# Introduction n the ubiquitous mobile computing environments, the mobile users may request diverse kinds of services and applications by mobile devices from arbitrary locations at any time via on networks. Obviously, the behavior pattern, in which the location and the service are inherently coexistent with temporal associated, of mobile users becomes more complex than that of the traditional mobile agent systems. To assist the user get interested information on time is one of the promising applications, especially in mobile agent systems. In recent years, the temporal data mining method has been studied for extracting useful knowledge from temporal data sets. The research of temporal data mining has been proposed the Generalized Sequential Pattern, named GSP. The GSP algorithm extended the Apriori-like level-wise mining method with time constraint to find frequent patterns in sequential databases. And the SPIRIT method proposed frequent sequence patterns by regular expression constraint. More recently, the research of related temporal association rule has been studied for cyclic association. The problem of finding frequent patterns in mobile agent systems has been extended to that of Author ? : 2 nd year M-tech, Aditya Engineering College, Surampalem, Peddapuram(mandal) , East Godavari(dt), A.P, India. E-mail : sriramya71191@gmail.com Author ? : M.Tech Assistant Professor, Aditya Engineering College, Surampalem, Peddapuram(mandal) , East Godavari(dt), A.P, India. E-mail : subbu_polamuri@yahoo.com finding frequent user behavior patterns in mobile web systems. II. The work-flow of the system is divided two phases. This systems form that the logs for users' movement and users' service requests associated with temporal association can be stored in different databases. The first phase of system architecture, Data integration phase, is to collect and integrate users' logs into one dataset for efficient access and the temporary user information like current location to manage requests from subscribers. For this phase, the attributes related to user's temporary service requests will be extracted from the dispersed log files and joined to from an integrated log file by using the user's service identifier as the key. Mining phase is deployed novel data mining method to discover the frequently temporal mobile access patterns from the integrated log dataset. The best results are returned associated with temporally user's access pattern to the mobile agent. The mobile agent system is supported by both movement and service requests pattern of users with proper time information, which makes the system powerful and accurate. # Proposed approach # III. # Problem statement A temporal database typically stores relational data with include time-related attributes. These attributes include (a) timestamp(s) of access patterns which one of the attributes. Definition 1: A trajectory of a moving object T = {(x1,y1,t1), (x2,y2,t2),?,(xn,yn,tn)}, such that ti < ti+1 for all i ={1, ? , n}; each (xi, yi, ti) is the object's location at time ti. Even though objects follow the same routes, it is highly unlikely that trajectories have the identical sequences due to the noise and the deviation of the objects' movements. To discretise trajectory data, each (xi, yi) at timestamp ti is transformed to the id of the spatial region describing the object's location. Since the interval between consecutive timestamps is fixed, a spatio-temporal sequence S is represented as a set of location symbols li, which contain positions (xi, yi). Then, a trajectory is converted to a generalized sequence of location symbols. Trajectories can be partitioned into disjointed subsequences by detecting meaningful spatiotemporal changes in objects' movement. A segment between two change points forms a spatiotemporal region, which includes both a spatial and a temporal approximation of movements within the segment. Locations are not repeated exactly in every instance of a movement pattern. The database scan is to find all frequent mobile access patterns. Then, the frequent mobile access pattern is inserted into the header table in decreasing order of their sequence access pattern. Privacy preservation in spatio-temporal data sets is challenging because spatio temporal data sets are so rich in correlations, allowing many "privacy attack" strategies that are difficult to counteract and sometimes even to anticipate. The proposed method is believed to protect against a few obvious threats, namely, 1. Detection of frequent private/personal/individual locations due to self-correlations in historical spatio-temporal (trajectory) data sets, 2. Detection of the current position due to physical mobility constraints on objects (maximum speed, road network, spatio-temporal restrictions in general. Intuitively, the whole trajectory of a moving object can be enclosed into a single rectangle so that the anonymity of the trajectory is preserved. However, as the trajectories are often very long, the rectangles can be very big so that it becomes impossible for the data mining algorithms to return any useful results. The proposed method provides an anonymized format of the trajectory by cutting a long trajectory into pieces and enclosing each piece in an anonymization rectangle. This format can give opportunities for doing data mining without sacrificing location privacy. Movements of mobile users are often modeled as trajectories in 2D space. Data mining on the trajectory data has many applications, not only in LBSes, but also in telematics and Intelligent Transportation System. An existing solution for protecting the location and trajectory data in LBSes is to anonymize the users' location data by decreasing the spatio-temporal resolution of the locations. After this "anonymization" step, the exact location data becomes a set of spatio-temporal rectangles. Although such ambiguity protects the mobile users' privacy, it also reduces the accuracy of the data mining results. Motivated by these observations, this paper defines privacy requirements for mobile transaction data and presents a system for collecting mobile users' trajectory data in a privacy-preserving manner. Compared to existing solutions, the proposed system does not require trusted components, yet it protects the privacy of mobile users and preserves the accuracy of data mining by keeping the spatio-temporal data intact. The proposed solution assumes that clients on mobile phones can communicate through a wireless P2P network. The process of trajectory collection is divided in five stages as follows. First, in the client registration stage, a group of k clients obtains permission and parameters from the server for executing the trajectory sampling and anonymization, the trajectory exchange-, and the data reporting stages in a multithreaded fashion. In the trajectory sampling and anonymization stage, clients record their private trajectories and generate a set of k "cloaking" trajectories to anonymize their actual trajectory. In the trajectory exchange stage, clients exchange sets of k partial trajectory pieces with other clients in the P2P network. Finally, in the data reporting stage, clients send anonymous partial trajectory pieces to the server. IV. # Conclusion This paper has presented an approach for mining spatio-temporal patterns from trajectory data and introduced the problem of representing spatio-temporal properties with redundant location symbols. 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Not at all, take in raw data or intermediate calculations in a research manuscript. 1![Figure 1 : System Model](image-2.png "Figure 1 :") 2![Figure 2 : Sample Mobile Transaction Database Spatio-Temporal Anonymization](image-3.png "Figure 2 :") 3![Figure 3 : Anonymization Model](image-4.png "Figure 3 :") ![and Format of Manuscript, 6. After Acceptance.](image-5.png "") ![Figures: Figures are supposed to be submitted as separate files. Always take in a citation in the text for each figure using Arabic numbers, e.g. Fig.4. Artwork must be submitted online in electronic form by e-mailing them.Preparation of Electronic Figures for PublicationEven though low quality images are sufficient for review purposes, print publication requires high quality images to prevent the final product being blurred or fuzzy. Submit (or e-mail) EPS (line art) or TIFF (halftone/photographs) files only. MS PowerPoint and Word Graphics are unsuitable for printed pictures. Do not use pixel-oriented software. 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Position your understanding of the outcome visibly to lead the reviewer through your conclusions, and then finish the paper with a summing up of the implication of the study. The purpose here is to offer an understanding of your results and hold up for all of your conclusions, using facts from your research and generally accepted information, if suitable. The implication of result should be visibly described. Infer your data in the conversation in suitable depth. This means that when you clarify an observable fact you must explain mechanisms that may account for the observation. If your results vary from your prospect, make clear why that may have happened. If your results agree, then explain the theory that the proof supported. It is never suitable to just state that the data approved with prospect, and let it drop at that. Make a decision if each premise is supported, discarded, or if you cannot make a conclusion with assurance. Do not just dismiss a study or part of a study as "uncertain." Research papers are not acknowledged if the work is imperfect. Draw what conclusions you can based upon the results that you have, and take care of the study as a finished work You may propose future guidelines, such as how the experiment might be personalized to accomplish a new idea. Give details all of your remarks as much as possible, focus on mechanisms. Make a decision if the tentative design sufficiently addressed the theory, and whether or not it was correctly restricted. Try to present substitute explanations if sensible alternatives be present. One research will not counter an overall question, so maintain the large picture in mind, where do you go next? The best studies unlock new avenues of study. What questions remain? Recommendations for detailed papers will offer supplementary suggestions. ## XVII * Mining sequential patterns RAgrawal RSrikant Proc. of Intl. Conf. on Data Engineering of Intl. Conf. on Data Engineering 1995 * Rule discovery from time series GDas KILin HMannila GRenganathan PSmyth Proc. of Intl. Conf. on Knowledge Discovery and Data Mining of Intl. Conf. on Knowledge Discovery and Data Mining 1998 * Efficient mining of spatiotemporal patterns ITsoukatos DGunopulos Proceedings of International Symposium on in Spatial and Temporal Databases International Symposium on in Spatial and Temporal Databases 2001 * ST-DBSCAN: An algorithm for clustering spatial-temporal data DBirant AKut Data and Knowledge Engineering 60 1 2007