6932749
Adaptive stream resource management using Kalman Filters
To answer user queries efficiently, a stream management system must handle continuous, high-volume, possibly noisy, and time-varying data streams. One major research area in stream management seeks to allocate resources (such as network bandwidth and memory) to query plans, either to minimize resource usage under a precision requirement, or to maximize precision of results under resource constraints. To date, many solutions have been proposed; however, most solutions are ad hoc with hard-coded heuristics to generate query plans. In contrast, we perceive stream resource management as fundamentally a filtering problem, in which the objective is to filter out as much data as possible to conserve resources, provided that the precision standards can be met. We select the Kalman Filter as a general and adaptive filtering solution for conserving resources. The Kalman Filter has the ability to adapt to various stream characteristics, sensor noise, and time variance. Furthermore, we realize a significant performance boost by switching from traditional methods of caching static data (which can soon become stale) to our method of caching dynamic procedures that can predict data reliably at the server without the clients' involvement. In this work we focus on minimization of communication overhead for both synthetic and real-world streams. Through examples and empirical studies, we demonstrate the flexibility and effectiveness of using the Kalman Filter as a solution for managing trade-offs between precision of results and resources in satisfying stream queries.
10743717
Support vector machine active learning for image retrieval
Relevance feedback is often a critical component when designing image databases. With these databases it is difficult to specify queries directly and explicitly. Relevance feedback interactively determinines a user's desired output or query concept by asking the user whether certain proposed images are relevant or not. For a relevance feedback algorithm to be effective, it must grasp a user's query concept accurately and quickly, while also only asking the user to label a small number of images. We propose the use of a support vector machine active learning algorithm for conducting effective relevance feedback for image retrieval. The algorithm selects the most informative images to query a user and quickly learns a boundary that separates the images that satisfy the user's query concept from the rest of the dataset. Experimental results show that our algorithm achieves significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
7236137
Efficient top-k hyperplane query processing for multimedia information retrieval
A query can be answered by a binary classifier, which separates the instances that are relevant to the query from the ones that are not. When kernel methods are employed to train such a classifier, the class boundary is represented as a hyperplane in a projected space. Data instances that are farthest from the hyperplane are deemed to be most relevant to the query, and that are nearest to the hyperplane to be most uncertain to the query. In this paper, we address the twin problems of efficient retrieval of the approximate set of instances (a) farthest from and (b) nearest to a query hyperplane. Retrieval of instances for this hyperplane-based query scenario is mapped to the range-query problem allowing for the reuse of existing index structures. Empirical evaluation on large image datasets confirms the effectiveness of our approach.
18150673
The anatomy of a multimodal information filter
The proliferation of objectionable information on the Internet has reached a level of serious concern. To empower end-users with the choice of blocking undesirable and offensive websites, we propose a multimodal information filter, named MORF. In this paper, we present MORF's core components: its confidence-based classifier, a Cross-bagging ensemble scheme, and multimodal classification algorithm. Empirical studies and initial statistics collected from the MORF filters deployed at sites in the U.S. and Asia show that MORF is both efficient and effective, due to our classification methods.
2439726
Concept boundary detection for speeding up SVMs
Support Vector Machines (SVMs) suffer from an O(n2) training cost, where n denotes the number of training instances. In this paper, we propose an algorithm to select boundary instances as training data to substantially reduce n. Our proposed algorithm is motivated by the result of (Burges, 1999) that, removing non-support vectors from the training set does not change SVM training results. Our algorithm eliminates instances that are likely to be non-support vectors. In the concept-independent preprocessing step of our algorithm, we prepare nearest-neighbor lists for training instances. In the concept-specific sampling step, we can then effectively select useful training data for each target concept. Empirical studies show our algorithm to be effective in reducing n, outperforming other competing downsampling algorithms without significantly compromising testing accuracy.
6363863
MEMS-based disk buffer for streaming media servers
The performance of streaming media servers has been limited due to the dual requirements of high throughput and low memory use. Although disk throughput has been enjoying a 40% annual increase, slower improvements in disk access times necessitate the use of large DRAM buffers to improve the overall streaming throughput. MEMS-based storage is an exciting new technology that promises to bridge the widening performance gap between DRAM and disk-drives in the memory hierarchy. We explore the impact of integrating these devices into the memory hierarchy on the class of streaming media applications. We evaluate the use of MEMS-based storage for buffering and caching streaming data. We also show how a bank of k MEMS devices can be managed in either configuration and that they can provide a k-fold improvement in both throughput and access latency. An extensive analytical study shows that using MEMS storage can reduce the buffering cost and improve the throughput of streaming servers significantly.
15998300
Shortest path amidst disc obstacles is computable
An open question in Exact Geometric Computation is whether there re transcendental computations that can be made "geometrically exact".Perhaps the simplest such problem in computational geometry is that of computing the shortest obstacle-avoiding path between two points p, q in the plane, where the obstacles re collection of n discs.This problem can be solved in O (n 2 log n)time in the Real RAM model, but nothing was known about its computability in the standard (Turing) model of computation. We first show the Turing-computability of this problem,provided the radii of the discs are rationally related. We make the usual assumption that the numerical input data are real algebraic numbers. By appealing to effective bounds from transcendental number theory, we further show single-exponential time upper bound when the input numbers are rational.Our result ppears to be the first example of non-algebraic combinatorial problem which is shown computable. It is also rare example of transcendental number theory yielding positive computational results.
12117585
Distributed multivariate regression based on influential observations
Large-scale data sets are sometimes logically and physically distributed in separate databases. The issues of mining these data sets are not just their sizes, but also the distributed nature. The complication is that communicating all the data to a central database would be too slow. To reduce communication costs, one could compress the data during transmission. Another method is random sampling. We propose an approach for distributed multivariate regression based on sampling and discuss its relationship with the compression method. The central idea is motivated by the observation that, although communication is limited, each individual site can still scan and process all the data it holds. Thus it is possible for the site to communicate only influential samples without seeing data in other sites. We exploit this observation and derive a method that provides tradeoff between communication cost and accuracy. Experimental results show that it is better than the compression method and random sampling.
2005681
Hiding Secret Points Amidst Chaff
Motivated by the representation of biometric and multimedia objects, we consider the problem of hiding noisy point-sets using a secure sketch. A point-set X consists of s points from a d-dimensional discrete domain [0,N – 1]d. Under permissible noises, for every point $\left \langle x_1,..,x_d\right \rangle \in X$, each xi may be perturbed by a value of at most δ. In addition, at most t points in X may be replaced by other points in [0,N – 1]d. Given an original X, we want to compute a secure sketch P. A known method constructs the sketch by adding a set of random points R, and the description of (X∪R) serves as part of the sketch. However, the dependencies among the random points are difficult to analyze, and there is no known non-trivial bound on the entropy loss. In this paper, we first give a general method to generate R and show that the entropy loss of (X∪R) is at most s(dlogΔ+ d + 0.443), where Δ= 2δ+1. We next give improved schemes for d = 1, and special cases for d = 2. Such improvements are achieved by pre-rounding, and careful partition of the domains into cells. It is possible to make our sketch short, and avoid using randomness during construction. We also give a method in d = 1 to demonstrate that, using the size of R as the security measure would be misleading.
14264886
Geometric properties of watermarking schemes
A variety of image watermarking schemes have been proposed using orthogonal transformations, projections, and coding techniques to embed imperceptible watermarks into images. Analytical studies of the watermarking problem have typically been based on studying the performance limits of these known algorithms. In contrast, this paper formalizes the watermarking problem in an "algorithm independent" framework, representing any watermarking algorithm as a partition of the image space into a collection of sets, and defining requirements of these sets that must be met by any solution to the watermarking problem. Specifically, these requirements define and constrain the false-alarm ratio, distortion, robustness and security of a watermarking system. Using this formalism, we first characterize common features of algorithms that solve the watermarking problem. We show how the requirements defined earlier force important differences between watermarking signal sets and classical communication systems signal sets. Next, we show how common components of existing watermarking algorithms (e.g. transformations, projections, and coding) can be associated with specific requirements of the watermarking definition. Finally, we show how our new formalism of the watermarking problem provides a procedure for optimal design of watermarking systems to target specified false-alarm, distortion, and robustness objectives.
9658544
Effect of Malicious Synchronization
We study the impact of malicious synchronization on computer systems that serve customers periodically. Systems supporting automatic periodic updates are common in web servers providing regular news update, sports scores or stock quotes. Our study focuses on the possibility of launching an effective low rate attack on the server to degrade performance measured in terms of longer processing time and request drops due to timeouts. The attackers are assumed to behave like normal users and send one request per update cycle. The only parameter utilized in the attack is the timing of the requests sent. By exploiting the periodic nature of the updates, a small number of attackers can herd users' update requests to a cluster and arrive in a short period of time. Herding can be used to discourage new users from joining the system and to modify the user arrival distribution, so that the subsequent burst attack will be effective. While the herding based attacks can be launched with a small amount of resource, they can be easily prevented by adding a small random component to the length of the update interval.