Gauging Correct Relative Rankings For Similarity Search

© 2015 ACM.One of the important tasks in link analysis is to quantify the similarity between two objects based on hyperlink structure. SimRank is an attractive similarity measure of this type. Existing work mainly focuses on absolute SimRank scores, and often harnesses an iterative paradigm to compute them. While these iterative scores converge to exact ones with the increasing number of iterations, it is still notoriously difficult to determine how well the relative orders of these iterative scores can be preserved for a given iteration. In this paper, we propose efficient ranking criteria that can secure correct relative orders of node-pairs with respect to SimRank scores when they are computed in an iterative fashion. Moreover, we show the superiority of our criteria in harvesting top-K SimRank scores and bucket orders from a full ranking list. Finally, viable empirical studies verify the usefulness of our techniques for SimRank top-K ranking and bucket ordering

The Programmable City

AbstractThe worldwide proliferation of mobile connected devices has brought about a revolution in the way we live, and will inevitably guide the way in which we design the cities of the future. However, designing city-wide systems poses a new set of challenges in terms of scale, manageability and citizen involvement. Solving these challenges is crucial to making sure that the vision of a programmable Internet of Things (IoT) becomes reality. In this article we will analyse these issues and present a novel programming approach to designing scalable systems for the Internet of Things, with an emphasis on smart city applications, that addresses these issues

Distributed Optimal Lexicographic Max-Min Rate Allocation in Solar-Powered Wireless Sensor Networks

Understanding the optimal usage of fluctuating renewable energy in wireless sensor networks (WSNs) is complex. Lexicographic max-min (LM) rate allocation is a good solution but is nontrivial for multihop WSNs, as both fairness and sensing rates have to be optimized through the exploration of all possible forwarding routes in the network. All current optimal approaches to this problem are centralized and offline, suffering from low scalability and large computational complexity—typically solving O( N 2 ) linear programming problems for N -node WSNs. This article presents the first optimal distributed solution to this problem with much lower complexity. We apply it to solar-powered wireless sensor networks (SP-WSNs) to achieve both LM optimality and sustainable operation. Based on realistic models of both time-varying solar power and photovoltaic-battery hardware, we propose an optimization framework that integrates a local power management algorithm with a global distributed LM rate allocation scheme. The optimality, convergence, and efficiency of our approaches are formally proven. We also evaluate our algorithms via experiments on both solar-powered MICAz motes and extensive simulations using real solar energy data and practical power parameter settings. The results verify our theoretical analysis and demonstrate how our approach outperforms both the state-of-the-art centralized optimal and distributed heuristic solutions. </jats:p

The Programmable City

AbstractThe worldwide proliferation of mobile connected devices has brought about a revolution in the way we live, and will inevitably guide the way in which we design the cities of the future. However, designing city-wide systems poses a new set of challenges in terms of scale, manageability and citizen involvement. Solving these challenges is crucial to making sure that the vision of a programmable Internet of Things (IoT) becomes reality. In this article we will analyse these issues and present a novel programming approach to designing scalable systems for the Internet of Things, with an emphasis on smart city applications, that addresses these issues

High quality SimRank-based similarity search

SimRank is an influential link-based similarity measure that has been used in many fields of Web search and sociometry. The best-of-breed method by Kusumoto et al. [7], however, does not always deliver high-quality results, since it fails to accurately obtain its diagonal correction matrix D. Besides, SimRank is also limited by an unwanted“connectivity trait”: increasing the number of paths between nodes a and b often incurs a decrease in score s(a, b). The best-known solution, SimRank++ [1], cannot resolve this problem, since a revised score will be zero if a and b have no common in-neighbors. In this paper, we consider high-quality similarity search. Our scheme, SR#, is efficient and semantically meaningful: (1) We first formulate the exact D, and devise a “varied-D” method to accurately compute SimRank in linear memory. Moreover, by grouping computation, we also reduce the time of [7] from quadratic to linear in the number of iterations. (2) We design a “kernel-based”model to improve the quality of SimRank, and circumvent the “connectivity trait” issue. (3) We give mathematical insights to the semantic difference between SimRank and its variant, and correct an argument in [7]: “if D is replaced by a scaled identity matrix (1−γ)I, top-K rankings will not be affected much”. The experiments confirm that SR# can accurately extract high-quality scores, and is much faster than the state-of-the-art competitors