43,930 research outputs found
Connecting the dots: a multi-pivot approach to data exploration
The purpose of data browsers is to help users identify and query data effectively without being overwhelmed by large complex graphs of data. A proposed solution to identify and query data in graph-based datasets is Pivoting (or set-oriented browsing), a many-to-many graph browsing technique that allows users to navigate the graph by starting from a set of instances followed by navigation through common links. Relying solely on navigation, however, makes it difficult for users to find paths or even see if the element of interest is in the graph when the points of interest may be many vertices apart. Further challenges include finding paths which require combinations of forward and backward links in order to make the necessary connections which further adds to the complexity of pivoting. In order to mitigate the effects of these problems and enhance the strengths of pivoting we present a multi-pivot approach which we embodied in tool called Visor. Visor allows users to explore from multiple points in the graph, helping users connect key points of interest in the graph on the conceptual level, visually occluding the remainder parts of the graph, thus helping create a road-map for navigation. We carried out an user study to demonstrate the viability of our approach
A general framework for coloring problems: old results, new results, and open problems
In this survey paper we present a general framework for coloring problems that was introduced in a joint paper which the author presented at WG2003. We show how a number of different types of coloring problems, most of which have been motivated from frequency assignment, fit into this framework. We give a survey of the existing results, mainly based on and strongly biased by joint work of the author with several different groups of coauthors, include some new results, and discuss several open problems for each of the variants
Minimizing Flow Time in the Wireless Gathering Problem
We address the problem of efficient data gathering in a wireless network
through multi-hop communication. We focus on the objective of minimizing the
maximum flow time of a data packet. We prove that no polynomial time algorithm
for this problem can have approximation ratio less than \Omega(m^{1/3) when
packets have to be transmitted, unless . We then use resource
augmentation to assess the performance of a FIFO-like strategy. We prove that
this strategy is 5-speed optimal, i.e., its cost remains within the optimal
cost if we allow the algorithm to transmit data at a speed 5 times higher than
that of the optimal solution we compare to
Implementation of Distributed Time Exchange Based Cooperative Forwarding
In this paper, we design and implement time exchange (TE) based cooperative
forwarding where nodes use transmission time slots as incentives for relaying.
We focus on distributed joint time slot exchange and relay selection in the sum
goodput maximization of the overall network. We formulate the design objective
as a mixed integer nonlinear programming (MINLP) problem and provide a
polynomial time distributed solution of the MINLP. We implement the designed
algorithm in the software defined radio enabled USRP nodes of the ORBIT indoor
wireless testbed. The ORBIT grid is used as a global control plane for exchange
of control information between the USRP nodes. Experimental results suggest
that TE can significantly increase the sum goodput of the network. We also
demonstrate the performance of a goodput optimization algorithm that is
proportionally fair.Comment: Accepted in 2012 Military Communications Conferenc
- …