K.Veena Reddy Annamacharya Institute of Technology And Sciences, CS, DISADVANTAGES OF EXISTING SYSTEM

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1 Fast and Efficient Nearest Neighbor Search with Keywords K.Veena Reddy Annamacharya Institute of Technology And Sciences, CS, Abstract-Search engines are used to explore/find any information from anywhere. Usually the main aim of using spatial queries in a search is to find the efficient and nearest neighbor retrieval, involve solely conditions on object geometric properties. Today, several smart applications incorporate novel sorts of queries that aim to search out objects satisfying each and every spatial predicate on their associated texts. As an example, rather than considering all the restaurants, a nearest neighbor question would instead evoke the eating place that's the nearest among those whose menus contain South Indian dishes, North Indian Dishes all at a comparable time. Currently, the simplest answer to such queries relies on the Information Retrieval tree (IR), which, as shown during this paper, encompasses a few deficiencies that seriously impact its potency. Impelled by this, we have a tendency to develop a replacement access methodology known as the Spatial inverted guide that extends the predictable index to address third-dimensional information, and comes with algorithms so as to respond nearest neighbor queries with keywords in real time. Index Terms- fast and efficient search, nearest neighbor question, third-dimensional information, Information Retrieval tree. 1 INTRODUCTION A spatial database manages multidimensional objects and provides fast access to those gadgets based totally on unique selection criteria. The importance of spatial databases is pondered with the aid of the benefit of modeling entities of fact in a geometrical manner. For example, locations of restaurants, hotels, hospitals and so forth are frequently represented as points in a map, while larger extents such as parks, lakes, and landscapes often as a mixture of queries. As an instance, in a geography records device, variety search can be deployed to discover all eating places in positive vicinity, even as nearest neighbor retrieval can discover the restaurant closest to a given address. Fast and efficient nearest neighbor search additionally known as closest factor search or similarity seek. It's far an optimization problem for finding closest (or maximum comparable) points. Nearest neighbor search which returns the nearest neighbor of a question factor in a set of factors, is an essential and widely studied hassle in lots of fields, and it has wide range of applications. We can search closest factor by using giving key phrases as enter; it is able to be spatial or textual. 2 RELATED WORK Keyword search is the most mainstream data disclosure system in light of the fact that the client does not have to know either an inquiry dialect or the basic structure of the information. The web search tools accessible today give keyword seeks on top of sets of archives. 3 EXISTING SYSTEM A spatial database use to manage multidimensional objects. Some spatial databases handle more complex systems together with three-d objects, topological coverage s, linear networks. Whilst traditional databases are designed to manage various numerics 15 and individual forms of information, extra capability needs to be delivered for databases to method spatial information kind s At the point when an arrangement of question watchwords is given by the client, the internet searcher returns all records that are connected with these inquiry catchphrases. Answer for such inquiries depends on the IR2-tree, however IR2-tree having a few downsides. Productivity of IR2-tree severely is affected on account of a few downsides in it. The answer for beating this issue ought to be looked. DISADVANTAGES OF EXISTING SYSTEM It will fail to provide real time answers on difficult inputs. Its closer neighbors are missing at least one of the query keywords. DRAWBACKS OF THE INFORMATION RETRIEVAL TREE The IR2-tree is the first get to system for noting NN questions with keywords. Similarly as with numerous spearheading arrangements, the IR2-tree likewise has a couple of disadvantages that influence its effectiveness. The most genuine one of all is that the quantity of false hits can be truly huge when the object of the last result is far from the question point, or the outcome is just unfilled. In these cases, the question calculation would need to stack the archives of numerous items, bringing about costly overhead as every stacking requires an arbitrary access 4 PROPOSED SYSTEM In this paper, we plan a variation of modified file that is streamlined for multidimensional focuses, and is therefore named the spatial Inverted record (SI-list). The technique effectively fuses point

2 arranges into a traditional list with additional space, attributable to a sensitive minimized stockpiling plan. In the mean time, a SI-record protects the spatial region of information focuses, and accompanies R-tree based on each modified little space overhead. Subsequently, it offers two contending courses for inquiry preparing. 1. We can (successively) consolidate various records all that much like combining conventional rearranged records by ids. 2. On the other hand, we can likewise influence the R-trees to skim the purposes of every single applicable rundown in rising request of their separations to the question point. As showed by trials, the SI-file fundamentally outflanks the IR2-tree in question productivity, frequently by a component of requests of size. ADVANTAGES OF PROPOSED SYSTEM It offers two competing ways for query processing. It uses Inverted indexes (I-index), it have proved to be an effective access method for keyword-based document retrieval. 5 SYSTEM ARCHITECTURE 3) First R*-tree then inverted file. Key Search and Distance Search: Key search and Distance search can be applied through the java coding implementation. <center><h3 style='font-weight: bold; font-size:18px; texttransform: uppercase;color: #9f1e33'>Search By Items</h3> <form method="post" action="ressearch.jsp"> <center> <table> <tr> <input type="text" name="food" id="food" placeholder="food name"/> <input type="text" name="location" id="food" placeholder="location"/> <input type="submit" name="submit" value="search"> </tr> </table> </form> <% String food=null; String Location=null; food=request.getparameter("food"); Location=request.getParameter("location"); if(food!=null) { try { Fig 1 System Architecture 6 IMPLEMENTATION Fast and Efficient Nearest Neighbor Search can be implementing through various phases: 1. Search Techniques 2. Map view 3. Distance Search Search Techniques: Location based search is one which is used to find the specific spot or region. This kind of inquiry contains area data, it ought to be recorded and additionally message data. This paper uses index structure, to handle textual and location based queries, with help of inverted files and R*-trees. It considered three strategies to combine these indexes namely: 1) Inverted file and R*-tree double index. 2) First inverted file then R*-tree. 16 Class.forName("com.mysql.jdbc.Driver").newInstance(); Connection con = DriverManager.getConnection("jdbc:mysql://localhost:3306/fastsear ch","root",""); Statement st=con.createstatement(); ResultSet rs= st.executequery("select * from restaurant where Food='"+food+"' and Location='"+Location+"' order by Distance ASC" ); out.print("<table border='1' style='font-family: ARIAL;font-size: 18px;font-weight:initial;'>"); out.print("<tr>"); ID </th>"); Name: </th>"); Type:</th>"); out.print(" <th style='font-family: ARIAL;font-size: 18px;fontweight:bold;'>Restaurant Address:</th>"); out.print("<th style='font-family: ARIAL;font-size: 18px;fontweight:bold;'>Mobile:</th>"); Location:</th>"); out.print("<th style='font-family: ARIAL;font-size: 18px;fontweight:bold;'>Famous food:</th>"); out.print("<th style='font-family: ARIAL;font-size: 18px;fontweight:bold;'>Distance:</th>"); out.print("</tr>"); while(rs.next()) {%> <tr> <td style="color: #666666"><%= rs.getstring("id")

3 <td style="color: #666666"><%= rs.getstring("name") <td style="color: #666666"><%= rs.getstring("type") <td style="color: #666666"> <%= rs.getstring("address") %> <td style="color: #666666"><%= rs.getstring("mobile") <td style="color: #666666"><%= rs.getstring("location") <td style="color: #666666"><%= rs.getstring("food") <td style="color: #666666"><%= rs.getstring("distance") </tr> <% } } catch(exception e) { out.print("error occured due to "+e); } } else { } %> </table></center> UI Implementation: Fig 2 Admin feeding location information Fig 3 List of Restaurants In The Specific Location Map View and Restaurant Search: This is used to map the restaurant and food results. <center><img src="images/foods.png" width="200" height="200"> <center><h3 style='font-weight: bold; font-size:18px; texttransform: uppercase;color: #9f1e33'>Browsing Restaurants</h3> <form method="post" action="browsing.jsp"> <center> <table> <tr> <input type="text" name="hname" id="hname" placeholder=" Enter hotelname"/> <input type="text" name="location" id="hname" placeholder="enter Location"/> <input type="submit" name="hname" id="hname" value="search"/> </tr> </table> </center> </form> <%String resname=null; String location=null; resname =request.getparameter("hname"); location=request.getparameter("location"); if(resname!=null) { try { Class.forName("com.mysql.jdbc.Driver").newInstance(); Connection con = DriverManager.getConnection("jdbc:mysql://localhost:3306/fastsear ch","root",""); Statement st=con.createstatement(); ResultSet rs= st.executequery("select * from restaurant where Name ='"+resname+"' and Location='"+location+"'"); out.print("<table border='3' style='font-family: ARIAL;font-size: 18px;font-weight:initial;'>"); out.print("<tr>"); ID </th>"); Name: </th>"); Type:</th>"); out.print(" <th style='font-family: ARIAL;font-size: 18px;fontweight:bold;'>Restaurant Address:</th>"); out.print("<th style='font-family: ARIAL;font-size: 18px;fontweight:bold;'>Mobile:</th>"); Location:</th>"); out.print("<th style='font-family: ARIAL;font-size: 18px;fontweight:bold;'>Famous food:</th>"); out.print("<th style='font-family: ARIAL;font-size: 18px;fontweight:bold;'>Distance:</th>"); out.print("</tr>"); while(rs.next()) { %> <tr> <td style="color: #666666"><%= rs.getstring("id") <td style="color: #666666"><%= rs.getstring("name") <td style="color: #666666"><%= rs.getstring("type") <td style="color: #666666"> 17

4 <%= rs.getstring("address") %> <td style="color: #666666"><%= rs.getstring("mobile") <td style="color: #666666"><%= rs.getstring("location") <td style="color: #666666"><%= rs.getstring("food") <td style="color: #666666"><%= rs.getstring("distance") </tr> <% } } catch(exception e) { out.print("error occured due to "+e); } } else { } %> </table></center> UI Implementation: Fig 7 Mapped Results Fig 5 Secure user panel login for searching the location Fig 8 Searching Based on Items Fig 9 Search results for food based restaurants Fig 6 User query feeding system 18

5 7 CONCLUSION We have seen a lot of utilizations requiring an query that has the capacity productively spatial inquiries that are incorporated with keyword look. The current answers for such queries either bring about restrictive space utilization or are not able to give genuine time answers. Not just that the SI-record is reasonably space temperate, additionally it can perform keyword increased closest neighbor look in time that is at the request of many. Moreover, as the SI-record depends on the traditional innovation of modified record, it is promptly incorporable in a business web crawler that applies enormous parallelism, suggesting its prompt mechanical benefits. ACKNOWLEDGMENT I would like to thank my project guide Mrs. Rupa Devi, Annamacharya Institute of Technology And Sciences for her constant support. My sincere thanks to HOD and the management of Annamacharya Institute of Technology And Sciences. [14] G. Cong, C. S. Jensen, and D. Wu. Efficient retrieval of the top-k most relevant spatial web objects. PVLDB, 2(1): , [15] I. De Felipe, V. Hristidis, and N. Rishe. Keyword search on spatial databases. In ICDE, pp , [16] Y. Zhou, X. Xie, C. Wang, Y. Gong, And W.-Y. Ma, Hybrid Index Structures For Location-Based Web Search, Proc. Conf. Information And Knowledge Management (Cikm), Pp , [17] Yufei Tao And Cheng Sheng Fast Nearest Neighbor Search With Keywords Ieee Transactions On Knowledge And Data Engineering, Vol. 26, No. 4, April [18] Shilpa B. Patil "Survey of Searching Nearest Neighbor Based on Keywords using Spatial Inverted Index" International Journal of Engineering Research and General Science Volume 2, Issue 6, October- November, ISSN AUTHOR PROFILE: K Veena Reddy is currently pursuing her Mtech in computer science from Annamacharya Institute of Technology and Sciences. She holds a distinction in B.Tech and her research areas include data warehousing and data mining. REFERENCES [1] S. Agrawal, S. Chaudhuri, and G. Das, Dbxplorer: A System for Keyword-Based Search over Relational Databases, Proc. Int l Conf. Data Eng. (ICDE), pp. 5-16, [2] 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 Int l Conf. Management of Data, pp , [3] G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S.Sudarshan, Keyword Searching and Browsing in DatabasesUsing Banks, Proc. Int l Conf. Data Eng. (ICDE), pp ,2002. [4] X. Cao, L. Chen, G. Cong, C.S. Jensen, Q. Qu, A. Skovsgaard, D.Wu, and M.L. Yiu, Spatial Keyword Querying, Proc. 31st Int l Conf. Conceptual Modeling (ER), pp , [5] X. Cao, G. Cong, and C.S. Jensen, Retrieving Top-k Prestige- Based Relevant Spatial Web Objects, Proc. VLDB Endowment, vol. 3, no. 1, pp , [6] X. Cao, G. Cong, C.S. Jensen, and B.C. Ooi, Collective Spatial Keyword Querying, Proc. ACM SIGMOD Int l Conf. Management of Data, pp , [7] B. Chazelle, J. Kilian, R. Rubinfeld, and A. Tal, The Bloomier Filter: An Efficient Data Structure for Static Support Lookup Tables, Proc. Ann. ACM-SIAM Symp. Discrete Algorithms (SODA), pp , [8] Y.-Y. Chen, T. Suel, and A. Markowetz, Efficient Query Processing in Geographic Web Search Engines, Proc. ACM SIGMOD Int l Conf. Management of Data, pp , [9] I. De Felipe, V. Hristidis, and N. Rishe. Keyword search on spatial databases. In ICDE, pp , [10] D. Zhang, Y. M. Chee, A. Mondal, A. K. H. Tung, and M. Kitsuregawa. Keyword search in spatial databases: Towards searching by document. In ICDE, pp , 2009 [11] R. Hariharan, B. Hore, C. Li, and S. Mehrotra, Processing Spatial- Keyword (SK) Queries in Geographic Information Retrieval (GIR) Systems, Proc. Scientific and Statistical Database Management (SSDBM), [12] X. Cao, G. Cong, and C. S. Jensen. Retrieving top-k prestige-based relevant spatial web objects. PVLDB, 3(1): , [13] Y.-Y. Chen, T. Suel, and A. Markowetz. Efficient query processing in geographic web search engines. In SIGMOD, pp ,