System Modeling

Posted on April 26, 2015 by Muna Al Abri

The system requirements that have been collected from team members (since the permission for an ethical approvement got delayed) and feasibility study have been modelled using textual use cases for the most outstanding functions and that make the core of the small.world application. This gives a better understanding of what the system is expected to do and how the user is expected to use it.

is an example of the developed use cases. for a detailed view, read use cases.

Game Theory and recommendation systems

Posted on April 25, 2015 by Muna Al Abri

Recommendation systems are quite widely used in the web. Amazon use it to recommend books, youtube to recommend videos based on other users preferences who are similar to the active user. Recommender systems helps users to express their interests. This has helped massively on the explosion of information on the internet. Imagine if Amazon didn’t have a recommender system, users won’t be able to be exposed to the same amount of goods they are currently exposed at.

Small.World will be using a recommender system that will help matching friends who share other friends and interests. (Azadjalal, Moradi and Abdollahpouri, 2014) came to a conclusion that most people rely on recommendations by friends rather than by strangers and this is our emphasis on Small.World. Recommendation will be made on how much the active user trust others.

The main concern is how to improve this matching ? To answer this question, let’s take a look at one of the most important features of Recommender systems which is “Collaborative Filtering” that predict ratings for active users on things/people based on other user’s opinion. Those ratings can be improved using trust statements. The paper suggests a novel method to improve trust in Recommendation systems using game theory (Azadjalal, Moradi and Abdollahpouri, 2014). Using Pareto Dominance concept which is a state of allocation of resources where making an individual in the network better is impossible without making everyone else on the same network worse-off (Wikipedia, 2015). the concept doesn’t take equality under consideration. It is applied to income distribution where the rich gets richer and the poor gets poorer.

Game theory is used to help identifying those trustable users who correctly have the same interests of the active user and who therefore should be the candidate [FOAF]. The method suggested applies pre-filtering process that reduces the chances of choosing the least trusted users from those who are trusted. To do that, the method uses a process with five steps to predict trust for each user as the fig.1 shows.

Figure 1. Algorithm of the proposed method (Azadjalal, Moradi and Abdollahpouri, 2014)

Trust is a hot topic on the social networks. Algorithms for trust in recommendation systems are developed to help users make wise decision. Small.World tends to use the proposed algorithm because of the matching requirements. Also, Small.World tends to adopt the latest and most efficient algorithm if it will meet our needs.

Refrences:

Azadjalal, M., Moradi, P. and Abdollahpouri, A. (2014). Application of game theory techniques for improving trust based recommender systems in social networks. In: The Fourth International Confrence on Computer and Knowledge Engineering. [online] Sanandaj, Iran: IEEE Computer Society, pp.261-266. Available at: http://ieeexplore.ieee.org [Accessed 24 Apr. 2015].

Wikipedia, (2015). Pareto efficiency. [online] Available at: http://en.wikipedia.org/wiki/Pareto_efficiency [Accessed 28 Apr. 2015].

Blog Summary

Posted on April 25, 2015 by Jingyuan Li

The table below presents blog posts logically classified and organised according to the mark scheme.

Post Categories List	Blog Posts
Welcome and project brief	Welcome and Project Brief – Neil Amos
Analysis of existing similar tools	It’s more than Facebook – Online Social Networks with Interesting Features – Miroslaw Jaroszak
Related academic work	Are you trustworthy ? – Muna Game Theory and Recommendation systems – Muna Branding and Design – Edmund Baird The differences between a scenario and a use case – Jingyuan li
Links to related news items in the tech media	Let’s Get Rid of All That Noise – Miroslaw Jaroszak
Surveys and focus groups	Requirements Gathering Survey – Muna
Mockups and Ideas	Task Allocation – Muna Project Schedule-Gantt Chart – Awezan Project Process – Miroslaw Jaroszak Mockups – Edmund Baird
Scenarios and Personas	Scenarios – Jingyuan Li
Tech demos	System Requirements – Miroslaw Jaroszak Small World Prototype – Muna System Modeling – Muna
UML diagrams	Class Diagram – Awezan Use Case Diagram for SmallWorld – Awezan
Overview of standards and protocols	REST API – Jingyuan Li Location Determining via SMS – Miroslaw Jaroszak Version Control and Development Languages – Jingyuan Li
Social / Ethical considerations	Ethical Challenges Might Face SmallWorld – Awezan Design and Accessibility Considerations – Edmund Baird Say goodbye to the awkward moments – Muna Next Antisocial person ? – Muna
Economic context	Cashing in on success: Advertising or Subscription? – Neil Amos Making Small.World Pay – Neil Amos
Related Technology	Geosocial Networking – Awezan

Scenarios

Posted on April 25, 2015 by Jingyuan Li

A scenario provides the description of the use’s interaction with the application.
For the Small.World, let us assume the following roles:
Bob -a student who is studying the Master of Science programme in England.
He has a flatmate named Alice comes from Holland in the university.
He also has a good friend named Tess in his class.
Alice -Bob’s flatmate.
She expects to meet her parents in Amsterdam after the end of the second semester.
Tess -Bob’s classmate.
She has a nice plan about traveling to Amsterdam during the easter holiday.
System: The Small.World.

The scenarios take place in the following manner:

1.Bob enters the website of the Small.World.
2.Bob registers by his facebook account at the first time.
3.Bob allows the Small.World to search and add all his friends who use this application.
4.Bob customizes the Small.World’s layout.
5.Alice downloads the mobile app of the Small.World.
6.Alice logs in using her Google+ email account for free.
7.Alice relates her Google+ account to her facebook account.
8.Alice selects keep her personal information secret.
9.Tess receives a recommodation message from Bob about the Small.World.
10.Tess downloads the Small.World app into her phone following the link in the text message.
11.Tess logins in with her facebook account.
12.Tess puts her first review about movies by her phone.
13.The Small.World tracks Alice’s movement when she comes back to Amsterdam.
14.Tess checks-in at her new location when she travels to Amsterdam.
15.The Small.World displays Tess’s current location in a multi-layered map.
16.Bob receives a push notification-”Your friends Alice and Tess are both in Amsterdam,would you like to reccommand them to be friends?” from the Small.World.
17.Bob shares the basic information about Alice and Tess in the Small.World.
18.Bob reccommands Alice and Tess to add each other through the Small.World.
19.Alice adds Tess into her friends’ list.
20.Alice says Hello to Tess in the chat section.
21.Alice reviews Tess’s past posts.
22.Bob hosts a three-people’s video conference to discuss Tess’s travel plan in Amsterdam.
23.Tess adds more personal information in the Small.World app, such as personal hobbies and preference on food and places.
24.Alice finds a video about Anne Frank Huis and shares with Tess to recommend tess
to travel together.
25.Tess receives a sound notification from Small.World.
26.Tess accepts Alice’s recommendation.
27.Tess posts a review of locations and events.
28.Alice shares Tess’s review.
29.Alice makes an appointment with Tess in the comment area of the review to travel
together at 7th, April.
30.Bob tags Alice and Tess on a same map.
31.Tess turns on the GPS tracking.
32.The Small.World displays the current location of Tess in both online and offline mode.
33.The Small.World tracks Tess’s location in real time by GPS.
34. Tess receives an alert on presence of Alice within 5 miles proximity.
35.Alice selects a restaurant from the advertisements in Small.World app.
36.Tess gives a good feedback for the restaurant through the app.

Geosocial Networking

Posted on April 22, 2015 by Awezan

The advancement in geographical information system (GIS) made the geosocial networking born. Such networking is enriched by geographic services such as geo tagging and geo coding by using coordinates taking from global positioning system (GPS) technology (Ruiz Vicente et al., 2011).

Geo tagging is using geographic metadata to different media such as photo and video. Geo tagging technology is really useful because it gives the exact location of a friend who checked in on to a service. On the other hand, geo codding is the process of adding location information by geographic coordination to identify the addresses such post code or street name etc.

The Geosocial networking is matching users with their place such as (meeting groups, concerts, restaurants etc…) to socialize and facilitating meeting groups and plan activity. Also it gives the opportunity to meet others who are geographically closer and who have similar interests. It is also increase business ability to target more audience and find out what is near you is much easier

However, some times this type of technology leads to privacy invasion by disseminating too much personal information on the web. Pleaserobme.com is a website that criticize ironically to make people aware of oversharing.

Geosocial Networking Concept (Ruiz Vicente et al., 2011)

What is missing in the existing social networking now is building acommunity in real life. This can be achieved by Small.World through using geosocial networking and also enabling friends of friend to see your activities to broaden the network. Meeting people’s with the same interest in the around geographic location give you more options to make more friends in real life.

Refrences

Ruiz Vicente, C., Freni, D., Bettini, C. and Jensen, C. (2011). Location-Related Privacy in Geo-Social Networks. IEEE Internet Comput., 15(3), pp.20-27.

Class Diagram

Posted on April 19, 2015 by Awezan

The Class is the static structure of the system. The diagram illustrates the system classes, attributes, operations and interrelationship between them (Ambler, 2014). The class diagram is divided into two main sections, class name with blue background, attributes beginning with a minus sign (-) indicate a private attribute.The arrows show the associations between classes with labels indicating the nature of the relations. For example, User class is associated with Profile class, it means each user has it is own profile.For simplicity, only the main operations are included in these classes. As in requirements mentioned, the User class has Chat, Post, and Message class. To make the class diagram clearer and easily understood, some operations have been eliminated from the figure.

Are you trustworthy ?

Posted on April 13, 2015 by Muna Al Abri

Trust is involved in human activities in a daily basis. Measuring trust in a digital world can be more complicated than real life. There are various methods that help in calculating trust and evaluating users based on trust value.There are significant number of models in trust in social networks. Small.World tends to follow reputation-based trust. This comprises two type of trust:

Global Trust: it is a quantitative approach where each user scored a value. The global trustworthiness then based on these scores.
Local Trust: it is a qualitative approach where the trust based on personal bias (Ziegler & Lausen, 2004).

The Local Trust are following uses three properties of trust in social networks:

Transitivity : If A trusts B and B trusts J then A will also trust J

Asymmetry : If A trusts B it does not follow that B trusts A

Personalization :Trust is held from the view of a node – it is therefore local trust (Millard & Imran, 2015).

the usage of Online Social Network (OSN) trust methodologies can boost the privacy and security for the user. A user needs to know if they can trust someone to look at there profiles or how much they can trust them. The post discuss two methods: Trust-Online Social Network (T-OSN) and Trust Indexing algorithm (TI).

The evolution of OSN encouraged people to meet strangers from all over the world and become friends. But this has also, led to some problems such as identity theft and stalking. Methods that calculate trust and help users to make decisions are usually complex or require huge resources. T-OSN is a trust model that has been created specially for OSN that can also be used on other applications such as mobile message forwarding and peer-to peer file sharing network (Li and Bont, 2011).

the model is effective, simple and require the least resources possible. the model is based on two things :

1- Number of friends (Degree)

2- Contact frequency (Contact interval)

and then a user trustworthiness can be calculated for each user. The theory suggests that if a user has more friends and more frequent communication with friends, then this user is highly secure to interact with. So the user will get a higher trustworthiness.

Another method is called “Trust Index” (Tang et al., 2012). It has the same idea as Page Rank that is being used in Google search engine. The algorithm is based on distance that counts hops. How far the user from his/her peer.

Each user will have a TI and a list of the ranking of other users, those who are more trustworthiness will be on the top of the ranking. The paper claims that TI successfully been able to classify trustworthiness users from those who are not (Tang et al., 2012).

Google have introduced Google+ circles where recommendations of people where based on common interests or people that the user might be connected to (Tang et al., 2012). Spam was not an issue. Small.World will follow the same path where trust will come in top of the recommendations that the users will get.

choosing a method to evaluate can be tricky where there is no ultimate solution. It depends on the type of the OSN and what factors will be involved in the algorithm.

research on this area is highly encouraged to create a more secure environment for OSN.

References:

Millard, D. and Imran, M. (2015). Trust: Part I.

Li, M. and Bont, A. (2011). T-OSN : a trust evaluation model in online social networks. In: IFIP Ninth International Conference on Embedded and Ubiquitous Computing. [online] Melbourne, Australia: IEEE Computer Society, pp.469-472. Available at: http://ieeexplore.ieee.org/xpl [Accessed 12 Apr. 2015].

Tang, R., Lu, L., Zhuang, Y. and Fong, S. (2012). Not Every Friend on a Social Network Can be Trusted: An Online Trust Indexing Algorithm. In: IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. [online] Macau SAR, China: IEEE Computer Society, pp.280-285. Available at: http://ieeexplore.ieee.org [Accessed 12 Apr. 2015].

Ziegler, C. and Lausen, G. (2004). Spreading activation models for trust propagation. IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE ’04. 2004.

This post is written by : Muna & Awezan

Small.World Prototype

Posted on April 7, 2015 by Muna Al Abri

Here is a glance of the system functions put into action, the prototype was used to gather the team members feedback to enhance it. Mockups were built based on the feedback given on the prototype below.

Click on the video link below for the prototype

Small_World_prototype_720

Tool used: http://www.justinmind.com