Towards Efficient and Scalable Data-Intensive Content Delivery: State-of-the-Art, Issues and Challenges

This chapter presents the authors’ work for the Case Study entitled “Delivering Social Media with Scalability” within the framework of High-Performance Modelling and Simulation for Big Data Applications (cHiPSet) COST Action 1406. We identify some core research areas and give an outline of the publications we came up within the framework of the aforementioned action. The ease of user content generation within social media platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges: derivation of real-time meaningful insights from effectively gathered social information, a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general, etc. In this article we present the methodology we followed, the results of our work and the outline of a comprehensive survey, that depicts the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centers supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. The challenges of enabling better provisioning of social media data have been identified and they were based on the context of users accessing these resources. The existing literature has been systematized and the main research points and industrial efforts in the area were identified and analyzed. In our works, in the framework of the Action, we came up with potential solutions addressing the problems of the area and described how these fit in the general ecosystem

Utjecaj relevantnosti konteksta na predviđanje ocjena u sustavu za preporuke filmova

Recommender systems are a popular and a highly researched way of helping users get to their desired content in the huge amount of available data, and services online. Understanding the situation in which users consume the items was shown to improve the recommendation process. For that reason, context-aware recommender system (CARS) employs contextual information in order to enhance the user’s model and to improve the recommendations. An issue that is still open is how to decide which pieces of contextual information to acquire and how to incorporate them into CARS, since using irrelevant piece of contextual information could have a negative impact on the recommendations. We propose a methodology for detecting which pieces of contextual information contribute to explaining the variance in the ratings, based on statistical testing. We also inspect the impact of the detected relevant pieces of contextual information on the ratings prediction based on the matrix-factorization algorithm. The experiment was conducted on the MovieAT database. The results showed a significant difference in the ratings prediction using the relevant and the irrelevant pieces of contextual information. We also confirmed the positive impact of the relevant, and negative impact of the irrelevant pieces of contextual information with respect to the uncontextualized model.Sustavi za preporuke (eng. recommender systems) predstavljaju čest i vrlo istražen način pružanja pomoći korisnicima u svrhu pronalaska željenog sadržaja u velikoj količini dostupnih podataka i usluga. Pokazalo se da uvid u situaciju u kojoj korisnici koriste sadržaj doprinosi kvaliteti preporuka. Zbog toga, konteksta svjesni sustavi za preporuke (eng. context-aware recommender systems CARS) koriste kontekstne informacije kako bi poboljšali model korisnika i time kvalitetu preporuka. Jedan od neriješenih problema je kako odlučiti koje kontekstne informacije je potrebno sakupiti i kako ih upotrijebiti u CARSu, budući da upotreba nebitnih kontekstnih informacija može imati negativan utjecaj na kvalitetu preporuka. Mi predlažemo metodologiju za otkrivanje onih kontekstih informacija koje doprinose objašnjavanju varijabilnosti ocjena za sadržaje, utemeljenu na statističkom testiranju. Tako.er, istražujemo utjecaj otkrivenog bitnog konteksta na predvi.anje ocjena utemeljeno na algoritmu faktorizacije matrica. Eksperiment je proveden na bazi podataka MovieAT. Rezultati su pokazali znatnu razliku u predvi.anju ocjena prilikom korištenja bitnog i nebitnog konteksta. Ujedno je potvr.en i pozitivan utjecaj bitnog, odnosno negativan utjecaj nebitnog konteksta, u odnosu na sustav koji ne koristi kontekst, što upućuje na važnost i kvalitetu detekcije