15318356
Variance estimation over sliding windows
Capturing characteristics of large data streams has received considerable attention. The constraints in space and time restrict the data stream processing to only one pass (or a small number of passes). Processing data streams over sliding windows make the problem more difficult and challenging. In this paper, we address the problem of maintaining ∈-approximate variance of data streams over sliding windows. To our knowledge, the best existing algorithm requires O(1/∈2 log N) space, though the lower bound for this problem is Ω(1/∈ log N). We propose the first ∈-approximation algorithm to this problem that is optimal in both space and worst case time. Our algorithm requires O(1/∈ log N) space. Furthermore, its running time is O(1) in worst case.
10973415
A testbed for evaluation and analysis of stepping stone attack attribution techniques
This paper describes a testbed for experimentally evaluating stepping stone attack attribution techniques. There is a lack of comprehensive experimental evaluation of many different stepping stone attack detection schemes. Therefore, there are no objective, comparable evaluation results on the effectiveness and limitations of these schemes. In this research, we designed and built a scalable testbed environment that can evaluate all existing stepping stone attack attribution schemes reproducibly, provide a stable platform for further research on this area and be easily reconfigured, expanded, and operated with user-friendly interface. This testbed environment has been established in a dedicated stepping stone attack attribution research laboratory. An evaluation of proposed stepping stone techniques is currently underway
9889457
A New Approach to Automated Retinal Vessel Segmentation Using Multiscale Analysis
Computer based analysis for automated segmentation of blood vessels in retinal images help eye care specialists screen larger populations for vessel abnormalities. However, automated retinal segmentation is complicated by the fact that the width of retinal vessels can vary from very large to very small, and that the local contrast of vessels is unstable, especially in unhealthy ocular fundus. We propose a novel method that takes these facts into account. Our method includes a multiscale analytical scheme using Gabor filters and scale production, and a threshold probing technique utilizing the features of retinal vessel network. Our method is good for detecting large and small vessels concurrently. It also offers an efficient way to denoise and enhance the responses of line filters, allowing the detection of vessels with low local contrast
3730862
Distributed Resource Allocation in Stream Processing Systems
Distributed stream processing architecture has emerged as appealing solution to coping with the analysis of large amount of data from dispersed sources. A fundamental problem in such stream processing systems is how to best utilize the available resources so that the overall system performance is optimized. We consider a distributed stream processing system that consists of a network of cooperating servers, collectively providing processing services for multiple data streams. Each stream is required to complete a series of operations on various servers. We assume all servers have finite computing resources and all communication links have finite available bandwidth. The problem is to find distributed schemes to allocate the limited computing resources as well as the communication bandwidth in the system so as to achieve a maximum concurrent throughput for all output streams. We present a generalized multicommodity flow model for the above problem. We develop a distributed resource allocation algorithm that guarantees the optimality. We also provide detailed analysis on the complexity of the algorithm and demonstrate the performance using numerical experiments.
14328389
Alternative scalable algorithms for lattice-based feature location
Considering the scalability of using formal concept analysis to locate features in source code, we present a set of alternative straightforward algorithms to achieve the same objectives. A preliminary experiment indicates that the alternative algorithms are more scalable to deal with the large numbers of data to some extent.
11528752
Redundancy in Complete Sets
We show that a set is m-autoreducible if and only if it is m-mitotic. This solves a long standing open question in a surprising way. As a consequence of this unconditional result and recent work by Glaser et al. [12], complete sets for all of the following complexity classes are m-mitotic: NP, coNP, $\bigoplus$P, PSPACE, and NEXP, as well as all levels of PH, MODPH, and the Boolean hierarchy over NP. In the cases of NP, PSPACE, NEXP, and PH, this at once answers several well-studied open questions. These results tell us that complete sets share a redundancy that was not known before. In particular, every NP-complete set A splits into two NP-complete sets A1 and A2.
11411902
Kinetic connectivity for unit disks
We describe a kinetic data structure (KDS) that maintains the connected components of the union of a set of unit-radius disks moving in the plane. We assume that the motion of each disk can be specified by a low-degree algebraic trajectory; this trajectory, however, can be modified in an on-line fashion. While the disks move continuously, their connectivity changes at discrete times. Our main result is an O(n) space data structure that takes O(log n\slash \kern -1pt log log n) time per connectivity query of the form ``are disks A and B in the same connected component?’’ A straightforward approach based on dynamically maintaining the overlap graph requires Ω (n 2 ) space. Our data structure requires only linear space and must deal with O(n 2 + e ) updates in the worst case, each requiring O(log 2 n) amortized time, for any e>0 . This number of updates is close to optimal, since a set of n moving unit disks can undergo Ω (n 2 ) connectivity changes.