Efficient Notification of Meeting Points for Moving Groups via Independent Safe Regions Abstract: In applications like social networking services and online games, multiple moving users form a group and wish to be continuously notified with the best meeting point from their locations. To reduce the communication frequency of the application server, a promising technique is to apply safe regions, which capture the validity of query results with respect to the users’ locations. Unfortunately, the safe regions in our problem exhibit characteristics such as irregular shapes and dependency among multiple safe regions. These unique characteristics render existing safe region methods that focus on a single safe region inapplicable to our problem. To tackle these challenges, we first examine the shapes of safe regions in our problem context and propose feasible approximations for them. We design efficient algorithms for computing these safe regions, as well as develop compression techniques for representing safe regions in a compact manner. Experiments with both real and synthetic data demonstrate the efficiency of our proposal in terms of computation and communication costs. Existing System: Previous work on moving query processing can be classified into two categories: (i) Report query results to a single user continuously, e.g. kNN, circular range queries , moving window(rectangle range) queries ; (ii) Detect relationships among moving objects, e.g., proximity detection, constraints monitoring. Proposed system: We propose a novel monitoring problem, Efficient Notification of Meeting Points (ENMP) for multiple moving users: given a group of moving users U, a set of points of interest (POI) P, ENMP continuously reports the optimal meeting point po ∈ P to users in U such that their maximum distance toward po is minimized. ENMP is motivated by many applications in social networks, location-based games and massively multi-player on-line games. A real application relevant to ENMP is EchoEcho2, invented by Google Venture. Echo Echo assists users to browse their friends’ real-time locations and share their own. As a highlight feature, Echo Echo allows a user to continuously observe his/her friends’ locations regarding to a predetermined meeting point. Mobile users with such interests have also been investigated in the collaborative system research . Problem Statement: 1) Multiple safe regions are irregular, which are hard to compute comparing with a single safe region; 2) Multiple users change their locations dynamically and unpredictably and thus multiple safe regions cannot be pre-computed as Voronoi cell. Algorithum: Divide-and-conquer verification method. Implementation Modules: 1. Optimal meeting point 2. Maximal safe regions 3. Independent safe regions 4. Search Space and Safe Regions Optimal meeting point: Finding an optimal meeting point (OMP) for a group of people (or a set of objects) at different locations is an important problem in spatial query processing. There are many real-life applications related to this problem, such as determining the location of a conference venue, deciding the pick-up location of a tourist bus, and planing tactics of artificial intelligence in realtime strategy games. Formally, given a set Q of query points in a spatial setting P, an OMP query fetches the point o∈P that minimizes a cost function defined over the distances from o to all points in Q. Since there are infinitely many locations in a given space setting, it is infeasible to examine all of them to find the OMP, and thus, the problem is challenging. In this paper, we study OMP queries in the following two spatial settings which are common in real-life applications: Euclidean space and road networks. In the setting of Euclidean space, we propose a general framework for answering all OMP query variants and also identify the best algorithms for particular types of OMP queries in the literature. In the setting of road networks, we study how to access only part of the road network and examine part of the candidates. Specifically, we explore two pruning techniques, namely Euclidean distance bound and threshold algorithm, which help improve the efficiency of OMP query processing. Extensive experiments are conducted to demonstrate the efficiency of our proposed approaches on both real and synthetic datasets. Maximal safe regions: The minimum distances (from p1 to a circle, and from p2 to a square) and the maximum distances (from p3 to the circle, and from p2 to the square). At time-stamp t1 (t2) , the optimal meeting point with the regarding locations of u1-u3 is p1 (p2). The independent safe regions for three users u1-u3 are R1-R3. Note that the safe regions (for the optimal meeting point) can have irregular shapes and we will elaborate this issue shortly. In this paper, we adopt the client-server architecture which is widely used in moving query processing.. The server manages a dataset P of points-of-interest (e.g., restaurants, cafes) and indexes it by an R-tree. A group of users U wishes to receive notifications of their optimal meeting point po ∈ P from the server continuously. Besides the result po, the server also reports a safe region Ri to each user ui ∈ U. The optimal meeting point remains unchanged if every user ui moves within his safe region Ri. Therefore, these safe regions serve to reduce the communication. Independent safe regions: The current user locations are u = 3 and v = 6, so the current optimal meeting point is a .For readability, the cells are colored based on their optimal meeting points (see Figure 4b). It appears that the cells with the same color form a connected ‘hyper-region’ in the high-dimensional search space, e.g., the diamond-like ‘hyper region’ for point a. Unfortunately, we are unable to decompose such a high-dimensional ‘hyper-region’ into independent safe regions { Ri|mi=1} for the users. First, two cells with the same color are not necessarily connected in the spatial domain. For instance, both combinations ⟨3, 9⟩ and ⟨5, 0⟩ for ⟨u, v⟩ take a as the optimal meeting point. However, user v cannot travel from location 9 to 0 directly without visiting locations 1-4, which have other optimal meeting points. Second, the maximal safe region of a user is restricted by that of another user. Search Space and Safe Regions: The advantage of circular safe regions is that they can be computed efficiently. However, they suffer from the drawback that they cannot serve as tight approximations of maximal safe regions. For instance Figure 7(a) contains two users u1-u2 and three points p1-p2 and po. Since p1 is the next optimal meeting point, according to Equation (6), the radius for circles are determined by two distances ∥ po, u1∥ and ∥ p1, u2∥ . Thus, the circular safe regions are depicted . In the next section, we propose a tighter approximation of maximal safe regions, named the tile-based safe regions. Configuration:H/W System Configuration:- System - Pentium –IV 2.4 GHz Speed - RAM - 256MB(min) Hard Disk - 40 GB Key Board - Mouse - Logitech Monitor - 15 VGA Color. 1.1 Ghz Standard Windows Keyboard S/W System Configuration: Operating System :Windows/XP/7. Application Server : Tomcat5.0/6.X Front End : HTML, Java, Jsp Scripts Server side Script : Java Server Pages. Database : Mysql 5.0 Database Connectivity : JDBC. : JavaScript.