Research Paper VALID REGION ESTIMATION FOR NEAREST NEIGHBOR QUERIES N. Mythili, Dr.C.Jayakumar

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1 Research Paper VALID REGION ESTIMATION FOR NEAREST NEIGHBOR QUERIES N. Mythili, Dr.C.Jayakumar Address for Correspondence Master of Computer Applications, St. Joseph s College of Engineering, Chennai, India Computer Science and Engineering, RMK Engineering College, Chennai, India ABSTRACT In Spatial Query processing, among various queries, Nearest Neighbor Queries are taken into consideration. While sending Nearest Neighbor Queries, a mobile client will suffer to get the exact nearest spatial object since the mobile client is in a moving condition after sending a query. To avoid this, we propose a novel method to construct the Estimated Valid Region so that the mobile client will have the assurance of the nearest object what he received previously till he is in the Estimated Valid Region. We propose a formula to calculate the area of the Estimated Valid Region and also we propose a technique to help the mobile client to determine where the Estimated Valid Region starts and ends. KEYWORDS Spatial Query processing, Nearest Neighbor Queries, Spatial object, Estimated Valid Region. I INTRODUCTION In modern days, Mobile Network plays a vital role in human life. Query processing makes a mobile client to handle and search things easily what he needs in the particular moment. Spatial query processing deals with spatial objects needed by a mobile client. Spatial objects are objects of spatial data types. Spatial data types are data types like polygon, rectangle, square etc. Spatial objects are objects which are related to geo space. Many terms are used for database systems which supports pictorial, image, geometric or spatial database system. A Spatial database system is a database system that offers spatial data types in its designing and querying. During implementation, spatial database supports spatial data providing spatial indexing. While moving, the mobile clients are in need of spatial objects like hospitals, restaurants, schools, coffee shops, medical shops etc. Location-based service is any information that is attained on a mobile device using geographical position [6]. The information is provided related to the location of the mobile client. It gives the information depending on the mobile client s personal need. There are various examples for location based services. For example, Find the nearest restaurant?, Give the information about the CBSE schools in this area. These kinds of queries are based on the location where the mobile client stands. Spatial Query processing in today s modern life faces the following challenges [8]: 1. High Server Load. There may be numerous mobile users in a particular network area. Those mobile users may ask more queries to the database server. Since we deal with Nearest neighbor queries, a doubt arises in the mobile client while moving from the prior place to the next. Hence the request will be made by him again and again. Now the database server will receive same type of queries from various mobile users so that the database server will be overloaded. 2. Power Consumption. The mobile client will always have a doubt that whether the result he received is correct or not. Hence the mobile user makes the request again and again. So the mobile client s device will loose it s power consumption. 3. Access time. Getting response in an early manner is a challenging job for a mobile client since everybody is moving from the current place to the next place often. Various kinds of spatial queries are available. We deal with Nearest Neighbor queries. Nearest Neighbor queries are of important queries among all the queries. Nearest Neighbor queries deal with nearby spatial objects surrounded by the mobile client. For example, Find the nearest hospital? makes the query as Nearest Neighbor query (NN Query). The query like Find the 5 nearby hospitals? makes the query as KNN query. Where k=5 in the above example. While moving, according to the mobile client, the result for the query comes from the database server doesn t look like a valid answer. Since the mobile client is moving, he will be far away from the result object which was assumed as the nearby object for the mobile client. Hence the mobile client sends the same query again and again to get the exact result from the server. These queries are given to the database server in a continuous manner and the queries are said to be continuous queries. Because of these continuous queries, the server load is increased. Since the server load is increased, it is very tough to access the result from the server. Since there are more queries received from a single mobile client, the number of same type of queries received from several mobile clients will be excessive. Hence the waiting time of the mobile clients is increased. To avoid the server load, a proxy server is designed in between the mobile client and the database server. The proxy receives the query before the query reaches the server. If the query is available in the proxy cache, the proxy itself sends the result to the mobile client instead of forwarding it to the server. It reduces the server load. Figure 1 Valid Region VR Figure 2 Estimated Valid Region

2 EVR To reduce the server load, continuous queries of same type should be avoided. To avoid these types of queries, a valid region [6] is created as shown in figure 1. The valid region is the region in which the result is valid. The result indicates the nearest spatial object. Within the valid region, the previous result the mobile client received is the valid result. Hence there is no need to send the same type of query to the server. In this way, the proxy load is also reduced. In this paper, we have constructed an Estimated Valid Region with new formulae. As shown in figure 2, Estimated Valid Region (EVR) is a region which connects the outer nearby objects of the query point of the mobile client [21]. This Estimated valid region is submitted to the mobile device along with the result. Hence the mobile client submits the query only once & while moving he waits till he travels in the same valid region. Hence the power consumption of the mobile device & the server access are saved. We calculated the Estimated Valid Region to reduce the distance so that the mobile client will get the exact nearby value. Also we calculated the area of the Estimated Valid Region after constructing a polygon. Here we also calculated the area of an irregular polygon with different lengths for all sides. Also we propose a method to calculate the length of the area and the starting point and the ending point of the length. II RELATED WORK Various research works have been published in the area of spatial query processing. A lot of survey has been done in server based query processing. But later, proxy based query processing is going on. Wei-Shinn ku, Roger Zimmerman and Haixun Wang have introduced location based services. Peer to peer sharing is introduced so that latency is reduced [6]. Nearest Neighbor queries, window queries and range queries are dealt. EVR tree is used here to store the data. This method allows to check whether the spatial object retrieved from the peer is correct or not. Kyriakos Mouratidis, Spiridon Bakiras and Dimitris Papadias have proposed Continuous monitoring of spatial queries in wireless broadcast environment [11]. In this, a centralized database server is placed and it continuously monitors the position of the moving mobile client and updates the result. Air indexing scheme is used to update the clients and queries of the mobile clients. Baihua Zheng, Wang-Chien Lee and Dik Lun LEE have proposed a continuous nearest neighbor search [5]. Hilbert curve index is proposed to access location based data via wireless broadcast. Hilbert curve is used to map multi dimensional space points to single dimensional space. Dingming Wu, Man Lung Yiu, Gao Cong and Christian S.Jenen have proposed top-k spatial keyword query [19]. It supports a group of important location based services. IR tree is described. R tree is extended with inverted files. The two components of the inverted file are a vocabulary of distinct words and a positioning list of each word. Haojun Wang and Roger Zimmermann have proposed a computational method to calculate the distance among the vertices which is helpful in spatial query processing [15]. A data structure SD tree is introduced to store the distance information and network connectivity. A continuous mobile network-distance based range query algorithm is introduced. Construction of BP tree is done. Regions of sub domain are stored in a data node. Searching is done with the support of the data structure. The insertion, deletion and searching of nodes are described. Ying Cai addressed the challenge of giving region monitoring services [3]. Yunjun Gao, Baihua Zheng, Gencai Chen and Qing Li introduced a new form of search in spatial queries namely OLS search [10]. This method takes inputs as data object set, a critical distance and a spatial region and gives the resultant object. Khaleel Mershad and Hassan Artail have proposed a server based method called Smart Server Update Mechanism[12]. It avoids many issues related with push based cache consistency approaches. Ling Hu have proposed Voronoi diagram that partitions the space into n different regions, such that every spatial object in the set of objects includes in only one region and each point in that region is nearer to the object than to any other object in the collection in the Euclidean space [22]. It computes VRs based on the query place, spatial objects. Information from the spatial index thus allowing efficient query processing at the service provider. Jiun Long Huang and Chen-Che Huang have proposed a proxy-based approach to window queries and continuous Nearest Neighbour queries in mobile environments [21]. A Region is created to avoid continuous queries given by the same mobile client in the same area. Cache is used to store the details of mobile data. The information stored in the cache are later used for mobile users. Kwangjin Park and Young-Sik Jeong have used Cache replacement [24]. The target object which is faraway from the user is replaced. Jianping Gou, Lan Du, Yuhong Zhang and Taisong Xiong have proposed a classifier which classifies according to different distance [20]. The various objects with different distances from the mobile user are given weightage. The object having less distance will have higher weightage. Sze Man Yuenn, Yufer Tao and Xiaokui Xiao have proposed superseding Nearest neighbour SNN core [13]. It finds each object which supersedes the other neighbour. Finally the SNN core contains only one object which supersedes all the other objects in the database. Using index method, the number of data objects is reduced so that Nearest neighbor search is done in an efficient manner. Yu Gu, Ge Yu and Xiaonan Yu have applied Incremental search method for KNN search [25].

3 III PROPOSED SYSTEM N. Mythili et al., International Journal of Advanced Engineering Technology E-ISSN Table 1 Symbol Notations Figure 3 System Architecture Figure 4 EVR Tree Figure 5 Grid Index Figure 3 shows the Network architecture. Database Server is formed. In addition to that, Proxy server is also modeled. Mobile clients are there in the mobile environment to send queries and receive answers. To receive the query from the mobile client and to send it to the server, a base station is placed in between them. The position or the coordinate point where the mobile client sends the query is said to be query point of the mobile client. When a mobile client sends a query from a query point, the base station receives and forwards the query to the server. Before the query reaches the server, the proxy receives the query. The Proxy builds an EVR tree for Nearest Neighbour queries. The EVR tree is built in such a way that the tree is divided into 2 nodes. Each node denotes a separate region. The leaf node below the root node contains the spatial objects which are already identified in the corresponding region. The end leaf node will contain the data object which is needed for the mobile client from the query point and is clearly depicted in figure 4. The proxy searches the EVR tree for the query of the mobile client which is received from the query point. If the query point is present in the EVR tree, the region related for the query point is found out. Finally, the spatial object related to the corresponding region is accessed in the corresponding leaf nodes. The Table 1 shows all the symbols which are used in this paper. We also described what the symbols are indicating. The spatial object is returned back to the mobile client. The EVR tree is updated after the query access. Figure 6 EVR Creation A Grid index is formed which is shown in figure 5. It is divided into number of cells. If the cell is occupied with any cached object, the cell is cached and if the cell is not occupied with any cached object, then the cell is uncached cell as illustrated in figure 5. Whenever the proxy does not find the query point in EVR tree, it searches in the grid index. Figure 7 Algorithm for Query Processing

4 Whenever the mobile client sends the query through the mobile device, it reaches the proxy first. The proxy receives the query and it does the following. The algorithm for Query processing is explained in figure 7. The figure 7 gives an overall view of the whole procedure for generating the Estimated Valid Region. EVR Construction If the query point is not found in EVR tree, the proxy server makes the NN query to 2NN query and sends it to the Database server. The Database server sends 2 nearest spatial objects S1 and S2 to proxy. Now the proxy server calculates the two distances from the query point to S1 and S2. The minimum distance of the spatial object must be the first nearest object from the query point. This distance is calculated by using the Euclidean distance formula (1) Distance = sqrt[(x2-x1)^2+ (y2- y1)^2] (1) Where (x1,y1) is the coordinate position of first spatial object and (x2,y2) is the coordinate position of second spatial object.from these two distances of two nearest spatial objects, the radius is calculated. Combining these 2 radii, one minimum radius is found using the formula (2) Radius = ½ * sqrt (dx^2+dy^2) (2) Where dx is the distance between the x coordinates of two nearest spatial objects and dy is the distance between the y coordinates of two nearest spatial objects. Construction of EVR by generating two circles and creating the polygon as EVR is shown in figure 6. By having the query point of a mobile device M(xm,ym) as the centre and keeping this minimum radius as radius, the first circle is drawn. The first nearest object S1 is taken as the query point S1(xs,ys). Now keeping this as a query Point, 2NN query is sent to the database server by the proxy. Two nearest objects S2 and S3 are received from the server. S2 is the spatial object which is already used to find the radius for the first circle. Five parametric points on the first circle are calculated from the points P1(Π/2), P2(3Π/2), P3(3Π/4), P 4(7Π/4), P5(Π), P 6(3Π/4), P 7(3Π/2). For the second circle, the distance between the query point S1 and the third nearby object S3 is calculated. The radius is calculated using the distance found. Radius = (Distance between the query point S1 and the spatial object S3) / 2 (3) Using the above equation for radius calculation (3), the second circle is drawn. In the second circle, only two parametric points P6(3Π/4) and P7(3Π/2 ) are calculated. These parametric points are chosen to cover all the points of the circles. In the second circle, only two parametric points are calculated to avoid having longest nearby objects from the query point. The parametric points are calculated through the following equations: px1 = xm + ra1 cos(ѳ+π/2), py1 = ym + ra1 sin(ѳ+π/2) px2 = xm + ra1 cos(ѳ+3π/2), py2 = ym+ ra1 sin(ѳ+3π/2) px3 = xm + ra1 cos(ѳ+3π/4), py3 = ym + ra1 sin(ѳ+3π/4) px4= xm + ra1 cos(ѳ+7π/4), py4 = ym + ra1 sin(ѳ+7π/4) px5 = xm + ra1 cos(ѳ+π), py5 = ym + ra1 sin(ѳ+π) px6 = xs + ra2 cos(ѳ+3π/4), py6 = ys + ra2 sin(ѳ+3π/4) px7 = xs + rs2 cos(ѳ+3π/2), py7 = ys + ra2 sin(ѳ+3π/2) Where (px1,py1) is the coordinate point of p1 (px2,py2) is the coordinate point of p2 (px3,py3) is the coordinate point of p3 (px4,py4) is the coordinate point of p4 (px5,py5) is the coordinate point of p5 (px6,py6) is the coordinate point of p6 (px7,py7) is the coordinate point of p7 By connecting all the vertices P1(px1,py1), P2(px2,py2), P3(px3,py3), P4(px4,py4), P5(px5,py5), P6(px6,py6) and P7(px7,py7), a polygon is constructed. The polygon connecting all the parametric points of the two circles with vertices P1, P2, P3, P4, P5, P6, and P7 becomes the Estimated Valid Region of the query point of the mobile client. The EVR is given to the mobile client. Since minimum radius is calculated, there is no need to have lengthy distance to find the nearest object for the mobile client. This EVR is given to the mobile client in metre 2. The mobile client may be anywhere else. But after the mobile client gets the EVR, the mobile client is free to move around the given valid region area with the previous result received. The result received from the proxy server is valid till the mobile client is within the EVR area. The area is calculated for the area surrounded in the polygon structure. Since the polygon constructed is of 8 sides and all the sides are of irregular lengths, the area is calculated using the formula (4). Area = ½ x1y1+x2y3+x3y4+x4y5+x5y6+x6y7+x7y8+x8y1- x2y1-x3y2-x4y3-x5y4 x6y5-x7y6-x8y7- x1y8 sq units (4) For the polygon constructed in the figure 6, since the vertices named are not in order, the formula to calculate the area of the Estimated Valid Region will be the following equation (5). Area = ½ x1y1+x3y5+x5y2+x2y4+x4y7+x7y8+x8y6+x6y1- x3y1-x5y3-x2y5-x4y2 x7y7-x8y7-x6y8- x1y6 sq units (5) After calculating the area of the constructed polygon, the mobile client is ready to move around the polygon within the calculated area. IV PERFORMANCE EVALUATION We evaluated the performance of our proposed method. Using Java simulator, we implemented and made various experiments on a Windows platform.we compared the proposed approach with the proxy based approaches in the related work for NN queries. We compared the proxy based approach with the server based approach. It is done to understand the advantage of proxy based architecture. The metrics used to measure the performance are Query answering time, Client hit ratio and Server load. The main performance metric is the query answering time. The query answering time is defined as the time elapsed at the time the query is submitted to the moment when the result for the query is retrieved with the estimated valid region. The client cache hit ratio gives the advantage of cached Estimated Valid Regions for the mobile clients and power consumption of mobile devices. Whenever the client hit ratio is more, the number of submitted queries will be less. During data transmission, when the number of submitted queries

5 is less, the power consumption of mobile devices will be less. The server load means the total time taken to compute the result in the database server. We mean the computation time rather than the number of queries stored in the server since the computation time includes the creation of Estimated Valid Region. The data objects stored at the LBS server are indexed by an EVR tree. The time taken for a continuous query is of mean 30 seconds. The mobile client sends the Nearest Neighbour query to the server. The parameters used in the performance evaluation are mentioned in Table 2. Table 2 System Parameters Parameter Value Number of mobile clients Query intervals Exp. dis. with =6 hrs Query duration Exp. Dis. with =30 sec Wired network bandwidth 100Mbps Wired network latency 0.5 Mbps Data object size 256 bytes Simulation time 24 hrs The performance of the spatial query processing for nearest neighbor query is given in the following chart. client cache hit ratio is primarily determined by EVRs and is almost independent of the proxy size. From figures 9 and 10, it can be seen that the query response time and the server load become shorter and lower, respectively, as the proxy size decreases. The reason is that a smaller grid size enables the proxy to resolve more NN queries by cached objects and EVRs. Figure 11 Query Response Ratio Query Response Ratio is shown in figure 11. Figure 8 Query Sending Time Figure 9 Query Response Time Figure 12 Server Load Ratio The above graphs represent the performance of Nearest Neighbor queries versus proxy cache size. When the mobile client wants to make a new request with a spatial query and if the mobile client is in its valid region and not in Estimated Valid Region, the client will send the query along with its ID and Estimated Valid Region. Now the proxy realizes with the EVR and there is no need to send the data object along with the EVR to the mobile client. The proxy only sends the Estimated Valid Region alone to the mobile client so as to reduce the traffic. As the hit ratio of the proxy cache decreases, the proxy is able to response for new queries and for new mobile clients who are coming to the particular mobile network. Based on number of clients, we use Lee s and Gao s approach. In our approach, Query response time is two times better than the Gao s approach. Due to low cache hit ratio, the performance in terms of server load and query response time in Gao s approach is poor. The following graphs of figure 13, figure 14 represent the performance of NN queries versus number of clients. Figure 10 Server Load Time For Nearest Neighbor queries, from figure 8, we come to know that by the increase of proxy size, the number of queries sent by the mobile client is reduced. For Nearest Neighbor queries, we observe from figure 8 that the mobile client query sending ratio is almost decreasing for all proxy sizes, because this method gives the exact solution to reduce the number of queries sent by the client. This is because the Figure 13 Query Sending Ratio

6 Figure 14 Query Response Time Figure 15 Server Load Time Server Load Ratio is shown in figure 12. Query sending Ratio is shown in figure 13. Query Response time is shown in figure 14. Server Load Ratio is shown in figure 15. V CONCLUSION AND FUTURE WORK In this paper, we dealt with Nearest Neighbor queries. For Nearest Neighbor queries, Estimated Valid Region is constructed. EVR is constructed by creating two circles. To draw the circles, a new formula is used to calculate the minimum radius with two different lengths. After constructing the circles, seven parametric points on the circle are calculated. By connecting all the parametric points, an irregular polygon is formed. This irregular polygon becomes the EVR of the query point where the mobile client stands. We introduced a new formula to calculate the area of the irregular polygon (EVR). After calculating the area of the EVR, it is given along with the query result to the mobile client. Since the mobile client receives EVR along with the result, there is no need to send the continuous queries of same type again and again. Also using the minimum radius, there is a guarantee that the mobile client will have the shorter distance to reach the nearby spatial object. By avoiding continuous queries, server load is decreased. In today s modern world, everybody is in an urged manner to get anything. Hence, the mobile client needs the response immediately after sending the query to the server. Waiting time of the mobile client is reduced. Besides all these works, still new efforts can be taken to reduce the time taken to get the response. Also we have dealt only with Nearest Neighbor queries. All the other types of queries can be dealt in future. REFERENCES 1. 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