News feed platforms, such as Facebook and Twitter, are growing continuously. Their primary requirements are scalability, low request latencies and high availability  for read and write requests. This requires to scale out the system to multiple machines . STOU and Graphity were reported as high performant algorithms to power a news feed system.
The aim of this thesis is to improve and benchmark Lare - A new technology for stateful single-page applications. Lare is a front- and backend technology, devel- oped to easily improve web sites with the use of AJAX but without the disadvan- tages of it regarding browser functionality, SEO and user experience.
In the last years, crowdsourcing has become a popular technique for evaluating systems, as well as for cleansing, enhancing and labeling data. While there are many success stories both in research and industry, there are still many challenges to overcome.
We look at a very popular construct in probabilistic modelling: the Dirichlet process (DP). Starting with the Chinese restaurant metaphor, we see how context can be modelled using DPs and understand some of the underlying assumptions.
Bis vor wenigen Jahren konnte zwischen virtuellen Spielen und Spielen in der physischen Welt klar unterschieden werden. Die wachsende Verbreitung von Mobiltelefonen mit hoher Rechenleistung, verschiedenen Sensoren und ständiger Internetverbindung ermöglicht es, Spiele zu entwickeln, die virtuelle und physische Umgebung vereinen.
Quite often, Linked Open Data (LOD) applications pre-fetch data from the Web and store local copies of it in a cache for faster access at runtime. Yet, recent investigations have shown that data published and interlinked on the LOD cloud is subject to frequent changes.
In anticipation of RDF graphs exceeding one trillion triples, the W3C tested RDF stores whether they can deal with such huge graphs. This amount of data can be stored in a cloud at a reasonable price. But storing a graph in a cloud consisting of several individual computers raises several issues like the triple placement or the efficient processing of interactive queries.
In this talk I will review and evaluate models of network evolution based on the notion of structural diversity. I show that diversity is an underlying theme of three principles of network evolution: the preferential attachment model, connectivity and link prediction. I show that in all three cases, a dominant trend towards shrinking diversity is apparent, both theoretically and empirically.
Identifying suitable data sets is a crucial task in several fields of application, like data analysis, but the task itself is highly complicated and mostly heavily related to manually skimming through a vast amounts of data.