1929844
Outlier detection for high dimensional data
The outlier detection problem has important applications in the field of fraud detection, network robustness analysis, and intrusion detection. Most such applications are high dimensional domains in which the data can contain hundreds of dimensions. Many recent algorithms use concepts of proximity in order to find outliers based on their relationship to the rest of the data. However, in high dimensional space, the data is sparse and the notion of proximity fails to retain its meaningfulness. In fact, the sparsity of high dimensional data implies that every point is an almost equally good outlier from the perspective of proximity-based definitions. Consequently, for high dimensional data, the notion of finding meaningful outliers becomes substantially more complex and non-obvious. In this paper, we discuss new techniques for outlier detection which find the outliers by studying the behavior of projections from the data set.
5368193
Progressive modeling
Presently, inductive learning is still performed in a frustrating batch process. The user has little interaction with the system and no control over the final accuracy and training time. If the accuracy of the produced model is too low, all the computing resources are misspent. In this paper we propose a progressive modeling framework. In progressive modeling, the learning algorithm estimates online both the accuracy of the final model and remaining training time. If the estimated accuracy is far below expectation, the user can terminate training prior to completion without wasting further resources. If the user chooses to complete the learning process, progressive modeling will compute a model with expected accuracy in expected time. We describe one implementation of progressive modeling using ensemble of classifiers.
206711990
Performance comparison of active-sender and active-receiver policies for distributed caching
The authors propose a distributed caching approach to off-loading data access requests from overloaded data servers in a distributed system to nodes that are idle or less busy. Helping out the busy servers on data accesses, the idle or less busy nodes are called mutual servers. Frequently accessed data are cached in the main memory of mutual servers in addition to server and client local caches. The authors evaluate several data propagation strategies among data servers and mutual servers. Simulation results show that the active-sender passive-receiver policy is the method of choice in most cases. Active-sender policies are best able to exploit the main memory of other idle nodes in the expected normal condition where some nodes are overloaded and others are less loaded. All active policies perform far better than the policy without distributed caching.<<ETX>>
35606110
An Analytical Model of Reconstruction Time in Mirrored Disks
Abstract While data replication has been widely used as a means of increasing the data availability and system reliability for critical applications, we still lack a good analytical model of the recovery process after disk failure. In this paper, we present a detailed and accurate model of the reconstruction process which takes into account the disk characteristics as well as the differing characteristics of external accesses and reconstruction reads. A functional equation is derived to represent the delay faced by reconstruction reads, and a solution is given. External access delays are found based on this. Approximate closed form solutions are derived, and are shown by numerical comparison to be very accurate.
36453760
Integrated approach to buffer management and query optimization
33875270
On optimal batching policies for video-on-demand storage servers
In a video-on-demand environment, batching of video requests is often used to reduce I/O demand and improve throughput. Since viewers may defect if they experience long waits, a good video scheduling policy needs to consider not only the batch size but also the viewer defection probabilities and wait times. Two conventional scheduling policies for batching are first-come-first-served (FCFS) and maximum queue length (MOL). Neither of these policies lead to entirely satisfactory results. MQL tends to be too aggressive in scheduling popular videos by only considering the queue length to maximize batch size, while FCFS has the opposite effect. We introduce the notion of factored queue length and propose a batching policy that schedules the video with the maximum factored queue length. We refer to this as the MFQ policy. The factored queue length is obtained by weighting each video queue length with a factor which is biased against the more popular videos. An optimization problem is formulated to solve the best weighting factors for the various videos. A simulation is developed to compare the proposed MFQ policy with FCFS and MQL. Our study shows that MFQ yields excellent empirical results in terms of standard performance measures such as average latency time, defection rates and fairness.
7510592
A matching pursuit technique for computing the simplest normal forms of vector fields
This paper presents a matching pursuit technique for computing the simplest normal forms of vector fields. First a simple, explicit recursive formula is derived for general differential equations, which reduces computation to the minimum. Then a matching pursuit technique is introduced and applied to the Takens-Bogdanov dynamical singularity. It is shown that unlike other methods for computing normal forms, the technique using matching pursuit does not need any algebraic constraints which are required for the existence of the simplest normal form. The efficient method and matching pursuit technique, which have been implemented using Maple, can be "automatically" executed on various computer systems. A number of examples are presented to demonstrate the advantages of the technique.
16104127
Bilateral Filter for Meshes Using New Predictor
A new predictor of bilateral filter for smoothing meshes is presented. It prevents shrinkage of corners. A major feature of mesh smoothing is to move every vertex along the direction determined by the mean curvature normal with speed defined by the predictor. It prevents unnatural deformation for irregular meshes. In order to remove thenormal noise, we use adaptive Gaussian filter to smooth triangle normals.
3748150
Mesh blending
A new method for smoothly connecting different patches on triangle meshes with arbitrary connectivity, called mesh blending, is presented. A major feature of mesh blending is to move vertices of the blending region to a virtual blending surface by choosing an appropriate parameterization of those vertices. Once blending is completed, the parameterization optimization is performed to perfect the final meshes. Combining mesh blending with multiresolution techniques, an effective blending technique for meshes is obtained. Our method has several advantages: (1) the user can intuitively control the blending result using different blending radii, (2) the shape of cross-section curves can be adjusted to flexibly design complex models, and (3) the resulting mesh has the same connectivity as the original mesh. In this paper, some examples about smoothing, sharpening, and mesh editing show the efficiency of the method.