Juwel Rana

An Architecture for Mobile Social Networking Applications

Mobile Semantic Web provides a new way of developing context-aware social networking applications. Social networking applications are changing the way of communication by using user’s context-information. For example, micro-blogging has become a smart way of conveying the current situation and activity by using user context. There is currently a significant difference between using social networking applications on a static computer compared to a mobile device, even if current mobile devices are powerful and have good connectivity. The difference is primarily related to the mobility aspect since the user contexts may change more frequently and the user may not be able to interact with the mobile device. In this paper we identify common characteristics of current social networking applications and how they attract users. Finally, we propose an agent-based system architecture that is based on a distributed platform for developing semantic and semi-automated mobile social networking applications.

Challenges for Mobile Social Networking Applications

This paper presents work in progress regarding utilization of social network information for mobile applications. Primarily a number of challenges are identified, such as how to mine data from multiple social networks, how to integrate and consolidate social networks, and how to manage semantic information for mobile applications. The challenges are discussed from a semantic Web perspective using a driving scenario as motivation.

Enriching and Simplifying Communication by Social Prioritization

This paper presents a framework for Aggregated Social Graphs, the ASG Framework, for developing applications that harness information available on social networking sites and telecom platforms to make communication simpler and richer. The framework aggregates users social graph by harnessing contact and social information from multiple communication environments and then calculating the social strength between the users, based on the users communication history. The framework discovers communication patterns in different level which is eventually used to simplify and enhance communication services by inviting appropriate users for collaboration, selecting suitable tools for communication and prioritizing information flows over the cloud. The framework also enables new types of applications such as social search clients, smart dialers or contact applications.

Dynamic Media Distribution in Ad-hoc Social Networks

This paper proposes a social distribution mechanism for finding, connecting and propagating contents in social networks using context and communication history of users together with meta information about the content. The proposed mechanism operates by propagating invitation messages to a prioritized group of users to build up an ad-hoc social network from the original social network while at the same time preventing spamming the users with unwanted invitation messages. The proposed mechanism makes it possible to implement and deploy a wide variety of services targeting the specific needs of a user. As a proof-of-concept, the paper shows how the proposed social distribution mechanism can be used to invite users to a shared space, which can contain various kinds of collaboration tools allowing a group of users to communicate and solve problems together. In order to investigate the efficiency of the proposed mechanism, the paper also presents result from a simulation study. The result shows that the social distribution mechanism should consider both social strength and context for propagating social media contents as some of the recipients may have equal interest of the social media but different level of trust about the originator.

Harnessing the Cloud for Mobile Social Networking Applications

The cloud computing model inherently enables information from social networking services Twitter, Facebook, LinkedIn, and so forth, context-based systems location, activity, interests, etc. and personal applications call logs, contacts, email, calendar, and so forth to be harnessed for multiple purposes. This article presents an agent-based system architecture for semantic and semi-automated applications that utilize the cloud to enrich and simplify communication services, for instance by displaying presence information, prioritizing information, and dynamically managing groups of users. The proposed architecture is based on the concept of aggregated social graphs, which are created from harnessed information about how people communicate. This article also presents challenges in achieving the envisioned architecture and introduces early prototyping results.

Using a Rich Context Model for Real-Time Big Data Analytics in Twitter

In this paper we present an approach for contextual big data analytics in social networks, particularly in Twitter. The combination of a Rich Context Model (RCM) with machine learning is used in order to improve the quality of the data mining techniques. We propose the algorithm and architecture of our approach for real-time contextual analysis of tweets. The proposed approach can be used to enrich and empower the predictive analytics or to provide relevant context-aware recommendations.

End-user creation of social apps by utilizing web-based social components and visual app composition

This paper presents a social component framework for the SatinII App Development Environment. The environment provides a systematic way of designing, developing and deploying personalized apps and enables end-users to develop their own apps without requiring prior knowledge of programming. A wide range of social components based on the framework have been deployed in the SatinII Editor, including components that utilize aggregated social graphs to automatically create groups or recommending/filtering information. The resulting social apps are web-based and target primarily mobile clients such as smartphones. The paper also presents a classification of social components and provides an initial user-evaluation with a small group of users. Initial results indicate that social apps can be built and deployed by end-users within 17 minutes on average after 20 to 30 minutes of being introduced to the SatinII Editor.

Ranking algorithm by contacts priority for social communication systems

The paper presents the ranking algorithm by contacts priority with application for social communication services. This algorithm makes it possible to rank contacts in different social communication services by their priority for the user, and find a preferable communication tools for every contact in the social graph. It is proposed to determine priorities of the contacts using communication history. The ranking algorithm is based on the Markov chains theory and social strength calculation approach. The paper exploits the opportunities for measuring social strength for the contacts and also prioritizes communication tools for simplifying communication. The results are supported by an example.

Real-Time Data Analytics: An Algorithmic Perspective

Massive amount of data sets are continuously generated from a wide variety of digital services and infrastructures. Examples of those are machine/system logs, retail transaction logs, traffic traci ...

A Handset-centric View of Smartphone Application Use

Studying the use of applications on smart phones is important for developers, handset designers and network operators. We conducted a study on Android devices by installing an instrumentation application, Device Analyzer, on participants’ handsets. Over a 4 month period we collected 10.9 billion records from 674 different users. In this paper we describe how to use the research study features of Device Analyzer to control participant selection and to access information (with consent) that is withheld for privacy reasons from the main dataset. We describe our data processing architecture and the steps required to preformat and analyse the data. Our data contains 3329 distinct applications (from the Google Play store) but despite this, on average, a user makes use of only 8 unique applications in a week. Almost 100% of our users make use of some email application on their phone. Fewer users (85%) made use of the Facebook application but 4–5 times more frequently than for email with sessions lasting almost twice as long. We also investigated whether different applications have correlated usage using a network analysis and a principal component analysis. We see that application usage tends to correlate by vendor more than by activity. This is potentially due to vendors integrating or cross-promoting services between applications.