Ensemble Reverse Spatial Keyword Search

Transcription

1 Ensemble Reverse Spatial Keyword Search K.Sitamaha Lakshmi 1, Ms.D.N Rajeswari 2, 1 M.Tech Andhra Loyola Institute of Engineering & Technology, Vijayawada. A.P., India. 2 Assistant Professor, Andhra Loyola Institute of Engineering & Technology, Vijayawada., A.P., India. Abstract: Query is the most useful request to get the data from many sources. Spatio-textual queries retrieve the most similar places based on the query. Many users want the Spatio-textual results accurately. Various existing system work on this and can t reach there mark to get the better output. In this paper, a novel ranking query is implemented to get the better results compare with existing system. To improve the performance of the proposed spatio-textual we adopted kcr-tree to maintain the upper bound and lower bound results. Keywords: Spatio-textual, reverse keyword, kcr-tree. 1. Introduction Spatial information is otherwise called geospatial information or geographic information. Spatial information is normally put away as directions and topology, and is information that can be mapped. A spatial database, or geo database is a database that is optimized to store and question information that speaks to objects characterized in a geometric space. Most spatial databases allow representing simple geometric objects such as points, lines and polygons. clients i.e. greatest number of clients are keen on that administration. The R-KNN question in choice emotionally supportive network is a case of a monochromatic inquiry as the database objects and the question are of a similar sort i.e. eateries. The use of R- KNN in profile based promoting is a case of a bichromatic inquiry as the database articles are clients and the question is administration to be started by the organization. Location-based administrations require the clients to report their correct area constantly. A client who wouldn't like to send his/her correct area needs to quit utilizing the area based administrations gave by the specialist co-op. The information gathered by such servers can be utilized to concentrate the client conduct, going to examples, and stalking individual areas. There is a need to discover a way with the end goal that the client can appreciate the advantages of utilizing area based administrations while saving his/her area security. A system for saving area protection of a client and characterized three sorts of Nearest Neighbor questions in particular, Public Query over Private Data, Private Query over Public Data and Private Query over Private Data. Location-based administrations are the administrations offered to clients in light of their areas. Area based administrations have numerous differing applications like finding the closest stores in a specific area, location-based advertisement, analyzing wildlife and traffic movements, location-based gaming etc. The progressions in database and portable innovation and quickly expanding fame of area based administrations brings about tremendous measures of information being gathered in databases. Location-based administrations have pulled in huge consideration from the industrial and research group. R-KNN (Reverse k-nearest Neighbor) query discovers applications in choice emotionally supportive networks where the assignment is to open another office like eatery in a zone to such an extent that it will be minimum impacted by its rivals and pull in great business. Another application is a profile based advertising, where an organization keeps up profiles of its clients and needs to begin another administration with the end goal that the administration is affected by most extreme number of 30 Fig: 1, System architecture.

2 2. Literature Survey The manual work and the existing arrangement of the application are extremely tedious. To defeat the disservice of existing system, proposed system is fabricated.this arrangements with the Related works of the existing paper, their impediments and the related work of the proposed system are discussed. A. Efficient Probabilistic Reverse Nearest Neighbor Query Processing On Uncertain Data Given a query object q, a reverse nearest neighbor (RNN) query in a typical certain database gives back the items having q as their nearest neighbor [3]. Another test for databases is managing uncertain articles. In this paper we consider probabilistic turn around closest neighbor (PRNN) inquiries, which give back the unverifiable items having the question protest as closest neighbor with an adequately high likelihood. I propose a algorithm for productively noting PRNN questions utilizing new pruning systems considering separation conditions. We contrast our calculation with cutting edge approaches as of late proposed. My test assessment demonstrates that the approach can altogether out perform past methodologies. In addition, we have indicated how our approach can without much of a stretch be reached out to PRKNN (where k >1) query processing for which there is at present no proficient solution. B. Retrieve Top-K Prestige Based Spatial Locations The location-aware keyword query returns positioned objects that are close to a question area and that have literary portrayals that match query keywords [7]. This query happens innately in many sorts of versatile and customary web administrations and applications, e.g., Yellow Pages and Maps administrations. In any case, a pertinent outcome protest with adjacent articles that are likewise applicable to the inquiry is probably going to be best over a significant question without important closeby items. The paper proposes the idea of glory based pertinence to catch both the literary importance of a question an inquiry and the impacts of adjacent items. In view of this, another kind of inquiry, the Locationmindful top-k Prestige-based Text retrieval (LKPT) query, is recommended that recovers the top-k spatial web objects positioned by both prestige-based relevance and location proximity [4]. We propose two algorithms that figure LKPT queries C. Spatial Keyword Query Processing: An Experimental Evaluation. Geo-textual lists play an critical part in spatial keyword querying. The current geo-textual files have not been looked at deliberately under the same trial system. This makes it hard to figure out which ordering method best backings particular functionality With the multiplication of online items with both a related geo-area and a content portrayal, the web is securing a spatial measurement. In particular, web clients and substance are progressively being geo-positioned and geo-coded. In the meantime, literary portrayals of purposes of intrigue, e.g., cafes and tourist attractions are progressively getting to be distinctly accessible on the web. Spatial keyword queries are being supported in real-life applications, such as Google Maps where purposes of interest can be recovered, Foursquare where geo-tagged documents can be recovered and Twitter where tweets can be recovered. Spatial keyword querying is additionally accepting expanding enthusiasm for the examination group where a scope of procedures have been proposed for proficiently handling spatial keyword queries. Three types of spatial keyword queries: a) Boolean KNN Query: Retrieve the k objects nearest to the user s current location (represented by a point) such that each object s text description contains the keywords tasty, pizza, and cappuccino. b) Top-k KNN Query: Retrieve the k objects with the highest ranking scores, measured as a combination of their distance to the query location (a point) and the relevance of their text description to the query keywords tasty, pizza, and cappuccino. c) Boolean Range Query: Retrieve all objects whose text description contains the keywords tasty, pizza, and cappuccino and whose location is within 10 km of the query location. 3. Existing System Existing reviews chiefly concentrate on the best way to productively locate the top-k result set given a spatioliterary question. In any case, in numerous application situations, clients may think that its hard to absolutely figure their question watchwords and rather like to pick them from hopeful catchphrase sets. A complete assessment of the existing spatio-textual indexes are given in [4]. Various variations of the spatiotextual query have likewise been contemplated as of late. Rocha-Junior and Nørv ag researched the spatio-textual query in street systems. An as of late proposed mck

3 query recovers m protests inside a base width that match the given keywords. 3.1 Existing System Algorithms Collaborative filtering is one of the most popular recommendation techniques, which has been widely used in many recommender systems. In this section, we give a brief survey of CF algorithms, and summarize recent work on CF-based Web service recommendation. PROPOSED SYSTEM The spatio-textual query was proposed in [3]. It retrieves a gathering of spatial web objects with the end goal that the group s keywords cover the query keywords and the items are the closest to the query area. Fan et al. [9] concentrated the spatio-textual likeness seek on regions of interest (ROIs) that contain region based spatial data and textual depictions. The proposed upgraded estimation for computing QoS similarity between various clients and between various administrations. The estimation considers the customized deviation of Web administrations' QoS and clients' QoS encounters, keeping in mind the end goal to enhance the precision of similarity computation. Although a few CF-based Web service QoS expectation techniques have been proposed in recent years, the execution still needs significant change we propose a location-aware customized CF strategy for Web service suggestion. The proposed strategy use both areas of clients and Web administrations while choosing comparative neighbors for the objective client or administration. To assess the execution of our proposed technique, we lead an arrangement of comprehensive investigations utilizing a real-world Web service dataset. In view of the above improved similitude estimation, we proposed an area mindful CF-based Web service QoS prediction strategy for administration suggestion. 4.1 Advantages In addition to the prediction accuracy, another advantage of our method is its high efficiency of QoS prediction. This indicates that our method is more scalable than traditional CF methods when applied to large-scale service recommender systems. This indicates that our method is more scalable than traditional CF methods when applied to large-scale service recommender systems. The reason is that, in most cases we can limit similar neighbor searching to a small subset of users (or Web services), especially when K is small. 4. Proposed System Algorithms Efficient Query Processing Algorithm We first formally define notations for the convenience of describing our method and algorithms. The Top-K similar neighbor selection algorithm is often employed The Top- K similar neighbor selection algorithm can be employed to select K Web services that are most similar to the target Web service. We can see that the algorithm first searches local users for similar users. This algorithm has a high probability of finding users similar to the active user in his/her local region. Prediction coverage is also an important metric for evaluating a QoS prediction algorithm MODULE DESCRIPTION Collaborative Filtering (CF) Web Service Recommendation Fig: 2, Proposed Architecture Incorporating QoS Variation into User and Service Similarity Measurement

4 Incorporating Locations of Users and Services into Similar Neighbor Selection User location information handler Service location information handler User-based QoS prediction Service-based QoS prediction Hybrid QoS prediction Location Representation Location Information Processing Collaborative Filtering (CF) Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating) CF techniques can be generally decomposed into two categories: model-based and memory-based [12],[13]. Memory-based CF is also named neighborhood-based CF. Depending on whether user neighborhood or item neighborhood is considered, neighborhood-based CF can further be classified into user-based and item based. Web Service Recommendation Various recommendation techniques have recently been applied to Web service recommendation, such as the content- based link prediction-based. Their argued that, for every pair of ac-tive user and target Web service, both the QoS experience of the users similar to the active user and the QoS values of the services similar to the target service can be em-ployed for QoS prediction. However, these previous ap-proaches failed to exploit the characteristics of QoS in the similarity computation. Based on the traditional CF approaches, several enhanced methods have been proposed to improve the prediction accuracy. This is probable if the Web services are deployed in a high performance Cloud environment. If the QoS is good enough (as in this instance), a small variation of QoS values over all users is likely to be ob-served. Some Web services may have a very poor QoS for all users. Incorporating QoS Variation into User and Service Similarity Measurement Previous QoS prediction methods assume that the co-invoked Web services have equal contribution weights when computing similarity between two users. We argue that the personalized characteristics (e.g., QoS variation) of both Web services and users should be incorporated into measuring the similarity among users and services. Web service QoS factors, such as response time, avail-ability and reliability, are usually userdependent. From different Web services, we can derive different personal-ized characteristics, based on their QoS values, as perceived by a variety of users. Some Web services may have a very good QoS for all users. For example, the availabil-ity is always 100%. This is probable if the Web services are deployed in a high performance Cloud environment. If the QoS is good enough (as in this instance), a small variation of QoS values over all users is likely to be ob-served. Some Web services may have a very poor QoS for all users. For example, the availability is always below 50%. This is probable if the Web services are deployed in a network environment with poor performance and bandwidth. These Web services are also likely to have small variation of QoS values over different users. Many other Web services may have a relatively large variation of QoS over different users. For example, the availability varies from 50% to 100% for different users. These Web services are considered to be user-sensitive. The following example explains why Web services with different QoS variations could contribute differently when computing the similarity between service users. User location information handler: This module obtains location information of a user including the network and the country according to the user s IP address. It also provides support for efficient user-querying based on location. Service location information handler: This handler acquires additional location information of Web services according to either their URLs or IP addresses. The location information includes the network and the country in which the Web service are located. It also provides functionalities for supporting efficient locationbased Web service query. User-based QoS prediction: After a certain number of similar users are identified for the active user, this function aggregates the QoS values they perceived on target Web services, and predicts the missing QoS values for the active user. Service-based QoS prediction: After a certain number of similar services are identified for a target Web service, 33

5 this function aggregates their QoS values to predict the missing QoS values for the active user Hybrid QoS prediction: This function combines the userbased QoS prediction and the service-based QoS prediction results, making final QoS predictions. The cold-start problem and data-sparsity problem in QoS predictions are also addressed in this module 5. Location Information Representation, Acquisition, and Processing This section discusses how to represent, acquire, and pro-cess location information of both Web services and ser-vice users, which lays a necessary foundation for imple-menting our location-aware Web service recommendation method. Location Representation We represent a user s location as a triple (IPu, ASNu, CountryIDu), where IPu denotes the IP address of the user, ASNu denotes the ID of the Autonomous System (AS)1 that IPu belongs to, and CountryIDu denotes the ID of the country that IPu belongs to. Typically, a country has many ASs and an AS is within one country only. The Internet is composed of thousands of ASs that interconnected with each other. Generally speaking, intra-as traffic is much better than inter-as traffic regarding transmission performance, such as re-sponse time [34]. Also, traffic between neighboring ASs is better than that between distant ASs. Therefore, the Inter-net AS-level topology has been widely used to measure the distance between Internet users [34]. Note that users located in the same AS are not always geographically close, and vice versa. For example, two users located in the same city may be within different ASs. Therefore, even if two users are located in the same city, they may look distant on the Internet if they are within different ASs. This explains why we choose AS instead of other geographic positions, such as latitude and longitude, to represent a user s location. SIMILARITY COMPUTATION AND SIMILAR NEIGHBOR SELECTION In this section, we first formally define notations for the convenience of describing our method and algorithms. We then present a weighted PCC for computing similarity between both users and Web services, which takes their personal QoS characteristics into consideration. Finally, we discuss incorporating locations of both users and Web services into the similar neighbor selection. Similar Neighbor Selection Similar neighbor selection is a very important step of CF. Selecting the neighbors right similar to the active user is necessary for accurate missing value prediction. In conventional user-based CF, the Top-K similar neighbor selection algorithm is often employed [8]. It selects K users that are most similar to the active user as his/her neighbors. Similarly, the Top-K similar neighbor selection algorithm can be employed to select K Web services that are most similar to the target Web service. There are several problems involved, however, when applying the Top-K similar neighbor selection algorithm to Web service recommendation. Firstly, in practice, some service users have either few similar users or no similar users due to the data sparsity. Traditional Top-K algorithms ignore this problem and still choose the top K most ones. Because the resulting neighbors are not actually similar to the target user (service), doing this will impair the prediction accuracy. Therefore, removing those neighbors from the top K similar neighbor set is better if the similarity is no more than 0. Secondly, as previously mentioned, Web service users may happen to perceive similar QoS values on a few Web services. But they are not really similar. Considering the location-relatedness of Web service QoS, we incorporate the locations of both users and Web services into similar neighbor selection. User-based QoS Value Prediction In this subsection, we present a user-based location-aware CF method, named as ULACF. Traditional user-based CF methods usually adopt for missing value predictions. This equation, however, may be inaccurate for Web service QoS value prediction for the following reasons. Web service QoS factors such as response time and throughput, which are objective parameters and their values vary largely. In contrast, user ratings used by traditional recommender systems are subjective and their values are relatively fixed [29]. Therefore, predicting QoS values based on the average QoS values perceived by the active user (i.e., r (u) ) is flawed. Moreover, Eq. (9) does not distinguish local and remote users that are similar to the active user. Intuitively, given two users that have the same estimated similarity degree to the target user, the user closer to the target user should be placed more confidence in QoS prediction than the other. Item-based QoS Value Prediction In this subsection, we present an item-based locationaware CF method, named as ILACF. Based on the 34

6 similar consideration as ULACF s, we use Eq. to compute the predicted QoS value for a service based on the QoS values of its similar services. Integrating QoS Predictions Due to the sparsity of the user-item matrix, to make the missing value prediction as accurate as possible, it s better to fully explore the information of similar users as well as similar services. Therefore, we develop a hybrid location- aware CF, named as HLACF, which integrated the user-based QoS prediction with the itembased QoS prediction. The following four cases will be considered in integrating QoS predictions. Experimental Results: The query points in a small query region nearly produce the same results and therefore the point based RST query algorithmic program is run only once within the sampling-based algorithmic program. When the region size will increase, the performance of Sample degrades sharply. A larger query region can increase the range of the results for various query points within the region. To upload on dataset after you are build RST. RST tree created after you are search any query. It displays nearest places. The comparison chart for both regions based and point based search. Thus, the sampling-based algorithmic program needs to run the point-based RST Q algorithmic program repeatedly to get the accurate region based results. References [1] K. Norvag, J. B. Rocha-Junior. Top-k spatial keyword queries on road networks. In EDBT, pp , [2] L. Chen, G. Cong, C. S. Jensen, and D. Wu. Spatial Keyword Query Processing: An Experimental Evaluation. PVLDB, 6(3): , [3]G. Cong, C. S. Jensen, D. Wu. Efficient retrieval of the top-k most relevant spatial web objects. PVLDB, 2(1): , [4] C. Doulkeridis, Y. Kotidis, and K. Norvag, A. Vlachou. Reverse top-k queries. In ICDE, pp , [5] Anind Dey, Louise Barkuus. Location-Based Services for Mobile Telephony: a Study of Users Privacy Concerns [6] M. A. Cheema, X. Lin,W.Wang,W. Zhang, and J. Pei. Probabilistic reverse nearest neighbour queries on uncertain data. TKDE, 22(4): , [7] X. Cao, G. Cong, and C. S. Jensen. Retrieving top-k prestige-based relevant spatial web objects. PVLDB 3(1): , [8] G. Cong, C. S. Jensen, D. Wu. Efficient retrieval of the top-k most relevant spatial web objects. PVLDB, 2(1): , [9] I. De Felipe, V. Hristidis, and N. Rishe. Keyword search on spatial databases. In ICDE, pp , [10] Y.-Y. Chen, T. Suel, and A. Markowetz. Efficient query processing in geographic web search engines. In SIGMOD, pp , Conclusion In this paper, the proposed system RSTQ (Reverse Spatio-Textual Queries) is used to retrieve the location based top k results. Based on the reverse keyword and ranking and the accurate keyword the results will retrieve. Showing the results with accuracy is done with KCR tree. In this paper, the location based results and similar keyword based results are also shown. About Authors 35

7 engineering. I am K.Sitamaha Lakshmi completed my Btech from DJR College of Engineering and Technology, presently pursuing my Mtech in Andhra Loyola Institute of Engineering & Technology, Vijayawada. My research Interested in Data mining, cloud computing and software Ms.D.N Rajeswari received her M.Tech Degree from VIGNAN UNIVERISTY and B.tech from JNTU Kakinada. She had published many papers in reputed journals and presented her papers in International conferences too. She had stood as an Academic topper for postgraduation. Currently she is working as an Assistant Professor in Andhra Loyola College. 36