
  algorithms.
• A goal of a search engine presentation is
• optimize the speed of the query.
• find the documents where word occurs.

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Nearest neighbor search (NNS)

• It also identified as closeness search.
• parallel search is an optimization problem for
  finding closest points in metric spaces.

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Existing system

• The existing application proposed by Tao and
  Shang builds a novel access method.
• This is called as spatial inverted index.
• This index is different from conventional index.
• It can deal with multi-dimensional data
  compatible with NN queries.
• This method has higher performance when compared
  with IR2-tree in terms of query response time.

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Drawbacks of Existing System

• The existing system does not apply the proposed
  indexing to a search engine kind of application.
• It does not provide a prototype application to
  demonstrate proof of concept practically.

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Advantages of Proposed System

• Faster NN search.
• Prototype application to demonstrate the proof
  concept.
• Search engine to demonstrate the faster searches.

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What is to be developed?

• The goal of propose system is to combine keyword
  search with the existing location-finding
  services.
• Let P be a set of multidimensional points.
• each point p belongs to P is associated with a
  set of words, which is denoted as Wp and termed
  the document of p.

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Problem Statement

• Input
• Spatial Network S, Node q from S
• Output
• k-nearest neighbors of q
• Objective
• Facilitate fast shortest path queries based on
  different search criteria's
• Constraints/ Assumptions
• Static spatial network
• Contiguous (connected) regions

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Example

• if p stands for a restaurant, Wp can be its menu.
• if p is a hotel, Wp can be the description of its
  services and facilities.
• a nearest neighbor (NN) query specifies a point q
  and a set Wq of keywords.
• It returns the point in Pq that is the nearest to
  q, where Pq is defined as.

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Sample dataset of hotel object
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Technology used

• The IR2-Tree
• DBXplorer
• Nearest Neighbor Search
• Spatial inverted list
• Merging distance browsing

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The IR2-Tree

• the IR2-tree combines the R-tree with signature
  files.
• Signature file in general refers to a
  hashing-based framework.
• The IR2-tree is an R-tree where each (leaf or
  nonleaf) entry E is augmented.
• On conventional R-trees, the best-first algorithm
  14 is a well-known solution to NN search.

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depth-first search
Example of bit string computation
Example of an IR2-tree. (a) shows the MBRs of the
underlying R-tree and (b) gives the signatures of
the entries
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DRAWBACKS OF THE IR2-TREE

• the number of false hits can be really large.
• The signature should have Vð1Þ bit for every
  distinct word of W.

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DBXplorer

• A System for Keyword-Based Search over Relational
  Databases.
• A significant amount of the worlds enterprise
  data resides in relational databases.
• Enabling keyword search in databases that does
  not require knowledge of the schema is a
  challenging task.

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Architecture of DBXplorer
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Algorithm use
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Space and Time Requirements
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Spatial inverted index

• Query processing with an SI-index can be done
  either by merging, or together with R-trees in a
  distance browsing manner.
• The spatial inverted list (SI-index) is
  essentially a compressed version of an I-index

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Theoretical analysis
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System requirement specificationH/W system
configuration

• Processor pentium-3.
• Speed 1.1 GHz.
• RAM 256MB (min).
• Hard Drive 20GB.
• Floppy Drive 1.44MB.
• Key board standard windows keyboard.
• Mouse two button mouse.
• Monitor - SVGA

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S/W system configuration

• Operating system windows XP/07/08.
• Application server Tomcat 6.x.
• Front end java.
• Script JavaScript.
• Server side script JSP.
• Database oracle 10g.
• Data connectivity JDBC.

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Conclusion

• Thus the proposed system remedied the situation
  by developing an access method called the spatial
  inverted index (SI-index).

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Referances

• S. Agrawal, S. Chaudhuri, and G. Das, Dbxplorer
  A System for Keyword-Based Search over Relational
  Databases, Proc. Intl Conf. Data Eng. (ICDE),
  pp. 5-16, 2002.
• N. Beckmann, H. Kriegel, R. Schneider, and B.
  Seeger, The R-tree An Efficient and Robust
  Access Method for Points and Rectangles, Proc.
  ACM SIGMOD Intl Conf. Management of Data, pp.
  322-331, 1990.
• X. Cao, L. Chen, G. Cong, C.S. Jensen, Q. Qu, A.
  Skovsgaard, D.Wu, and M.L. Yiu, Spatial Keyword
  Querying, Proc. 31st Intl Conf. Conceptual
  Modeling (ER), pp. 16-29, 2012.
• Y.-Y. Chen, T. Suel, and A. Markowetz, Efficient
  Query Processing in Geographic Web Search
  Engines, Proc. ACM SIGMOD Intl Conf. Management
  of Data, pp. 277-288, 2006.
• G.R. Hjaltason and H. Samet, Distance Browsing
  in Spatial Databases, ACM Trans. Database
  Systems, vol. 24, no. 2, pp. 265-318, 1999.

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Thank you !!!