instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
Knowing the largest rate at which data can be sent on an end-to-end path such that the egress rate is equal to the ingress rate with high probability can be very practical when choosing transmission rates in video streaming or selecting peers in peer-to-peer applications. We introduce probabilistic available bandwidth, which is defined in terms of ingress rates and egress rates of traffic on a path, rather than in terms of capacity and utilization of the constituent links of the path like the standard available bandwidth metric. In this paper, we describe a distributed algorithm, based on a probabilistic graphical model and Bayesian active learning, for simultaneously estimating the probabilistic available bandwidth of multiple paths through a network. Our procedure exploits the fact that each packet train provides information not only about the path it traverses, but also about any path that shares a link with the monitored path. Simulations and PlanetLab experiments indicate that this process can dramatically reduce the number of probes required to generate accurate estimates.
answer:
Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning

question:
In many domains it is desirable to assess the pref erences of users in a qualitative rather than quan titative way. Such representations of qualitative preference orderings form an important compo nent of automated decision tools. We propose a graphical representation of preferences that re flects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a rep resentation is often compact and arguably natural. We describe several search algorithms for domi nance testing based on this representation; these algorithms are quite effective, especially in spe cific network topologies, such as chainand tree structured networks, as well as polytrees.
answer:
Reasoning With Conditional Ceteris Paribus Preference Statements

question:
The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method.
answer:
Dynamic Discovery of Type Classes and Relations in Semantic Web Data