spatial preference newsfeed system for android mobile users

IRACST - International Journal of Computer Science and Information Technology & Security (IJCSITS), ISSN: 2249-9555 Vol.6, No3, May-June 2016

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SPATIAL PREFERENCE NEWSFEED SYSTEM FOR ANDROID MOBILE USERS

Sangeeta Ruth Department of Computer Science and Engineering,

B.S.Abdur Rahman University, Vandalur, Chennai, India

Srividhya Raghavan V Department of Computer Science and Engineering,


Smrithi J Department of Computer Science and Engineering,


Saira Banu,Asst.Prof (Senior grade) Department of Computer Science and Engineering

B.S.Abdur Rahman University Vandalur, Chennai, India

Abstract— A spatial preference news feed system LANF(Location Aware News Feed) generates news feeds for a mobile user based on their spatial preference and non-spatial preference .Diversity is a very important feature for location-aware news feeds because it helps users discover new places and activities. Hence this system proposes a new framework called D-MobiFeed. D-MobiFeed is a framework used for prioritizing and scheduling the messages for the mobile users and also specifies the minimum number of message categories for the messages in a news according to the relevance of the user’s preferences. This system focuses the queries raised by the users and schedules the news feeds for a mobile user to gather current and predicted locations, such that each news feed contains messages belonging to different categories.

Keywords: Location-aware news feeds, spatial preference, scheduling, D-MobiFeed

I. INTRODUCTION

Android produces an easy application structure that allows you to build innovative apps and games for mobile devices in a Java language setting. The documents cataloged in the left navigation provide details about how to build apps practicing Android's various APIs. If you're new to Android developments, it's important that you understand the following fundamental abstraction about the Android app structure: Applications provide different entry points Android apps are built as a combination of distinct components that can be invoked individually. For instance, an individual activity provides a single screen for a user interface, and a service complete performs work in the background. From one component you can start one more fundamental using resolved. You can even start a component in a different application, such as an activity in a maps app to show a direction. This model provides various entry points for a single application and allows any application to behave as a user’s “delinquency” for an action that other applications may invoke.

LANF provides a new platform for its users to get spatially related message updates from either their friends or favorite news sources. GeoFeed distinguishes itself formal

existing news feed systems in that it enables users to post message with spatial extent rather than static point locations, and takes into account their locations when computing news feed for them. GeoFeed is equipped with three different approaches for delivering the news feed to its users, namely, spatial pull, spatial push, and shared push. This work considers a mobile environment that makes our location- and diversity-aware news feed system unique and more challenging. With the geographical distance between a message and a mobile user in a relevance measure model, the relevance of a message to a mobile user is changing as the user is moving. Such a dynamic environment gives us an opportunity to employ location prediction technique to improve the quality of news feeds and the system efficiency. With our location prediction techniques, we aim at improving the quality of news feeds by scheduling multiple location- and diversity-aware news feeds for mobile users simultaneously. Experimental results show that, when k = 5, over 75% news feeds contain messages belonging to one category and about 20% of news feeds are related to two categories. We argue that diversity is a very important feature for location-aware news feeds because it helps users discover new places and activities.

Diversity-aware recommender systems in D-Mobifeed is the only metric used to evaluate its quality as a recommender system is the relevance of messages to users (i.e., accuracy). However, it is argued in that, developing recommender systems with accuracy as the single goal has many drawbacks, and the recommender community should move beyond the conventional accuracy metrics. One promising direction that Has drawn recent interest is to diversify the recommendation lists. Ziegler et al. proposed an intra-list similarity metric to measure the overall diversity of a recommendation list, where the similarity between products is derived from their taxonomy-based categorization Diversity-aware web search systems is a process of web search systems which differs from that of recommender systems since it involves an explicit user query (i.e., keywords). The query, however, is also ambiguous and has more than one interpretation. One possible way to address this problem is to produce a set of diversified results that cover different


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interpretations of the target query. Specifically, the search result diversification approaches in the literature can be classified as either implicit or explicit. Implicit approaches assume that similar documents will cover similar aspects of a query.

App components are the essential building blocks of an Android app. Each component is a different point through which the system can enter your app. Not all components are actual entry points for the user and some depend on each other, but each one exists as its own entity and plays a specific role—each one is a unique building block that helps define your app's overall behavior. There are four different types of app components. Each type serves a distinct purpose and has a distinct lifecycle that defines how the component is created and destroyed.

An activity represents a single screen with a user interface. For example, an email app might have one activity that shows a list of new emails, another activity to compose an email, and another activity for reading emails. Although the activities work together to form a cohesive user experience in the email app, each one is independent of the others. As such, a different app can start any one of these activities (if the email app allows it). For example, a camera app can start the activity in the email app that composes new mail, in order for the user to share a picture. An activity is implemented as a subclass of Activity and you can learn more about it in the Activities developer guide.

A content provider manages a shared set of app data. You can store the data in the file system, a SQLite database, on the web, or any other persistent storage location your app can access. Through the content provider, other apps can query or even modify the data (if the content provider allows it). For example, the Android system provides a content provider that manages the user's contact information. As such, any app with the proper permissions can query part of the content provider (such as content providers are also useful for reading and writing data that is private to your app and not shared. For example, the ad sample app uses a content provider to save notes. A content provider is implemented as a subclass of Content Provider and must implement a standard set of APIs that enable other apps to perform transactions.

A Broadcast receiver is a component that responds to system-wide broadcast announcements. Many broadcasts originate from the system—for example, a broadcast announcing that the screen has turned off, the battery is low, or a picture was captured. Apps can also initiate broadcasts—for example, to let other apps know that some data has been downloaded to the device and is available for them to use. Although broadcast receivers don't display a user interface, they may create a status bar notification to alert the user when a broadcast event occurs. More commonly, though, a broadcast receiver is just a "gateway" to other components and is intended to do a very minimal amount of work. For instance, it might initiate a service to perform some work based on the event. A broadcast receiver is implemented as a subclass of Broadcast Receiver and each broadcast is delivered as an Intent object. For more information, see the Broad cast Receiver class.

Fig. 1: Process of a mobile environment based on location prediction and user’s preferences

This project focus on a mobile environment, where mobile users are moving in a road network and can predict their locations using the GPS and can select the categories according to the user’s preferences. Our problem is unique and more challenging as D-MobiFeed considers the geographical distance factor between messages and mobile users in the relevance measure model, and thus, the relevance of message s to users could be changing as they are moving. In addition, D-Mobi Feed has an opportunity to employ a location prediction technique to improve the quality of news feeds by scheduling multiple (i.e., n + 1, where n is a look-ahead step) location and diversity-aware news feeds for mobile users simultaneously. The main reason is that computing each news feed individually

II. RELATED WORK

In this paper, we illustrate about the different systems and the various models that the other authors have researched about which contributes to the development of our application.

Geolocation and Assisted GPS. Currently in development, numerous geolocation technologies can pinpoint a person's or object's position on the Earth. Knowledge of the spatial distribution of wireless callers will facilitate the planning, design, and operation of next generation broadband wireless networks. Mobile users will gain the ability to get local traffic information and detailed directions to gas stations, restaurants, hotels, and other services. Police and rescue teams will be able to quickly and precisely locate people who are lost or injured but cannot give their precise location. Companies will use geolocation based applications to track personnel, vehicles, and other assets. The driving force behind the development of this technology is a US Federal Communications Commission (FCC) mandate stating that by 1 October 2001 all wireless carriers must provide the geolocation of an emergency 911 caller to the appropriate public safety answering point. Location technologies requiring new modified, or upgraded mobile stations must determine the caller's longitude and latitude within 50 meters for 67 percent of emergency calls, and within 150 meters for 95 percent of the calls. Otherwise, they must do so within 100 meters and 300 meters, respectively, for the same percentage of calls. Currently deployed wireless technology can locate 911 calls within an area no smaller than 10 to 15 square kilometers. It is argued that assisted-GPS technology offers superior accuracy, availability, and coverage at a reasonable cost.


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A New Method of Relevance Measure and Its Applications. Relevance analysis is a regular and important task in many technical fields. We can get the relevance score by measuring (or quantifying) the result of relevance analysis. In this paper, we have reviewed two main tools for relevance measure, which are the covariance and the mutual information, and we have discussed that there may be some problems in relevance measure if we use the above two methods, then we give the definition on Partial Condition Entropy (PCE) based on the information theory and presented a new method for relevance measure by using the PCE. There are mainly three advantages for relevance measure by using our method: (1) The relevance degree can be compared more easy than other methods because the score of relevance calculation is equal to a numeral between 0 and 1;(2) By using the method, we can not only know whether there is relevance between the considered events but also get a special score that represents the relevance degree of these events; (3) When we calculate the PCE, we needn't know all the conditional probability density, so our method is more flexible than the calculation of mutual information. To demonstrate the usefulness of our method for relevance measure, we apply it to the sentence relevance analysis in Natural Language Processing (NLP). We find that our result of relevance measure is a more truly reflection on the relationship between the sentences.

Location-aware user tracking and prediction system. Modern location-aware services and applications use context and prediction methods to adapt to the needs of users and changes in the environment. The growing availability of WLAN and mobile devices offers significant opportunities for location aware services. But the use of WLAN or RFID technologies alone provides a less accurate estimation of a user's current location. In this paper, we introduce an ontology-based location tracking system. It makes use of both WLAN and RFID technologies, and includes a prediction method for identifying a user's current location and predicted future location. Our system architecture better serves the client by using location and context information.

Ranking spatial data by quality preferences. A spatial

preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of these methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters.

III. SYSTEM MODEL

In this section, we present the architecture diagram of

our system model

SYSTEM ARCHITECTURE

Fig. 2 Architecture diagram of D-MobiFeed model

This project focus on a mobile environment, where mobile users are moving in a road network. Our problem is unique and more challenging as D-MobiFeed considers the geographical distance factor between messages and mobile users in the relevance measure model, and thus, the relevance of message s to users could be changing as they are moving. In addition, D-Mobi Feed has an opportunity to employ a location prediction technique to improve the quality of news feeds by scheduling multiple (i.e., n + 1, where n is a look-ahead step) location and diversity-aware news feeds for mobile users simultaneously. The main reason is that computing each news feed individually. USE CASE DIAGRAM

Fig. 3 Use case diagram of the application

A use case diagram is a graph of actors, a set of use

cases enclosed by a system boundary, communication (participation) associations between the actors and users and generalization among use cases. The use case model defines the outside (actors) and inside (use case) of the system’s behavior. The following diagram depicts the use case diagram for the proposed system. The use case “Check classification patterns” represents that the user can obtain rules or classification patterns out of the original data set. This use case extends computation of information gain, because we make a division among tupelos based on the information gain computed for each attribute. The attribute with maximum information gain is selected as the split criterion. The use case “Check privacy preservation” represents


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that the user can check the level of privacy on the data set. This use case extends generalization of data as level of privacy is directly related to the level of generalization.

IV. MODULES

Location Prediction. The location prediction function is designed to predict a mobile user’s locations based on an existing path prediction algorithm. MobiFeed aims at maximizing the total relevance of news feeds by utilizing a location prediction technique. Dynamic environment gives us an opportunity to employ location prediction technique to improve the quality of news feeds and the system efficiency. Existing diversification problems focus on retrieving an individualist of items with a certain level of diversity. In contrast, with our location prediction techniques, we aim at improving the quality of news feeds by scheduling multiple location- and diversity-aware news feeds for mobile users simultaneously. In addition, D-Mobi Feed has an opportunity to employ a location prediction technique to improve the quality of news feeds by scheduling multiple (i.e., n + 1, where n is a look-ahead step) location and diversity-aware news feeds for mobile users simultaneously. As an internet based platform our facility, which integrates both satellite and GSM tracking for seamless global coverage, can securely transmit asset locations to your computer or smart phone via web browser or our custom app. static registered locations for users logging on to GeoFeed. So, once a user logs off Geo Feed, we compute the cost and decision models for that user based on its registered location. User locations can be registered with Geo Feed either explicitly from the user or implicitly by detecting that a user is frequently logging on from a certain location. If a user has multiple registered static locations, e.g., home and work, Geo Feed treats each location separately, where the cost and decision models can be different for each registered location.

Relevance Measure. The relevance measure function is

implemented by combining the vector space model with non-spatial and spatial factors to determine the relevance of a message to a user. In MobiFeed the only metric used to evaluate its quality as a recommender system is the relevance of messages to users (i.e., accuracy). However, it is argued in that, developing recommender systems with accuracy as the single goal has many drawbacks, and the recommender community should move beyond the conventional accuracy metrics. One promising direction that has drawn recent interest is to diversify the recommendation lists. Diversity-aware web search systems is the process of web search systems differs from that of recommender systems since it involves an explicit user query (i.e., keywords). The query, however, is also ambiguous and has more than one interpretation. One possible way to address this problem is to produce a set of diversified results that cover different interpretations of the target query. Specifically, the search result diversification approaches in the literature can be classified as either implicit or explicit. Implicit approaches assume that similar documents will cover similar aspects of a query. Unfortunately, relevance measure alone is unable to capture the broader aspects of user satisfaction. Although users expect to receive messages that are highly relevant to their interests, they may prefer a location-aware news feed with a certain level of diversity (i.e., the messages in a news feed belong to a certain number of

categories). In conventional web search or recommender systems, topic diversification is a key method to improve user satisfaction. This work considers a mobile environment that makes our location- and diversity-aware newsfeed system unique and more challenging. With the geographical distance between a message and a mobile user in a relevance measure model, the relevance of a message to a mobile user is changing as the user is moving. To this end, we propose D-MobiFeed, a framework that takes both the relevance and diversity of news feeds into account when scheduling news feeds for moving users. This h-diversity constraint brings a brand new challenge to D-MobiFeed, i.e., the trade-off between relevance and diversity of news feeds.

News Feed Scheduler. The news feed scheduler works

with the other two functions to generate news feeds for a mobile user at her current and predicted locations with the best overall quality. . The decision problem is modeled as a maximum flow problem and enables D-MobiFeed to decide whether it can fulfill the h-diversity constraint for a news feed. For the optimization problem, we design an efficient three-stage heuristic algorithm to maximize the total relevance of news feeds under the h-diversity constraint. Experimental results based on a real social network data set crawled from Foursquare and a real road network show that D-MobiFeed can efficiently provide location- and diversity-aware news feeds when maintaining their high quality in terms of relevance. In conventional web search or recommender systems, topic diversification is a key method to improve user satisfaction. This work considers a mobile environment that makes our location- and diversity-aware newsfeed system unique and more challenging. We design a location-aware news feed scheduler that works with our location prediction and message relevance measure functions to provide news feeds for mobile users. Upload the entire activity to the admin a new system enables a user to specify the minimum number of message categories (h) for the messages in a news feed. In Mobi-Feed, schedule news feeds for a mobile user other current and conclude location. Admin allow the authentication to the member then only access the information to the member. Entire activity stored the map. The main reason is that computing each news feed individually.

V. ALGORITHM

HEURISTIC SCHEDULING ALGORITHM

In MobiFeed, our objective is to schedule news feeds for a mobile user other current and predicted locations, such that each news feed contains messages belonging to at least h different categories, and their total relevance to the user is maximized.

A heuristic algorithm is one that is designed to solve a problem in a faster and more efficient fashion than traditional methods by sacrificing optimality, accuracy, precision, or completeness for speed. Heuristic algorithms often times used to solve NP-complete problems, a class of decision problems. In these problems, there is no known efficient way to find a solution quickly and accurately although solutions can be verified when given. Heuristics can produce a solution individually or be used to provide a good baseline and are supplemented with optimization algorithms. Heuristic algorithms are most often employed when approximate solutions are sufficient and exact solutions are necessarily computationally expensive.


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In this paper, we use Heuristic scheduling algorithm. Using Heuristic scheduling algorithm information is filtered and categorized according to the user’s preferences. A heuristic is a function that ranks alternatives in search algorithms at each branching step based on available information to decide which branch to follow. The heuristic scheduling algorithm is divided into three stages in this application. The first stage in this algorithm describes the basic details of the various categories that are provided in the application. The second stage describes about the detailed functionalities and the responsibilities of the various categories for the better understanding of the user. The third stage gives details about the ratings and the feedbacks of the various categories which are prioritized and ranked according to the user’s preferences.

VI. USER STUDY AND EXPERIMENTAL RESULTS

USER STUDY

In this paper, we proposed the D-MobiFeed framework in the application. We formulated the heuristic scheduling algorithm and developed an application in the android platform. The application was developed and provided in an .apk format to the users through smart phones. The user was given the opportunity to study the application which was developed in the android platform using android studio. The application was split into three modules such as the user’s home screen, member’s home screen and administrator home screen. The functionalities of each home screen in the application is given below according to the study of the user.

User’s home screen provides the various menus that are available for the user. The user is able to find the location based on his preferences using the Find location and Place menu. The user also has a special functionality to select the categories of their preferences and view the appropriate news feeds. The user can view his own profile and can also edit his profile. This screen also provides a remarkable feature to view the searched history of the places. The Find location feature provides a list of various categories that are predefined in the application and enables the user to select the preference of their choice. Based on the user’s preferences the location is predicted with the help of GPS (global positioning system) and provides the list of items (i.e. banks, ATM, company) available in the current area of location where the user is present. The user can also fetch the details of that item by clicking on the pointer to get the full details such as the name, location, contact details, website, functionality, description etc.

In the Member’s home screen, the member can add a place which involves registering the location for his item(i.e.bank,ATM,company).The member must select the category according to his preference and fill in the details such as the name, address, latitude and longitude positions, number, email-id, website details, functionality, description, password. The member can also edit the place details in future if there are any changes or updates regarding the place. The member’s requests are sent to the admin for approval. The member can also view his own profile and edit his profile.

In the Administrator’s home screen, the administrator has the ability to grant permission to the member to register his place in the application. The administrator has the functionality to approve and also disapprove the member’s request. The

administrator can also add places and can also edit his places based on future updates. The administrator has the feature to view the fetched item in the map before it has been approved and also the item details that are provided by the member. The item gets automatically updated in the application once the administrator approves it by adding the item and also a message is sent from the administrator to the member that the item has been approved. EXPERIMENTAL RESULTS

In this paper, we illustrate the results in the form of

screenshots that are taken during testing of the android application by the user. The user was provided with the application in his/her smart phone and was granted with the permission to use the application. The various screenshots of the menus and functionalities of the application are illustrated below in the form of screenshots that were experimented to produce the results.

Signup is the initial step where the user and the member has separate signup screens to register themselves to access this application. They must provide details like name, number, e-mail id, password, address and city.

Fig. 4 User Signup


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Fig. 5 Member Signup

Fig. 6 User’s home screen

Fig. 7 Selection of different categories done by the user

Fig. 8 Searched History


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Fig. 9 Profile of the user

Fig. 10 List of predefined categories in the find location menu

Fig. 11 Location tracked with the help of GPS and provides the ATM’s available in that location

Fig. 12 Newsfeed provided about the company when the user clicks the pointer in the map


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Fig. 13 Member’s Home screen

Fig. 14 Location registration done by the member

Fig. 15 Administrator’s Home screen

Fig. 16 Administrator can add and delete the member’s Request


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Fig. 17 User’s List

Fig. 18 Member’s List

VII. REFERENCES [1] Wenjian Xu, Chi-Yin Chow, “A Location- and

Diversity-aware News Feed System for Mobile Users”, pp. 1, May 2015.

[2] M.Kalaivanan, K.Vengatesan, “Recommendation System based on statistical analysis of ranking from user”, pp. 479-484, Feb 2013.

[3] G.Adomavicius, Y.Kwon, “Improving aggregate Recommendation Diversity Using Ranking-Based Techniques”, Vol. 24, pp. 896-911, May 2012.

[4] J. Bao, M. F. Mokbel, and C.-Y.Chow, “Location Aware Newsfeed”, pp. 54-65, April 2012.

[5] Jie Bao, Mohamed F. Mokbel, “GeoRank: Efficient location-aware news feed ranking system”, pp. 54-65, April 2012.

[6] R.S.Saranya, S.M.E.Saraswathi, “Ranking spatial data by quality preferences”, pp. 776-781, March 2012.

[7] I. Al Ridhawi , M.Aloqaily , A.Karmouch , N.Agoulmine, “A location-aware user tracking and prediction system”, pp. 1-8, June 2009.

[8] M. S. Zhong ; L. Liu ; R. Z. Lu, “A New Method of Relevance Measure and Its Applications”, pp. 595-600, Aug 2007.

[9] A. Machanavajjhala, J. Gehrke, D. Kifer, M. Venkitasubramaniam, ”L-diversity: privacy beyond k-anonymity”, pp. 24, April 2006.

[10] G.Adomavicius,A.Tuzhilin,“Toward the generation of recomender systems: a survey of the state-of-the-art”,

Vol. 17, pp. 734-749, June 2005. [11] G. M. Djuknic and R. E. Richton, “Geo-location And

Assisted GPS”, Vol. 34, pp. 123-125, Feb 2001. [12] P.E.Hart, N.J.Nilsson, B.Raphael, “A Formal Basis for

the Heuristic Determination of Minimum Cost Paths” Vol. 4, pp. 100-107, July 1968

VIII. AUTHOR’S PROFILE

Ms.Sangeeta Ruth was born on 15th july 1995, in Chennai, Tamilnadu. She did her schooling in Rosary Matriculation Higher Secondary school in Chennai (Tamil Nadu). She is currently pursuing her Bachelor of Technology degree in B.S.Abdur Rahman University. Her areas of interests include Web Development, Android, Cloud computing and Web Designing. Her e-mail ID is: [email protected]


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Ms. Smrithi J was born on 3rd September 1994, in Chennai, Tamilnadu. She did her schooling in Sri Sankara Vidyalaya Higher Secondary school in Chennai (Tamil Nadu). She is currently pursuing her Bachelor of Technology degree in B.S.Abdur Rahman University. Her areas of interests include Web Development, Android, Cloud computing and Web Designing. Her e-mail ID is : [email protected]

Ms.Srividhya Raghavan V was born on 8th October 1994, in Chennai, Tamilnadu. She did her schooling in Sri Sitaram Vidyalaya Higher Secondary school in Chennai (Tamil Nadu). She is currently pursuing her Bachelor of Technology degree in B.S.Abdur Rahman University. Her areas of interests include Web Development, Android, Cloud computing and Web Designing. Her e-mail ID is : [email protected]

Mrs. Saira Banu received the B.E degree in Computer Science and Engineering from the Madras University in 2003. She completed the Master’s in Computer Science and Engineering and received the degree from Anna University in 2006. She is working as Assistant Professor senior Grade, in the department of Computer Science and Engineering in B.S. Abdur Rahman University. She is perusing her research in improving the quality of VOIP Application. She has presented papers in various conferences and published paper in the reputed journals in the area of VOIP. Her email ID is : [email protected]

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spatial preference newsfeed system for android mobile users