3768369
Efficient Data Mining for Path Traversal Patterns
The authors explore a new data mining capability that involves mining path traversal patterns in a distributed information-providing environment where documents or objects are linked together to facilitate interactive access. The solution procedure consists of two steps. First, they derive an algorithm to convert the original sequence of log data into a set of maximal forward references. By doing so, one can filter out the effect of some backward references, which are mainly made for ease of traveling and concentrate on mining meaningful user access sequences. Second, they derive algorithms to determine the frequent traversal patterns-i.e., large reference sequences-from the maximal forward references obtained. Two algorithms are devised for determining large reference sequences; one is based on some hashing and pruning techniques, and the other is further improved with the option of determining large reference sequences in batch so as to reduce the number of database scans required. Performance of these two methods is comparatively analyzed. It is shown that the option of selective scan is very advantageous and can lead to prominent performance improvement. Sensitivity analysis on various parameters is conducted.
16088118
An effective hash-based algorithm for mining association rules
In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear in a sufficient number of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets first and then, identifying, within this candidate set, those itemsets that meet the large itemset requirement. Generally this is done iteratively for each large k-itemset in increasing order of k where a large k-itemset is a large itemset with k items. To determine large itemsets from a huge number of candidate large itemsets in early iterations is usually the dominating factor for the overall data mining performance. To address this issue, we propose an effective hash-based algorithm for the candidate set generation. Explicitly, the number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly. Extensive simulation study is conducted to evaluate performance of the proposed algorithm.
16144421
Fast algorithms for projected clustering
The clustering problem is well known in the database literature for its numerous applications in problems such as customer segmentation, classification and trend analysis. Unfortunately, all known algorithms tend to break down in high dimensional spaces because of the inherent sparsity of the points. In such high dimensional spaces not all dimensions may be relevant to a given cluster. One way of handling this is to pick the closely correlated dimensions and find clusters in the corresponding subspace. Traditional feature selection algorithms attempt to achieve this. The weakness of this approach is that in typical high dimensional data mining applications different sets of points may cluster better for different subsets of dimensions. The number of dimensions in each such cluster-specific subspace may also vary. Hence, it may be impossible to find a single small subset of dimensions for all the clusters. We therefore discuss a generalization of the clustering problem, referred to as the projected clustering problem, in which the subsets of dimensions selected are specific to the clusters themselves. We develop an algorithmic framework for solving the projected clustering problem, and test its performance on synthetic data.
371162
Security architectures for controlled digital information dissemination
Besides securing transmission of digital information at lower layers, several application-level security solutions for controlled dissemination of digital information have been developed using cryptographic, watermarking or use-control technologies. These dissemination control solutions have been designed for different business purposes. Little research, if any, identifies security architectures for controlling or tracking digital information dissemination in general. The identification of such will provide a foundation for developing appropriate security solutions for organizations' secure dissemination of digital information, and provide a better understanding of current application-level security solutions. We identify eight application-level security architectures based on the following three elements: virtual machine, control set and distribution style. Some of the architectures provide control and tracking capabilities for dissemination and usage of digital information, while others provide only tracking capability. We describe the architectures and compare their capabilities, merits and demerits. In addition, we review briefly some of the required mechanisms, including watermarking and use-control technologies. Also, we relate some of commercial solutions to our security architectures in order to provide insight on the current availability of our solutions architectures.
9959276
Injecting RBAC to secure a Web-based workflow system
Web-based workflow systems have recently received much attention because they can support dynamic business processes over heterogeneous computing systems. Most existing web-based workflow systems, however, provide minimal security services such as authentication of users and network security. In this paper we describes an experiment in injecting role-based access control (RBAC) into an existing web-based workflow system. Specifically, we ensure that each task can only be executed by users belonging to a specific role. In order to achieve this, we define a simplified RBAC model to meet our needs and describe the security architecture to be applied to an existing web-based workflow system. We describe our implementation using commercial off-the-shelf (COTS) technology to demonstrate the feasibility of this approach. Our implementation uses X.509v3 certificates with role attribute, and employs a user-pull style where the client requests a client certificate from the role-server and presents it to the workflow system. A major goal of our implementation is to have minimal changes to the existing web server and no changes to the browser. We also discuss alternative architecture such as server-pull with LDAP (Lightweight Directory Access Protocol).
12592675
A New Approach for Active Stereo Camera Calibration
By active stereo we mean a stereo vision system that allows for independent panning and tilting for each of the two cameras. One advantage of active stereo in relation to regular stereo is the former's wider effective field of view; if an object is too close to the camera baseline, the depth to the object can still be estimated accurately by panning the cameras appropriately. Another advantage of active stereo is that it can yield a larger number of depth measurements simultaneously for each position of the platform on which the camera system is mounted. Panning and tilting over a large angular range, while being the main reason for the advantages of active stereo, also make it more challenging to calibrate such systems. For a calibration procedure to be effective for active stereo, the estimated parameters must be valid over the entire range of the pan and tilt angles. This paper presents a new approach to the calibration of such vision systems. Our method is based on the rationale that an active stereo calibration procedure must explicitly estimate the locations and the orientations of the pan and tilt rotating axes for the cameras through a closed-form solution. When these estimates for the axes are combined with the homogeneous transform relationships that link the various coordinate frames, we end with a calibration that is valid over a large variation in the pan and tilt angles.
9022148
Parallel Searching in Generalized Monge Arrays
Abstract. This paper investigates the parallel time and processor complexities of several searching problems involving Monge, staircase-Monge, and Monge-composite arrays. We present array-searching algorithms for concurrent-read-exclusive-write (CREW) PRAMs, hypercubes, and several hypercubic networks. All these algorithms run in near-optimal time, and their processor-time products are all within an 
 $O (\lg n)$ factor of the worst-case sequential bounds. Several applications of these algorithms are also given. Two applications improve previous results substantially, and the others provide novel parallel algorithms for problems not previously considered.
34356748
Model-Based Human Motion Capture from Monocular Video Sequences
Generating motion and capturing motion of an articulated body for computer animation is an expensive and time-consuming task. Conventionally, animators manually generate intermediate frames between key frames, but this task is very labor-intensive. This paper presents a model-based singularity-free automatic-initialization approach to capturing human motion from widely-available, static background monocular video sequences. A 3D human body model is built and projected on a 2D projection plane to find the best fit with the foreground image silhouette. We convert the human motion capture problem into two types of parameter optimization problems: static optimization and dynamic optimization. First, we determine each model body configuration using static optimizations for every input image. Then, to obtain better description of motion, the results from all static optimizations are fed into a dynamic optimization process where the entire sequence of motion is considered for the user-specified motion. The user-specified motion is defined by the user and the final form of the motion they want. A cost function for static optimization is used to estimate the degree of overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. A cost function for dynamic optimization is the user-specified motion based on the static optimization results as well as image fitting. Our method is used to capture various human motions: walking, pushing, kicking, and handshaking.