RSkNN: kNN Search on Road Networks by Incorporating Social Influence
RSkNN: kNN Search on Road Networks by Incorporating Social Influence management report in data mining.Although kNN search on a road network G(r), i.e., finding k nearest objects to a query user q on G(r), has been extensively studied, existing works neglected the fact that the q’s social information can play an important role in this kNN query. Many real-world applications, such as location-based social networking services, require such a query. In this paper, we study a new problem: kNN search on road networks by incorporating social influence (RSkNN). Specifically, the state-of-the-art Independent Cascade (IC) model in social network is applied to define social influence.
One critical challenge of the problem is to speed up the computation of the social influence over large road and social networks. To address this challenge, we propose three efficient index-based search algorithms, i.e., road network-based (RN-based), social network-based (SN-based), and hybrid indexing algorithms. In the RN-based algorithm, we employ a filtering-and-verification framework for tackling the hard problem of computing social influence. In the SN-based algorithm, we embed social cuts into the index, so that we speed up the query. In the hybrid algorithm, we propose an index, summarizing the road and social networks, based on which we can obtain query answers efficiently. Finally, we use real road and social network data to empirically verify the efficiency and efficacy of our solutions.
The existing system incorporates road network and social network. Independent Cascade (IC) model in social network is applied to define social influence. One of the challenge was to speed up the computation of the social influence over large road and social networks. To address this challenge, three efficient index-based search algorithms was proposed, i.e. road network-based (RN-based), social network-based (SN-based) and hybrid indexing algorithms. In the RN-based algorithm, employs a filtering-and-verification framework for dealing with the hard problem of computing social influence. SN-based algorithm, embed social cuts into the index, so to speed up the query. In the hybrid algorithm, index was proposed, summarizing the road and social networks, based on which query answers can be obtained efficiently. In proposed system recommendation is given based on the reviews of trusted users.
With the ever-growing quality of mobile devices (e.g., smartphones), location-based service (LBS) systems (e.g., Google Maps for Mobile) are wide deployed and accepted by mobile users. The k-nearest neighbor (kNN) search on road networks could be a basic drawback in LBS. Given a question location and a group of static objects (e.g., restaurant) on the road network, the kNN search drawback finds k nearest objects to the question location. Alone with the favored usage of LBS, the past few years have witnessed an enormous boom in location-based social networking services like Foursquare, Yelp, Loopt, Geomium and Facebook Places. All told these services, social network users are usually related to some locations (e.g., home/office addresses and visiting places).
Such location info, bridging the gap between the physical world and also the virtual world of social networks, presents new opportunities for the kNN search on road networks. The said example motivates U.S. to think about the social influence to a user once process the kNN search on road networks. Specifically, alphabetic a question user q would really like not solely retrieving k geographically nearest objects , however get an outsized social influence from q’s friends UN agency are to . Therefore, during this paper, we have a tendency to study a completely unique query: kNN search on a road-social network (RSkNN), and propose economical question process algorithms. Specifically, given Gs, Gr and q, the RSkNN search finds k nearest objects (Aq = ) to question q’s location on Gr, specified the social influence SI(or) to Q through q’s friends, UN agency are to or, is a minimum of a threshold.
In this paper a kNN search on road networks by incorporating social influence (RSkNN). Independent Cascade(IC) model in social network is applied to dene social influence. One critical challenge of the problem is to speed up the computation of the social influence over large road and social networks. In Supercomputing. Under certain reasonable assumptions that even if no revetment is used in the un-coarsening phase, a good bisection of the coarser graph is worse than a good bisection of then graph by at most a small factor. Models for the processes by which ideas and influence propagate through a social network have been studied a number of domains, Including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of word of mouth in the promotion of new products.
In this paper, The System can provide novel ranking methods that are different from the ICM, typical methods of social network analysis, and Page Rank method. Moreover, It experimentally demonstrate that when the propagation probabilities through links are small, they can give good approximations to the ICM for sets of influential nodes. A novel hybrid genetic algorithm (GA) that globally optimal partition of a given data into a specie number of clusters. To circumvent these expensive operations, it hybridize GA with a classical gradient descent algorithm used in clustering, viz. K-means algorithm. Hence, the name genetic K-means algorithm (GKA).The location-aware influence maximization problem. One big challenge in location-aware influence maximization is to develop an efficient scheme that offers wide influence spread. To address this challenge, it propose two greedy algorithms with 1 1/e approximation ratio. To meet the instant speed requirement, it propose two efficient algorithms with (1 1/e) approximation ratio for any (0,1).
In this paper User’s interests are modeled by check-inactions. Here a Spatial-aware Interest Group (SIG) query that retrieves a user group of size k where each user is interested in the query keywords and they are close to each other in the Euclidean space. It prove that the SIG query problem is NP-complete. Approximation algorithms have developed in response to the impossibility of solving a great variety of important optimization problems. Too frequently, when attempting to get a solution for a problem, one is confronted with the fact that the problem is NP-hard. While this is a significant theoretical step, it hardly qualifies as a cheering piece of news. Three simple efficient algorithms with good probabilistic behavior two algorithms with run times of O(n(log n)2) which almost certainly nd directed (undirected)
Hamiltonian circuits in random graph so fat least cn log n edges, and an algorithm with a runtime of O(n log n) which almost certainly a perfect matching in a random graph of at least cn log n edges. In this paper the systematic work on GeoSN query processing. Proposed a general frame work that offers exile data management and algorithmic design. Architecture segregates the social, geographical and query processing modules. Each GeoSN query is processed via a transparent combination of primitive queries issued to the social and geographical modules. Design a heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments . The study influence maximization in the linear threshold model, one of the important models formalizing the behavior of influence propagation in social networks. It show that computing exact influence in general networks in the linear threshold model is P-hard, which closes an open problem left in the seminal work on influence maximization.
H/W System Configuration:-
Processor : Pentium IV
Speed : 1 Ghz
RAM : 512 MB (min)
Hard Disk : 20GB
Keyboard : Standard Keyboard
Mouse : Two or Three Button Mouse
Monitor : LCD/LED Monitor
S/W System Configuration:-
Operating System : Windows XP/7
Programming Language : Java/J2EE
Software Version : JDK 1.7 or above
Database : MYSQL
PROCESS BLOCK DIAGRAM
In architecture design, the first block is user here user will login to the system by providing password and username. In RNIndex Search block IRN is road network index, Gs is social network and q is query is provided as input to the block and upper bound and lower bound is calculated. Next block is Filtering step Gs is road network and qs is query set is offered as input and tight upper bound and lower bound is analyzed that is SI(Or). Next block is Verification block where Gr is road network, Gs is social network and q is query is delivered as input and true value of SI(Or) is estimated. It confirms if Or is a valid answer. In SN Based Search block Gs is social network and SG is delivered as input and SI(Or) is estimated. Next block is Min Cut Cover in which Vr and Vs are accepted is used to calculate Gr s is graph constructed for Gs and Gs and k is size of cutmark is provided as input and D the optimal cutmark set is analyzed. Combination of both RNIndex Search and SN Based Search is done in Hybrid Indexing. There is use of Database. Nearest location is given by using these algorithm and recommendation is provided. In road network according to latitude and longitude the distance are measured. By using filtering and sampling we get fine result through which we can get nearest location as per the query.
In design style, the primary block is user here user can login to the system by providing secret and username. In RNIndex Search block IRN is road network index, Gs is social network and Q is question is provided as input to the block and edge and boundary is calculated. Next block is Filtering step Gs is road network and qs is question set is obtainable as input and tight edge and boundary is analyzed that’s SI(Or). Next block is Verification block wherever Gr is road network, Gs is social network and Q is question is delivered as input and true price of SI(Or) is calculable. It confirms if Or could be a valid answer. In atomic number 50 based mostly Search block Gs is social network and SG is delivered as input and SI(Or) is calculable. Next block is Min Cut cowl during which Vr and Vs area unit accepted is employed to calculate Grs is graph created for Gs and Gs and k is size of cutmark is provided as input and D the best cutmark set is analyzed. Combination of each RNIndex Search and atomic number 50 based mostly Search is finished in Hybrid compartmentalisation. there’s use of information. Nearest location is given by victimization these algorithmic rule and recommendation is provided. In road network in line with latitude and great circle the space area unit measured. By victimization filtering and sampling we tend to get fine result through that we are able to get nearest location as per the question.
RESULT AND DISCUSSION
- User Home Page:
After login into the page we get user home page with Check In Point and Current Location. In check point user will add the place he/she visited. In current location user will enter their current location to get the nearest location to that of the current location.
- Check Point Page:
In this check point page visited user will fill the information of that place that they had visited. Review is also given in the same page. It will include all the information that is needed so that recommendation can provided.
- Admin Home Page:
In this database in maintained. Google map and data sets are seen by the admin.
- Graph Page:
In this graph positive and negative review graph is shown. This is done with the help of reviews of the user which may be positive or negative.
- Review Page:
In this Review page, review is seen by the user who want to visit that particular place. Through category and speciality reviews is seen of the person who has visited.
In this paper there is a feasible solution and query is answered within a specific time. There is a joint social and road processing on networks stored in a distributed manner. There is a use of datasets for getting the past records of most popular visited places. There is a use of GPS for finding the road network and there will be a dummy network for implementing this work. Future work can be adding that location where user have not been visited and that recommendation can be provided by number of person visiting that particular place.
In RSkNN: kNN Search on Road Networks by Incorporating Social Influence management report in data mining paper there’s a possible resolution and question is answered at intervals a specific time. there’s a joint social and road process on networks keep during a distributed manner. there’s a use of datasets for obtaining the past records of most well liked visited places. there’s a use of GPS for finding the road network and there’ll be a dummy network for implementing this work. Future work will be adding that location wherever user haven’t been visited which reccomendation will be provided by variety of person visiting that individual place.